300 million active users sing the praises of Spotify—the most successful music-streaming services out there today, easily beating out comparable services from Apple, Amazon, and Google. The service now boasts some 138 million paying subscribers—double that of its closest competitor. The secret to Spotify’s success is its uncanny ability to propose personalized music recommendations through features such as its ‘Discovery Weekly’ playlist. Every Monday, Spotify users are treated to 30 new songs that they have typically never heard of but usually love. Over the past five years, Spotify users have spent more than two billion hours tuned into these personalized playlists, discovering new music they adore, and growing ever-more-loyal to the platform as a result. 

This got me thinking — “What is it exactly that Spotify does to make personalized discovery so successful? And what would it take for an online grocer to “replicate this success?”

At least part of the answer comes in how Spotify thinks about music. Since 2014 the service has owned The Echo Nest, a spinout from the MIT Media Lab that creates personalized music taste profiles based on the listening patterns of each user. But rather than using only broad heuristic approaches like musical genres, Spotify breaks each song you listen to down into over fifteen detailed aural characteristics ranging from basic descriptors like musical key, time signature, and volume to far more esoteric conditions, including “speechiness,” “positiveness,” and “danceability.” In other words, the Spotify personalization algorithm cares as much (or more) about the detailed “ingredients,”“texture,” and “flavor” of a song as it cares about the broad category that the song lives in. 

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You can probably see where I’m going with this. But before we get there, let’s take a look at how things might pan out if Spotify did it a different way. Let’s say I listened to the Norah Jones’ song Come Away with Me. A simple analysis might show that this is a jazz song, or what some might call “easy listening.” It would be a fair guess that I might like to hear more tunes from Norah Jones and other artists in her genre. But one of the most popular jazz/easy listening artists of all time is Kenny G, and–with apologies to Kenny G fans– I don’t want to hear that. If the algorithm was a little smarter (it is, by the way), it would know that Norah Jones is a female vocalist with relatively young, pop sensibilities, while Kenny G is an instrumentalist whose core fan base is at least two to three times my age. The algorithm would do far better recommending Billie Eilish, Amy Winehouse, or even Lady Gaga.

In truth, I tend to prefer electronic music, and what really gets me going is a great drumline. Whether it’s indie, house, or even modern R&B, I’ll take a fast and funky rhythm pattern over a killer bass line any day of the week. Spotify knows this about me. They dig deep into the alchemy of the songs I stream and manage to distill out the essential elements that make my personal tastes unique to me. The algorithm is smart enough to know that I might like a particular song by an artist because of the instrumentation, tempo, lyrics, or melodic style without really being a fan. That’s why my Spotify recommendations often contain suggestions of music from artists and genres outside of what I normally stream. And sure enough, most of the recommended songs have something about them which makes me feel they’re “my” kind of music!

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So how might this work for grocery? Just like a song is defined by numerous characteristics, so too is a food product: There is its broad food group, such as protein, vegetable, dairy, or grain; its subgroup, such as beef, chicken, or fish; it’s ontology as a main ingredient, side dish, or sauce; its country of origin; whether it is fresh or packaged; whether it is a versatile, stand-alone recipe item or a convenient, pret-a-manger meal; its caloric count and nutritional content; and more esoteric considerations such as whether it is gluten-free, vegan, or hallal. Food items can also be thought of in terms of recipes and what other items the product is typically paired with—such as the relationship between bread, peanut butter, and jam, or that between pasta, mozzarella, and tomato sauce. There are flavor profiles such as sweet, salty, spicy, or umami. There is the brand, whether the item is frozen, canned, or bottled. The complete range of characteristics for any one food item is just incredibly robust.

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Brands are parallel to songwriters, and food products are like songs. Rhythm, instrumentation, and melody are homologous to texture, ingredients, and flavor. With the right algorithmic approach, you can tease out a dozen or more taste affinities from food that resemble the musical affinities I talked about earlier. That will allow you to help shoppers discover new food products that truly appeal to them, rather than using only generic heuristics. Instead of “customers who bought X also tried Y,” you can use “since you are a vegan customer who frequently buys spicy foods that are easy to make, we thought you might like to try this dairy-free pepper jack cheese and soy-based, organic, taco filling. By the way, since you have purchased light, imported beers in the past, you might want to try this new Sol Limon y Sal beer to go with that!”

