While ChatGPT falls short in this area, alternative approaches have come of age, mostly without people noticing. One of the earliest forms to hit the business scene was co-authored by John D.C. Little at MIT. Little developed algorithms that mimicked PhD econometricians in the way that they detect trends, variations and anomalies in vast data sets such as those generated by optical scanners at grocery stores.
To make the intelligence actionable, an “authoring” or “generative” layer of algorithm writes a memo to the marketing manager. An example: “Your competitor is promoting in Cleveland and it seems to be a tactical experiment. You have three useful options: 1. Watch and learn; 2. Mess up their experiment by increasing your prices four percent; or 3. Gain share in Cleveland by moving your product to the end of the aisle.”
The final layer is distribution. The memo is sent to the manager in real time, via email, text message or other means. This article by Little highlights how the technology was deployed at Ocean Spray Cranberries, a fruit processing cooperative.
INSEAD’s journey into AI-powered business intelligence
Full disclosure: I am Little’s academic “grandson”; his student Leonard Lodish was my dissertation chairman at the Wharton School. At INSEAD, the former Dean of Executive Education, Arnold De Meyer, gave our TotoGEO AI lab a small budget to create executive education materials that were tailored to each individual attending a two-week programme. The common subject covered was strategic planning but a participant from the semi-conductor industry, for example, would receive course materials focused on that industry while another participant in the same room might receive materials on the toothpaste industry.
It worked. No matter how obscure the participant’s industry (e.g. copper oxychloride), the course programme had maximal relevance and impact. Feedback from the participants included “can I meet the analyst who prepared the materials?” With this encouragement, we set about putting our AI-powered approach on steroids.
The idea is simple. Prior to my academic career, my work involved estimating the market potential for cellular telephone networks across granular geographies. These estimates proved useful for cell site optimisation modelling. I also worked in the Caribbean, Asia, Africa and the Middle East, estimating the export potential of firms, some of which made rather obscure products like shower curtain rings or toilet seats. Turns out, unsurprisingly, that the more obscure the product, the less anything is published on it – just try Googling the market potential for shower curtain rings in Sri Lanka.
Foreign direct investment to such countries is hampered by the lack of data required to conduct full due diligence. Indeed, information asymmetries between buyers and sellers have long been cited as a reason why companies fail to sign all-important contracts. This problem is especially acute for small, underserved communities, especially those in emerging economies. By focusing on the long tail of products across traditionally remote geographies, AI algorithms can help reduce these asymmetries, thereby increasing investments, employment and value-creation opportunities within these regions.
At INSEAD’s TotoGEO AI lab, we set about creating algorithms leveraging various economic theories (proposed by the likes of John Maynard Keynes, Franco Modigliani, Milton Friedman and Irving Fisher, among others) to extrapolate from sparse data sets. This involves accurately estimating the consumption of a specific product category in one country and applying those consumption patterns in other countries after making the necessary adjustments for local conditions. Once estimates are generated, the algorithm takes care of the entire value chain of content creation, including all meta data, marketing collateral and distribution. MAID plc was an early distributor. Others soon followed.
Reports generated by our algorithms were priced under US$1,000, no matter how obscure the product, covering markets across all countries and cities. Even if data are not readily available online – the algorithm mimics the economist facing a “data desert” (i.e. where only sparse data are available, or too “dirty” to use in their raw format).
The Uber pitch deck and other reactions
Over the years, hundreds of Fortune 500 companies have either purchased one-off studies or subscribed to entire catalogues generated by TotoGEO’s algorithms. Perhaps the most interesting is Uber (then known as UberCab), which cited one of our reports in its now famous 2009 pitch deck: