In today's world of the real-time, connected customer, are you REALLY going to revert back to segmentation and offline data modeling?
As countless B2C companies have implemented rules-based marketing automation tools and hit major scalability and complexity challenges, advanced segmentation leveraging offline data modeling by data scientists, is regaining traction.
That's right, while people, products and places are more connected with brands than ever before, large B2C enterprises are becoming so frustrated trying to implement, maintain, and optimize rules-based approaches that they are reverting back to decades-old technology!
Admittedly, there are some attractive aspects of advanced data modeling using tools like SAS, R, or SPSS. They can directly leverage your company's IT investments in big data warehouses like Hadoop that have been made over the past several years. They enable scientific approaches via advanced correlations/regressions to be applied to the marketing problem leading to fairly robust propensity models. These models can then inform that marketer who to target with which offer in an effort to maximize returns against a broad population (an improvement over best guess or intuition).
Unfortunately, however, this advanced modeling & segmentation quickly becomes a bottleneck in any organization, requiring teams of data scientists to build out and deploy robust models followed by weeks or months to have a marketer try and implement the complex targeting rules needed to fulfill the model. Worse yet, because these models are static and have typically been built/back-tested against historical data (vs. what is happening in the market right now), they are often outdated by the time they get to market and/or demonstrate serious performance degradation shortly after implementation.
So, the virtuous, non-scalable hiring/investing in more resources continues, with little to no demonstrable improvement and no end in sight.
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