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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.
Understanding the Role of Feedback Loops
Marketing and sales have traditionally lacked an approach to the classic “SENSE –>RESPONSE” feedback loop commonly exploited in the engineering world. Feedback loops enable systems to change input mix and system characteristics to enhance output. The lagged effect of marketing actions and the fact that customer response is, more often than not, the result of the cumulative effect of multiple actions taken by the firm make it hard to establish causality and establish a clear feedback loop. It is this lack of a feedback loop that limits firms’ ability to assess the ROI of their marketing and sales efforts. Absence of feedback loops further results in a disconnect between episodic strategy formulation (the realm of senior management) and the constant execution in the field that is typically managed at the frontline.
AI prediction models can pick up trends at a granular level, such as at the level of individual transactions. The field information provided by these models can be used to update and tweak marketing and sales strategy faster and more frequently, enabling firms to close the gap between strategy and execution.
Here’s an example: A 200-year-old North American manufacturing firm had significantly increased its marketing lead-generation activities but had yet to achieve a significant increase in sales. The firm was convinced it had a marketing problem. It used an AI model to analyze the data and found that the increased marketing spending had indeed generated high-quality leads, but not higher overall sales. Subsequent analyses revealed that the manufacturer’s limited sales resources were part of the problem. The sales team had cherry picked the best leads from the incremental marketing spend, but ignored a corresponding number of leads it would otherwise have followed up on.
The company now understood it had a sales-capacity issue, not a marketing problem. The analysis enabled the manufacturer to appropriately balance sales and marketing expenses to generate stronger revenue. Without the benefit of the data analysis, the siloed nature of the marketing and sales organizations would have made it difficult and time-consuming to do such a cross-functional study or reallocate resources quickly.
This disconnect is further illustrated by the example of a consumer-electronics company that ceased doing business in Russia consequent to its invasion of Ukraine. The company knew what its revenue shortfall would be due to lost sales in Russia and associated markets, but faced the difficult question of how to optimally reallocate the marketing spend to other markets to try to offset the lost sales. An AI-optimized scenario planning exercise suggested the best way to reallocate the available marketing budget and quantified the expected net drop in sales and increase in marketing budget necessary to offset the loss by increasing sales in other regions. The analysis also revealed that it would be too expensive to increase marketing to fully offset the losses from Russia. But it still enabled the firm to optimally reduce sales losses by reallocating existing marketing promotion budgets to other regions.
Flipping the Segmentation Process
As a result of the feedback-loop focus, we see the use of AI models also changing the practice of segmentation. In theory, segmentation is defined as the process of identifying a group of customers who have a common set of needs (to develop a unique product/solution to serve that segment), that share common identifiable characteristics (to be able identify customers in the target segment), and that are likely to react in a similar manner to actions taken by the firm (to design the engagement strategy and exploit economies of scale). In practice, most firms in the analog world focus on the first two parts of the definition, i.e., common set of needs and common characteristics. This approach therefore takes the form of an outside-in approach: “Let’s figure out what this group truly needs and then design the right product to serve these needs better than anyone else and, as a result, be able to extract a higher price.”
In AI-based prediction models, the practice of segmentation is focused on the third part of the definition of segmentation, i.e., the likelihood that all customers in a segment are likely to react similarly to marketing and sales actions taken by the firm. For example, an AI-based prediction model might ask which customers are better served by the sales force in the field or the tele-sales team, or which customers are most likely to respond positively to a specific price promotion campaign. Firms can use an AI model’s predictions to align the appropriate marketing and sales resources to serve each demand opportunity.
Considering the unmatched targeting abilities of predictive models, it is easier to take organizational (or expected near-term organizational) capabilities as a given and find the customers most likely to match those capabilities. This is especially true in a rapidly changing environment where market conditions and customer behavior can change far faster than organizational capabilities can evolve.
Where Are We Headed Next with AI-based Prediction Models?
The availability of customer specific data and ability of AI and machine learning to provide better predictions is poised to force companies to create integrated customer-facing organizations that fuse traditional marketing and sales functions. Ideally, this will, help organizations deliver a superior customer experience that results in enhanced profitability.
Here’s one more example: An international manufacturer wanting to improve its marketing function using AI models initially focused on prioritizing sales opportunities. Analysis of its data, however, found that, dollar-for-dollar, efforts by the field sales force focused on retaining existing channel partners had a greater impact on revenue than a similar amount spent solely on marketing. In fact, optimizing spend across channel partner retention, marketing, and sales had a greater impact on overall business KPI for a given level of overall spend than would have been achieved had the focus remained exclusively on sales-opportunity prioritization. Truly automated approaches to AI can “let the data speak” to help identify entirely new avenues across traditional marketing and sales activities with the potential to impact business KPIs and optimally balance resourcing between those activities.
Digitally native firms may make quick progress on integration of AI models, but we are concerned that legacy firms that grew up in the analog world are going to run into two major stumbling blocks and fall behind their competitors. The first is the siloed nature of their sales, marketing, and support organizations, which will impede enterprise-wide integration of customer-facing functions. The second stumbling block is that the only entities that can break this stalemate — the CEO and board — are often ignorant of how AI-based prediction models can redefine the way firms engage with customers and market segments.
Boards, unless they have members with tech expertise, are unlikely to demand the organizational transformations needed to make this happen. Ample evidence of this is found in traditional, sales-led enterprise software firms, that have struggled to defend themselves from nimble digitally native competitors that take a holistic approach to serving customers and understanding the opportunities in their data.
Will machines take over marketing and sales functions? No. Marketing and sales will not be run entirely by machines. We still need humans to make non-obvious decisions. When it comes to updating strategy, a human will always be needed to ensure the validity of AI-generated recommendations before acting on them. Humans are needed to monitor outcomes on an ongoing basis in order to provide continuous feedback to the AI models.
Remember, despite all its strengths, AI tools are far from infallible. AI at its best is a tool that augments human capability, and could reshape how we make decisions in functions such as marketing and sales and maintain a competitive advantage.