Personalized recommendations like these create a 5% or greater basket uplift. They also replicate online the joy of food discovery that customers experience in-store. With the brick-and-mortar experience, colorful endcap displays, dump bins, and aisles of exciting choices appeal to the “gathering” aspect of our hunter-gatherer evolution. It’s enjoyable and even slightly addictive. Online and in mobile shopping apps, the “Spotify approach” can empower grocers to replicate that joyful discovery process by presenting the most attractive options to customers within the very limited space available. That goes well beyond lifting basket size. By delivering an incredible user experience to shoppers, effective personalization also increases loyalty, shopping frequency, and net promoter score. 

And that—with a tip of the hat to Spotify—should be music to grocers’ ears.

Personalized grocery shopping recommendations are undeniably effective. 91% of consumers prefer to receive personalized recommendations. And when done well, they consistently deliver in excess of 5% basket uplift. But did you ever think about why recommendations work so well? Understanding the psychology behind why shoppers respond to recommendations can help retailers understand how to make them more effective. Here’s a dive into the four motivations that lead shoppers to respond to personalized recommendations.

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You are shopping online for groceries, whittling down your list of needs, when a photo recommendation pops up for an item you have not yet selected. Almost unconsciously, you pick the item, add it to your virtual cart, and carry on. “Great, that’s one less thing I have to search for,” you probably think to yourself, as you click and scroll your way through your journey. You have just experienced personalized recommendation at its best. When executed correctly, personalized shopping recommendations are undeniably effective. According to a sizeable Accenture survey, a whopping 91% of consumers prefer to shop with retailers who provide them with relevant, personalized recommendations. In our own work here at Halla, we have consistently seen personalized recommendations deliver basket uplift exceeding 5%— even in physical stores where the shopper must walk to the recommended item to select it. Some studies even attribute a 200% performance increase when calls to action are personalized, rather than generic. It’s clear that personalized recommendations work.

But did you ever stop to think about why? Understanding the psychology behind why recommendations work can really help retailers understand how to make recommendations more effective. To get to the bottom of this, the natural place to start is to ask a parallel question—”Why do people buy things?” If we can comprehend the basic motivating factors behind making a purchase in the first place, we can understand how to help buyers find what they need. Let’s take a look at a few of those motivations:

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1. It meets a key objective: Whether a recommended product meets a simple or a complex expectation, understanding a shopper’s objective goes a long way towards an immediate purchase as well as long-term loyalty. For a cost-conscious shopper, for instance, you can offer an alternative generic product with close characteristics to the brand-name product they are already aware of. When you see a shopper add discount sliced bread and jam to their cart, for instance, proposing your high-quality, low-cost, house brand of peanut butter is likely to hit the mark.Consider also, how to serve shoppers with food allergies or dietary restrictions. If past behavior suggests lactose intolerance, for example, you could offer lactose-free or A2 protein vanilla-flavored ice cream to go with that pie crust and apple filling. If it looks like your shopper is actually planning for a birthday party, you can offer candles and disposable partyware. Whatever it is, anticipate what your shopper is looking for, and why. Then make personalized recommendations that help them find what they need.

2. It provides genuine value: A second way you can be smart about personalization is to make recommendations that deliver real value. While that may align quite nicely with meeting budgetary objectives as we just saw, for some shoppers at some times, value has little to do with cost. It can be very hard to understand the real volume of a product like boxed cereals or tortilla chips when buying online. A SKU that’s cheaper is not necessarily better value—and shoppers will feel cheated when the product you recommended is finally delivered. The key is to make sure that whatever you are offering consistently meets or exceeds the value ratio of what shoppers normally encounter on their own. If they are searching for paper towels and you introduce a bulk-pack of their favorite brand which is not normally available in stores, they will remember you well. Also keep in mind that brand affinity or perception of quality is especially important for fresh items like lettuce and avocados and for luxury items like gourmet wine and cheese.

