External data such as store foot traffic, reviews, ratings, business filings, social profiles, median incomes in the zip codes, and website traffic can offer additional signals that can help CG companies to find the right business and prioritize them for their sales force.
Time is short since competitors are trying to win over those surviving and novel retailers too. A dramatic shift in conditions opens new markets that might previously have been locked up by a particular brand. Retailers are willing to try new brands, the buying public’s loyalties have changed - they just might not remember their previous preferences. All this means CG brands are in a race to get back to situational fluency - and are ingesting huge amounts of external data to achieve it. That comes at a financial cost but also costs in terms of time from overstretched data science teams. Buying the wrong data signals that are not predictive just adds to complexity, not clarity.
In the post-pandemic world where the historical data and lead generation and scoring models are no longer effective, organizations are increasingly powering machine learning and analytics models with external data to open up new doors and help bring new customers into the fold. They’ve exhausted the gains from improving their algorithms and are realizing it’s the data, that fuel those models, which will yield the best outcomes.
The consumer goods enterprises that have external data acquisition and management as a part of their data management strategy will have a clear advantage when it comes to capturing shelf-space as retailers come out of the pandemic.
Omer Har is the co-founder and chief technology officer of Explorium.