Digital means different things to different people. Social. Mobile. Cloud. The Internet of Things. Big Data. All of the above. Ask five executives and you’re likely to get five different answers. But no matter how you define it, interest in all things digital has skyrocketed in recent years. According to a recent survey, 76 percent say digital technologies are disrupting their industry to a great or moderate extent. And, consider this graph showing the popularity of the search term “digital transformation” on Google.

Given the intense focus on digital, a certain amount of digital fatigue or even digital exhaustion at this point may be understandable. Many companies have already made significant investments in digital, and it may be temping to think that they’re finished now—or at least close to it. But for the most part, the short answer is “No. Not yet.”

Companies that fail to innovate, adapt and transform aren’t likely to be around for long. According to the consulting firm Innosight, the average lifespan for a company in the S&P 500 dropped from 61 years in 1958 to 25 years in 1980 to 18 years today. What’s even more astonishing is that, at the current churn rate, 75 percent of the S&P 500 will be replaced in 25 years.

In the face of these changes, most companies face additional digital gaps that will take additional time, energy, investment and leadership to address.

The (Digital) Underground

When most senior leaders think about digital they tend to think primarily in terms of the presentation layer—in terms of direct customer interaction. Things like mobile apps, social media, and website design spring to mind. But, these initial efforts just begin to scratch the surface of the real transformation that is needed.

For companies to survive and succeed in the digital era, they need to make sure their underlying core operating systems—the layers before presentation to customers—are integrated and scaleable. And, this requires them to address common gaps related to the way work gets done inside their companies.

 The Culture Gap

“Our traditional teams are too slow. We’re not prototyping fast enough, not innovating fast enough. We need to systematize change.” – Beth Comstock, Vice Chair and CMO of General Electric

Cultural change is hard and requires tremendous leadership. So, its no surprise that many companies are still far from integrating new ways of working and new management mind-sets into their digital DNA. What exactly does it mean to change a culture in this regard? Every industry and company is different, but a few common themes have emerged.

  • Willingness to Address Deep Change

For many organizations facing digital disruption, the degree of change required to remain competitive can feel staggering. Long established ways of working, talent strategies, business processes, ingrained beliefs, incentives, technologies, organizational structures, etc. all must be reconsidered and potentially changed. When deeper change is needed, top executives play a crucial role. They have to make clear why change is needed as well as the scope and focus of the change.

Consider the case of David Krantz, the CEO of YP, as he led a transformation of YP, formerly known as the Yellow Pages, from print to digital. For decades, the yellow pages book provided consumers with a quick and easy directory of businesses. As more and more consumers shifted to digital, YP shifted as well, creating online and mobile products. However, the company wanted to do much more than simply create an electronic version of the yellow pages. The digital transformation of the company included the creation of a full suite of digital marketing solutions for small and mid-sized businesses, the introduction of search engine marketing, and new placements on local online directories.

Krantz understood that the people part of the digital transformation is the most important part of the equation. He decided to get in front of every employee over a year and a half period—a challenge in a large company with a very distributed sales force. As Krantz describes it in a recent interview with Harvard Business Review, “I decided I wanted to get to those offices in their locations. So I got to Baton Rouge, and Modesto, California, and Sacramento. And often were places where people told me they had never seen a senior executive before in person, let alone the CEO of the company.”

  • Speed and Agility

“We are moving from a world in which the big eat the small to a world in which the fast eat the slow.” – Klaus Schwab, Executive Chairman and Founder of the World Economic Forum

When organizations embark on digital transformation journeys, many realize that they need to become faster and more agile. As Scott Cook, CEO of Intuit, remarked,  “I got sick of wasting months and years of engineering time on products that just weren’t going anywhere and weren’t changing customers lives. So, as a result, we’ve recently blown up the way in which we develop products.” Cook and other executives are introducing Lean and Agile principles and techniques to use both money and human creativity more efficiently and effectively. They are reducing the time it takes to introduce a new product, a new feature, new marketing or a new customer experience. Inspired by lessons from Lean manufacturing, they rely on empirical results to validate learning. This is brought about by rapid experimentation. The approach focuses on learning what customers actually want and measuring actual progress, and it encourages organizations to shift directions with agility.

  • Cross-Functional Collaboration

Transformation requires business and technology groups to end any form of troubled relationship and create a true digital partnership. The very nature of digital engagement with customers creates new interactions in new channels and generates new data—all of which demands cross-functional collaboration to deliver an integrated customer experience. When CMOs consider some of the best sources of customer data, they often discover that it exists internally—in data stored in the customer service systems managed by the COO. And, consider the case of CMO and CIO alignment. Once they were leaders of functional silos but now require much tighter alignment and closer collaboration.

At Akamai, investments in marketing and technology alignment have paid off handsomely. Akamai’s CMO, Brad Rinklin, and CIO, Kumud Kalia, have been co-captains of Akamai’s digital transformation and have worked steadily to bring IT and marketing into better alignment. The pair were recently interviewed by CIO magazine. “Considering we’re a $2 billion company, Kumud’s team helped marketing launch a brand-new website with a brand-new [content management system] and a brand-new marketing automation system while overhauling all of our marketing lead processes and doing an integration into our Salesforce automation engine,” Rinklin says, adding that all those projects were done in one calendar year and there was no downtime. Overnight, we went from our old system to our new system and it all worked fantastically,” he says. “We couldn’t have done that as a marketing team alone or just asking for some IT help. We had to do this as a partnership.”

  • Business Process Change

Teams working to build new digital capabilities in their organizations often fall into the trap of myopically focusing on technology while downplaying the importance of communication, change management, and business process impacts. When Jaemi Bremner and her team were implementing a new marketing automation capability at Intuit, they recognized the degree of change this new capability would require to the way work gets done. As Bremner describes it, the change in business processes was like going from an “orchestra,” where team members followed a carefully crafted, pre-defined plan, to a “jazz band,” where teams could experiment and change course based on customer feedback and behavior. While 22% of the program budget went to license costs, 45% of the budget was devoted to “enablement”—to internal communication, process change and training. Bremner shared a diagram similar to the one below to illustrate conceptually how her team considered the role of technology, data, people and process in the program.

This allowed them to inform Marketing leadership earlier of the types of talent enhancements that would be needed. It allowed them to proactively influence the key individuals whose support would be critical for adoption and feedback. And, it allowed Intuit to re-think its business processes with the goal of placing the customer and the customer’s experience front and center. Based on her team’s experience, Bremner recommends spending 50% of your budget on capability building to ensure your technology investment has ROI.

Integrating Digital into the cultural DNA of your organization takes time and effort, but the benefits can be significant and long-lasting. It can be the key to getting your organization moving at the pace of the Digital and Social Era and meeting customer expectations for a great experience. It can help teams collaborate across traditional functional silos. And, it can increase the likelihood that your technology programs, by incorporating people and process, will have a ROI.

Data has revolutionized marketing. Using data, marketers today can better understand their customers, deliver personalized one-to-one experiences, and drive significant bottom-line results. But along the path to data-driven marketing nirvana, most CMOs will encounter at least a few monsters. Here are some of the most common examples of nightmare scenarios and a few helpful antidotes in case you happen to see them.

The (Data) Swamp Thing

Many companies are rushing to embrace Big Data, and many of these have created Data Lakes. A Data Lake, a term coined by James Dixon, CTO of Pentaho, is a large, enterprise-wide data management platform such as Hadoop where a vast amount of raw data is stored in its native form until needed. Data Lakes offer organizations lots of new capability and flexibility. However, in their rush into Big Data, some IT departments have moved quickly and haphazardly to fill their Data Lakes with customer and marketing data. Focused on success metrics like how quickly they “fill the lake,” they move data without much regard for how it will be used. Without data definitions, quality metadata, and data lineage, the data in a Data Lake can be difficult to put to good use. In these cases, the Data Lake morphs into a CMO’s worst nightmare: “the data swamp.” Like Peter Sondergaard of Gartner says, “Data is the oil of the 21st century. For all of its value, oil is useless thick goop until it’s refined into fuel.” A data swamp makes this refinement nearly impossible.

The Antidote

To stop this monster in its tracks, insist on the following:

  • A Business Question or Hypothesis-Driven Approach. Often the biggest challenge is to follow the 80-20 rule and identify the 20% of the data that provides the right insights. Prioritize data based on business value and business need.
  • An agile, iterative approach to managing, analyzing and activating data.
  • Robust, high-quality data definitions, metadata and data lineage.


The Frankenstein (of Fractured Experience)

How many times has something like this happened to you? You browse the web, shopping for something you’re really interested in and excited about, say a new golf club. You do your research, find your ideal club, and make the purchase. Maybe you didn’t get the best price, but you’re heading into the weekend with your new club, with reservations at your favorite course, and a forecast for beautiful weather. Everything is going great. Until you get the email. The email that reads “Hey, here’s 20% off the price of that new golf club you’ve been shopping for.” And, its from the same company you purchased the club from yesterday. Aren’t those marketers clever?

