Data Axle believes brands can address these shortcomings through the use of retention models.
Marketers know it costs less to retain a customer than to acquire a new one. But many struggle to keep existing customers engaged due to budget or resource constraints. VP of analytics, Michael Krueger, believes brands can address these shortcomings through the use of retention models.
Mike provides thought leadership to the development of data science solutions that benefit our clients’ data enrichment needs and marketing opportunities. He oversees a talented team of data scientists that have a similar natural curiosity in the impact data and machine learning can have in achieving business goals. Mike brings over 20 years of extensive experience ranging from machine learning, segmentation, analytics, BI and market research.
Retention models help brands target their existing customers more effectively based on a desired outcome, for example the likelihood to grow customer value over time. Whether the marketer wants to cross-sell products to increase basket size or upsell customers to a new product, retention models are important tools for developing targeting strategies that yield the best results.
There are several different retention models; the ones we most frequently develop are:
Retention models utilize historical first-party data to predict future behavior. As a refresher, first party data refers to the data collected by the marketer about their existing audience such as email activity, purchase history, and browse behavior. Generally, we recommend using enough purchase data (e.g. sales amounts, number of transactions, transaction dates, items purchased) to cover multiple purchase cycles. If purchase data is not available, retention models can be built using other first-party data sources, including product registration, coupon redemption, form submissions, and consumer engagement data.
To make retention models more accurate and effective, we recommend supplementing first party data with additional consumer data such as age, gender, presence of children, income, interests, employee size, etc. This will help us build more comprehensive models
One pitfall is marketers tend to target programs too narrowly. Marketers often focus marketing initiatives on high-scoring consumers, while excluding low-ranked ones. Instead, marketers should test the model and initiatives across a broader base of consumers for two reasons:
A second pitfall is marketers’ tendency to overwrite scores as they are updated over time. Maintaining historic scores helps identify pockets of opportunity to target segments when they are likely to respond. For example, if a consumer’s score increases dramatically, he or she may be ready for a next purchase through a more aggressive call-to-action or a (re)activation campaign.
We generally expect retention models to perform well for 18-24 months after initial development. The timing may vary, but model performance tends to deteriorate over time due to:
Regardless of the scenario, it’s best to measure score changes over time so brands can make necessary adjustments and proactively plan for model re-development.