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.

Photo copyright: alphaspirit / 123RF Stock Photo

Summer Reading List

June 4, 2014 — 1 Comment

Summertime is here. As the pace of work slows a bit and you escape to the beach or the mountains, it’s a great time to catch up on reading.

So, what will you bring with you? Here’s a list of great reads for marketers. Some are classics. Some are new. Some are edifying. Some are inspiring. All will help shift your perspective and bring new insights to you and your organization.

Enjoy your downtime. You deserve it. And, happy reading!


by Bob Lord & Ray Velez

Over the course of their careers, Bob Lord and Ray Velez have had a front row view of the convergence of marketing and technology. As CEO and CTO of Razorfish, a large, global digital marketing agency, they provide their unique perspective on how to succeed in the digital and social era.

Bob and Ray make the case for close collaboration between marketing and IT departments and provide a blueprint for culture and business process change. They explain the new landscape of customer expectations and dive into technologies such as interactive marketing, cloud computing, omnichannel commerce and ubiquitous computing.

Favorite Quotation:

“The villain throughout this book is the silo.”  –  tweet this


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by Nilofer Merchant

By now, much has been written about the impact of social media on business. Most of this has focused on opportunities to leverage social media from a marketing or customer service viewpoint. Much less, however, has been written about how social media changes more fundamental aspects of business. How will social media change the way we approach strategy? Will it impact the structure of firms? How will it change how people work together as well as corporate culture? And, how will social media help unlock more of the talent that exists within each individual?

In her book 11 Rules for Creating Value in the #SocialEra, Nilofer Merchant addresses these topics, exploring emerging themes and raising new questions for us to consider.

Favorite Quotation:

“Anyone can, but not everyone will.”  –  tweet this


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by Harley Manning & Kerry Bodine

As two of the most prominent thought-leaders in customer experience, Harley Manning and Kerry Bodine couldn’t have chosen a better time to write their book, Outside In. Businesses across many different industries are recognizing that creating positive, compelling experiences for their customers is more important than ever. Leaders in customer experience have superior financial performance and sustainable success.

However, customer expectations are increasing every year, and the proliferation of digital, social, mobile and physical touch points makes creating consistently great experiences very challenging. Harley and Kerry offer practical advice and share success stories from leading firms.

Favorite Quotation:

“You need your customers more than they need you.”  –  tweet this


The Pursuit of Social Business Excellence51fdHShxCqL._BO2,204,203,200_PIsitb-sticker-v3-big,TopRight,0,-55_SX278_SY278_PIkin4,BottomRight,1,22_AA300_SH20_OU01_

by Vala Afshar & Brad Martin

Vala Afshar and Brad Martin assert that the digital and social era provides businesses with the tools to extend their values, core beliefs and guiding principles to their employees, customers, and partners. This creates an unprecedented opportunity for social and cultural transformation.

Social collaboration—with mutual benefit for all involved—is the key to true connection and requires the ability to listen, learn, share, engage and add value in a way that’s scaled and amplified like never before.

Vala and Brad argue that the transformation to a social business requires technology, but technology should not be the focus. To truly benefit from the opportunities of the social era, businesses must focus on culture and people first and foremost.

Favorite Quotation:

“A social business simply cares more.”  –  tweet this



by Jay Baer

Jay Baer is one of the most widely-recognized thought leaders in marketing today, and he’s working to turn traditional marketing upside down. Instead of marketing that’s needed by companies, Youtility is marketing that’s wanted by customers. It is marketing that is useful, helpful, personalized and provided for free. It is the key to building long-term, trust-based relationships and customer loyalty. And, it is very effective in cutting through the clutter of the advertising and marketing that’s bombarding people every day. It is pull marketing, that which is desired by customers, rather than push marketing, that which only interrupts and annoys customers.

To win attention these days, marketers must start with a new question: “How can we help?”

Favorite Quotation:

“The difference between helping and selling is just two letters.”  –  tweet this


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by Ted Rubin & Kathryn Rose

Ted Rubin and Kathryn Rose make a very interesting observation in their book, Return on Relationship. The social and digital era has brought focus and attention to developing relationships with customers and nurturing them over time. This, oddly enough, almost takes us back full circle to the “before mass advertising days” of traditional, face-to-face selling.