Unfortunately, this scenario is all too common. When I mention examples like these, almost everyone has a horror story of their own to share. Often more than one. Organizations implement re-marketing capabilities, using your behavior in one channel to follow up later with messages and offers in another channel. The trouble comes when data silos exist across these channels and there’s a lag in how quickly the data is integrated. So the decision to send you an email with a discount for that new golf club doesn’t see the transaction data showing that you just purchased it.

The Antidote

To avoid this nightmare, insist on the following:

  • A Focus on Customer Experience. Keep people, your prospects and customers, constantly in mind in terms of understanding their decision journey, improving their experience and meeting their needs and expectations.
  • The Right Data. Make sure your decisioning systems and marketing automation systems have access to the right data, in real time or relevant time depending on the use case.
  • Identity Resolution. A strong identity resolution solution, so you’ll know that you’re interacting with the same prospect or customer, even if they’re using multiple devices and browsers.


Avoid these monsters, and sleep well.

When most people think of advertising and marketing, an image of the “Mad Men” era agency comes to mind. But with surprising speed, the rise of digital–and the accompanying explosion of customer data–is reshaping marketing. Using big data, marketers today can better understand their customers, deliver personalized one-to-one experiences, and drive significant bottom-line results. They can align their marketing activities with overall goals and KPIs and gain insight into what’s working and what’s not.

However, in the rush to incorporate data science into marketing, new opportunities and approaches to customer intelligence have emerged as well. Many companies are discovering they can do more with big data if they focus on an often underrepresented element in their analysis–humans. Humans are more than a collection of data points; more than a summary of impressions, clicks, pageviews, likes, and transactions. While people generate more data than ever before, the challenge is that insights don’t just pop out of the data–you have to know where to look.

Become customer-obsessed; focus on the humans

The companies that successfully integrate big data into marketing are those that relentlessly focus on their customers. The goals and KPIs they set and measure themselves against, the way they structure and analyze their data, and how they incorporate customer insights into their activities are all driven by this customer obsession. Here are a few examples:

  • Promoting savvy, engaging content that connects with certain customer segments in just the right way, fueling their interest in a brand and it’s products and services.
  • Strengthening customer relationships by providing content that builds trust and establishes thought leadership—as well as demonstrating a keen interest in the customer’s interests. (What’s the most common attribute of interesting brands? They’re interested in you.)
  • Understanding the buying cycle of each customer segment through customer intelligence, lead management, lead scoring and lead nurturing.

Today, customer data, knowledge, and insights are more valuable and of more strategic importance than ever before. That’s largely because power has shifted over time from companies towards their customers. Customers have more options, greater access to pricing information, and, through social media, greater means to share their experiences with others, both good and bad. They have more power, choice and influence than ever before.

In this age of the customer, the only sustainable competitive advantage is knowledge of and engagement with customers.” – Forrester Research

Companies that succeed in this environment are those that become obsessed with understanding their customers, such as Amazon and Salesforce. These companies go to great lengths to exceed customer expectations by leveraging customer information and insights. They are masters at gathering data, turning data into insights, and making decisions and changes in faster and faster feedback cycles. In other words, they find out what’s working and what isn’t and adjust appropriately at lighting speed.

Blend behavioral science with data science

Whatever else it produces, an organization is a factory that produces judgments and decisions.” – Daniel Kahneman

Over the past decade, behavioral economists have changed how we look at consumer buying behavior. Most companies have historically viewed their customers in rational terms—as if purchase decisions were primarily guided by rational choices to maximize personal gain and utility. However, the image of the consumer making unfettered “rational” choices has given way to a greater understanding of economic activity and the many “irrational” factors that influence purchase decisions. We’ve learned that economic life is pervaded by culture, driven by relationships, and influenced by emotion.

At first glance, the worlds of data science and behavioral science may seem to be light years apart. Data science is concerned with analytics, technology, machine learning and big data. Conversely, behavioral science is concerned with human psychology. Upon further inspection, however, data science and behavioral science can be combined into a marketing dream team. Much of big data, after all, is customer behavior data continuously gathered through digital touchpoints and channels. Behavioral science can help guide companies in where to look for insights and how to interpret the data. It can help them develop and deploy marketing and experiences that go with—rather than against—the psychology of human decision making.

What’s Next?

For many companies, now is the right time to re-think how they gather, structure and analyze their customer data. It is time for new metrics and KPIs that are developed from the customer’s point of view. It is time to integrate silos of customer data that are fragmented across multiple internal and external systems. Does your company have a single view of the customer? Or is customer data scattered across multiple systems? When it comes to marketing activity, do you have an integrated view of all marketing interactions with a customer? Or is this data isolated for each separate marketing channel? One of the keys to humanizing big data is making sure that your data foundations are integrated and customer centric.

In their book, Built to Last, Jim Collins and Jerry Porras coined the phrase “the tyranny of OR.” They describe how choosing between seemingly contradictory concepts—focusing on this or that—often leads to missed opportunities. Breakthroughs, they argue, happen at points of integration: art and science; form and function; creativity and technology. When it comes to big data marketing, one of those points of integration is the combination of data science and behavioral science. CMOs can maximize their results by hiring and developing diverse talent across these domains and by building multi-disciplinary teams made up of both skillsets.

We’re in the early stages of tapping into the potential presented by big data marketing. The opportunities to apply data to marketing are growing—but only if we know where and how to look.

One of the best kept secrets in online marketing is that most campaign attribution data is completely wrong.” – Eric Peterson, CEO Web Analytics Demystified

There’s an old joke about marketing. A CEO looks across the boardroom table, frowns, and says, “I know that half the money I spend on marketing is wasted. The trouble is I don’t know which half.” This has been the unfortunate reality in marketing for decades—and still is the case in many companies. We know that marketing works. But oftentimes not how or why. And, almost certainly not which marketing investments are wasted or less effective. Before the digital and social era, it was almost impossible to understand with any certainty which marketing tactics were really moving the needle. As Yogi Berra once said, “You’ve got to be very careful if you don’t know where you are going, because you might not get there.”

Fortunately, times have changed, and CMOs now have the opportunity to more accurately measure, improve and predict the results from their marketing investments. Customers are rapidly adopting digital devices such as smartphones, tablets and wearables. Media is increasingly digitized. And, social media usage is growing across almost every consumer segment. The net result? We now have an unprecedented amount of data with which we can understand how marketing influences customer behavior across the full lifecycle of interactions with a company.

Why Data-Driven Attribution?

One of the hottest topics in marketing right now is data-driven attribution, the process of assigning credit to marketing events across multiple interaction channels. Most companies are in the early stages of accurately attributing marketing results to their marketing investments, and the practice of simply assigning all the credit to the last marketing touch is still common. However, these companies are missing out on an incredible opportunity. By some estimates, top performing marketing organizations are five times more likely to use advanced attribution models.

Simply put, data-driven attribution matters because it helps us develop marketing programs that cost less and deliver more. There are two primary reasons for this. The first reason is that consumer behavior has shifted dramatically in the past few years. McKinsey estimates that 56 percent of consumer journeys now include multi-channel, multi-device interactions. The traditional concept of the marketing funnel is now ancient history. It has been replaced by decision journey—the non-linear, multi-channel and consumer-driven path to purchase.

And, the second reason is quite simply that we now have the data and the capabilities to better understand how marketing drives purchase behavior. One of the wonderful things about digital is that it is an inherently measurable medium. As more advertising and marketing shifts into the digital world, we have more data that we can use to evaluate effectiveness. Marketers can now measure how campaigns perform for different customer segments and optimize their media buys and creative elements over time to improve effectiveness. So, we have more and more data, but what about capabilities? There’s good news on that front as well. Cloud computing, big data technologies, and growing ranks of data scientists are combining to deliver the data, visualizations, and algorithms required for data-driven attribution. These forces are also driving down the cost of these capabilities, and enabling even mid-sized and small companies to benefit.

How to Make It Happen?

Companies that succeed with data-driven attribution—and data-driven marketing in general—are those that take a systematic, disciplined approach to identifying all the touchpoints they have with prospects and customers and creating an integrated view. These companies tend to treat customer data and insight as a core competency and source of competitive advantage. They invest in the hard work to identify marketing channel data, integrate it, and marry it to their customer profiles.

Step 1 – Inventory sources of marketing and customer data

Once marketing organizations take a closer look at relevant data sources, many discover that, while they have volumes of data internally, much of the data they need has yet to come through the front door. It remains locked away with advertising agencies, web analytics vendors, or other third parties. The first step in building data-driven attribution capabilities is to inventory the data sources that are needed—including those that are already available and those that must be pulled into the company.

