If you read a lot, like me, you might notice almost daily there’s a new study that contradicts some earlier research. Something causes cancer — then it’s good for you. You know the drill. What’s going on here? Do we simply not know what our research is saying? Can nobody correctly interpret the data?
None of this would mean much to CRM except that with the advance of big data and analytics, the front office — that is, the relationship between vendors and customers — is coming to resemble many other endeavors that rely on data analysis. Here is my take on all of this.
About Correlation
Very often the research we get in the popular press and in business interactions represents the findings of correlation studies. Simply put, correlation tells how strongly two things or events relate to one another, and it takes some sophistication to understand.
We can think of correlation as probability, but we need to understand what it means. A coin toss has a 50/50 chance of coming up heads or tails, so a 50 percent probability is exactly neutral.
If something had a 40 percent chance of happening, it would be negatively correlated. In other words, the probability of something not happening would be greater. However, a 40 percent chance of something happening is not zero, which is why we still get rain on days when there’s less than a 50 percent chance of it.
So, a probability of greater than 50 percent is what we’re usually looking for, and the higher the number, the better the correlation. A 90 percent probability is interesting, but 60 or 70 percent — not so much, for reasons that are obvious by now.
Still, a 90 percent correlation is not a sure thing. Using the weather analogy, we sometimes see sunny days when rain has a 90 percent chance of occurring.
In business, we’re beginning to use correlation a lot, but that disappoints many because correlation alone won’t tell us about another important part of the story, causation.
About Causation
Causation is the reason behind the correlation. It’s the data that, added to the correlation data, will provide the information necessary to make a decision. So, for example, a sales person evaluating prospects might look for high correlation between a prospect’s need profile and the vendor’s solution.
That’s a good start, but it’s missing something very important. It says nothing about the prospect’s motivation, which might be found only through more traditional means, like making a sales call.
What? Correlation isn’t enough? Consider this: At the correlation level, a prospect in need of a solution looks just the same as one that just bought something from your competitor. Causation, in this case, is another word for a buy signal. If you look at buy signals and not just correlation, a customer that just bought will look very different in this one dimension than one that is still looking.
In sales and marketing analytics, we’re mostly focused on correlation, and that means we’re far from foolproof in making our predictions. I am not trying to get on anyone’s case, but the fact that we’re so vested in correlation simply tells us where we are in the lifecycle of analytics as applied to CRM. There’s more work to do.
Another way to look at the situation is through the lens of qualitative vs. quantitative data. So far, I’ve been focused on quantitative analysis — like getting those 90 percent signals. When we’re dealing with quantitative findings, very often we’re looking at correlation data.
Consider the Candy Bar
Finding causation requires more sophistication, but it is often qualitative findings that tip the balance. Interestingly, you can develop quantitative findings over qualitative findings, but it takes a little more work. You need to ask questions differently, and you might need to score the answers to get a quantifiable result.
Finding causation starts with asking open-ended questions. In my book, Solve for the Customer, I use the example of creating a new candy bar. The quantitative approach might ask about preferences, like do you like coconut, prefer milk chocolate or dark, like peanuts, almonds, pistachios, nougat — the possibilities are almost limitless.
At the end of your research, you might have a very detailed understanding of how much your target audience likes various components of a candy bar, but you wouldn’t be any closer to making something that would sell.
The qualitative approach is less sexy in many minds, because it implies that you won’t get enough information to work with — but consider this. In designing a candy bar, it would benefit you a lot if you also asked open-ended questions about what people like most about them, or their favorite memories involving candy bars, or how they fit into a person’s day.
Those questions are almost limitless too, and the answers would surprise you and possibly tell you a lot about unmet needs in a crowded market.
Taste Tests
If you don’t believe that’s useful, consider the story of Howard Moskowitz. Back in the day, there were two competing makers of jarred spaghetti sauce — Ragu and Prego. Prego was the perennial No. 2 in the market and wanted to take the lead, so it hired market researcher Moskowitz to figure out how.
At the time, there also were just two kinds of sauce on the market — plain and spicy. That was it, just two. Moskowitz hired chefs to make what ultimately became 45 kinds of sauce, many with chunks of things in them — like tomato, meat and other veggies.
Moskowitz discovered that about one third of the American public wanted chunky sauce — but incredibly, there was none on the market. Previous research was concentrated on getting quantitative answers to questions about existing choices, which could be boiled down to “how do you like our sauce?” There were no open-ended questions about what caused people to like spaghetti or Italian food.
The Moskowitz taste tests provided the open-ended questioning leading to discovery of a new market that’s been worth billions ever since.
My point in all this is that you need both quantitative and qualitative information to arrive at correlation and causation if you hope to understand customers. If you’ve embarked on an analytics journey, that’s great — but keep looking and formulating your strategy.
Buying a single product is definitely not the end of the journey, but a beginning. If you’re a vendor, don’t make the mistake of thinking that your single product is the final answer to market need. It’s a stepping stone, and you need to position yourself accordingly.
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