What It Means to Be a Data-Driven Marketer in 2015


Share on LinkedIn

The quickest way to make a CMO roll their eyes is to tell them they should be making more data-driven decisions. Of course we should, everyone knows that. Unfortunately there are a lot of “buts” attached to making this a reality. “But the data I need is fragmented throughout the organization.” “But I have no way of linking customer data across channels.” “But we don’t have the analytical skills internally to analyze the data.” “But I can’t trust available data.” The list goes on. There are, however, marketing leaders among us who actually do a very good job at leveraging data to inform marketing decisions. The problem is, when we talk about big data and data-driven decisions for marketers, it tends to veer toward boil-the-ocean concepts that are too big and audacious for even the largest marketing teams and budgets to take on. So what’s the practical reality from CMOs who are successful at making consistent data-driven decisions? What does “data-driven” mean in 2015? Thinking About Data Last year Gleanster Research surveyed over 9,700 senior marketing professionals in companies of all sizes in Europe and the US. And while there are lots of things your organization should be doing with customer data, not all of them are realistic. You need to devote limited resources, finite time, and tight budgets to leverage data-driven marketing decisions.

There are a handful of somewhat obvious rules and guidelines that successful data-driven marketers consistently follow. The one caveat here is that these things are somewhat difficult to quantify from a research perspective. They are derived from conversations, analysis, and hands-on experience. Consider these the soft skills you’ll need to stay out of the rat holes and maintain credibly as a marketing leader who values the numbers.

Data is everybody’s friend.

Every organization is applying analytics to marketing decisions – meaning your colleagues and peers also want to be data-driven marketers. At times even the same data produces different perspectives from internal stakeholders. Everyone’s got data to support their decisions. It’s important to realize that there are no one-size-fits-all answers in analyzing marketing data – but there are directional and discernible trends. Always test assumptions. There are no magic insights you can derive from any form of analysis, even if you pay statisticians boatloads of money. Test, validate, and test again. You don’t have all the answers, but rather a process for uncovering the most informed decision. What you bring to the equation that is unique is your interpretation of the data and the actions you recommend for marketing optimization.

Everyone is risk averse.

Risk comes from not knowing what we are doing. For marketers there’s a risk in sticking your neck out there and analyzing customer data. What if the data is inaccurate? What if you don’t have the full picture? Tenacity and perseverance in using data to inform marketing decisions pays off. You may not garner the credibility you want initially, but if you always return to the data, risk-averse leaders will gravitate toward your insights rather than to a peer who relies on anecdotal assumptions. When you analyze data, always try to remove the risk from the findings, dig a little deeper, test alternatives. It’s not a “big data” challenge. Sure, there’s a ton of data on customers at every organization. Your job in marketing is not to analyze all of it. It’s to prioritize decisions and figure out where the path of least resistance lies to improve conversion, save costs, save time, and increase revenue. Pick one or two duties in your job and fix something. Anything, big or little. The practical reality is that you don’t have to analyze big data in marketing. You have to pick small samples and populations of data that can inform one or more decisions. World hunger is solved one slice of bread at a time – and every slice makes a difference in the aggregate. Someday machine learning will help us uncover the gems in big data, but today you have to start somewhere, with currently available data, in areas where you can effect change.

Simplicity is the ultimate sophistication.

That’s actually a quote from Leonardo Da Vinci. Marketers are overworked and underpaid. You have regular duties and responsibilities in your job, and normally they don’t account for committing time to analyzing data. So you need to look for leverage in how you commit time and resources. If you’re a senior leader, don’t waste your team’s time chasing questions that can’t be answered. Prioritize a few decisions that will make a difference for the organization and consider that a huge win when they prove valuable.

Context begets analytics.

The challenge with data is analysis paralysis. You have to know when to stop digging and take action. Marketers’ unique skillset for the organization is their creativity and emotionally charged perspective on how to drive a visceral reaction from a target audience. Those skills come from having context about what drives your buyer – what makes them purchase, engage, share, and react. For marketers the insights in customer data usually aren’t black and white. It’s the interpretation of the data as much as the analytical process. Sometimes marketers sell themselves short because they aren’t statisticians or metric oriented. The truth is, the most skilled statistician probably can’t provide the context you can when looking at the same data. They can isolate correlations, but those just tell you where to dig further. Marketers are incredibly valuable because they can layer context over analysis, so be confident in the value you offer.