There are many potential data sources to consider. To help jump start your thinking, here’s a checklist of some of the most common examples:

  • CRM
  • email campaign
  • direct mail campaign
  • web and mobile clickstream
  • call center
  • paid search
  • organic search
  • affiliate marketing
  • social media
  • Internet video
  • Internet radio
  • display ads
  • mass media
  • stores / branches
  • marketing automation
  • marketing cost
  • customer/prospect demographics
  • customer/prospect behavior

Step 2 – Integrate data and build an event stream

Once you have a good handle on all of the potential data sources that are relevant to your data-driven attribution efforts, the next step is to begin to integrate some of that data. Start with data from your most important marketing channels and go from there. These sources can be consolidated using traditional data warehousing techniques; however, the large volume and unstructured nature of some of the data sources will benefit from big data technologies.

The data foundation for attribution is the marketing event stream. Essentially, this is a data structure that ties together all marketing interactions that occur with each individual. It requires detailed, atomic-level data from each marketing channel and then matching that data up with individual prospects and customers. In some cases, information about the customer or prospect is known and in some cases the prospect is completely anonymous. One of the core challenges is identity resolution—tying online and offline interactions to individuals who could be engaging across multiple channels and devices.

A marketing event stream may look something like the example below, where multiple interactions over multiple days occur prior to conversion.

Step 3 – Develop rules-based attribution models

The next step in the process is developing your first attribution models. The best starting point is simple, rules-based attribution. There are several examples of this illustrated in the diagram below. Last touch attribution means that we assign all of the credit for a conversion event to the last marketing touch that occurred. First touch is the opposite of that, all the credit goes to the first touch. And, even-weighting is assigning equal credit to all the marketing touches in the event stream. Each of these rules-based models is imperfect and incomplete; however, they are good places to start and develop a better understanding of conversion paths.

Step 4 – Test & learn; Develop algorithm-based attribution models

Evolving to a more sophisticated and more accurate approach to attribution often means developing an algorithm-based model. Many companies find that a test and learn approach helps refine a model that does a much better job of assigning credit. Test your hypothesis using a small percentage of your marketing budget and analyze the results. The cost and effort associated with this approach are challenging; however, the payoff can be huge and a source of competitive advantage.

 Step 5 – Ensure that insights lead to action 

The key to success with data-driven attribution is ensuring that insights lead to better decisions and actions. The process of integrating data and building attribution models is difficult, time-consuming and costly. And, all of that investment is wasted if insights never see the light of day. To avoid this situation, focus on people and process aspects of data-driven attribution, not only data and technology. Proactively think through the business processes that will need to change: everything from marketing strategy, to budget planning, to campaign optimization. How will data visualizations be distributed? Do you have the right people available who can review the data and craft a compelling business story that influences decision-making? What existing KPIs and measures will be disrupted? Who is likely to support this change? Who is likely to resist it?

Next Steps

Developing a fully optimized data-driven attribution capability can be difficult and costly, but getting started doesn’t have to be. Companies can often begin with data from their most important marketing channels and use simple conversion path analysis and rules-based attribution techniques. This is a great way to prove the value of data-driven attribution and assess the people, process and technology changes that are needed.

Another key consideration is whether to outsource this capability or to keep it in house. Many companies are recognizing that customer and marketing data is a source of competitive advantage. And, these companies are investing more to build out related capabilities as an internal core competency. However, other companies find that they can outsource to an attribution vendor who brings their marketing channel data together and produces insights with a more affordable approach. This is an important decision, and senior leaders should ensure that it aligns with their overall business strategy and competitive capabilities.

Data-driven attribution is an important part of the future of marketing. It can help make marketing more efficient, effective and customer-focused—it will allow you to guess less and know more. And who wouldn’t like that?

We want to know what consumers are looking for, what their values are, and how can we meet their needs. It’s not just about Big Data; it’s about translating that into the truth.” — Gayle Fuguitt, President and CEO of the Advertising Research Foundation

Marketing leaders are using big data to get ahead. For example, the grocery giant Kroger attributes much of its growth (e.g., 43 consecutive quarters of growth at stores open at least 15 months) to its investment in UK-based dunnhumby, which helps Kroger analyze shopping data through its loyalty card. Using big data, Starbucks segments their loyalty customers before applying rules and targeted, individual offers. Citibank uses big data marketing analytics to determine which products and services (such as credit cards and savings accounts) to offer their customers. The list of success stories goes on and on.

Companies are learning that the most important ingredients in the recipe for big data results go well beyond technology. Here are 5 keys to getting the most out of your investments.

1. Begin with the end in mind

When done well, investments in the enterprise marketing stack should show clear ROI. And, the key to getting big ROI from big data marketing? Focusing on the insights and decisions that are desired.

Big data is not about the data or technology, but about the business decisions that the insights enable. The best approach to big data is business question or hypothesis-driven. Often the biggest challenge is to follow the 80-20 rule and identify the 20% of the data that provides the right insights.

2. Break down the silos

One of the greatest challenges with leveraging data for marketing is that so much of it is locked away in physical or organizational silos. One of the keys to big data marketing is integrating data from multiple channels, from multiple departments, and from outside sources, such as ad server and web analytics vendors. It is extremely difficult for marketers to get a comprehensive view of the consumer decision journey—or even how their campaigns are performing—when their data is so fragmented.

3. Invest in talent development/acquisition

Many companies fail to examine how far they have evolved their talent and how this impacts the types of marketing data and technology investments that are appropriate. There is a good analogy in car buying: smart car buyers don’t over-invest a Ferrari when a Mini Cooper will do. Because marketing has changed so much in such a short period of time, many in marketing are struggling to catch up. Those who rose through the ranks of traditional marketing are now faced with the need to develop very different skills and mindsets.

Consider the importance of data and analytics in marketing today. Companies that invest in new analytics platforms but fail to develop their marketing talent struggle to achieve the intended results. This may not necessarily be because they’ve over-invested in technology. It may simply be because they have underinvested in developing their talent or bringing in new talent. Given the pace of change in marketing, the marketing department should have some of the highest levels of investment in professional development anywhere in the organization.

4. Build cross-functional, efficient digital marketing processes

It is unfortunately common to find companies with new technologies and old business processes. Are marketing and IT still struggling to agree on the best interaction model? Are business processes taking longer to complete than needed? Are the handoffs between marketing and sales inconsistent? Is marketing optimized for agile, fast-iteration approaches?

All of these may be signs that investments in optimizing business processes haven’t kept pace with investments in marketing technology and big data. Optimized processes lead to better working relationships between marketing and IT and marketing and sales, happier employees, and rapid cycle times.

5. Make sure Marketing is in the driver’s seat.

Finally, make sure that Marketing is front and center—understanding and driving the marketing big data environment—from data to insights to decisions. The IT department is a critical enabler of big data marketing. However, the role of IT should be to manage and provide the technology capability. How that capability gets used must be in the hands of marketers. When this is not the case, it indicates that technology investments have gotten ahead of organizational readiness.

It’s no secret that new technologies—along with an explosion of digital data—are disrupting traditional approaches to advertising, marketing and sales. In a survey conducted by Adobe, most marketers agreed that marketing has changed more in the past two years than in the past 50. Marketers are suddenly spending much more on technology and driving more technology decisions. In fact, they now spend over $20 billion annually on marketing technology, a market that has grown by over 67 percent in just two years. In advertising, Adtech-driven programmatic and real-time bidding (RTB) approaches are taking over digital advertising. Business Insider forecasts that these approaches will represent 50 percent of digital ad sales by 2018, and many large companies are already shifting large portions of their spending this way.

With this type of rapid, technology-fueled change, innovation often occurs in silos. It is only later that broader patterns and adoption emerge. That accurately describes the situation today with Adtech and Martech: technologies and approaches that have evolved separately at a rapid rate and are only now beginning to fuse together. The connection between the two can be summarized in one word: data.

What Is Adtech? What Is Martech?

The lines of demarcation between Adtech and Martech are blurry, and widely-accepted definitions of each are hard to come by. However, here’s a shot at defining them.

  • Adtech – Adtech is short for advertising technology. The term refers to the technologies and approaches used for managing, delivering, targeting and measuring digital ads. The primary users of Adtech today are publishers and advertising agencies.
  • Martech – Martech is short for marketing technology. The term refers to the technologies and approaches used for managing and measuring all digital marketing activities. The primary users of Martech today are the digital marketing and e-commerce groups within organizations.

There are many similarities between Adtech and Martech. Some of the things that Adtech and Martech have in common are rapid change, new entrants, enterprise vendor acquisitions, the importance of data and, perhaps most notably, COMPLEXITY.

To get a sense of this complexity, which can feel almost overwhelming at times, one need look no further than the crowded and fragmented vendor landscape. For several years, the investment bank, LUMA Partners, has published these landscapes, now considered essential reading in Adtech and Martech circles.

Here’s the LUMAscape for Adtech (Digital Display Advertising):

And, here’s the LUMAscape for Martech (Marketing Technology):

AdTech: The Rise of Programmatic Advertising

Programmatic advertising, the use of software and data to purchase display ads that may be targeted to particular audiences, represents one of the most significant waves of innovation to ever hit advertising. Using programmatic approaches, advertisers and their agencies use Demand Side Platforms (DSPs) and Data Management Platforms (DMPs) to purchase display inventory on ad exchanges. DSPs provide centralized media buying from multiple sources, and DMPs centralize cookie data and help advertisers create segments that can be specifically targeted. For example, advertisers can target display ads for a new pet product to a segment of consumers who are believed to be pet owners. Publishers, in turn, use Supply Side Platforms (SSPs) to make their inventory available. SSPs provide outsources media selling and network management services for publishers.