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3. It saves them time/trouble: Online shopping offers a clear convenience advantage over trudging through the aisles and then hauling groceries back home from a brick-and-mortar store, right? Not according to a joint investigation between Google and Bain & Company. It turns out that only 42% of 8,000 online shoppers surveyed said that shopping online saved them time versus shopping in store! That’s crazy. This represents a great opportunity for grocers to improve the online experience using personalized recommendations. To save shoppers time, why not recommend bulk items that your shoppers always buy? They can transfer the entire list to their cart at once or prune selections as needed. Or how about proactively recommending items that fit recipes that shoppers seem to be building? If you see fresh basil, garlic, and olive oil—you should be recommending parmigiano reggiano, pine nuts, and a nice box of rigatoni to go with that pasta pesto they have in mind. Not only does this save shoppers time, it also helps them to avoid the decision paralysis that plagues the online shopping experience.

4. It fits their personality: Finally, personalized recommendations should be the very embodiment of the “know your customer” adage. One of the deepest, most universal human desires is to be understood, ranking not far behind basics like food, water, and shelter in the hierarchy of human needs. There’s no sense, then, in offering fresh butcher’s cuts to a shopper that is vegan, or suggesting junk food to a customer who identifies as a health nut. Bad recommendations don’t just fall flat, customers may even feel offended that “you think I would like that?!

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Understanding your shoppers’ personality traits is key to knowing if you should stick to the basics for a conservative shopper, for instance, or if you can delight a more adventurous customer with new food ideas that match their palate, diet, and personality. Don’t just use simple “people like you bought this” heuristics. Really dig into the psychology of food and know your customers’ “why.” Your recommendations will perform far better if you nudge shoppers in the direction that they already want to go, instead of shoving them somewhere that isn’t natural. Recommendations work because they help customers meet their genuine needs in a valuable, convenient, and personal way. The closer you can get to understanding your customers and why they buy things in the first place, the more powerful, and profitable your recommendations will be.

At the end of June, Kroger announced that it received FDA approval to sell a home COVID-19 test, with anticipation of 60,000 units per month in the near term. That a supermarket is getting involved in national health concerns  really should not be a surprise. More than any other lifestyle decision that we make, “what we eat” has the largest impact on our preventative healthcare. Now that the pandemic has put grocery into the innovation hotseat, why shouldn’t “supermarkets” be on the short list of players in the multi-trillion dollar American healthcare landscape? As grocery moves online, and digital consumption data becomes more important to business, grocers ought to be leaning hard into healthcare. It’s time grocers establish their rightful place in what is becoming the hottest space in business, before the tech giants and new direct-to-consumer (DTC) players edge all of the others out.

Americans are sick of being sick

Over 100 million Americans have high blood pressure, 122 million more have diabetes or pre-diabetes, and nearly 220 million Americans are either overweight or clinically obese. Not only are these chronic conditions major causes of premature death and the source of 90% of US health care costs, they also make sufferers vulnerable to threats like COVID-19. And the greatest contributor to chronic disease is, of course, the food choices we make. 

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A silver lining of the pandemic is that it abruptly forced consumers to think more about preventative health. In a survey of 23,000 consumers across 18 countries in May, 2020, 63% said that they became significantly more health conscious in response to the virus. 80% said that they would reduce sugar intake and eat healthier, immunity-boosting foods. This has helped accelerate an existing trend toward personalized health-conscious food selection which has been two decades in the making. Organic food sales have grown into a $55 billion dollar market. The number of Americans identifying as vegan expanded 3,000 percent over the past 15 years. The ketogenic diet is on track to exceed $15 billion in market value by 2027. Allergy-safe foods, gluten-free selections and other health-conscious choices account for billions more.   When Nielsen surveyed grocery shoppers around the world on home dining preferences, 70% said that they actively make dietary choices to help prevent adverse health conditions. As this trend towards eating healthier continues, grocers are in a unique position to make a massive impact for good, along with a tidy profit for themselves.

