Artificial Intelligence Brings Agnosticism to
Growth Mapping in the CPG World
By Vinit Doshi

Looking for sustainable growth opportunities in mature markets for CPG products can feel like a Herculean task. There are too few resources against too many opportunities.

In recent years, however, more manufacturers and retailers have turned to Growth Mapping to increase market share and revenue. They are identifying profitable growth spaces, then developing competitively advantageous brand positioning and product offerings.

Growth Mapping aligns what consumers want with what manufacturers should bring to market. It analytically combines different segmentations – products (behavioral), occasion (needs-based), and consumer (attitudinal) – to identify and unlock precise sources of volume-driven growth, be it from existing, neighboring, or new categories, channels, markets, and brands. It is an integrated, consumer-led approach to understand and prioritize the role of all factors affecting consumer choice. The appraoch includes occasion, situational variables, attitudes, wants and needs, and constraints associated with a particular market.

Growth Mapping Balancing Act
CPG manufacturers have a lot of information of their own on which they can base their strategic decisions. They are awash in a sea of ever-larger amounts of data from point-of-sale (POS) data, tracking, custom market research, social media, focus groups, panel analytics, and so on. Yet these data often languish in siloes, leading companies to disconnected pieces of information. These clashing frameworks can leave them feeling paralyzed and without a cohesive, winning strategy on where to play and how to win.

Another factor in the delicate balancing act of finding growth is limited resources. In the CPG world, differentiating one step ahead of competitors and being first to market are critical to success. Yet, we cannot spend forever diagnosing in-market performance, surveying consumers and naval gazing at every piece of data. Nor can we guess the right answers based on intuition. Our decisions need to be deep and complete, reflecting the real world, and based on data-driven analyses that are rigorously focused on the relevant questions. This can get very complex, time consuming, and expensive.

Growth Mapping solves this by integrating across consumer segments, usage occasions, need states and category and brand behaviors. It pinpoints the most promising growth spaces, including dormant and underserved demand. Finding these growth spaces requires an objective and rigorous understanding of what factors matter most to consumers. Such analytics take great time and effort, and specialized data science and translator skills. The more complex the market, the larger the data challenge, the longer it takes and more expensive it becomes.

We marketers often start with great intentions, believing we are objectively going where the data takes us. But the truth is that precisely because of the time, people, and budget constraints, we place limitations on the quality of data and analytics used to make critical decisions. Teams routinely rule out the data elements and analyses that they think won’t impact the outcome, and choose to look at simpler data in isolated and linear ways. Inevitably, this influences the outcomes of our work and lowers the likelihood of finding the most viable growth spaces for our brands.

AI Makes No Assumptions
The analysis of such large magnitudes of complex data points, measured across innumerable numbers of consumers and occasions, makes it almost impossible to use conventional approaches to find causation and patterns. Traditional analytics still rely on human analysts, can take months, and are often constrained by subjective assumptions, leading to sub-optimal results and missed opportunities.

Artificial Intelligence (AI) and machine learning are two related technology innovations that have made it out of “the lab” and into real-world applications we use every day, often without realizing. They work in a seemingly simple way. Through iterations of supervised learning, they learn to perform tasks, or recognize patterns, in a sense writing their own code algorithms. A simple parallel is learning to walk – there’s a lot of falling over involved before you finally master it.

Once an algorithm reaches a certain level of understanding, it can perform tasks on data sets that are far larger than any group of humans could analyze, and with no predisposition about which patterns and relationships must exist within. AI doesn’t grow tired or make mistakes; it will dispassionately crunch data with brute force, but with a guiding hand until it gets the job done; that is, until it finds your growth opportunity. It is not only many times faster than human analysts or working with individual analysis tools, but it’s particularly adept at searching beyond the boundaries of our biases to suggest new insights about where and how our brands could compete.

Adding AI and machine learning to Growth Mapping means that self-imposed restraints on data, time, and even budget are now a thing of the past. Those looking for transformational growth spaces can explore a myriad of growth hypotheses in a systematic and automated way. No longer do the restrictions of human fallibility and time need to influence the process. Growth Mapping can now be truly agnostic and achieve better results, faster and more objectively.

Future Is Here – Grasp It
By creating an understanding of what consumers really want, and why they behave the way they behave, Growth Mapping enables CPG companies to re-align core value levers from market definition and product innovation to pricing and promotions for sustainable volume-driven growth within and beyond their core business. AI is opening up the true potential for Growth Mapping to be agnostic and exploring possibilities with unprecedented breadth and objectivity. In tightly fought markets where first-mover advantage remains so important, can you afford not to have AI on your side?

Vinit Doshi is a Senior Expert from Periscope By McKinsey, a provider of a suite of Marketing & Sales Analytics Solutions to help companies achieve sustainable revenue growth. For more information:

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                                                                   June 2018