Programmatic advertising has taken the world by storm in recent years. Consider these recent trends:

  • According to its global chief marketing officer, Google has targeted 60 percent of its digital marketing budget for programmatic campaigns.
  • The world’s biggest media spender, Proctor & Gamble aims to buy 70 to 75 percent of its U.S. digital media programmatically.
  • Recent research from eMarketer indicates that U.S. programmatic digital display ad spending will grow 137.1% to over $10 billion this year. And, the forecast is for that number to double by 2016.

MarTech: The Rise of Data-Driven Marketing

For years, marketers have struggled with measurement, personalization and targeting, and e-commerce. In Adobe’s 2013 study, aptly titled Digital Distress, these ranked lowest when marketers were asked about how well their organizations were prepared to execute different types of marketing activities. Only 43 percent felt well equipped to execute e-commerce while, by contrast, 70 percent felt well equipped to execute brand building, the traditional “bread and butter” of marketing.

In many marketing organizations today, measurement is limited to whatever is available in Google Analytics, Adobe Analytics or other web analytics reporting system. And, lower value insights such as pageviews and clicks are what is most commonly reported. But what about insights into who is visiting? Or their level of engagement? Or better insight into the effectiveness of different pieces of creative content? Or the ability to create a personalized customer experience? Or the ability to offer different types of incentives to attract the best customers?

Marketers, hungry for these types of insights and capabilities, are turning to Adtech technologies to expand their enterprise marketing stacks. And, DMPs are one of the most common examples of this trend.

Data: The “Connective Tissue” of Martech and Adtech

As described earlier, DMPs allow companies to centralize data, both their own online and offline data as well as third party data, and activate that data though multiple marketing channels. While DMPs are still primarily used by advertising agencies, they are increasingly used by marketers. In fact, marketing technology vendors have been quick to acquire DMPs and bolster their capabilities.

Using a DMP, marketers can measure how campaigns perform for different customer segments and optimize their media buys and creative elements over time to improve effectiveness. Marketers are using DMPs to better understand the characteristics and behavior of their online visitors and present tailored experiences and offers. DMPs also help address the need to move closer to real-time execution in the digital and social era. They provide rapid capability to develop customer insights and push those insights to digital ad execution and marketing automation systems. In short, DMPs help marketers better understand the context of individuals as they move through their decision journeys and then act based on those insights.

Interest in DMPs has been surging in the past few years as the technology becomes a critical component of the marketing technology ecosystem. In fact, some predict that DMPs will eventually emerge as the one-stop shop for all marketing data needs.

What’s Next?

Many marketers will determine that the data, capability and insight provided by DMPs are a valuable core competency. When an advertising agency or other third party manages a DMP on behalf of a company, data can be activated only in the channels managed by the agency. This creates execution gaps and analytical blind spots. Given the strategic importance of data and analytics, many CMOs will decide that the DMP and related capabilities belong in-house, where they become part of the broader marketing technology stack. In fact, according to those surveyed by Winterberry Group, 62 percent said their company has already implemented a DMP or plans to do so within the next 12 months.

Adtech and Martech will continue to converge in the coming years, as future marketing campaigns unfold in real-time across multiple channels. Advertisers, publishers, marketers and, most importantly, consumers all benefit as a result. As William Gibson famously remarked,

The future is already here — it’s just not very evenly distributed.

I feel the need…the need for speed.” – Maverick and Goose

What does Top Gun have to do with modern marketing? To understand the connection, we need to tell the story of John Boyd.

Boyd was a US Air Force fighter pilot and military strategist whose theories have been incredibly influential. People have gone so far as to call him one of the most remarkable unsung heroes in US military history and greatest military theorist since Sun Tzu. He was a fighter pilot during the Korean War and helped establish the Fighter Weapons School at Nellis Air Force base—a school similar to the one featured in the movie Top Gun.

Boyd developed a fast-cycle theory, delivered in hundreds of lectures and influencing fields as diverse as military tactics, business competition and business process design, that became known as “Boyd’s Law.” After spending much of my career helping companies implement data-driven marketing, I’m convinced that Boyd’s Law is alive and well in marketing also.

Lessons from Top Gun

Top Gun.001

Boyd wasn’t just a good fighter pilot—he was a great one. During the 1950s, combat pilots across the US knew that Boyd had a standing offer for what was know as the forty-second challenge: take a position behind his plane, and in 40 seconds or less Boyd would have his opponent in his own gun-sights or pay $40. Colonel Boyd never lost the bet in more than 3,000 hours of flying.

While Boyd was an experienced pilot with quick reflexes, his invincibility in mock combat came from an insight he developed after the Korean War. Boyd answered a question that had captured his interest during the war. He wanted to understand why the relatively slow US F-86 dominated the far superior MIG-15.

Boyd realized that all fighter pilots go through a cycle of observing, orienting, deciding and acting; something he termed the OODA loop. Boyd’s crucial insight was that the speed with which pilots completed the OODA loop mattered much more that the quality of decisions in each step. That was why the slower F-86 could win out over the faster MIG-15. The F-86 provided better visibility for pilots and could roll faster. The key to winning wasn’t a plane that could fly faster and higher but one that could change course quicker and enable a faster OODA loop. This answer became the foundation for Boyd’s fast-cycle theory known as Boyd’s Law.

What Does Top Gun Have To Do With Marketing?

Personalized, one-to-one marketing has long been represented the “holy grail” for marketing. For years, marketers have sought to present the right offer and message, to the right customer, at the right time, in the right place, and in the right context. Marketing has moved from a “one size fits all” to a “one size fits one” approach—and with good reason. Personalized marketing can be as much as ten times more effective than static approaches and has consistently improved conversion rates, average transaction size, and customer loyalty.

Over many years helping clients implement personalized marketing, I’ve observed some common patterns. Companies building personalization and data-driven marketing capabilities tend to make similar investments. They invest in data infrastructure to integrate information from various marketing channels and customer touch points with their CRM data. These days that often includes investments in Big Data technologies, such as Hadoop and NoSQL data infrastructure, to deal with the growing amount of data as well as increased variety and complexity. Companies also typically build centralized repositories of offer and message data to improve consistency across channels. And, they often invest in some form of decision engine that can make personalization decisions in real-time. These investments represent the new “hierarchy of needs” for modern, data-driven marketing and take the form of the example below.

marketing hierarchy.001

One of the most important factors in the success of personalized marketing, however, is speed and agility. The faster companies can execute “test and learn” cycles—where new offers, messages, treatments, and rules are implemented, evaluated and improved—the better the results.

In fact, personalization that includes these fast cycle times will often perform better than personalization that uses better decision engines, more sophisticated rules and segmentation strategies, or higher quality data. On the surface, this phenomenon seems counter-intuitive. That is, until you consider Boyd’s Law. Here’s why I believe speed and agile are new imperatives for marketing.

Many large, Fortune 500 companies have invested millions of dollars in their personalization capabilities. These companies have some of the best data analysts and data scientists, robust data, and extremely sophisticated rules engines to drive marketing messages and offers. However, this level of sophistication often includes a hidden downside: complexity. The complexity of the decision rules, the technology environment and data infrastructure means that it can take months to execute a full “test and learn” cycle. In other words, it can take several months before teams understand how the offers, creative elements, messages and decision rules are performing and can makes changes to optimize performance. Over the course of a year, these companies are lucky to execute three or four “test and learn” cycles.

On the other hand, I’ve worked with companies that have implemented much less complex and sophisticated (and often much less costly) approaches to personalization that have performed extremely well. Compared to static approaches, these implementations have led to dramatic increases in conversion rates and average purchase amounts—often in six months or less. On the surface, this is just as puzzling as the dominance of the F-86 over the MIG-15 was to John Boyd. In both cases, the answer is the same: fast cycle time and agility. When implementing personalization, these companies optimized for business process and cycle time rather than sophisticated decision engines, segmentation strategies, and business rules. Rather than a “test and learn” cycle of months, these companies implement “test and learn” cycles in a day or week.

Process Is The New Black

The key takeaway from John Boyd is clear. While it is tempting to over-emphasize data and technology when implementing data-driven, personalized approaches, it is important to keep in mind that these are only foundational elements. Companies will be best served by optimizing for rapid “test and learn” cycle times, even if this means starting with less sophisticated data and analytics. While fast cycle times are more challenging to implement, the payoff is significant. Granted, process isn’t generally the most exciting topic for marketing and technology folks. But, when optimized processes and rapid cycle times deliver higher conversion rates, revenue, and competitive advantage, the results are very exciting indeed.

