Understanding consumer preferences is critical for any business, and this becomes more complex in the ever-changing retail industry. Advanced statistical techniques like Bayesian linear regression come in handy to decode these preferences, helping businesses align their offerings accordingly. For a decade, we worked with Kohl’s, a giant in the retail industry, using Bayesian regression to understand customer preferences and drivers of choice, and here’s how we did it.
The key challenges we faced were understanding the real drivers behind customers’ retail choices and leveraging these insights to gain a competitive edge. Two factors always topped the ladder for choices when selecting a retailer – value and convenience. Knowing that Kohl’s locations were closer for most customers than going to the mall gave us an advantage over our main competitor, JCPenney, at the time.
The Bayesian Regression Approach
To further understand customer preferences and their drivers of choice, we used the Bayesian regression. Drivers of choice are the order of importance that your customers place on the outcomes like price, quality, location, and more. The Bayesian regression helped us test all these touchpoints to see where our interactions were strongest and weighed the most.
We collected data from various sources such as customer surveys, store visits, and purchase histories. Then, we ran a series of Bayesian regression analyses, treating these variables as probabilistic rather than fixed values.
We asked customers to make choices and to make sacrifices. This approach, based on trade-off analysis, helps reveal true preferences. When customers are asked to give up something to gain something else, their decisions often highlight what they value the most.
After analyzing the data, the Bayesian regression models revealed the true drivers of choice. While value and convenience were indeed significant, other factors also played a critical role – factors we might not have considered otherwise.
These insights enabled us to tailor our strategies to what customers valued most, further driving preference for Kohl’s over competitors. For instance, if quality emerged as a more substantial driver than previously assumed, we could shift focus towards high-quality products in marketing campaigns and store displays.
Bayesian regression, with its flexibility and robustness, is a powerful tool for understanding customer preferences in an intricate industry like retail. By focusing on drivers of choice and asking customers to make tough decisions, we were able to unearth the real factors that influence their preferences, allowing us to tailor our strategies and gain a competitive edge in the marketplace.