Inside Strategic Segmentation Blog Series – Part Three
Strawman Planning and Hypothesis Generation To Maximize Segmentation Impact
Written by Chris Diener, Senior Vice President, Analytics
To maximize the impact of any segmentation study, it needs to address the highest priority marketing decisions. In Part 3 of our series, we outline a specific strawman planning exercise you can conduct with key stakeholders to ensure alignment around priority application areas.
At the kick-off of any segmentation project, you need to interview key stakeholders that will be end-users of the segmentation, or important for implementation. Depending on the scope of the study, this typically includes senior leaders across marketing, customer insights, product development, analytics, and other areas, as well as media agencies. Ultimately we want to connect the dots between the client’s goals and expectations for the segmentation with the market insights we design the study to unlock.
Blog series at a glance:
- Part 1 – Aligning your segmentation approach with strategic priorities
- Part 2 – The Importance of making segmentation immediately actionable
- Part 3 – Strawman planning and hypothesis generation to maximize segmentation impact (this post)
- Part 4 – The ability to reach the segments differentially or accurately
- Part 5 – Prioritizing action steps within each segment: TURF or Next Best Move
Strawman segmentation and hypothesis generation
During stakeholder interviews, we ask client partners to describe how they think the market is already segmented, or to describe the segments they see in the market. What types of customers would benefit most from the products or services you offer? What features, benefits, or messages do customers value most?
Based on that, we work up hypothetical “strawman” segments with the client, including the characteristics that define each segment, what available data or knowledge exists to support that hypothesis, and what information we don’t know. For each segment, we also ask: what is it about this segment of customers that might lead them to select different products or services, or prefer certain kinds of communications?
As we flesh out each of our hypothetical segments, we dive deeper with each client partner to understand how they view each segment, and what is valuable about that group of customers. We also listen closely to understand what specifically defines value across each area of the business. This process not only gives us tremendous insight into the clients’ organization and how they think. It also directly informs the methodology for our study and what dimensions of data should have the highest priority.
For example, when conducting a segmentation study for an online services company, we saw the Strawman planning process dramatically changed the direction of the project. The client was initially looking to segment the market based on attitudes around consumption by existing visitors. Based on feedback from key stakeholders, we shifted the segmentation to focus on expanding the market - identifying specific areas where they could compete. In addition to feature importance, we also looked at usage occasions over time.
In doing this, we discovered a very practical element of targeting reach that had a higher level of importance than previously expected. If the segments could not be differentiated based on media habits and their underlying attitudes, it would be met with high levels of resistance internally.
As we discussed each of our strawman segments with stakeholders, we also uncovered other perceived challenges based on how the company and competitors were marketing to those segments.
Maximizing the impact of market segmentation
Once we have developed our hypotheses around each segment, we work with clients to narrow down the three most important priority areas our segmentation study will focus on, using the 3 by 3 rule we discussed in the previous post. Based on that, we develop a well-defined plan and strategy - clearly mapping the key decisions the segmentation will inform in the first three months, while also validating the desired results with senior leaders.
Developing a robust data set for your segmentation
Ultimately we need to make sure the output of our segmentation maps to the specific business problems we’re solving for across areas. For instance, if a company is looking to validate a new product or service idea, our segmentation study will need to include attitudinal research around customer needs and what combination of messaging and offers are most likely to drive them to adopt or purchase. If our goal is to better target our advertising and media spending, then we need to identify what combination of variables is most predictive of customer behavior and identify differences in customer groups based on their buying or lifestyle patterns.
Figure 1
Applying the right data and techniques to segmentation analysis
It’s not enough to segment customers purely based on a customer’s demographics, psychographics, or transactional data. To inform any segmentation analysis, you need to develop a robust data set for your segmentation based on strategic priorities. What data and insights will be most predictive of customer future behavior? Is the basis actionable for developing and implementing strategy?
There are a variety of analytics techniques we can use to identify better data and deliver actionable insights for any segmentation. A great example is the research Professor Eric Bradlow, GBK Co-Founder and Vice Dean of Analytics at Wharton has done on the phenomenon of the “hot hand” as it relates to the way consumers tend to buy products and services or consume content.
Eric’s research finds that “customers who consume or buy content in bunches, then go away and come back and buy in bunches, are more valuable to companies than customers who buy at a steady pace. Not only are RFM crucial components to calculating CLV; there is one additional dimension that MUST be factored in: a customer’s clumpiness or as some refer to it, binge consumption.”
When done well, market segmentation studies not only have the power to redefine who your company’s most valuable customers are – their key motivations and needs – but also to pinpoint more accurately what specific products, services, messaging and variables are most likely to move them to action, and maximize CLV.