The test of a first-rate intelligence is the ability to hold two opposed ideas in the mind at the same time, and still retain the ability to function.” – F. Scott Fitzgerald

In their book, Built to Last, Jim Collins and Jerry Porras coined the phrase “the tyranny of OR.” They describe how choosing between seemingly contradictory concepts—focusing on this or that—often leads to missed opportunities. Breakthroughs, they argue, happen at points of integration: art and science; form and function; creativity and technology.

Today, a new type of professional has emerged: the “hybrid.” Hybrids combine concepts that have traditionally been considered separate, distinct and sometimes contradictory into their education, training, job roles and leadership. As a result, they are helping organizations benefit from digital disruption, creating new models for change, and bridging functional boundaries. The path they’re charting may well be a new model for professional development and leadership roles of the future.

There was a time when conventional career wisdom was to choose an area of focus and work hard to develop expertise, reputation and relationships within that domain. Young people in their 20s fretted if they hadn’t yet found this focus, worried that their career prospects might not shine as bright. Workers in large companies developed their careers within organizational silos, rising through the ranks of functions like finance, marketing, operations or IT. Pundits cited the “10,000-hour rule”—the idea that this amount of time is required to reach meaningful expertise and success in any field. As a result, many knew a great deal about their specialty but few knew about—or even expressed interest in—other areas.

However, events occurred that caused some people to re-evaluate this model. Chief among these were a faster pace of change, disruption resulting from rapid adoption of mobile, social and digital technologies, and increasing customer expectations. Companies that wanted to survive and thrive in this environment needed to focus on innovation and change. And, innovation and change often benefit from the integration of seemingly contradictory concepts. Professionals with deep expertise in functional areas were still badly needed, but something was missing. Enter the hybrids.

Who Are Hybrids?

Hybrid professionals have emerged over time, often driven by curiosity and emerging needs. They developed expertise in one area early on but, for various reasons, diverted some of their energy, time and attention to learning another area as well. The knowledge they acquired, the relationships they developed, and the nature of their work all began to change as a result. In some cases, hybrid professionals honed their skills across disciplines because of a formal training program, such as corporate leadership programs that rotate participants through brief stints in various functions. However, in many instances, people grew into hybrids as a result of a shifting landscape that required and rewarded the integration of and collaboration between disciplines. Hybrids typically stand out in organizations. Consider the case of a former entrepreneur who is now leading internal innovation in a Fortune 500 company. Or the case of a marketing professional who now seems to know as much about technology as the IT guys. Often referred to as generalists or multi-specialists, hybrids bring value to organizations that is only now beginning to be better recognized, understood, encouraged and rewarded.

Characteristics of Hybrids

The most obvious characteristic of hybrids is that they have expertise in more than one discipline. However, there are several other traits that many hybrids seem to share.

  • Collaboration – Hybrids enjoy collaborating with others and bring a team-oriented approach to their interactions. They tend to have a mindset of WE instead of ME. They assume positive intent when dealing with other teams or departments.
  • Curiosity – Hybrids are often very intellectually curious and have broad interests in other disciplines. They enjoy learning new things and growing through new experiences. They share an intense interest in other people and value backgrounds and experiences that are different from their own.
  • Intrinsic Motivation – Many hybrids are motivated by purpose more so than money. They gain satisfaction in their work through a degree of autonomy, a freedom that allows them to learn about and participate in other disciplines. They are more concerned with the results of their work than with who received credit or traditional notions of who does what.
  • Rebelliousness – A little rebellion against the status quo every now and then is not such a bad thing. Hybrids aren’t afraid to cross organizational and functional boundaries. They have less respect for “the way it’s always been done” and tradition, and they have the capability to deconstruct behavioral norms and ossified patterns.

Hybrid Examples

Examples of hybrids are increasingly easy to find. Hybrids are emerging in multiple industries and geographic regions. While a comprehensive list of examples would be very long, there are several examples that have become more common in recent years.

Marketing + Technology

As I’ve written in previous blog posts (e.g., Why Marketing Technology Is Set To Explode), marketing is going through the most significant, technology-fueled transformation in its history. Direct, digital relationships with customers are now more critical to company success than ever before, and marketing technology is enabling this. All of a sudden, CMOs are spending almost as much on technology as their CIO counterparts. In fact, Gartner predicts that CMOs will have larger IT budgets than CIOs by 2017.

Not only is technology much more important to marketing than before but it is also more complex. In recent years there has been an explosion in marketing technology options from both startups and established players. Vendors have released major advances in lead generation, lead scoring, web analytics, marketing automation, data management, tag management, CRM, etc. And, customers ever increasing digital interactions with companies has resulted in massive volumes of data. For example, Google says that 90 percent of the world’s data was generated in just the past two years.

This environment has given rise to the marketing technologist. Marketing technologists are hybrids who bring deep expertise in both marketing as well as relevant marketing and digital technologies. CMOs are rapidly hiring “Chief Marketing Technologists” and “Marketing Operations Leader” roles. Thought leaders in marketing, such as Jay Baer, Scott Brinker, Laura McLellon, Paul Roetzer, and Mayur Gupta, have chronicled the rise of the marketing technologist, the talent gaps that exist, and the need for more hybrid professionals in marketing. For example, the July issue of Harvard Business Review is devoted to the new basics of marketing and includes an excellent article, “The Rise of the Chief Marketing Technologist,” by Scott Brinker and Laura McLellon. They describe the evolution of this role and make the case that the future of marketing belongs to the generalists, the hybrids.

Big Company + Startup

In big companies projects have to scale and Lean Startup help us to do it” – Beth Comstock, CMO of GE

Another example of hybrids is in the combination of entrepreneurial, startup expertise with big company, innovation expertise. As large companies pursue innovation and respond to digital disruption, many of them are looking to startup models and former entrepreneurs for help.

Startups create something new and of value under conditions of extreme uncertainty. Now, many people are adapting startup practices, such as Lean Startup, within large companies in order to accelerate innovation. The Lean Startup approach helps companies use both money and human creativity more efficiently and effectively. Rapid iterations and feedback loops allow teams to test their earlier hypotheses of customer value. Customers provide feedback and teams observe and gather data around customer behavior, usage, and experience. The knowledge gained through this process allows teams to adjust and pivot in order to maximize value.

There’s a big reason that large companies need to innovate and act more like startups—those that don’t aren’t likely to be around for long. According to the consulting firm Innosight, the average lifespan for a company in the S&P 500 dropped from 61 years in 1958 to 25 years in 1980 to 18 years today. What’s even more astonishing is that, at the current churn rate, 75 percent of the S&P 500 will be replaced in 25 years. This background provides the context for understanding why executives in big companies feel tremendous pressure to innovate.

More and more brands are creating innovation labs, often staffed with former entrepreneurs, and traditional companies are starting to embrace entrepreneurship as a core competency. Innovation lab models vary—some are located within corporate headquarters while others often located in outposts such as Silicon Valley or other hubs of digital and tech talent. Beyond driving innovation, these labs also serve as a recruiting tool by helping to shape the image of large companies and attract digital talent.

Hybrids in this space typically have some entrepreneurial background or startup experience. In some cases their startup was acquired by the larger company. In other cases, they have built and sold a successful startup and pursuing innovation in a large company is their next career challenge. To be successful these hybrids need to blend their entrepreneurial experience with the realities of working within a large company. Selling their ideas, building relationships and support, and navigating a complex corporate environment are all skills they master in order to be effective.

Form + Function

Have you noticed how easy and enjoyable many smartphone apps are to use? How intuitive, sticky and user-friendly cloud-based applications such as Evernote and Salesforce have become?

With the rise of cloud-based, on-demand software, more and more software offerings are designed to cater explicitly to the needs of users. They typically require little or no training, are intuitive and easy to use, offer simple designs and feature-sets, and provide much more enjoyment. In the past, vendors could get away with ugly, cumbersome, difficult-to-use software. Today, they simply can’t. In fact, Net Promoter Scores for software vendors are more correlated to customer experience than product performance.

At Evernote, a maker of a free application that helps you remember everything across all of the devices you use, the company has made a radical shift in it’s approach to building software. As Phil Libin, Evernote’s CEO, describes it, they went through a very intentional effort to make experience and design the focal point of their process. In the past, Evernote followed a traditional software development approach that can be described as “feature first.” Several years ago, Evernote changed their approach from “feature first” to “experience first.” They now start with the design and allow everything to follow from there. They recognized that they had talented designers on staff, but they needed to include designers earlier in the process and invite them to all the key meetings.

Design is not just what it looks like, design is how it works.” – Steve Jobs

These designers are hybrids who have blended expertise in both form and function. A concept popular within modernist architecture and industrial design circles, marrying form and function requires designers who can optimize the attractiveness (the “form”) of their designs with usability and usefulness (the “function”). Many of these hybrids started out as web or software designers with a focus more akin to graphic designers, designers primarily concerned with the presentation of text, images, colors, and other creative elements. However, as more websites became interactive and more attention was placed on software usability, these designers shifted into hybrid roles. They began to develop expertise in user experience and human-computer interaction. They developed skills in ethnographic research and other approaches to developing deeper user understanding and even empathy with their users. By combining these disciplines, hybrid designers create beautiful interfaces and websites that are also intuitive, easy to use, simple, and enjoyable.

