In the realm of business, artificial intelligence (AI) is commonly touted as nothing short of a marvel—a powerful tool ready to solve complex business and marketing issues autonomously. Yet, its effective implementation demands more than faith; the precision and care needed in deploying AI is like the insight and foresight of a skilled navigator charting a course. Without the guidance of a well-crafted strategy and the implementation of precise safeguards, the flow of data-driven decisions risks devolving into a frustrating logjam of inefficiencies, missteps, squandered resources, and missed opportunities.
Consider the case of AI in its most alluring form— as an out of the box solution delivering Machine Language AI that promises to transform your first-party data into a vein of gold. Mined as in lead engines and advertising platforms which entice advertisers with the allure of optimized, "look-alike" modeling -- the promise of finding prospects who mirror your customer base. And the certainty of eliminating the inefficiency of non-performing campaigns, However, can AI optimize the propensity to reach a positive outcome on its own? Not really. To get to that level of impact – it’s going to take a level of calibration that advertisers may find surprising.
Take an online retail example, where the data illustrates a stark disparity.
Without taking the added step to develop value-based customer segments – any improvement in prospect generation would maintain the inefficiency of the Law of Averages. Conversely, in this case, by applying what is akin to the Pareto Principle (the 80/20 Rule) the advertiser found that their best third of customers accounted for a vast majority (87%) of sales, while the lowest third contributes a mere 3%. This begs the question; how much should an advertiser invest in digital media, acquiring look-a-like customers vs. just going after high-value ones? Without guidance – AI will do as it’s told and assume all customers are equal. Conversely, delivering a disproportionate resource level to acquire high value vs. medium value prospects arms AI to do what it does best – deliver the desired target audience with the best CPA available. As the expert – it will be up to you to determine how to best align resources, select efficient media and deliver relevant content. Treatment differentiation should include a combination of all three…
Take as another example - Google’s Performance Max—an AI-fueled tool that promises to find and target future customers, aligned with your previous results and future goals. It offers automated precision – at a level not seen before – and the ability to continuously optimize results based upon what works best. Yet, no AI can craft success in isolation. It thrives on shared data; it requires the lifeblood of feedback to perform attribution effectively and also the advertisers trust that Google has their best interest – not their own – at heart.
The essence lies in correctly funneling back near-real-time customer acquisition data, not just in volume but with thoughtful consideration of performance variability. Consider how seasonality, geographic differentiation, or purchase frequency might impact conversion, and align your strategies accordingly. For example, an e-commerce retailer selling winter craft kits or a higher education institution that follows the normal school year schedule, a model heavily weighted towards these seniors – but will that also work for the spring term? Unlikely! Knowledgeable performance marketers must adjust their AI models to account for actual cause and effect of customer behavior that doesn't necessarily conform to annually flat purchasing patterns
The real issue as performance marketers have long known – is that advertisers may not be asking their scoring engine, in this case machine language AI, the right question to solve. To truly harness AI, advertisers must understand that it is a dynamic tool, not a static solution and adjust accordingly. With close guidance – AI can offer a truly dynamic approach that keeps refining and improving performance over time.
To harness the full spectrum of AI's capabilities, it's imperative to delve beyond conventional data sets, integrating both first and third-party insights to attain the desired precision.
Unfortunately, the confidence advertisers have in the value of their own data while understood, will also typically limit their success. So beyond first-party data, AI may need to be complemented with discerning data points from both first and third-party sources.
Following up on a very successful out of the box AI solution that doubled sales, expanding the variables with daily updates of first and now third-party data, led to a tripling of sales among their best customers. By refining the AI model to a fine edge it cut through the market's noise, in a manner that was significantly faster than a static model.
Crafting custom and derived variables as opposed to solely using what was readily available – clearly made an enormous difference in this AI-built model – but it demanded navigation as opposed to just turning it over to the smart automation of AI/ML.
AI is not a panacea—even when it works perfectly. Moreover, it is a precision tool that requires a keen operator to wield its full value. Optimization is about homing in on the specifics, the minute details that turn data into insights, insights into strategies, and strategies into market leadership.
Are you ready to calibrate your approach and unlock the full potential of AI with a strategy that resonates with your unique business cadence? The 100+ CMOs and CSOs of Chief Outsiders, the world’s largest Fractional CXO consultancy are ready to assist your organization. Let's discuss how to turn your AI investment into your competitive edge. Start Here