Ted and Kathryn encourage business people to drop efforts to tie social investments to traditional Return on Investment (ROI) metrics. Instead, they advocate thinking about the true value of social strategy in terms of Return on Relationship (ROR). ROR is a back-to-basics measurement approach to assessing how well you’re developing true engagement with customers.

* Oh, and one more thing, Ted wears cool socks, which you’re sure to discover if you follow him on Twitter.

Favorite Quotation:

“Relationships are the new currency.”  –  tweet this


The Epic Collision of Marketing & DataDaveBirckhead_EpicCollision_3Screens

by Dave Birckhead

I’d like to round out this reading list by humbly adding my own eBook. I wrote The Epic Collision of Marketing & Data to serve as a conversation starter to explore how marketing, technology and data are coming together in rapid and powerful ways. The purpose of the book is to: cut through the clutter and hype surrounding big data and marketing; provide clear, straightforward descriptions; and present ideas and concepts that can immediately be put into practice.

The book is also published on a new platform called Snippet that makes it an ideal beach read. Snippet is available via a browser, iPhone or iPad and has totally re-shaped the reading experience to allow better navigation and social interaction while reading.

I hope you enjoy the book!

Favorite Quotation:

“Marketing is going through the most significant, technology-fueled transformation in its history.”  –  tweet this


My new e-book, The Epic Collision of Marketing & Data, was recently published on the Snippet platform and is available on browsers as well as the Apple iPad and iPhone. You can read more about the book on the Snippet website here.

The trend towards data-driven marketing in being accelerated and enabled by rapid advances in analytics approaches, big data technologies and cloud computing. Many business people are surprised when they learn just how much technologies have advanced, how much the cost has dropped and how rapidly data-driven insights can be gleaned.

What’s the Big Deal About Big Data?

By now, everyone involved in marketing and technology has heard quite a lot about big data. Big data has been one of the most-used technology buzz words for the past several years. But, many of us are still left wondering exactly what it is and how our organizations can benefit. In this section, I’ll cut through the hype, provide a clear definition of big data, and explore a few potential uses, especially those that will have increasing significance for marketers in the years to come.

So, first of all, what exactly is big data? Big data is a term used to describe the vast—and increasing—amounts of data available to organizations today.

You may hear people use terms like structured and unstructured data. Structured data means data that can be identified because it exists in a structure, most commonly a database where the data is stored in rows and columns. Unstructured data, on the other hand, has no identifiable structure. For example, images, videos, music files, emails, and documents are all considered to be unstructured data.

Large amounts of structured and unstructured data have been the norm in most organizations for quite some time. So, why all the buzz about big data? As it turns out, data volumes have been increasing, especially the volumes of unstructured data. Which makes sense when you think about it. Our increasing use of social media, digital photography, digital video, etc has led to vast amounts of data. Its not uncommon to purchase a new personal computer these days with a terabyte of storage to hold all the music, photos, videos, etc. expected to be stored there. A decade ago, companies with a terabyte of data had the largest data warehouses in the world at that time. Times have certainly changed.

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The Opportunity

The real reason you’re hearing so much about big data these days is the opportunity to take advantage of tremendous new insights that can be gleaned from the volumes of data available.

Tom Davenport, professor at Babson College and author of Competing on Analytics and Keeping Up With The Quants, has identified three different types of analytics that benefit from big data and new approaches.

Business analytics: Business analytics are the traditional reporting, dashboards, and business  intelligence infrastructure that has been in place in most organizations for many years. Advances in business analytics have led to the introduction of new key performance indicators (KPIs) and the capture of data from disparate sources across the enterprise.

Predictive analytics: Predictive analytics uses data from the past and present from internal and external sources in order to build statistical models to predict future events. For example, predictive models can be used to predict when customers are likely to leave or the future lifetime value of different customer segments.

Prescriptive analytics: Prescriptive analytics produce insights that tell organizations what to do. In marketing, prescriptive analytics can be use to optimize the mix of marketing investments and create pricing simulations.

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Enter Cloud Computing

Cloud computing is making 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 much more reliable and secure while a fraction of the cost of on-premise infrastructure.

What this means for marketers and other business people is that the ability to quickly ramp up computing infrastructure and integrate and analyze data has never been cheaper, faster or easier. In the past, developing this capability often meant engaging internal IT resources to create enterprise data warehouses which took many months and costs hundreds of thousands of dollars.

