Many of my colleagues have recently posted articles on Consumption Sensing, a forecasting approach that focuses on anticipating consumer demand. This approach has become even more vital during the pandemic as consumers’ needs and shopping behaviors change dramatically. But the new challenges don’t stop there.
Demand changed, but so has consumers’ expectations of what they should pay, as well as their response to promotions. Consequently, CPG companies must reevaluate “tried and true” practices. Moreover, loyalty is facing another test. In categories with shortages, consumers tried other brands, creating another opening for consumers to switch permanently. A recent study by McKinsey noted that as much as 30 to 40 percent of US consumers tried alternative brands or products during COVID-19.1 This trend of consumers switching to more affordable options is likely to exacerbate further during the post-pandemic recession. Therefore, CPG companies can no longer rely on past performance to make future pricing and promotional decisions.
What can CPG companies do? And how does consumption sensing play a role? Well, first, let’s take a step back and revisit what we mean by Consumption Sensing.
Traditional forecasting tends to rely almost entirely on internal and historical data – prior shipments, previous retail orders, syndicated POS data, etc. However, the concept behind Consumption Sensing is to bring in other external data and integrate it with historical data to anticipate what consumers will want in the future. Data sets could include employment reports, economic indicators, and social sentiment, as well as non-sales eCommerce data such as views, clicks, and ratings. By embedding these additional exogenous variables into the artificial intelligence (AI) models, a whole new picture comes to light about what consumers will want on the shelf next month or next season. The ability to analyze consumption shifts and future trends helps to pinpoint even where they will want it, i.e., which stores or channels need to carry what.
The good news is these same new, hyper-localized AI models that forecast future consumer demand can also be used to simulate hyper-localized pricing and promotional responses.
By leveraging both price elasticities and promotional drivers, and a future, consumer-centric view of demand, CPG companies can anticipate what level of base pricing will maintain or grow market share. They can also determine the best trade promotion investments for the given period based upon ROI and business growth. Consumption Sensing also extends to assortment planning, where companies can anticipate changes and their effect on sustaining loyalty or acquiring new consumers. The ability to make these decisions at a very granular level and focusing decisions that are independent by store and/or channel, helps achieve this goal. It is especially important given re-opening and, potentially, re-closing schedules, along with the impact of the recession, is going to vary greatly state-by-state, city-by-city, even neighborhood-by-neighborhood.
Lastly, you might think that this is a temporary problem. However, MANY experts have reiterated that it will be some time before we fully transition out of this crisis, and the world is likely never to go back to the way it was. Consumer shopping behaviors will forever be changed, and some would say that consumers were already expecting CPG companies should anticipate their needs. Those CPG companies that want to grow market share already know that the only way to expand is to be the first to have the right kind of new product on the retailer shelf, in the right location, at the right time, and at the right price.
So, where do you start?
First, as also mentioned in the McKinsey study referenced earlier, CPG companies that have not already done so need to start establishing a dedicated Revenue Growth Management (RGM) team and approach. By bringing together the siloed functions of pricing, marketing, assortment, and trade, they stand a much better chance at capitalizing on existing and future market share by working together with a single view of account growth and return on investment levers.
And second, this is also a good time for the RGM team to work hand-in-hand with their Demand Planning counterparts and invest in building the right, consumption sensing AI forecasting models that can feed both functions. Using a single source of truth will increase planning effectiveness and greatly improve efficiency as it works to eliminate the inevitable back-and-forth that occurs when everyone is working from a different forecasting point of view.