We’ve been thinking.
Growth is a tricky business because you can’t analyze your way to the future. At some point, you need to pick up your shovel and start digging. Many firms, when they realize this, “bet the farm” and put themselves in a vulnerable position. Others do...
As marketers, we’re often extremely focused on bringing new customers into the fold. And let me tell you there’s nothing wrong with that. In fact, it’s entirely necessary, especially during certain stages of your business lifecycle.
But here’s the deal – you’re most certainly leaving value on the table with customers that you already have.
Every loyalty program manager should be wrestling incrementality. But be ready for a tough battle. The questions are formidable. How much of the behavior in the program is incremental? What’s that behavior worth? Which members exhibit incremental behavior and what motivates them?
While not an easy pin fall – wrestling with incremenatly is a winnable match. By combing clever analysis tied to the use cases for incremental behavior with a mixed method approach using both quantitative and qualitative analysis, incrementality can be wrestled to the mat.
Most of us probably realize that there is incredible power in predictive models and machine learning models. These models can help predict our customers’ future behavior before it happens, create individualized customer experiences, and help us connect to our customers on a deeper level.
However, did you know that every predictive model comes with its own set of trade-offs? That’s right, many times, by maximizing predictive accuracy in one area, you can actually increase your error in another area.
Read on to learn how to balance these trade-offs and take advantage of predictive modeling.
Feel like there is gold in the hills of customer understanding? Disappointed that you don’t have solid insights to base growth strategies on? You’re not alone. This article provides some solid tips to get you started.
One of the key challenges in designing a loyalty program or any type of promotion is to avoid rewarding behavior that would have occurred anyway. We use the phrase “accidental beneficiaries” to describe rewarding existing behaviors. The cost of rewarding existing behavior is easily underestimated or overlooked in designing and assessing promotional initiatives.