“Cloud computing is often far more secure than traditional computing, because companies like Google and Amazon can attract and retain cyber-security personnel of a higher quality than many governmental agencies.”

- Vivek Kundra, former federal CIO of the United States

“Line-of-business leaders everywhere are bypassing IT departments to get applications from the cloud (also known as software as a service, or SaaS) and paying for them like they would a magazine subscription. And when the service is no longer required, they can cancel that subscription with no equipment left unused in the corner.”

- Daryl Plummer, Gartner analyst

Big Data Marketing Recommendations

The combination of new analytics approaches, big data technologies and cloud computing unlocks new capabilities that can be obtained for less money and in less time that ever before. These changes also put marketing leaders and line-of-business leaders in the driver’s seat and increase the pressure to bring new awareness and capabilities into their organizations.

As you consider how you can apply big data to marketing in your organization, we recommend the following:

  • Big data is not about the data or technology, but about the business decisions that the insights enable.
  • Big data insights have maximum value when the focus is on real-time insights connected with front-line execution.
  • Many business 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.

How to Become a Marketing Hero

Imagine the following scenario. A CMO presents the results of the latest marketing campaign to the CEO and other members of the executive team. Their expectations for the presentation are remarkably different from what they may have expected 10 years earlier. Today, CEOs and CFOs expect marketers to quantify their contributions to the bottom line. The CMO walks the team through the latest results, including the revenue impact and return on marketing investment. As the conversation turns from past results to future plans, the CMO uses data to show the predicted impact on revenue from the planned campaigns. The CEO and CFO agree and allocate additional budget to fund the campaigns.

This scenario is increasingly common but still rare in many organizations. The use of data to quantify marketing’s contribution to financial outcomes is quickly dividing marketers into the “haves” and “have-nots” of data-driven approaches. When marketing is able to speak the language of the CEO and CFO, alignment improves as well as the CMO’s standing in the organization. Data-driven marketers are much more likely to be invited into strategic planning sessions and be perceived as delivering greater value to the organization.

While only a fraction of organizations could be considered data-driven today, many marketers understand the need to move in this direction. There is often some degree of hesitation, and many marketers are unsure of how to take the initial steps. Wharton professor David Reibstein summarized this very well in a recent interview:

There are some CMOs who embrace finance, data, dashboards, and transparency. These CMOs are the ones who are being invited to the table. However, there are many who simply seem afraid. Some seem afraid of being held accountable. The irony is that without demonstrated value, they are actually more at risk of being fired. Some seem to be afraid of what the data will show, that perhaps the valuation isn’t as good as you’d like. However, this shouldn’t be something to be feared as the first time you measure it you are creating a baseline. The goal is simply to be able to figure out how to improve the valuation over time.

Marketing KPIs

The good news is that there are relatively simple and straightforward ways to get started. Many marketing Key Performance Indicators (KPIs) can be captured on spreadsheets and don’t require lots of additional data or technology investments. The key is to start with something simple and evolve over time.

While there are many different marketing KPIs, two stand out as perhaps the most important: Customer Lifetime Value and Return on Marketing Investment. Let’s explore each of these in turn.

Customer Lifetime Value (CLTV)

Calculating the lifetime value of a customer can create significant new insights for marketers. Customer Lifetime Value, or CLTV, is simply the projected profits from a customer over the entire relationship with that customer. CLTV helps organizations understand the upper limit on how much they should spend to acquire a customer.

Some marketers calculate the average CLTV across their entire customer base. However, this can cover up some of the interesting insights happening below the surface. As Don Peppers and Martha Rogers often advise: “not all customers are created equal.”  CLTV is also important in driving an improved understanding across the organization of how much more valuable certain customers or customer segments are than others. For example:

Customers in Segment A have a CLTV four times higher than the average customer. Even if it costs more to acquire customers in this segment, it can be highly valuable to target and acquire them.

