Aggregating Situational Data
Chapter 2: Principle 1—Growth comes by Capturing Situational Markets
Dear Friends,
Wow! I can’t believe the readership response to my last post about situational markets. This is such a geeky chapter. And many of you are deep into the topic. Thank you. Send me your questions and comments. Just reply to the email. I get them.
This next section is only partially done, but it’s ubergeeky. I’ll send more tomorrow, but here’s how it starts. Outline is below the post!
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Aggregating Situational Data
Recently, my co-host, Aransas Savas, and I had Craig Lutz on our podcast to talk about his book on conjoint analysis and max diff analysis. Craig is one of the original employees of Qualtrics. He provided some of the important analytical and programming support for the original Qualtrics solutions. Today Qualtrics is the leader in CX analytical tools. They are at the forefront of work in AI and CX and I suspect that Craig is playing a key role in the ongoing development of their analytical tools.
I asked Craig this question. Can you use max diff and conjoint techniques on situations? Instead of creating segmentation—the primary purpose of conjoint and max diff—based on like-minded individuals, can you segment data based on similar situations?
He said yes. He said that no one had ever asked him to do situational segmentation but that the principles still apply. The difference is that the situation, not the individual, becomes the unit of study. When companies start collecting data about situations the way they collect data about individuals, they begin to see their solutioning in a very different light.
Instead of trying to find the drivers that matter to the most similar people, they begin to look for drivers that matter to a wide range of people in similar situations. They understand how attitudes change based on situations. They are able to identify what makes a situation unique. Remember, new needs arise out of situations (think when statements) —not out of the fact that a group of people are like-minded. This gives the company a dramatic advantage in value creation.
If companies can aggregate, prioritize, and segment their data based on the where, how, and what behind needs, they can act. And if they understand which of those needs are most widely held, by people who otherwise have little in common, the company has a major opportunity to create more value.
Max diff and conjoint, Baysian analytics and customization modeling should all be focused on helping companies find the situations that matter most to the most people and then prioritizing the features the company should create to support individuals who experience those situations.
To do otherwise is to create ever narrower slivers of target audience groupings that lack a connection to the real world. Decisions are made in context, in situ. So start using your math to study the situations that arise.
Click here for book outline with links to posts
This post about Experience Strategy Certification is also helpful.