A typical large grocery has more than 30,000 SKUs, and yet the average grocery shopper purchases less than 300 of them over the course of any given year. Why is that? What determines the individual choices that food shoppers make? And how can grocers understand what their customers really want?
These questions and others speak to the heart of ‘food psychology’ —that set of hard-to-define values, attitudes, tastes, and behaviors that determine what a shopper is likely to pick up in a trip down the real world or virtual shopping aisle.
To begin, are your shoppers health-conscious buyers, who are willing to pay more for free-range, organic, non-GMO products? Are they family staple buyers, looking for the most nutritional value, cost and convenience for their shopping buck? Or are they epicurean, indulging in the best of gourmet options like imported wines and fine cheese? The beauty of online grocery shopping is that all of this can be determined from a shopper’s purchasing behavior.
Most online grocers are getting food psychology wrong
Where food psychology really becomes important in the online grocery world is when you want to offer highly relevant and helpful recommendations to users during their online shopping process. Acting on evidence that AI-powered recommendations increase retail purchase volumes up to 18%, it is understandable that grocers are incorporating this into their arsenal of e-commerce tools.
But more often than not, the recommendations online grocers make fall flat. Identifying shoppers based on the kind of simple lifestyle preferences stated above is too simple an approach. A true understanding of food psychology requires that we go much further.
For example, what if our presumptive epicurist was really just preparing for a fancy dinner party during their last session? That imported caviar she purchased was less an indicator of her genuine preferences than temporary circumstances. Likewise, although our family value buyer usually takes home staples like milk, eggs, and flour, that doesn’t help you understand that the ground beef, tomatoes, and onions in his cart actually reflect an intent to make a Bolognese sauce. If you knew that, you might have recommended dry white wine, garlic, bay leaf, and tagliatelle instead of another basic staple like bananas.
And again, perhaps our presumptive health-conscious buyer sometimes shops specifically for a vegan non-drinker, but also for a partner who prefers their steak bloody and their liquor strong. Recommending only Tofurkey patties and artisanal kombucha may not get you as far as you’d hoped.
When grocery shopping recommendations are made on narrow data and simplified paradigms without really understanding shoppers’ complex relationship with food, grocers face double-jeopardy: Not only do you miss the opportunity to earn a bigger shopping cart checkout, but the poor recommendations also annoy and alienate the shopper, who feels “sold to” instead of “served.”
There is a better way, and it’s really pretty intuitive.
To give good online shopping recommendations, online grocers need to:
- Take an inventory deep dive
Grocery items are remarkably data-rich. Consider a typical grocery SKU: The name, manufacturer’s description, price, category, and brand reveal whether this is a food product or a non-food product, and whether it is something applied, such as lip balm, or something to be used externally, like a spatula. Most food products can be further broken down into myriad sub-components according to their ingredient list, nutrient disclosure, and product attribute call-outs such as “free-range” or “microwave” that reveal important psychological attributes of consumers’ food preferences.
The way to then make actionable sense of all of that product information is through artificial intelligence techniques like natural language processing. NLP gives grocers a structured way to convey sophisticated human knowledge about food SKUs to a computer algorithm: “What is a protein?” “What is a vegetable?” “What is a grain?” “What is an alcoholic beverage?” and “What is a condiment?”—but also, “What is healthy?” “What is convenient?” “What is good value for money?” and “What is indulgent?”
- Every customer is unique
As we discussed above, while it may be useful to think of customers in terms of well-defined avatars, the reality is that each shopper is likely to express a wide range of purchase behavior based on what they are preparing, who they are shopping for, and variations in their personal preferences over time. It is never enough to simply pigeonhole a customer into a simplified caricature of shopper type. Instead, you must put together whatever you know about the shopper’s personal characteristics with past purchase behavior, response to previous recommendations, inferences from products purchased, and a wide range of other clues each customer displays during their interactions with your platform. Your recommendation engine must be built to serve market segments of “one,” and the work of defining who they are is never done. - Buying versus Shopping
Understanding products and customers is an important start. But to really present the right recommendations at the right time, you must also understand the actual context of the purchase. At the simplest level, that can refer to seasonal variations (imagine receiving “pumpkin spice” recommendations in the middle of July) or geography (preference in sweeteners, for instance, vary dramatically amongst different regions and populations).
But understanding the contextual relationship between the items a buyer has already put in their cart can also make all the difference in the world between good and bad recommendations. There is a unique relationship, for instance, between flour, butter, and a dozen Granny Smith apples—picking up that these belong to ‘from scratch’ apple pie recipes empowers you to propose complementary items like cinnamon, nutmeg and pie pans that customers really need.
- You are what you eat (so what are your customers eating?)
And really that brings us to the most complex and fascinating requisite of making good grocery recommendations— understanding food itself. Each one of us has our own personal flavor preferences and dietary habits. To get to the heart of what online grocery shoppers want, your recommendation engine has to know food, inside and out. That means gathering and classifying, millions of food ingredients, flavor profiles, products, dishes and recipes, and then building a machine learning algorithm with an unparalleled level of understanding of how customers relate to those attributes.
Grocery recommendations have a unique logic which can not be served by copy-pasting the kinds of recommendation algorithms used successfully by entertainment and general e-commerce players. To make really great grocery recommendations you need to break down your products into recognizable data, understand how your customers shop, take into account contextual variation in shopping habits, and then build an AI model that is specific to the psychology of food. Machine learning offers the ability to do this at a scale that humans could not dream of alone, and Halla is proud to offer the only truly grocery-specific recommendation engine on the market today.