While there is no standard calculation of CLTV, it is possible to begin with relatively simple calculations and evolve from there. Harvard Business School Publishing has made an online CLTV calculator available at:

Return on Marketing Investment (ROMI)

Return on Marketing Investment, or ROMI, is simply a measure of financial contribution attributable to marketing.  It is a measure of the net revenue attributable to marketing divided by the cost of the marketing effort. It can be calculated as:

ROMI = (Revenue attributable to marketing – cost of marketing) / cost of marketing

For example, consider a company that spends $75K on a new marketing campaign that results in $500K in new sales. ROMI would be calculated as:

($500K – $75K) / $75K = 5.7

So for every $1 invested in the marketing campaign, the campaign returned over $5.

ROMI is a powerful way for marketing to begin connecting with the language of the CFO and quantify financial contribution. And, it is a great place to start when moving down the path of data-driven marketing.

The Path Forward

For many marketers, the best approach will involve starting with simple versions of relatively few metrics. Most of these metrics can be calculated in little time using spreadsheets. It is also important to learn from what other marketers are doing. There is little value in re-inventing the wheel. Seek out other marketers and discuss marketing metrics that they have put into place. Take part in marketing networking groups and share your knowledge.

Marketing is changing at a rapid rate. Key to success going forward will be our ability to learn as individuals and help our organizations increase the number of “learning cycles” we complete each year. What are your successes and lessons learned with marketing metrics? What is your experience with the best way to get started?

The way consumers are researching and buying products has shifted significantly in recently years. Consumers 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.  They know what they want to purchase, when they want to purchase it, and they resent any attempt to force-feed them messages. Because of this dramatic shift, the traditional marketing funnel paradigm is being replaced.

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The new paradigm for marketing is the customer lifecycle that places customer needs, not business needs, at the forefront. The customer lifecycle 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 lifecycle also illustrates the fact that the consumer journey is not a linear path. Today, consumers flow easily across stages in an iterative cycle.
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In this stage, consumers discover an unmet need. They begin to think about a relatively narrow set of products and services that might meet this need. Most consumers start their decision process with brands they are already familiar with.


In the next stage, consumers begin to actively explore and evaluate their options. The consumer 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 process involves the consumer’s decision and actual purchase. Consumers tend to make their purchases in the most convenient channel.


In the Engage stage, consumers engage with the product or service they’ve purchased. The experience that consumers 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. Consumer activity during this stage can also help companies identify other potential needs.


During the Advocate stage, consumers 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 consumers to provide reviews.

What do Pandora, Netflix and Amazon all have in common? All three are world-class “segmenters.”

- Brian Halligan, CEO and Co-Founder of HubSpot

Another significant change in marketing is a growing understanding of how consumer behavior is increasingly varied, even within the same demographic segments. This has given rise to the concept of microsegments. Microsegments represent a more precise slice of a market and are typically identified through advanced technology and analytics techniques. Microsegments contain fewer consumers which allows for highly personalized predictive analysis and marketing optimization.

For years, marketers used demographic segments–not because this was the optimal approach but simply because demographic data was the only data readily available. Today, digital touchpoints provide much more information about consumer behavior, and this data can be combined with demographic data to create microsegments. For an example of the limitations associated with traditional demographic segmentation, see the illustration below comparing two individuals with similar demographic profiles.

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Once marketers have defined microsegments, they can use quantitative and qualitative analysis to understand how each microsegment behaves at each stage of the customer lifecycle. Marketing investments can be allocated where they will have the greatest impact.

For example, consider the hypothetical case of a company that has made significant investments in its website and website content updates. By using data to better understand its most valuable microsegments, the company finds that only a small percentage actually visit the website during the customer lifecycle. Instead, the company finds that these consumers tend to visit online review and comparison sites during the Explore stage. The company is then able to shift resources from the website to content provided to comparison sites. Better product images, information and reviews have a significant impact on the purchase decisions of these microsegments, and the company experiences a near-term revenue increase.

One of the most powerful ways that data and analytics can be used in modern marketing is in helping marketers define microsegments and then analyze the behavior of these microsegments during each stage of the customer lifecycle. The insights gained from this analysis provide a strategic overview and roadmap for marketing that guides activities and investments and helps deliver significant financial results.

As we look ahead to 2014, we expect the pace of change in marketing to only continue to accelerate. It seems like you can’t leave your desk these days to grab a cup of coffee without coming back to another change–a new marketing software entrant, an acquisition announcement, a major shift in consumer behavior, or an advance in marketing analytics. However, there are a few broader trends and patterns that have emerged. Here are 5 trends we believe will continue to have a significant impact on digital marketing in the new year.

