Making Use of Data: 5 Questions Marketers Need to Answer to Succeed

A recent Forrester/Dun and Bradstreet survey revealed that only about half of sales and marketing decisions at B2B companies with over 500 employees are made based on data. The following summary from MarketingProfs shows that challenges to the effective use of data abound: From gathering to application to measurement, all are seen as either “extremely” or “very” challenging by a large portion of the respondents.

The paradox here is that businesses are awash in data today. Companies are spending vast amounts on software, hardware, and services related to data acquisition, management, and display. Moreover, businesses are constantly talking about data and the need to make better use of it. Unfortunately, neither these solutions nor the organizational desire solves the problem. In fact, the solutions often make the problem worse because they are seen (and sold) as a silver bullet and enable managers to check a box saying they are pursuing a “data initiative.”

The reality is that the problem lies not in a lack of will or technology. It lies in a lack of focus—a lack of strategy around data management and analytics. When you can measure anything, you do…but more measures mean more data and more difficulty making it all align. More data also mean more potential interpretations—and a decreased likelihood of consensus around what the data mean and the implications.

To construct an effective data and analytics strategy, marketers need to answer the five questions below. You’ll see that several are much more about strategy than data. Why? Because to capture and leverage the right data, we must first be clear on strategy.

  • What are the key stages of the customer journey? The point of marketing and sales is to influence behavior towards revenue generation. If you don’t know the path customers will follow that will generate revenue, what good is your data?
  • What activities are we undertaking today to move people through those stages? Marketing and sales should be targeting points on the customer journey with activities designed to influence behavior. Organizing activities along the journey ensures that you will use data about those activities and customers in the right way.
  • What should be the measure(s) of success for the impact these activities are having? Once you have aligned activities with the journey, identify ways of assessing whether those activities are successful, specifically with respect to the goal of moving from one stage to the next.
  • How can we align data we have or could plausibly get with these measures? Effectively using data to make decisions depends on being able to capture the right data consistently and with confidence. If it’s hard to acquire data or the data is subject to skepticism in the organization, it will never be used.
  • What decisions will we make based on the data? Imagine if, for a given measure or data point, one month it’s high and the next month it’s low. What decision will that trigger? If you can’t articulate the decision that will flow from the data, why capture the data in the first place?

To be clear, answering these questions is NOT easy for most companies. Data strategy isn’t easy. It’s actually much harder than buying software or hiring consultants to deliver more data and more measures through more systems. But without that hard work to decide what data and what measurements can really impact business outcomes, it’s unlikely that surveys like the one above will show any better results.

EMI survey of investors reveals attitudes towards robo-advisors

In mid-2016, EMI partnered with Boston Research Technologies to conduct a national survey of investors aimed at understanding their attitudes towards robo-advisors. The survey captured input from over 700 respondents, distributed across age and wealth segments.

As shown in the infographic below, EMI’s analysis of the survey data paints a picture of the robo-advisor opportunity that doesn’t neatly align with the concept of it being an entry-level offering for the young mass affluent. In fact, interest in robos correlates more strongly with attitudinal segments than with age or wealth segments. This has a lot of implications for lead generation and nurturing programs, and product positioning and messaging. Moreover, the strong desire for “someone to talk to” and for a platform that is not difficult to use suggest a need for support and guidance that deviates from the “self-service” (and low-cost) vision for robos.

Attitudes towards Robos among investors

How To: Customer Segment Intelligence Gathering

In a previous post, we discussed the uses for and value of customer segment intelligence in SaaS companies. This post focuses on the approaches to gathering customer segment intelligence and provides a framework for developing an intelligence-gathering approach that meets intel objectives most efficiently.

In EMI’s experience, companies often under-invest in customer and market intelligence because they either perceive that doing research is too expensive and time-consuming or they have done research in the past that didn’t deliver value. With a focused research approach in which methodology is aligned with objectives, however, neither of these should be the case.

Start with an Objective

As the previous sentence suggests, successful research always starts with a clear, strategic objective. Often that objective will emerge out of anecdotal identification of potential gaps in knowledge, for example:

  • Penetration in a particular industry isn’t as robust as you would have expected based on the fit between the industry’s needs and your software’s benefits
  • Sales closes significant new contracts with several customers from an industry not explicitly targeted through marketing
  • A group of customers are not succeeding in implementing the software as quickly as other customers
  • Several larger customers cancel or reduce their subscriptions

In each of the examples above, observations of behavior leads to questions—What is driving less-than-expected industry performance? How did these customers find out about us and can we target more like them? What are the impediments to implementation? Why are larger customers cancelling? These questions can then be turned into research objectives: understand how to increase penetration in an under-penetrated or emerging segment; identify customer experience improvements that can improve time to implementation and retention.

Sometimes the need for research arises when a business begins to pursue a new, untested venture. Common examples would be the development of a new product/service, the enhancement of an existing product/service, or the pursuit of an entirely new market.

While the genesis of the research may be different, the approach must be the same. Discipline around objective definition is vital to avoid trying to answer every question about the de novo opportunity. For example, if the research needs are in the area of product development or enhancement, potential objectives are:

  • Collect “blue sky” input to build a list of potential new features for customer segments with high growth potential
  • Test the value to existing customers segments of new features that have already been defined

Each of these two objectives drives a different research approach. Whatever your objective, defining it and maintaining it as your lodestar throughout the research development process is the key to efficiently capture the insight you need and avoid gathering information you don’t need.

Identify Your Methodology

“Research” does not always mean a survey. In fact, it is likely that customer data analysis, secondary market research, in-depth interviews, and/or focus groups could be better methods for achieving your research objectives.

The key to determining which method will best meet your needs is articulating the kind of insights you need and the most logical source of those insights. For example:

  • Do you need to identify differences between customer segments or find what all customers have in common?
  • Are you trying to gather initial, guiding information to develop a list of potential new features or are you trying to understand the relative value of features that have already been conceived?
  • Do you want to understand what existing customers value or the size of the potential new market for your product?

The answers to questions like these will guide whether you pursue qualitative focus groups, interview or survey existing customer, analyze customer usage data, gather secondary research into industry trends, or conduct broad market surveys.

Know When to Stop

There is a famous quote from French author Etienne de Saint-Exupery: “Perfection is attained not when there is nothing left to add, but when there is nothing left to take away.” Perfection may neither be obtainable nor even a desirable goal in research, but the need to limit and control the compulsion to add—questions, completed surveys, interviews, data—is both strong and vital to research quality and utility. The goal should always be to do the least amount necessary to ensure well-grounded decisions and outputs. If you can’t articulate how a question will contribute to better decisions or outputs or how collecting more data is likely to change decisions or outputs, it’s time to stop.

Customer intelligence is a vital tool for identifying and scoping new market opportunities, grounding product development decisions in an understanding of customer value, and honing in on causes of under- or over-performance. Amassing this intelligence isn’t easy, but when approached in a disciplined, grounded way, it almost always produces a measurably positive ROI driven by new revenue and/or productivity gains.