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.