#1 – Rise of Marketing Technologists

CMOs will increase hiring of marketing technologist and marketing operations roles.

The growing importance of software to almost every facet of marketing is giving rise to a new role in many companies: the Chief Marketing Technology Officer. A cross between the traditional Chief Marketing Officer and Chief Technology Officer roles, the CMTO provides strategic guidance to how to best take advantage of the growing number of marketing technologies. These include traditional technologies such as customer relationship management as well as analytics, marketing automation, mobile marketing and social media marketing. Marketing technologists and marketing operations pros bring technology and analytics skills as well as deep marketing skills.

#2 – Trust and the Social Era

Businesses will invest in ways to measure and increase levels of trust with their customers.

In the past decade, behavioral economists have changed the way we look at consumer buying behavior. 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 and also depends on moral bonds of social trust. Today, companies with strong brands are winning in the marketplace because they understand a simple yet profound truth: that the social capital represented by trust is just as important as physical capital. In the Social Era, cultivating trust is one of the most important activities that companies pursue.

#3 – Advanced Marketing Analytics

Progressive firms will invest in predictive analytics, prospect scoring and defining customer lifetime value. 

The world is quickly being divided into the “haves” and “have nots” of marketing analytics. Firms that have moved toward data-driven marketing will continue to develop and advance their analytics capabilities. In 2014, we’ll see more marketing organizations move beyond traditional web analytics and into true marketing analytics and marketing intelligence capabilities. This will include advances in the ability to predict responses, understand customers, and quantify the lifetime value of different customer segments.

#4 – Marketing Agility

Marketing organizations will shift a greater portion of their budgets into “test and learn” and agile marketing tactics.

Agile is a discipline picked up from software developers and it’s being applied to marketing teams. Agile teams follow core principles that shape the way they work. Those principles include:

  • a bias toward action;
  • responding to change;
  • emphasis on collaboration (people and their interactions) and,
  • iterative work cycles that deliver something of value.

Companies are finding that the best way to approach the integration of creativity and analytics is through agile marketing techniques. Many are investing 30 percent or more of their marketing budgets into these types of “test and learn” tactics. With agile marketing, teams of creative and analytics pros work together to create ideas and then use data to inform and test as they are introduced.

#5 – Smarter Content Marketing

Marketers will improve their content marketing programs by using data to target content to specific segments and evaluate content effectiveness. 

Content marketing will continue to be a focus for many marketers in 2014. However, many will turn their attention to improving the quality of content rather than creating more and more of it. Central to this effort will be the use of data and analytics to better target specific content and to evaluate how well each piece of content is performing. Marketers will improve their ability to provide the right content, at the right time, via the right channels to the right customer segments.

 “If we look back just ten years, the CMO’s job was 90 percent creativity and 10 percent science. I think now we’re at 50-50.”

- Michael Lazerow, CMO of Salesforce Marketing Cloud

As marketing becomes increasingly data driven, the landscape and the types of professionals involved in marketing are evolving at a rapid rate. This has led to many online discussions and water cooler conversations debating topics such as right-brain vs. left-brain thinking and art vs. science. However, we strongly believe that this isn’t a question of “either / or.” It is a question the best ways to combine both capabilities. The success stories are emerging daily of companies that are blending creativity and analytics in order to create breakthrough results.

When new marketing concepts are being considered, there are the typical questions of “who are we trying to reach?,” “what is the context?,” and “what behavior are we trying to drive?” Creative thinking is required to come up with the right message, imagery, and experience that will support these goals.

Companies are finding that the best way to approach the integration of creativity and analytics is through agile marketing techniques. Many are investing 30 percent or more of their marketing budgets into these types of “test and learn” tactics. With agile marketing, teams of creative and analytics pros work together to create ideas and then use data to inform and test as they are introduced.

“Good design is about clarity over style, and accountability over indifference.”

- John Maeda

For example, marketing analytics team members can use data to help the team understand customer segments at a more granular level. Data about customers can provide insights into micro-segments based on behaviors, needs and goals. Marketing analysts are also able to leverage data to calculate the potential value of each micro-segment and how they behave across each stage of the consumer decision journey.

