The single biggest problem with most enterprise Voice of Customer (VoC) programs is that they focus far too much on top-line metrics like NetPromoter Score or Satisfaction and nowhere near enough on consumer drivers of choice. While it’s certainly important to know how well you are doing, it’s significantly more actionable to know what you can do to actually improve. Is this an either or? Certainly not. It would be foolish to give up on performance tracking nor is there any need for such a choice. Online survey tools are – mostly – designed to support easy enhancement of survey instruments and easy deployment of additional surveys. A relatively modest sample and a very small of number of questions should suffice to establish your top-line metrics – leaving you plenty of elbow room to explore the real drivers of consumer choice.
But while it sounds straightforward, this idea of exploring consumer choice is non-trivial. Perhaps the biggest reason it’s so rare is that it’s not that easy to figure out how to do it. Consumer decision-making remains (and probably always will remain) a hotly debated area of academic research. It feels like the type of question that is never so much solved as gradually refined.
So I’m not going to solve the problem in this blog, but I did want to lay out several ways to think about the problem along with accompanying research strategies. I don’t happen to think that there is one right way to approach this problem, since different industries and different products will likely involve rather different decision-making strategies. There’s little reason to expect that we will employ identical decision-making strategies in buying a house or choosing a cancer drug as we do in picking a brand of butter to buy.
High-Level Models
There are a number of high-level models of consumer-decision making. Do you need to know about and understand them? Maybe. I think there’s a considerable amount to be learned here. Economists have dramatically revised and deepened their understanding of actual choice behaviors in the last twenty years – building models that feel much closer to reality than traditional Utility maximization models. If you haven’t read any of these Behavioral Economics books, you really should.
But it’s also true that you can get along pretty well without thinking to that level of abstraction. Models of consumer choice can be summarized in high-level terms, as economic or psychological. I’d classify economic models as focused on the way consumers make decisions based on features, benefits, preferences, and risk. Utility theory is the classic economic model. Extensions of it like satisficing (a model built on the idea that consumers will find the first acceptable outcome) are designed to capture rules-of-thumb that consumers use to make actual decisions in the face of the challenges inherent in gathering information and making decisions.
Psychological models tend to provide more evaluative methods and concentrate on connecting product or experience attributes to a consumer’s internal values.
[Of course, a utility theorist can make this type of model fit into their system as well. Indeed, it’s a rather ridiculous aspect of broader utility theory that when taken a certain way it can explain anything and can become like any other theory. An argument can (and has been) made that when a solider jumps on a grenade he’s maximizing his overall utility. Since we’re not in college here, I’m going to assume nobody cares about this type of argument.]
From a marketing perspective, these two approaches tend to lead to very different types of research. And rather that focus on which model of consumer choice you think is true, it’s probably better to focus on which type of research seems to make the most sense for your company, product or service.
Economic Decision-Making Strategies
If you think that consumers tend to make decisions about your products/services largely on a feature/price basis, then you should probably focus on audience research tools that will help you decide questions like:
- How should I price my products?
- Which features do customers care most about?
- Which features do customers tend to want to group together (bundles)?
- Which features aren’t driving customer choices or preferences and might be eliminated?
If questions like these are your focus, one simple method to get at customer preferences is to directly ask. You could, for example, create a survey where you simply listed a set of features and asked customers to rate their importance. You could also ask customers to rank their importance.
Both methods can work (and get at slightly different things), but directly asking customers does have some problems. It’s hard, for example, to capture every alternative possible. And particularly where you’d like to investigate the relationship between a substantial number of factors, asking seems almost impossible. You just can’t explore every combination.
To facilitate this type of research, analysts developed a technique called conjoint analysis. In conjoint analysis you ask the consumer to choose between various options and then use statistical methods to infer the weightings being assigned to each factor. It’s rather like designing a small, carefully controlled test-bed where each decision helps you refine your estimate of the factors the customer is using (perhaps sub-conciously) to make a choice.
One of the key facts about conjoint analysis is that the choice dimensions need to be objective facts (things like screen-size not things like “snazzy design”). This makes it very well suited to understanding rational feature’s trade-offs but much less applicable to understanding things like “brand relationship.”
Psychological Decision-Making Strategies
If you think that key decisions (or at least the part of the decisions you don’t already understand) about your products/services are more psychological and value-laden than feature/benefit focused, you’ll probably want to think more about psychological methods for understanding the consumer.
Means-End Chains are an example of this type of decision-making model. In Means-End Chains, the idea is to link product attributes (like being colored red or being a sports car) to emotional or intellectual consequences (passion or heat) to personal values (I want to feel romantic).
You can see how different this is from conjoint type analysis. The goal of Means-End Chains is more about explaining why feature preferences exist than creating numerical weightings of them.
As with feature preferences, it’s possible to get at psychological decision-making strategies simply by asking direct questions. But you can see why this might be challenging. The path from attribute to value is necessarily a bit indirect and asking questions like “Do you want to feel romantic” or even “Does Red make you feel romantic” seems a bit of a stretch.
Instead, researchers have created a semi-structured approach to generating these Means-End Chains with a method called Laddering. The idea behind Laddering is to explore the “why” behind a response and to drive this deeper and deeper until you’ve associated the “why” to some fairly basic value.
Laddering is obviously much easier to do in a Focus Group or Personal Interview context than an online survey. In fact, I’d say that building a Ladder-based survey is probably impossible until you’ve done some basic research into potentially common ladders. But if you’ve done that research, you may well be able to extract some “golden” questions from that research that can be used to either construct or infer Means-End Chains for actual visitors to a site.
Picking Your Poison
Extending your VoC program beyond top-line measures of success is critical to driving decision-making and action. It isn’t enough to know whether you’re succeeding or failing – you have to know why as well. VoC programs can do that. But exploring consumer decision-making isn’t always as easy as sitting down and penning a few new survey questions (though that probably wouldn’t hurt). Depending on the nature of your products and selling, you’ll likely need to decide which model(s) of consumer decision-making seem most apropos and end up choosing between the economic or psychological camps. This is really the place where you have to pick your poison. Within those camps, there are as many different theories as there are theorists. But there are also some well-established research techniques like conjoint analysis and laddering that can help you devise much more sophisticated approaches to making your VoC program answer the really fundamental question – “Why did the customer decide.”
[Note: Since I’ve treated these problems at a very high-level I didn’t include any specific citations. But there are several books about Means-End Chains in Marketing Analysis available on Amazon and a huge number of works on Conjoint analysis which is widely used and has been around for quite some time.]
[2nd Note: Early-bird on X Change is ending soon so register now if you planning on attending this year!]
