The march toward personalization means marketing strategy finds itself under the expanding shadow of artificial intelligence. AI is being deployed in a growing number of functions.
I recently asked Jerry Roche, CEO of Trial Run, a fractal analytics firm, for his perspective on the impact of AI on marketing strategy.
Paul Talbot: Are AI capabilities changing the way we create and manage marketing strategy?
Jerry Roche: Given the need to serve personalized experiences to a large customer base, marketers can no longer rely on intuitive heuristics targeted to the lowest common denominator. They need to discover preferences, align messaging, personalize value, and employ the right channels for all.
This is, by definition, no longer a task that human cognition can serve, certainly not at scales that keep the business competitive. This evolving need of the marketing function warrants data and artificial intelligence that can aid human cognition to make sense of it.
In this new paradigm, marketers are competing as much in AI execution as in their core strategy. In fact, it may increasingly appear that the core strategy is only as good as the AI available to execute it.
Not only are marketers using data and AI to inform strategy, they are also using them to evaluate it.
Talbot: What do algorithms need to do in order to illuminate strategic rather than tactical questions?
Roche: Being customer-centric rather than product-centric should be the problem that algorithms need to solve. And it is the problem that they are uniquely capable of solving owing to the need to understand multitudes of preferences and patterns. This will lead to solving for the customer’s pain points and not those of the business.
A good way to start is to look at the customer lifecycle, identify key points of truth in the journey, and solve for those. Instead of focusing on how to reach your customer, learn how the customer reaches you and enable them. Once the algorithm focuses on the right objective, insights will be deeper and more precise.
Talbot: If you were a CMO working on updating or evaluating an existing marketing strategy, what could you reasonably expect AI to contribute to the process?
Roche: Irrespective of the techniques used, which can be predictive modeling, rapid experimentation or something else, advanced data science is influencing all the areas of strategic marketing.
Segmentation is key because it is the most straight-forward way to optimize your marketing strategy. But more than descriptive segmentation, companies are rapidly moving to behavioral segmentation.
Instead of the traditional ‘Males between 25-40 years of age’, we have been seeing more of ‘Adventure-seekers’. This segmentation is bound to go deeper, till we finally reach the ‘segment-of-one’ strategy.
AI can also put more weight to marketing when it comes to new product development. AI can help marketing teams know empirically that their knowledge of the market is strong enough to get a significant role in product development.
Then comes the all-important Customer Lifetime Value (CLV). To keep customers engaged at scale is an impossible task, if not for AI.
AI can do the deep listening to deliver content that builds strong relationships. With insights, it can measure CLV accurately and help you prioritize how much to invest in each customer.
Talbot: AI can help test and validate assumptions… can you give an example of how this can happen in the context of marketing strategy?
Roche: In our experience so far, data driven experimentation always leads to benefits as there are no failed experiments; only failed strategies.
Experimentation provides solid empirical evidence of consumer behavior. It helps establish cause & effect relationships and enables accurate attribution thereby helping to identify the driver behind the success or failure of current strategies.
It is also the only way to understand the impact of ideas which have never been tested as you gather real consumer insights on new ideas.