All of this can be used as input into the creative process. Creative professionals can generate ideas based on a more comprehensive customer understanding. Demographics, needs, behaviors, and context are all available to inform the creative process.

Once the idea and creative elements have been developed, the marketing can be packaged and launched, sometimes in the form of a pilot. Agile marketing teams begin to gather and analyze data almost immediately about how this is performing. Based on the data, the creative concept and elements can either be refined or introduced to a broader group.

Perhaps the most compelling example of the power of combining creativity with analytics is in content marketing. Content marketing is now the rage in marketing groups across the globe. With more channels than ever available, marketers are focused on creating more and more content to fill each each channel. However, more of the same content is almost always not the best solution.

There is a growing body of evidence that content aimed at the needs and interests of customers and prospects is much more effective than traditional product-focused content. As creative professionals develop new content, data and analytics can help determine how effective that content is in achieving goals such as engagement and conversion. Analytics can also help marketers understand the best timing, channel, segment and consumer decision journey stage for each piece of content.

When we look ahead to the future of marketing, one thing is abundantly clear–it won’t be a matter of “creatives” vs. “scientists;” it will be a matter of blending both.

One of the interesting developments that we’ve noticed recently is that more and more companies are making significant investments in their enterprise marketing technology stack. It’s no secret that direct, digital relationships with customers are now more critical to company success than ever before. Companies have responded by ramping up their spending on technologies that enable this. These include technologies such as inbound marketing platforms, social listening tools, content management systems and web analytics platforms.

While these technologies hold much promise, there are some dark clouds on the horizon. In the rush to build out digital marketing capabilities, some companies have over-invested in technology and their enterprise marketing infrastructure. There is a widespread belief in business that technology alone can solve business problems, and this has been the case over many years and many cycles of technology adoption. It is no different today.

Too many companies over-invest in marketing infrastructure. This isn’t because the systems are overly expensive or that they don’t deliver spectacular new capabilities. It’s simply temping to invest in bells and whistles that the organization isn’t yet ready to use.

Could this be the case in your organization? Here are several classic signs to look for:

1. Lack of clear ROI

When done well, investments in the enterprise marketing stack should show clear ROI. Perhaps even more importantly, they should enable the CMO to quantify the value of marketing, something increasingly important to marketers who want the respect of other executives.  As Wharton’s David Reibstein recently remarked in an interview with, “A lot has been written about the importance of marketers being able to connect their efforts to financial outcomes. I believe that marketers’ inability to do this has cost them their seat [at the executive table].”

If your company’s investments in enterprise marketing technology–and particularly investments in analytics platforms–have not enabled this important outcome, it is a clear signal that something is wrong. Enterprise marketing technology should link marketing activities to key financial outcomes. If this hasn’t happened, it may be time to pause and take a closer look at technology investments and the approach.

2. “Big bang” technology implementations 

Investments in enterprise marketing technology are sometimes made with a “check the box” mentality. Need a content management system? Checked it off. A new analytics platform? Yes, we’ve added that too.

There are two challenges with this approach: 1) decisions are sometimes made on a “one off” basis rather than in consideration of the full enterprise marketing stack and 2) the implementation of these of these solutions often takes the form of a “big bang,” monolithic approach with long cycle times.

If anything is certain in marketing these days it’s that change is a constant. Marketing needs will change. Keep projects short and focused.

3. Little or no talent development/acquisition

Many companies fail to examine how far they have evolved their talent and how this impacts the types of technology investments that are appropriate. There is a good analogy in car buying: smart car buyers don’t get 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. Cross-functional, efficient digital marketing processes are non existent 

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?

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

5. The marketing department is not in the driver’s seat. 

Finally, a classic sign of an overbuilt marketing infrastructure is when marketing is not front and center, understanding and driving digital marketing, from content creation and management to analytics.  The IT department is a critical enabler of marketing technology. 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.

If you’ve considered some of these examples, and you find them present in your organization, the question then becomes, “how do we respond?” The good news is that when companies over-invest in marketing technology relative to other critical enablers, it often presents a great opportunity. By turning to people and process enhancements, it is possible to unlock the value of previous technology investments. We’ve found incredible success with this approach recently.

And, what CEO or CFO wouldn’t like to see that? Significant financial return by leveraging much of what already exists. It is certainly a big step towards earning marketing, and the CMO, a seat at the executive table.


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