Humanist + Technologist

One of the most interesting examples of hybrids in recent years is the combination of humanist and technologist. There is growing awareness of the need for this hybrid focus and role, increasingly so in a digital era that includes the Internet of Things (IoT) and continued automation of processes and traditional roles. One of the ironies of the digital age is that even as the importance of technology increases, the first priority of organizations should be to humanize their interactions with their customers. In the words of uber-research analyst Ray Wang, “The constructs of B2B and B2C have changed to People to People (P2P). Contextual relationships, trust, transparency, and value exchange are the key pillars.”

While many people would still consider the phrase “humanist technologist” to be an oxymoron, there are a growing number of these hybrids who focus on both humanism and technology. They bring several priorities to their work:

  • Ensure that technology is people-centered and driven by deep understanding of users and their goals;
  • Bring focus to the role of trust in customer relationships and ensure trust is maintained through company behavior, customer privacy, and relationships; and
  • Keep the human face of a brand in focus and alive in interactions and experiences.

In many cases, these hybrids have backgrounds and expertise in technology but developed interests in disciplines like Design Thinking and Customer Experience (CX). Some have educational backgrounds in engineering, science, math and technology as well as in art, literature, or social work. They enjoy helping to use technology in ways that enhance enjoyment, interactions and relationships between people. They care about the nature of being human as well as the nature of technology and actively work to reconcile the two. In other words they focus on technology in service to people—they maintain that while technology is responsible for connecting people, it’s always people that matter.

Your 90 Day Plan

Given the current environment and pace of change, the ranks of hybrid professionals are likely to continue to grow. The value they bring to organizations is now better understood, encouraged and rewarded. However, there are still many barriers that make hybrid roles more difficult to pursue and introduce in organizations. As companies consider evolving the way they think about traditional roles and hybrid roles, here are a few recommendations to keep in mind.

  • Change Recruiting Practices. Hybrids are difficult to recruit. Not only are they hard to find, traditional recruitment practices often overlook them. Because they don’t fit neatly with traditional job descriptions and have more varied backgrounds, recruiters tend to pass them by. To hire hybrids, most companies will need to significantly revamp their recruiting and hiring processes.
  • Create New Role Definitions. Companies will need to work with their senior leaders to proactively understand hybrid needs and define new roles. New roles, such as “Chief Marketing Technologist,” are critical to many organizations, but it takes time to define and recruit for these types of roles.
  • Encourage Collaboration. Hybrids thrive in environments where collaboration and communication are the norm and are rewarded. Because they work at the intersection of disciplines, they need to have strong relationship-building and teamwork skills. Recruit professionals with these traits and reward this type of behavior.
  • Pursue Purpose. Purpose is a much stronger, more sustainable motivator than money, especially for hybrids. Pursue a purpose that your organization can rally around and engage hybrids who share excitement for your mission.
  • Build Leadership Support. Communicate the value of hybrid role to senior executives and encourage their support. Executive understanding and leadership in supporting hybrids is critical.

Information is the oil of the 21st century, and analytics is the combustion engine.” – Peter Sondergaard, Gartner

When most people think of advertising and marketing, an image of the “Mad Men” era agency comes to mind. But with surprising speed, the rise of digital–and the accompanying explosion of customer data–has revolutionized marketing.

Using technology and data, marketers today can better understand their customers, deliver personalized one-to-one experiences, and drive significant bottom-line results. To achieve these goals, they now spend over $20 billion annually on marketing technology, a market that has grown by over 67 percent in just two years. In addition, spending on big data hardware, software and infrastructure is forecast to grow to a total market size of $114 billion by 2018.

As the strategic importance of data has increased, new approaches to customer analytics have emerged as well. As customer interactions with companies grow and diversify, the need to integrate data faster and deliver real-time insights is critical. This post will explore the underlying trends driving companies to become more data-driven and invest in customer analytics. And, it will outline three types of approaches to capturing, managing, analyzing, and activating customer knowledge and insights.

Why Customer Data Is Critically Important

The Age of the Customer

Today, customer data, knowledge, and insights are more valuable and of more strategic importance than ever before. That’s largely because power has shifted over time from companies towards their customers. Customers have more options, greater access to pricing information, and, through social media, greater means to share their experiences with others, both good and bad. They have more power, choice and influence than ever before.

In this age of the customer, the only sustainable competitive advantage is knowledge of and engagement with customers.” – Forrester Research

Companies that succeed in this environment are those that become obsessed with understanding their customers, such as Amazon and Salesforce. These companies go to great lengths to exceed customer expectations by leveraging customer information and insights. They are masters at gathering data, turning data into insights, and making decisions and changes in faster and faster feedback cycles. In other words, they find out what’s working and what isn’t and adjust appropriately at lighting speed.

Changes in Customer Behavior

Customers have taken control of their purchase process. With websites, blogs, Facebook updates, online reviews and more, they use almost twice as many sources of information to make decisions as they did in the past and often engage with a brand dozens of times between inspiration and purchase. According to a survey conducted by Endeca Technologies, 50 percent of customers interact with an average of two touchpoints to research or purchase products, and 36 percent engage with an average of three.

Digital technology change is driving much of this. Customers are rapidly adopting new devices and new digital and social media touchpoints. In a sense, technology has turned our customers into moving targets. Consider how much your own shopping behavior has changed in recent years.

Farewell Funnel, Hello Decision Journey

Because of this dramatic shift, the traditional marketing funnel paradigm–the linear flow from leads, to prospects, to purchase that’s been with us since the “Mad Men” era–is being replaced. The new paradigm for marketing is the customer decision journey that places customer needs, not business needs, at the forefront. The customer decision considers the overall experience that a customer has with a brand and how that contributes to loyalty (or not). It also better reflects the ability of each individual to influence the purchase decisions of others to an unparalleled extent. The decision journey of most customers is non-linear and multichannel.

Customer decision journeys can be thought of as activities that fall into several different stages.


In this stage, customers discover an unmet need. They begin to think about a relatively narrow set of products and services that might meet this need. Most customers start their decision process with brands they are already familiar with.


In the next stage, customers begin to actively explore and evaluate their options. The customer is intent on purchasing. They begin to explore on the Internet, research options, read reviews, and pay closer attention to advertisements and promotions. In this stage, it is critical for brands to ensure that review and comparison information is widely available.


The third stage of the decision journey involves the customer’s decision and actual purchase. Customers tend to make their purchases in the most convenient channel.


In the Engage stage, customers engage with the product or service they’ve purchased. The experience that customers have during this stage determines loyalty and whether positive or negative feedback is shared via reviews and social media. They may also engage with customer service or a user community to receive support. Customer activity during this stage can also help companies identify other potential needs.


During the Advocate stage, customers share their experiences of the product or service with others. They may do this through reviews, social media or direct word of mouth. Companies can encourage this by providing invectives to customers to provide reviews.

Customer Insight Drives Business Results

Companies are finding that a better understanding of their customers and customer journeys can lead to significant business results. One of the reasons for this is that customer behavior has changed so much in such a brief period of time, and customer behavior continues to change. Technology has turned customers into moving targets. Companies can no longer assume that what worked last year–in terms of customer acquisition, engagement, retention and experience–will continue to work this year. Another reason for growing interest in customer journeys is that customer behavior now varies considerably across customer segments and even within broad, demographically-defined segments. Consider the case of two people with nearly identical demographic characteristics: both male; same generation, both born in 1946; grew up in England; 2 children; very wealthy; and both have large real estate holdings. However, most people would agree that these two people–Prince Charles and Ozzy Osbourne–have very different behavior! Mapping customer journeys and analyzing customer data allows business leaders to understand these differences and respond accordingly.

Here are a few examples of business outcomes that often result from improved customer knowledge and insight.

Marketing & Advertising

Companies are leveraging customer data to move ever closer to the elusive goal of truly personalized marketing: the right offer, at the right time, in the right location and context, to the right person. Even incremental improvements over traditional mass marketing approaches yield major gains in conversion and new customer acquisition rates. Gartner estimates that there is likely a 10X improvement in response rates for offers that are timely, relevant and convenient over those that aren’t.

Customer journeys and customer data are also being used by progressive companies to shape their marketing strategies and guide spending on marketing and advertising. For example, consider the case of a hypothetical company with an outdated website and the need to boost sales in the coming year. Conventional wisdom may lead the CMO to include a significant round of website investments in the marketing plan and budget. However, by mapping the customer journey of the company’s most important segments and analyzing the associated data, the company discovers that online reviews play a much more substantial role in the decision process for these customers. In fact, the majority don’t bother to visit the website at all. Armed with these insights, the CMO redirects investment to improve product photos on online review sites and encourages customers to post reviews. Total marketing spending doesn’t increase, but sales and return on marketing investment show marked improvement.