The role of food shopping in preventative healthcare is something that healthcare providers already know. In 2014, Zuckerberg San Francisco General Hospital launched a therapeutic food pantry where patients receive “prescriptions” for 25 pounds of healthy, restorative food every two weeks. St. Joseph HOAG Hospital, in Orange County, California, runs a “shop with your doc” program where MD’s and nutritionists accompany patients to help them select healthy choices at the local Ralph’s Supermarket. And, in a major boost to the recognition of the role of food providers in healthcare,  Congress established a pilot program this year to include medically tailored home-delivered meals within the scope of Social Security.

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Healthcare, technology, and consumer choices are converging
Meanwhile, major technology companies are investing heavily in healthcare, preparing to leverage their digital strengths for the coming revolution of precision medicine. Google acquired Fitbit for $2.1 billion dollars and has acquired access to health data of tens of millions of consumers through other deals. Apple is gobbling up health technology startups so fast and furiously that Morgan Stanley predicted that health will account for 25% of the tech giant’s revenue by 2027. Facebook tried to acquire patient data from hospitals nationwide in 2018, and is now investing in its own preventative healthcare app. And of course Amazon—owner of Whole Foods, I need not remind anyone—owns PillPack and Health Navigator, amongst others, and invested a staggering $3.5 trillion dollars into healthcare—in 2018 alone.

The die has already been cast to see a convergence of technology, grocery, and healthcare. COVID-19 sent grocers scrambling to invest in retail tech that would allow them to serve shoppers effectively online and in store. While online AI, smart carts, and other retail technology are reshaping grocery, there are serious opportunities in healthcare that supermarkets should get out in front of. If they don’t, it won’t be long before Amazon and other players springboard off their health assets  to encroach further into the food space. (It has already been speculated that Amazon is preparing Alexa to be a “virtual nutritionist” that will help users select products at Whole Foods.)

A few healthcare ideas that supermarkets can move on today

1. Make healthy recommendations:
Instead of making low-sugar and heart-healthy choices a quaint afterthought, why not retain nutritionists on staff the way that sommeliers are, to offer guidance, and implement choices? This could even incorporate the latest food science for longevity and chronic disease reversal. Halla is doing something like this online with our partner EatID. Online grocery shoppers can now select personal health preferences that prioritize foods to accommodate allergies, health conditions, dietary restrictions, weight-loss goals, and other healthy eating choices.

2. Build healthy private labels: Why not build a private brand around health, so that shoppers can easily recognize and select your pre-vetted line of delicious low-sodium, low-carb, heart-healthy, organic, or vegan food options. Shelf signage and online product details could call out health benefits so that health-conscious shoppers do not have to squint to decipher tiny nutrition labels.

3. Partner with healthcare players: Supermarket chains could go much further than that—even becoming central healthcare players. Why not partner with insurance companies and corporate wellness programs to incentivize and reward shoppers for making healthy choices? There are now tens of millions of American employees in corporate wellness programs. Insurers are eager to move from out-dated actuarial tables to real-time lifestyle data to determine premiums and reduce payouts. Grocers would make natural partners. 

4. Subsidize DTC testing: Some grocery chain should strike a deal with a direct-to-consumer health player like Viome or Thryve to offer subsidized gut flora testing, and like DNAFit or XCode Life to determine genetic and epigenetic characteristics. Can you imagine the loyalty that you could get by offering food and supplement choices custom-tailored to your shoppers’ personalized microbiome, genome, and epigenome?

Given how fundamental our food choices are to health, the people who deliver our food should have a much bigger seat at the healthcare table. The FAANG companies have their eye on healthcare. It won’t be long before Google, Apple, and Facebook follow Amazon into the food provision space. Supermarkets who move quickly now have the opportunity to position themselves very well for the coming convergence of grocery shopping and healthcare.

Garbage in, garbage out! Computer scientists like me are fond of emphasizing the importance of good data to achieving valuable outcomes. And it’s true—no matter what you are trying to compute, you won’t get far with bad data. But when it comes to online grocery shopping recommendations, the challenge is not that binary. Achieving good grocery recommendations algorithmically looks less like the black-and-white world of good versus bad data, and more like a gradient of value from “very simple and poor” to “considerably more complex, but truly exceptional.”

