Evolutionary Programming: The Survival of the “Fittest” Data Models

Darwinism, the universally understood “survival of the fittest” evolutionary theory, holds that reproductive success for a species depends on adapting to environmental changes over time. Natural selection weeds out the ill-prepared. Human beings developed opposable thumbs, for instance, just so we’d be able to scroll through an Instagram feed.

OK, so I’m not a biologist. But natural selection is a fitting analogy for customer engagement machine learning models that essentially determine winners and losers in the quest for personalized customer experiences that drive revenue. Evolutionary programming – also known as Evolutionary AI – is a Redpoint Automated Machine Learning proprietary process that basically works like natural selection; models that succeed in the environment they’re built for – hyper-personalization of a customer experience – survive to live another day. But whereas natural selection plays out over millions of years, evolutionary programming condenses this to real time. Automated continuous optimization ensures that an unsuccessful model becomes “fit”, if you will, to compete in the battle for driving revenue with personalization at scale.

Feeding the Customer Data Beast

Evolutionary programming is fueled by the continual ingestion of customer data from every source – first-party, second-party, and third-party, as well as structured, unstructured, and semi-structured. Simulations perform the “eat or be eaten” function of the jungle. Tuned to deliver a specific metric, a simulation anchors a closed-loop feedback cycle to let marketers know if the metric they’ve chosen – a KPI, ROI, customer lifetime value, etc. – is being met. Like an animal stalking its prey, the simulator strips away anything that is not laser-focused on delivering the chosen metric. It is pure survival instinct at work.

Importantly, what’s stripped away in this case is the need to write code for a data processing engine to help gauge a machine learning model’s performance. Building configurable models may be a common core competency, but in-line analytics that provides automated continuous optimization is what differentiates Evolutionary AI from anything else in the market today. It puts the power of AI into the hands of marketers, not data scientists. Evolutionary modeling tactics train, optimize, and automatically update fleets of models tuned to any business objective, such as acquisition, cross-sell, or retention. The modeling environment has capabilities to alter model type and parameters, fitness functions to assess models, and an efficient search mechanism to automatically select the best model – all without human intervention.

Automation ensures that marketers are free to align business objectives with the advanced analytic models, leveraging the predictive analytics to deliver dynamic customer journeys in the context and cadence of an individual customer.

Eyes are Always on the Prize of Personalization

A common misconception of machine learning models is that there is usually no more than a handful of models involved. This misconception took root largely because it takes vast resources to build and re-program models by hand. Evolutionary modeling completely changes this dynamic; Redpoint Automated Machine Learning customers are encouraged to have hundreds of models out in the field. Evolutionary modeling is so powerful that rending a next-best action for a customer in real time at the moment of interaction through a standard channel such as a website or a mobile app is the ground floor of its considerable reach.

Complex next-best actions or decisions are more than just possible, they are in fact quite common. For quite a few customers of Redpoint Automated Machine Learning that do have a considerable number of models in the field, a next-best action is returned only after the platform interfaces with several other models to capture, package, and deliver information back to the customer in milliseconds.

The platform provides marketers with the tools they need to intuitively access and manage models with a five-step wizard. The system takes marketers through the step-by-step process of re-training and moving models into production. Once set-up for a refresh, for instance, a model will re-train itself on new data on the hour, automatically generating a next-best action if any difference is detected – however insignificant. This is true lights-out modeling that, like the animal stalking prey, never stops doing what it is programmed for – in this case, unearthing any opportunity in the data to enhance a personalized customer experience. This capability has traditionally been beyond the reach of any hands-on approach, which might produce mediocre results but only after considerable expense, resources, and time.

Earlier in this space, I wrote a two-part series on AI as a revenue driver. If you have yet to do so, I encourage you to read Part I and Part II to understand how evolutionary modeling is a revenue-generating engine and why many data-driven organizations now consider marketing as a mission-critical line of business as the frontline for creating personalized customer experiences.


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