This column appeared in the December 2020 issue of Produce Business.
When I started my career in marketing and strategic management, we had to learn the 3 P’s by heart: P for Promotion; P for Price; P for Place. My marketing projects always had to start off with an analysis according to the 3P’s. Of course, this held ground in 1985, when the world was a lot more simplistic.
In 2021, it is my contention that we can easily replace the 3P’s with just one letter: The D for DATA. As the world of food retail turns, trillions and trillions of terrabytes of data are constantly being generated, collected, analyzed, stripped apart and spun around. All with one focus: optimizing profits and minimizing losses.
In food retail (including fresh produce), data drives the majority of decisions that are made. Every product purchased, along with its price and every single characteristic of the sale, is recorded in immense databases, with tables exceeding hundreds of billions of rows. Loyalty schemes allow retailers to gain insight into the transaction history of millions of customers. Add weather and demographic data, and it becomes clear that just about everything is interconnected. With this magnitude of information at his fingertips, the data scientist can now easily predict when customer X will again be buying a bunch of bananas, a head of lettuce or a watermelon.
As we speak, retailers are using AI (artificial intelligence) programs to scrape the websites of competitors all over the world to match product information and prices of thousands of products together into a database. That very same program is keeping a hawk’s eye on the retailer’s own physical stock of products and incoming deliveries.
With the help of Machine Learning algorithms, forecasts are continuously provided on customer demands on a highly local level. Faster than any human mind can ever do it, the program decides at how many units of a specific product should be ordered and at what price point it should be sold to achieve maximum margins and minimum wastage.
But data also decides where a product can best be merchandised to get maximum traction with the consumer. It does so based on extensive A/B experimentations that are conducted live in the stores and by comparing consumer buying behavior across a broad spectrum of products.
Gut-driven ordering, promotions, pricing or discounting is no longer the way to secure efficiency and profitability in a commercial organization. But hey, don’t listen to me. Simply ask any major retailer: AI-driven pricing and discounting is here to stay!
Where does this leave us, the traditional, old-school fresh produce people who grew up calculating prices and margins on the back of a cigar box?
I believe that the problem is not the bright young minds that run data and AI companies. The main challenge lies between the ears of my generation, the so-called ‘grey brigade’. If there is one thing that we excel at, it is refusing to change and maintaining the status quo.
In my work as an advocate for change (call it persuading companies to move with the times), I can tell you that every time I have started a discussion on becoming a data-driven organization, I have heard countless ‘yes, buts’.
‘Yes, but it will never work because a computer can never make decisions in the way that an actual human being can.’
‘Yes, but the world of fresh produce is based on stuff that a computer can never understand.’
‘Yes, but we have tried this type of avant-garde thinking before, and it did not work.’
Why then are the really innovative fresh produce companies in Europe betting heavily on data science and AI? Here are some answers:
Let’s face it: with fresh produce, the retailer needs all the help it can get! Fruit and vegetables are planted months in advance and cannot be harvested to order. Crops are picked according to the time of year and the weather. There is no delaying a harvest for one or two months! As soon as produce leaves the farm, it is a race against time to get it onto the shelves and out through the checkouts before the quality deteriorates.
For the retailer AND the grower, this means predicting the stocks that every store should receive in order to achieve maximum sales. It also means setting the best price to move the stocks whilst maximizing margin.
The bottom line is that:
• By introducing AI-driven tools, an innovative company adds scientific assistance to the decision-making process.
• Data tools resolve a lack of insights and inaccurate predictions that can lead to reduced quality and lower speed of decision-making.
• Data tools reduce the time and energy spent on the decision-making process, allowing the process to become substantially smoother and more efficient.
• These benefits ultimately lead to a higher frequency of taking well-informed action, which results in more revenue and margin.
There is much evidence proving that companies utilizing unbiased, analytical insight into their customers and overall operations have a serious advantage over their competitors. Will fresh produce companies move with the times and become data-driven? Only time will tell…
