Much has been written over the years about how firms lack visibility into the returns from their marketing investments. In an analog world, the perennial reason offered for this problem was difficulty establishing a causal link between investments made in marketing activities and the market (or customer) response to those actions.
In the digital world, a common way to build causal links is by running a large number of relatively cheap experiments through which firms can connect marketing and sales actions to a customer response. Firms can track customer responses throughout the journey from search to click to purchase, and even to consumption. The result has been an exponential increase in the amount of data on that journey to which firms have access.
We wanted to know why some firms are much better and faster than others at adapting their use of customer data to respond to changing or uncertain marketing conditions. Especially during the initial months of the pandemic in 2020, and more recently in 2022, when recessionary forces began to affect the nature of customer demand, some firms were able to analyze the burgeoning customer journey data and pivot, adapting their marketing and sales efforts much faster than their competitors. We have observed a common thread across these fast-acting firms is their use of AI models to predict outcomes at various stages of the customer journey — for example, using AI to analyze historical consumer behavior data and predict the likelihood of a customer responding favorably to a marketing campaign.
What else do we see happening in these firms? First, while their competitors respond reactively to actions taken by customers, these firms are taking a proactive approach to managing their customer relationships. They’re using AI to predict which customers are likely to churn and what corrective action can be taken to prevent the customer from defecting, while their competitors react after the customers have already left. And when their predictions go off track because of external changes or market conditions, they use that feedback to quickly reorient and redirect their marketing and sales efforts. Using AI models to predict customer response translated, in effect, to designing and running a large number of experiments that helped these firms respond to market changes faster than firms not using those tools.
Prediction Models Are Changing how Strategy Works
Consider the example of a global trading firm engaged in the sourcing and distribution of commodity bulk chemicals. In early 2019 the firm began using AI-based prediction models to understand the flow of opportunities through the various stages of clients’ RFP-based buying processes. The firm learned that quality-related factors were primary determinants of getting short-listed by clients. They began using this information to selectively pursue client opportunities.
By May 2020, however, the company’s AI-model predictions were proving to be wrong. Further analysis revealed that delivery-related terms were now better predictors of being short-listed by clients, and the firm quickly and successfully switched its engagement model globally. Firm leaders who would previously have received information about supply-chain issues through macroeconomic data or a revenue shortfall at the end of a couple of quarters were able, using AI to predict intermediate outcomes in clients’ buying processes, to rapidly switch the marketing and sales approach to better align with shifts in the marketplace.
We found another example at a major real estate property developer in the UK. A January 2020 analysis of optimal incentives to tenants suggested that, given a low likelihood of corporate space remaining unrented for more than 30 days, it should be conservative in offering incentives to existing corporate tenants. The analysis further showed flexible workspaces to be less profitable than renting out corporate office space given competitive cost pressures. By late February 2020, in the very early stages of the pandemic, the developer’s updated AI model suggested increasing the flex workspace footprint by 30% and offering generous incentives to lock in existing tenants. These recommendations led the developer to begin changing its sales strategy by the middle of March, much faster than competitors still relying on the first quarter (ending March) output of their marketing and sales models. A month’s or even a week’s lead can make a significant difference in a competitive market.
In the preceding examples, each firm had to specify goals when setting up its AI models to predict outcomes. A goal might be to achieve a specific customer-acquisition level when given a specific marketing budget. Well-designed AI models are about enhancing business outcomes — not just accurate predictions. They balance the benefit of a correct prediction against the cost of an incorrect one and work within organizational constraints like marketing budgets. Being trained using historical data, AI models provide firms with a better, more sophisticated and nimble understanding of the links between their actions and the market or customer response.
