What I Wish I Had
Closing Data Gaps with AI- Image Recognition
So, you think you can work for a start-up?
It’s the dream of many “corporate entrepreneurs” from top tier companies to reap the perceived benefits of a startup. The images of the early Silicon Valley startups working in their flat bill hats, board shorts, playing ping pong at 10am, free lunch and lattes, collecting massive gains on the equity given to them for their “big company” experience occupies your daydreams while sitting in your battleship grey cubical. “There must be a better way?”
What many who have crossed over into this world have come to realize is, today at least, this is a dream. Startups can be hard. In the pursuit of “punching above your weight class”, Startups need data… at least as good as the “Big Guys”, however, they have limited resources. Careful consideration on what investments can be made to garner insights that can help them compete for buyer mindshare. “What I wish I had is….” becomes the question most asked by Category Executives moving to startups.
What do we all learn from this?
Whether you work for a startup or an established brand in the CPG industry, garnering key insights from those shopping your brand is critical to getting invited to the “Thought Leader” table or keeping your seat. The truth is there are tons of data gaps today in the CPG space starting and ending at the store level. We all wish we had consistent store level POG, Pricing, Promotion and POS data throughout the year so we could understand what is REALLY happening at retail. Syndicated data can help us see macro trends; however, this data is often looking too far in the past or not detailed enough. How can I understand my brand’s performance, let alone how the competition is impacting the category? Big blind spots. I guess “What I wish I had…” might be what all category managers are asking- not just startups.
There is an argument that today data gaps are widening, not closing as Retailers leverage their own strategies to test pricing, assortment and positioning in micro-markets, out of sight from suppliers and vendors. The data coming from these tests are weighing much heavier into retailer decision making. Technology, owned by suppliers and vendors, will be the only way to get ahead of these tests so they don’t work against your brand.
Technology exists today to fill the gaps...
One tool that is gaining significant traction is AI-Image Recognition. CPG companies are using this technology to obtain real time, store level data by leveraging what the technology can see, then comparing these images with what’s expected. When store level point of sale data is added you can fill many of the gaps you have today. Imagine having store level details of what’s being executed for the entire category. This data can be implemented into price elasticity, volume and share modeling. Now THAT is driving “thought leadership” with your customers. Your boss will be asking you, “How did you get that buyer meeting?”
For more information on AI- Image Recognition, visit maxerience.com or contact Jason DeRienzo at email@example.com..