In fact, I propose that online grocery recommendations should be viewed as a journey starting at “dumb” and arriving at “wise.” Here’s how that works:

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1. Develop dumb data into apt information

The work of putting together recommendations always begins with raw data. That might include SKU numbers, product names, ingredients, and nutritional information. This work is simple, but the data output you can get from this is “dumb.” It is too raw to be of any real use. To transform this “dumb” data into “smart” (and therefore “good”) data, you need to clean, normalize and standardize it, then tag each piece with as many accurate and useful product qualities that you can.

For example, “Is it a grain, produce, or meat?” “Is it frozen, fresh, boxed, bagged, or canned?” “Is it imported or domestic?” Correctly, completely, and measurably labelling a large body of good data is essential to a robust grocery recommendations system. You must also be mindful of which data is structured and unstructured. This sure adds a bit of work, but it also moves you along the complexity-value journey from “data” to “information.”

But once you have trained your algorithms to recognize that the “information” qualities of products can be clustered together according to the multiple traits that each item has, you can bestow upon the algorithm something like “cheese knowledge.”

2. Knowledge is power; Give your data some!

Ok, but even information alone offers quite limited value. And if you ask me, the real shame of where we are as an industry right now is that this is precisely where most online grocers stop. They think that tagging raw data with various category or trait markers is the best that they can do for their shoppers. The truth is far from that. Instead of calling it quits at isolated bits of product information, a more sophisticated approach is to link that information together in significant ways. Take cheese, for example. Each cheese SKU has simple information qualities like whether it is Gruyere or Cheddar, whether it is yellow or white, whether it is sharp or mild, soft or hard, and whether it is in a wheel, a wedge, a tub, or packaged in slices. You can develop quite a robust ontology of information about cheese alone.

But then connecting and cross-referencing the classifications and qualities of cheeses relative to one-another and to the needs of customers moves you along the complexity-value journey from “information” to something like “real knowledge.” For example, let’s say a shopper purchased hard, yellow, sheep’s milk cheeses in the past. It is a little bit more complex to understand that Havarti and Camembert do not belong in the family of cheeses that this shopper prefers, and that recommending those cheeses is not going to get you very far.

But once you have trained your algorithms to recognize that the “information” qualities of products can be clustered together according to the multiple traits that each item has, you can bestow upon the algorithm something like “cheese knowledge.” It will then know to offer this shopper Manchego or Pecorino, cheeses perfectly in accordance with the shopper’s expressed tastes.

3. Feed your algorithm with a well of wisdom

But there is still further to go on our journey. What if your recommendation engine not only had a deep understanding of food qualities and how they interrelate between products, but it also understood human lifestyle preferences? What if it knew how various ingredients and food products are used together to construct popular dishes? What if it could understand how the priorities of a single college student differ from those of the primary shopper for a family of five? What if it knew about food allergies, and religious or health-oriented dietary restrictions?

This goes beyond simply recognizing the behavior patterns of consumers and looking for simple correlations.

Grasping these more abstract concepts is considerably harder than just labeling SKUs with simple metatags. It requires a large amount of reference information from recipes, menu items, and ingredient lists. It requires understanding cultural and regional dietary preferences. It requires deciding and prioritizing human decision-making heuristics. It requires advanced machine-learning in algorithms, so that they remain responsive to the behavior of real shoppers “in the wild.”

All of that adds up to a lot more complexity! But that complexity also boosts your recommendations way, way, up along the value spectrum journey.

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With this kind of engine driving recommendations, you could recognize not only that jam goes well with another spread on bread, but you would also know to suggest pumpkin butter to a shopper whose history suggests nut allergies. When you identify that a shopper is building a fruit salad, you would know to recommend fruits like berries and melons, and not those like tomatoes and zucchinis. You would offer sliced turkey or smoked salmon as substitutes for ham to a kosher shopper making eggs benedict, but only low-sodium smoked salmon to a shopper who is a hypertension-conscious pescatarian. And as for our cheese example, you would know that beef, lettuce, pickles, onions and tomato on a bun go better with sliced emmental than they do with cottage cheese.

