Segmenting Key Drivers: Addressing the Individuality of Your Customers

by Mike Deinlein

As insights professionals, we understand that not all consumers or customers are the same. They have unique needs, behaviors, or attitudes that influence how they interact with a certain business, product, or service. Yet, more often than not, we sometimes ignore the nuance and distinctiveness between customers when we tackle Key Drivers Analysis (KDA) in large-scale tracking programs. While this has its benefits – namely, clear recommendations on where to focus resources – an over-reliance on an aggregate KDA built for the “average” customer can obscure the foundational understanding that all customers are unique.

Key Drivers Analysis attempts to answer the single most important question for a customer experience researcher – “where should I invest my money to make the most positive impact for our customers?” It illuminates the relative importance of potential drivers on an outcome – Net Promotor Score (NPS), Overall Customer Satisfaction (OSAT), Customer Effort Score (CES), or a specific behavior – so companies can better direct resources to the areas that matter most. This is a novel goal and when done well, a Key Drivers Analysis can help businesses plan strategically and achieve a healthier return on their Customer Experience (CX) investments. However, such a lofty goal is sure to drive a multitude of opinions on the best approach to KDA. While one researcher may prefer correlation for the simplicity in aligning stakeholders, another prefers partial least squares regression for the storytelling potential, and yet another researcher prefers the most cutting-edge machine-learning technique for the highest possible predictive power. There is no easy answer to the best technique for KDA, but there still might be an answer to the best approach to KDA.

No matter what tool a researcher chooses, results are often reported at the aggregate level – an all-for-one and one-for-all approach to Key Drivers Analysis. This assumes two key factors:

All customers want the same thing.

All customers mean the same thing.

An aggregate model takes the individuality of customers and distills it down to the “average” customer. But no customer is truly average. Every customer has unique needs, behaviors, and reasons that drive their experience with your brand, and it is those differences between individuals that reveal insights and opportunities that an aggregate KDA often obscures.

So, how do we bring individuality back into Key Drivers Analysis? Well, we need individual-level impacts for our key driver model (warning, a quick analytic detour to follow). To bring this to life, we have created a new approach based on the newest advances in machine-learning explainability. The protocol is scalable to many different predictive, key-driver techniques – from simple methods like regression, to more advanced machine-learning algorithms like Bayesian Network Analysis, random forests, and neural nets. It’s an explanatory approach that sits on top of whatever technique best fits a situation’s needs, but has the added benefit of providing individual-level impacts.

So how does it work? The concept is derived from game theory where we try to understand…

how much each player – the features or predictors included in the model – helps us to predict the game – a respondent’s predicted outcome in our model, let’s say through customer satisfaction or NPS, for example.

By running countless permutations where certain “players” are iteratively included or excluded from our model, we can assess how well the presence of a feature helps us predict our outcomes for each respondent, the “game” in our model. For example, perhaps cost savings is more critical in predicting Respondent A’s satisfaction, but digital experiences are more critical for predicting Respondent B’s satisfaction. We call this the “relative contribution,” and it provides the respondent-level impact we need to bring individuality back to our KDA.