Our Technology: Revolutionary Forecast Modeling
Our experience is solving complex systems problems. We found that our Consumer Goods colleagues were dealing with similar problems. They were using data to drive marketing plans and forecasts but remained unsatisfied with outcomes. Clearly something was wrong.
Most teams use one set of tools to make plans, and are then burdened to somehow turn those plans into forecasts. This is backwards. We realized this was due to two specific limitations of common statistical and "black box" A.I. models. We built our technology to empower teams to use the forecast to know what to plan.
Most models don't operate at the level brands actually spend their money, like treating $1 of Facebook like $1 of TV.
Most models aren't able to partition causality amongst concurrent drivers, like every Black Friday season with low prices and high ads.
A Catalog of Causality
Our team of MBAs, PhDs, computational scientists, and applied mathematicians realized that we needed a different modeling approach to unlock true causality of demand and make better business decisions.
"Black box" and statistical models rely solely on historical data - they may have millions of data points, but they never see the variation you'd need to solve the concurrent causes problem.
Those models lack the causal structure to understand what has happened and what could potentially happen if they make a change, so we went to the literature to build a causal model of consumer purchasing.
Price effect on sales
We capture how likely consumers are to purchase at each retail channel as price varies
Susceptibility to Ads
Total impact of advertising
We capture how likely consumers are to purchase at each retail channel as media exposure varies
Impact of Seasonality
Varying Purchase Likelihood
We model true seasonality of a product, excluding all other input factors.