Evolutionary AI: Survival of the “Fittest”Evolutionary AI: Survival of the “Fittest”
Evolutionary AI: Survival of the “Fittest”
January 4, 2020
by George Corugedo, RedPoint Global
03 January 2020
We all remember learning about theuniversally understood theory of “survival of the fittest” in biology class.But have you thought about how it relates to customer engagement?
Natural selection holds that reproductivesuccess for a species depends on adapting to environmental changes over time.It weeds out the ill-prepared and favors those who have learned to acclimate tonew surroundings.
Similarly, customer engagement machinelearning models determine ‘winners and losers’ in the quest for personalizedcustomer experiences that drive revenue. Evolutionary AI works like naturalselection – models that succeed in the environment they’re built for (in thiscase the hyper-personalization of a customer experience) survive to liveanother day.
Whereas natural selection plays out over millionsof years, evolutionary programming condenses this process into real-time. Anothercaveat? With continuous optimization, marketers can re-program models so thatinstead of becoming stale or obsolete, they become ‘fit’ to continue fightingin the battle for driving revenue.
Data in, performance insight out
Evolutionary programming is fueled by the continualingestion of customer data from every source – first-party, second-party andthird-party – as well as structured, unstructured and semi-structured.
Tuned to a specific metric, machinelearning simulations can alert marketers on whether the metric (fitnessfunction) they have chosen – a KPI, ROI, etc. – is being optimized or not. Likean animal stalking its prey, the simulator strips away anything that is notlaser-focused on boosting the chosen metric, helping marketers focus on themost valuable activities.
In-line analytics that provide opportunitiesfor continuous optimization is what differentiates evolutionary AI from othermodels. It puts the power of AI squarely in the hands of marketers rather thanwith data scientists, allowing them to train, optimize and update models tunedto specific business objectives. Another benefit? Leveraging the predictiveanalytics to deliver dynamic customer journeys in the context and cadence ofeach individual customer.
Lights-out modeling for deeper intelligence
Evolutionary AI debunks the common misconceptionthat businesses should only have a handful of models running at any given time,which originated when it took vast resources to build and re-program models byhand.
Now, companies that use evolutionarymodeling are encouraged to have hundreds of models in the field at once. This approachis so powerful that establishing a next-best action for a customer in real-timeat the moment of engagement is the ground floor of its considerable reach.
It’s a platform that provides marketerswith the tools needed to intuitively access and manage models with astep-by-step process of re-training and moving them into production. Onceset-up for a refresh, a model will re-train itself on new data, automaticallygenerating a next-best action if any difference is detected. This is truelights-out modeling that never stops doing what it’s programmed for – in thiscase, unearthing every opportunity to enhance the customer experience.
Evolutionary modeling has emerged as a revenue-driving engine that gives marketers the intelligence needed to deliver on ever-increasing customer expectations.