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Causal-based forecasting methods are increasingly important—driving customer relevance by ensuring better back-end processes.
Endless articles, blog spots and water cooler conversations have expounded the importance of customer relevance in the digital age. Most focus “on-screen” and involve strategies for infusing customer interactions with more meaning in areas like advertising, search and shopping. What remains largely unexplored is a critical back-end process—one that happens behind computer and mobile screens—namely, forecasting. Discover why causal-based techniques are rising in prominence thanks to the Digital Age.
Traditional retail forecasting involved marketing, merchandising and supply chain each creating their own guesstimates (based in large part on past sales performance) of anticipated demand. Sometimes though not often they would share the projections with one another and a single number representing expected sales was agreed upon. There was little, if any insight into the efficacy of each function. Numerous questions remained unanswered or overlooked: What impact did the most recent two-for-one promotion have on sales? How did store-based stock levels compare with national levels after the holidays? How much were distribution issues impacting the overall sales? Which products needed more advertising and which could achieve the same results with less? With the dawn of the Digital Age, all that has to change.
With digital’s emphasis on relevance and “market of one” precision targeting, the failure to understand every link in a product value chain is no longer tenable. Understanding what stock you need where in order to meet demand can change daily even hourly depending on a range of variables such as weather, holidays, promotions or competitor incentives. Reducing forecasting errors by 50 percent could not only save millions, it can also give a huge intangible boost to the brand being seen to be available with minimum waste in an on-demand world is a socially enviable position to be in. That’s why retailers and manufacturers are increasingly turning to the more multi-faceted approach of causal-based forecasting.
Though not new, causal-based techniques have, up until now, been underutilized when calculating demand projections. If used, they have been confined within one functional group the impact of a “buy-one-get-one” marketing promotion is only factored into next quarter’s marketing forecasts and not considered by merchandising or supply chain in their projections. In today’s digital age, having an integrated, dynamic ability to forecast demand is increasingly critical: surges happen at lightning speed. Consider the demand upswing that can occur from discount promotions around the holidays.
The ability to accurately factor the promotion into shipping and fulfillment orders will have a significant impact. And things are only getting more complex. Emerging trends like Groupon and Yelp Deals, where virtually unlimited numbers of buyers can suddenly demand a given product in a compressed period of time will create new challenges for forecasting accuracy. Aligning marketing with merchandising and supply chain through causal-based forecasting is a powerful weapon for meeting the demand in today’s mercurial retail landscape.
While most forecasting techniques rely on past performance to gauge future demand, causal-based forecasting takes a much more granular look at both internal and external factors that affect sales. Those include:
Causal-based forecasting can then be used to identify how each of these factors affect sales uplift in an integrated fashion, linking marketing with supply chain and merchandizing. That way, each respective organizational link understands, in detail, the ebb and flow of products, both online and in-store, and can adjust efforts accordingly. Unlike traditional, manual approaches, causal-based techniques are automated and drive seamless insights to all key value chain players.
For one leading European retailer, causal-based approaches improved promotional forecasting by more than 25 percent across a range of more than 150,000 products. Here’s how: The company identified products for which demand varied according to fluctuations in the weather. The proportion of business volume affected by weather sensitivity over the period in question was then qualified. As a last step, weather-sensitive products were ranked according to their affect on turnover.
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