Customer Service

During the Engage stage of the customer journey, customers may interact with customer service or an online user community to receive support. By capturing and analyzing the data from these touch points, such as customer service notes and online forum postings, companies can identify customer pain points and issues proactively and update their customer service FAQs or other communications with existing customers. This not only improves customer experience, making it easier to resolve problems, but it also decreases customer calls into call centers and overall service costs.

Retention & Loyalty

Many companies have discovered patterns that customers exhibit before they cancel service, close an account or switch to a competitor’s product. Using customer data and analytics, these companies deploy and refine predictive models that help them retain customers with proactive approaches. Investments, in terms of offers and upgrades, can be made at the right time to increase the likelihood of retaining desirable customers.

Customer Experience

The experience that customers have with companies matters a great deal. According to research conducted by Peter Dahlstrom and David Edelman of McKinsey & Co, fully two-thirds of the decisions customers make are informed by the quality of their experiences all along their journey. Other recent research has highlighted the critical connection between experience and company financial performance. Customer experience leaders have significantly outperformed laggards in cumulative stock returns.

Approaches to Customer Knowledge and Insights

So we’ve established why customer journeys and customer data are so important and how companies can use the resulting knowledge and insights to drive business value. But how are they accomplishing this? What data are they capturing? And, how are they putting it to use?

It turns out that technologies and approaches have emerged in recent years to help make sense of all the data that is being generated in the digital and social era. We live in a world of rapidly increasing data volumes where data plays a key role in determining winners and losers in the competitive arena. It’s of little surprise then that large enterprise technology vendors and startups have rushed to introduce new technologies to help companies unlock, analyze and activate their customer data. Many of these solutions fall into three types:

  1. Evolved Data Warehousing Solutions
  2. Data Blending Solutions
  3. Data Management Platforms

Evolved Data Warehousing Solutions

For many years, traditional business intelligence and data warehousing technologies and approaches have been used to capture and analyze customer data. Beginning in the 1990s, companies pulled data from their transactional systems into separate, centralized data warehouses to support reporting and analysis. The typical extract-transform-load (ETL)-based approach to data warehousing captures data housed in disparate source data systems, transforms the data, and then moves it into the data warehouse, where the data is arranged in a way to help facilitate access. By centralizing data in the warehouse, companies could create a “single version of the truth” and avoid the errors and discrepancies that often plagued them when reports were created from various transactional and source data systems.

Traditional data warehousing solutions are expensive to build, but they play an important role in companies. They are used to generate reports and visualizations on the holistic company performance that go to executives and regulators. The data warehousing approach, architecture, and vendor ecosystem is very mature and has been honed over the past few decades. Traditional data warehousing isn’t going away anytime soon.

However, the explosion of data, particularly unstructured data, generated in recent years has strained the traditional data warehousing approach and underlying technologies. The foundational infrastructure of data warehousing has been the relational database, which stores data into tables (or “relations”) of rows and columns and is used for processing structured data. As the volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources) of data has increased, relational databases often aren’t able to provide the performance and latency needed to process large, complex data. This volume, variety, and volume (3V) description of data was created by industry analyst Doug Laney (currently with Gartner) in 2001 and is one of the most common ways of characterizing “big data.”


In response, a new generation of big data technologies have emerged that make capturing and analyzing customer data, even large volumes of it, faster, easier and more cost effective. While not yet as mature as traditional data warehousing, big data technologies represent an evolution of data management and analytics technologies, architectures, and approaches. Many consider the foundational infrastructure of big data to be the Hadoop ecosystem. At the core of this ecosystem is Apache Hadoop, open-source software for distributed storage and processing of big data. In addition, the ecosystem includes other software such as Hive, Pig and Spark. The Hadoop technologies break up and distribute data into separate parts and then analyze those parts concurrently.

NoSQL and Massively Parallel Processing (MPP)

In addition to Hadoop, big data technologies include technologies such as NoSQL and MPP technologies. NoSQL, which stands for Not Only SQL, refers to a range of database technologies that are good at processing dynamic, semi-structured data. For large volumes of strucutred, semi-structured, and unstrcutured data, NoSQL databases are more scalable and provide better performance than traditional, relational databases.

MPP technologies process very large volumes of data in parallel across hundreds (or even thousands) of processors. MPP has a lot in common with Hadoop. However, while Hadoop typically runs on inexpensive commodity hardware, MPP generally runs on expensive, specialized data warehouse appliances that are optimized for CPU, storage and network performance.

Many organizations find that they require both traditional data warehouse and big data environments. Big data cannot yet provide the level of trust, security and metadata management offered by the traditional data warehouse. However, the business value associated with customer analytics and processing greater volumes, velocity and variety of data drive the need for big data approaches.

Operating on top of data warehousing and big data environments are a host of reporting, analytics, visualization and data mining technologies. These technologies continue to improve and evolve as well, making it easier to both discover customer insights from the data and to communicate those insights in easy-to-understand ways that help inform decisions and actions.

Another recent technology development, cloud computing, benefits both traditional data warehousing and big data approaches by driving down the cost of computing and data storage. The good news is that, while data volumes are exploding, the cost of storing data continues to decrease every year. Cloud computing makes it easier than ever to spin up new computing resources—accomplishing this with literately a push of a button. And, cloud computing infrastructure is often half the cost or less. Companies such as Amazon have revolutionized cloud computing with Amazon Web Services (AWS) and have driven down cloud computing costs over the past several years. Many organizations have discovered that cloud computing infrastructure is more reliable and secure while a fraction of the cost of on-premise infrastructure.

Data Blending Solutions

While a single, centralized data warehouse or big data environment offers many advantages, analysts sometimes need access to different customer data sources for critical decisions. The process of bringing a new data source into a data warehouse or big data environment, through a traditional extract-transform-load approach, can be time-consuming. Traditional data warehousing and business intelligence approaches and technologies almost always have a serious challenge: a constant backlog of IT requests. In a recent research survey, Forrester found that about 63% of business decision-makers now use an equal amount or more of homegrown or shadow rather than traditional, enterprise BI applications.

Historically, analysts used tools like Microsoft Excel or Access in situations where they needed to analyze data not available in the data warehouse. But, in recent years a new type of solution, data blending (also sometimes referred to as data discovery), has emerged. Using data blending tools, analysts themselves can access, cleanse, and blend data from multiple sources without having to write a line of code. These tools allow customer data to be blended together from multiple internal sources as well as external sources immediately to support a more agile approach to customer analytics. This is increasingly important because if companies know what their customers are doing better than their competitors, or can get to those insights faster, then they have a very distinct advantage.

Data blending solutions often include direct connectors to the most common sources of internal customer data as well as connectors to packaged third party data from leading providers. Because they allow analysts themselves to blend data from multiple sources, they help meet the imperative for speed and agility. However, data blending solutions have important limitations and caveats. For this reason, they remain a piece of the overall customer intelligence and data warehousing solution for most organizations rather than a replacement for traditional BI tools and approaches.

One serious drawback with data blending is the risk of end user error when pulling together different data sources. Typically end users don’t understand the underlying data semantics which can lead to confusion and increase the likelihood of inaccurate results. For example, “revenue” in one data source may be monthly revenue while “revenue” in another data source may be quarterly revenue. If an end user doesn’t realize the difference when blending data from the two sources, inaccurate and misleading results are likely.

Today, data blending solutions offer speed and agility but lack the robustness, support for mission critical applications, and single version of the truth provided by traditional BI tools and approaches. Solution providers are rushing to address this gap by providing the data quality and consistency of traditional approaches along with the ease and speed of data blending.

Data Management Platforms (DMPs)

Another recent solution for customer data management and analytics is the Data Management Platform or DMP. A DMP allows companies to centralize data, both their own online and offline data as well as third party data, and use it to create target audiences and optimize their online advertising. Using a DMP, companies can measure how campaigns perform for different customer segments and optimize their media buys and creative elements over time to improve effectiveness. They also help provide visibility into Return on Marketing Investment (ROMI) across campaigns and customer segments.

DMPs emerged just a few years ago in response to the needs of online publishers and marketers who began to capture and integrate larger and larger volumes of data from a wider variety of sources. DMPs differ from data warehouses since they more provide more rapid data integration and are tied to execution systems, such as digital ad execution, content management and marketing automation systems. DMPs are optimized to allow marketers to define target audiences and then activate campaigns to reach those prospects and customers.

DMPs help address the need to move closer to real-time execution in the digital and social era. They provide rapid capability to develop customer insights and push those insights to digital ad execution and marketing automation systems. Today, the primary use cases for DMPs include:

  • Providing an automated, sophisticated approach for using customer data in digital ad targeting
  • Enabling media efficiencies and programmatic approaches to marketing and advertising
  • Developing insights that drive improvements to customer experience

In addition to these use cases, there are two high-level benefits of DMPs that are driving interest and adoption.