Frame the opportunity, not the problem.

Every organization has challenges – especially with respect to analysis. According to Gleanster Research 8 out of 10 CMOs at large enterprise organizations believe they could be doing a better job leveraging available data to inform marketing decisions. But your job when analyzing data is ALWAYS to uncover the opportunity to make a better decision, improve process, or boost key performance indicators. Don’t waste time identifying the problems with the availably of data or the internal use of data. Stay focused on effecting changes with insights informed by data, and eventually you start to stand out as someone who knows how to dig into the data and act accordingly.

Act like a 2-year-old from time to time.

No, don’t throw a tantrum. Ask why. Ask why a lot. Why helps delve into the heart of your analysis even after you think you have come to a conclusion. “Why don’t we look at…x?” “Why is this the ideal conclusion?” Why also gives marketers credibility because it’s an analytical question. All too often senior leaders default to “well I think…” and they may or may not have the right answer. But why is going to drive the entire organization down a discovery path. “I think” closes it out and dictates a decision.

Let’s talk about what data-driven success in marketing actually looks like in 2015. The one caveat to this is that I’d like to address some of the more accessible tactics that are pervasive across the top 20% of the market, not one or two outlier examples.

Micro Segmentation Over 1:1 Personalization

Even when data is readily available to inform highly targeted engagement, someone actually has to produce the creative and copy to trigger the engagement. According to Gleanster, the use of personalization in email campaigns results in 350% higher conversion over generic copy. What’s interesting about that is that 76% of organizations report they primarily use CRM data and basic demographic data for personalization (not behavior triggers and other implicit data). That means the vast majority of value from a personalization standpoint is currently derived from basic segmentation. But only 5 out of 10 marketers indicate that they are effective at segmentation. Segmenting and targeting copy and creative remains an untapped opportunity to maximize marketing spend for over half of organizations. When it’s overwhelming to think about engaging buyers across each stage of the customer lifecycle, don’t let that bleed into engaging high-priority segments of your target audience in one stage of the customer lifecycle – by role, title, region, income, and other basic demographic attributes. Shockingly, only about 3 out of 10 organizations take the time to target segments of customers with unique messaging. This is, of course, a lot of work for marketers, even if you cherry pick 3-5 segments. You need domain knowledge about the target audience, unique creative, copy, and a refined value proposition. From a data-driven decision standpoint you should get started on prioritizing your target audiences by reviewing your customer data and looking for the 3-5 largest segments of current buyers. There are always attributes that uniquely segment your customers – when you uncover them you can isolate which areas to focus your finite marketing resources on targeting with creative, copy, and messaging. If you know what your current customers look like, use that to inform how you target new customers. That literally means reducing the volume of spend that goes toward generic brand awareness and casting the acquisition net wide looking for any opportunity.

Automating Up-Sell and Cross-Sell Campaigns

Marketing is the only function in the business that actively communicates across the entire spectrum of the customer lifecycle, from the inquiry to a loyal customer. That raises two very interesting questions that data-driven marketing has answers for:

  • Should marketing own the customer lifecycle?
  • How should marketing allocate time, budget, and effort across the customer lifecycle?

It turns out marketers are spending disproportionally more time on customer acquisition and virtually no time on customer retention and expansion. In fact, the average B2B midsize firm gets 70% of its revenue from customer acquisition efforts, versus the average Top Performing firm that gets 50% from up-selling and cross-selling according to a recent report by Gleanster Research and Act-On (Rethinking the Role of Marketing). Think about it: customer data that is readily available in CRM can be used to initiate revenue-generating campaigns to customers at the later stages of the customer lifecycle. Revenue from customer engagement is not only more profitable, it can be attributed to marketing. As marketers you should be using available customer data to automatically trigger campaigns for up-selling and cross-selling to known customers within tools like marketing automation. More importantly, marketers can finally link conversion to real revenue and demonstrate their impact on up-sell revenue. There are actually finite products and services that marketers would need to configure automated campaigns around, and for the most part these campaigns are “set and forget” – and triggered by data in CRM. There’s no better opportunity for marketing to demonstrate their influence on revenue and use data to derive leverage from campaign creation efforts.

A/B Testing on Landing Pages and Email Campaigns

According to the 2014 Gleanster Marketing Resource Management report, only 60% of small and midsize firms conduct A/B tests on email, landing pages, and website properties. It’s actually shocking to learn how much you really don’t know about your customers when you run A/B tests on creative and copy. This is an area that is supremely overlooked by marketers, and the capabilities often exist in multiple tools including web content management, email marketing, and marketing automation. It can, however, be a time-consuming endeavor, and many marketers are reluctant to even guess at what to test. But you can learn a ton about your target audience by running A/B tests. From a credibility standpoint, if you have aspirations to be perceived as a data-driven marketer, go test assumptions that have been made by the organization online. Your boss can’t argue with the numbers, whether they prove your strategy is on the mark or needs some revision. This is low-hanging fruit, and it’s underutilized.

Machine Learning Is Your Best Friend

One consistent theme that keeps coming up in our advisory sessions is that marketers want help in data analysis. Thanks to advances in computing power, data analysis that previously took days can now be done in seconds and often in the cloud. Machine learning applies rules to data sets and looks for correlations between data. Does this do the job of a marketer? Heck no! What machine learning does for marketing is help isolate trends that should be investigated further. Marketers still need the context about customers and products to translate those correlations in the data into action. Today there are predictive learning and data visualization tools that will automatically apply rules to data sets and color-code correlations – all these tools require is the skillset to load the data. Turnkey “machine learning” solutions are still in their infancy, but are rapidly gaining traction as low-cost, user-friendly ways to augment analytical capabilities. For very large data sets, machine learning is really the only way to uncover trends manual human analysis would likely overlook. The caveat here is that we tend to see predictive capabilities primarily used in mid-to-large organizations with more resources (people, money, skills) to leverage the tools. Also, the first-generation tools are priced a bit high for the average small or midsize company. Keep in mind that the quality of the data determines the quality of the analysis, which is a huge obstacle for most organizations. However, over 50% of large enterprise firms that participated in the 2014 Digital Marketing survey had used predictive tools with tremendous success, and marketers in particular were bullish on the future potential of these tools for their organizations.

Republished with author's permission from original post.

Ian Michiels
Ian Michiels is a Principal & CEO at Gleanster Research, a globally known IT Market Research firm covering marketing, sales, voice of the customer, and BI. Michiels is a seasoned analyst, consultant, and speaker responsible for over 350 published analyst reports. He maintains ongoing relationships with hundreds of software executives each year and surveys tens of thousands of industry professionals to keep a finger on the pulse of the market. Michiels has also worked with some of the world's biggest brands including Nike, Sears Holdings, Wells Fargo, Franklin Templeton, and Ceasars.


  1. This is a really comprehensive, and useful, assessment of the big data application landscape for marketers. When Peppers and Rogers first began looking at 1:1 personalization and understanding customer behavior on a micro-segmented basis, they were anticipating a time when multiple customer-related data streams would be easier to converge and analytical techniques would see broader usage, i.e. beyond those who are super math proficient and tech-savvy (http://customerthink.com/do-you-wonder-why-big-data-gets-so-much-attention-going-forward-will-that-continue/). With new software suites like IBM’s recently-introduced Watson Analytics, that’s getting closer and closer.

    Also, the points made regarding landing pages and email campaign testing – and, for me, websites in general – are extremely well-taken. Most direct marketing and web site development companies are great at the tech side of analytics; however, there is very little evidence of mare advanced content research techniques being applied (http://customerthink.com/whats_the_real_goal_of_web_site_visitor_experience/)


Please use comments to add value to the discussion. Maximum one link to an educational blog post or article. We will NOT PUBLISH brief comments like "good post," comments that mainly promote links, or comments with links to companies, products, or services.

Please enter your comment!
Please enter your name here