Enabling Microsegments

One of the most powerful ways that data and analytics can be used in modern marketing is in helping marketers improve segmentation, moving beyond the traditional large, demographically-defined segments towards more granular, demographically and behaviorally-defined microsgements. DMPs enable the definition of microsegments by allowing companies to overlay their own first party customer data with additional third party data that provides greater insights into behavior and interest in products and services.

Enhancing Personalization

Truly personalized marketing and customer experiences–the right offer, at the right time, in the right location and context, to the right person–continues to be the holy grail of marketing. DMPs enable personalization techniques that tailor the ads, content, offers, promotions, and experiences for prospects and customers, levels of personalization that are now considered essential. Without a doubt, marketing has moved from a “one size fits all” to a “one size fits one” approach and static, mass market approaches are no longer enough.

Interest in DMPs has been surging in the past few years as the technology becomes a critical component of the marketing technology ecosystem. In fact, some predict that DMPs will eventually emerge as the one-stop shop for all marketing data needs.

Comparing Approaches and Technologies

When it comes to customer analytics, most companies will find that these three approaches and technologies need to be used in tandem. It is generally less of a case of “either/or” and more of a case of “and/also.”

Data warehousing, optimized over several decades, represents a mature, highly-evolved approach and ecosystem of technologies. Data warehouses support mission critical reporting and analytics needs, providing a “single version of the truth,” data quality, security and robustness, and they aren’t going to disappear anytime soon.

Data blending solutions, while newer and less mature, meet a real business need to integrate and analyze customer data faster than ever. They help generate insights quickly and enable greater agility when it comes to customer analytics and actionable insights. However, these solutions have important limitations when it comes to the potential for end user error and inaccurate results. Software providers are rushing to address these challenges. In the meantime, these solutions should be used with care.

Data management platforms play a critical role by helping to integrate greater volumes and variety of sources of customer data and activate this data. Through connections to execution systems such as ad execution, content management, e-commerce, and marketing automation solutions, DMPs help companies activate their customer data and operate closer to real-time–moving ever closer to truly personalized marketing.

Your 90 Day Plan

The path forward for customer knowledge and insights is an exciting one. Given its rising importance, the rapid growth in new approaches and technologies to support customer analytics comes as no surprise. However, this complexity makes charting a course forward more challenging than ever. As companies consider evolving their approaches to customer analytics, here are a few recommendations to keep in mind.

  • Customer analytics is not about the data or technology, but about the business decisions that the insights enable.
  • Customer insights have maximum value when the focus is on real-time insights connected with front-line execution.
  • Many customer insights can be found by mashing up different data pools. But, it is important to begin with whatever data is available today.
  • The best approach is business question or hypothesis-driven. Often the biggest challenge is to follow the 80-20 rule and identify the 20% of the data that provides the right insights.
  • Where possible, begin with simple and then evolve to more sophisticated approaches. For example, is it possible to approach early attempts at multi-channel, multi-touch marketing attribution with heuristic approaches? Can you begin predictive modeling using simple, linear regression models that are easy to understand and implement?
  • Keep people, your prospects and customers, constantly in mind in terms of improving their experience and meeting their needs and expectations.
  • Don’t just focus on customer acquisition and retention data. There is additional value in insights derived from the full life cycle of prospect and customer touchpoints.
  • Gain an outside perspective. Consultancies can help provide an assessment of where you are today and recommend roadmaps and best practices based on their experience with other clients.
  • Rather than approach customer analytics in terms of a single business use case, consider a full range of uses when determining appropriate levels of investment and communicating the full strategic value.
  • Make learning and talent development a key part of the agenda.
  • Take an agile, iterative approach to managing, analyzing and activating data.
  • Approach customer analytics as a journey rather than a one-time project. Most companies require cultural, organizational and process change to become more data-driven–not just a new data store or technology–and this evolution takes time.
  • Success with transforming to data-driven marketing also requires executive support and involvement. Persuade senior executives to champion and support these efforts.

Personalization techniques are now essential for success in both B2C and B2B e-commerce. This post will describe the need for personalization, explore how personalization approaches have evolved, and share recommendations for implementation that help avoid common pitfalls.

As the amount of spending via e-commerce channels continues to increase, the landscape has become more competitive than ever. E-commerce giants such as Amazon and Walmart have advanced the state of the art in their battle for customers and share of wallet. New entrants, including venture-backed startups, aggressively target niche markets. For firms engaged in e-commerce, the need to break through the noise, deliver exceptional customer experiences, and optimize revenue has never been greater.

What’s Driving Personalization?

Personalization techniques that tailor the content, offers, promotions, and experiences for visitors help firms achieve these goals and are now considered essential. Without a doubt, e-commerce has moved from a “one size fits all” to a “one size fits one” approach and static websites are no longer enough. There are four major forces driving the adoption and advancement of personalization.

Customers expect a great experience.

Many firms recognize that the experience their customers have as they interact with them is more important that ever. Customers expect simple, easy, and personalized e-commerce experiences. And, if a site fails to deliver that experience, an alternative is simply a click away. People are also more likely to share their experiences, both positive and negative, with others via social media.

Customer behavior has changed dramatically in recent years.

With the adoption of new technologies, customer shopping behavior has shifted significantly. Customers are now much more likely to browse and research an item in a physical store but eventually purchase the item online. And, the opposite is also true. Customers research online, but purchase in a physical store. Mobile and social technologies have increased the number of touch points most firms have with their customers. Regardless of the interaction channel, customers expect a seamless, consistent experience.

The volume, velocity and variety of data available are all increasing.

According to IBM, 90 percent of the data in the world today was created in the last 2 years alone. Firms have access to more data than ever before. This includes their own first party data, such as information about their customers stored in their CRM system, as well as third party data they purchase from others, such as customer demographic data, past purchases, and web browsing history. Firms engaged in e-commerce are very interested in how to best use this data to increase value.

Firms seek new ways to increase e-commerce revenue.

As the e-commerce landscape becomes more competitive, firms are exploring new ways to optimize their e-commerce revenue. Personalization techniques have proved very effective at increasing conversion rates, average transaction size, and customer loyalty. By 2018, Gartner predicts that B2B firms with effective personalization with outsell their competitors without this capability by 30 percent. According to a study conducted by Cisco, 70 percent of the largest segment of online consumers said that a personalized shopping experience would lead them to increase their purchases.

The Evolution of Personalization Techniques

Personalization techniques include a broad variety of technologies and processes that tailor content for visitors based on their characteristics, behaviors, and interactions. Personalization includes techniques such as segmentation, A/B and multivariate testing, product recommendations, and behavioral targeting. Over the years, behavioral targeting techniques have evolved considerably. Behavioral targeting uses information about a visitor’s past and current activity to customize content.

The first type of behavioral targeting techniques to come into general use were rules-based. With this approach, business rules are defined in advance and optimized over time through a manual, trial and error process. Rules-based techniques have a few limitations. They operate on segments of visitors but don’t customize content for each individual visitor. They are also resource intensive and require ongoing investment to define, test and refine the rulesets.

As behavioral targeting techniques evolved, a new type of approach, based on algorithms rather than rules, began to emerge. These techniques became know as automated or algorithm-based targeting. With automated targeting, software is used to automatically identify micro segments of visitors and evolve statistical models that increase in effectiveness over time. Automated approaches can improve over time—without manual human intervention—and they can find patterns that humans may not be able to detect.

Personalization Best Practices

When embarking on a effort to introduce or improve personalization techniques, there are several best practices to keep in mind in order to avoid common pitfalls. Personalization can be very effective in improving customer experience and increasing revenue. However, effective implementation as well as ongoing management and monitoring are crucial.

Tie investments in personalization to business objectives.

It is very important to define the success metrics for personalization and to measure and monitor against Key Performance Indicators (KPIs) on an ongoing basis.

Don’t surprise visitors with how they are being targeted.

Make sure that personalization techniques aren’t overly aggressive. This is can be the difference between what visitors perceive as an exceptional, tailored experience from one they may perceive as creepy. Keep in mind that most customers don’t mind the use of their past purchases with your firm or stated preferences in customizing content. However, many people are less comfortable with the use of their web browsing history or social media updates.

Actively measure and monitor personalization processes.

All personalization processes should be constantly managed and monitored. Care must be taken to make sure that personalization techniques continue to improve customer experience and sales. This is especially true for automated behavioral targeting techniques where the statical models driving customization are always evolving.

Take an iterative, agile approach to personalization.

The best approach to personalization involves experimentation. Through ongoing test and learn cycles new customized content, promotions, pricing, and products can be explored to determine what leads to improvements.

The Road Ahead

Personalization techniques have evolved over the years and become more sophisticated as available data, personalization technologies, customer expectations and e-commerce competition have all increased. Personalization is now an essential technology component for success in e-commerce and one that can have a significant impact on customer experience and sales. Firms should consider the types of personalization techniques available and evaluate whether rules-based or automated behavioral targeting techniques are appropriate. They should adopt an ongoing, test-and-learn approach to personalization that leads to continuos improvement. And, finally, they must keep customer expectations of privacy in mind to ensure a positive customer experience.

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