This goes beyond simply recognizing the behavior patterns of consumers and looking for simple correlations. At the top end of the complexity-value spectrum, your recommendations algorithm can achieve something that looks an awful lot like what we would consider “wisdom.”

In today’s world of AI-driven e-commerce, garbage in, garbage out is not even table stakes anymore. The winners in online grocery will be those who get furthest along the complexity-value journey. It turns out that tennis great (and once-army-computer-programming-instructor) Arthur Ashe was right…success is a journey!

A year ago there were two grocery shopping channels: offline and online. Today we find ourselves with an omni-hybrid model where multiple channels are serving and competing in similar and increasingly intertwined functions. In addition to the convergence of offline and online that most are familiar with, now wholesalers, restaurants, and even CPG brands are beginning to cross swords where they never did so in the past. As we move from full-on lock-down to what most believe will be a long stretch of uncertainty, grocers need to contend with this strange new and nebulous normal by shoring up their data capabilities and adjusting their user experience norms. Where shall we begin?

Well, everyone knows about the most obvious convergence—the one between online and offline. Before the pandemic, online grocery was for computer-savvy 18-35 year olds while traditional stores were for everyone else. If the two channels intermingled at the fringes, it was not by much. Thanks to COVID-19, new users from every demographic piled online to order from the safety of home, leading to a five-fold shift in market share. As a result, online grocers have become primary shopping sources, competing with brick-and-mortar stores for customers of all generations. 

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That’s not the only line being blurred by the times, though. During the crisis, food wholesalers who normally sell to restaurants opened up direct-to-consumer bulk provision of eggs, meats, fish, and produce, competing with bulk retailers like Sam’s Club and Costco. Shuttered restaurants began selling everything from flour to toilet paper, as customer demand overwhelmed traditional grocery channels.

Meanwhile, the existing blurred lines between dine-in restaurant service and prepared food or grab-and-go offerings of groceries has reached a fevered pitch. You might conjecture that some of this channel convergence was a real anomaly—desperate measures cooked up by embattled businesses for the short term. You could be right. Only time will tell. But there’s another odd convergence that’s come about which might be even more worrisome for grocers. I’m talking about the trend of consumer packaged goods brands selling direct to consumer (DTC). Following in the footsteps of everything from eyeglass companies like Warby Parker to DNA testing like 23andMe.com, consumer brands are now dabbling with endpoint sales to the folks who normally consume their products anyway. Just look at Frito Lay’s snacks.com, where grocery shoppers can have everything from Lay’s classic potato chips to Rice-a-Roni to Captain Crunch breakfast cereal shipped directly to their door, with a minimum order of just $15.00, and free shipping. It isn’t just the PepsiCo family, either. We have seen DTC entrants from brands as diverse as Heinz, KitKat, and Clorox emerge since the beginning of the crisis.

But hold on there. It’s not the end for traditional grocers, nor for their online counterparts. Far from it.

How can conventional grocers possibly compete with that? Are we seeing the beginning of the likes of what iTunes did to Tower Records and Amazon did to the neighborhood bookstore? Will DTC wholesalers like Public Goods and DIY meal kit services like Hello Fresh take a bite out of the traditional grocery supply chain the way that Dollar Shave Club disrupted personal care a few years ago? The answer is “yes,”—if  buying somewhere else truly feels more convenient and natural to the consumer than buying from you.But hold on there. It’s not the end for traditional grocers, nor for their online counterparts. Far from it. In fact, this might just be the wake-up-call that the grocery industry needs to begin taking the new paradigm of personalized customer service seriously. Look, data and AI-driven personalization is already a relatively mature approach in general e-commerce. When you go to buy a book, a set of earphones, or a new dining room set, you can be sure that the internet has you drilled down to a tee. The likes of Amazon, Best Buy, and Overstock have been tracking, buying, and parsing data about you for years. They know who you are, what you like, and how you buy…seriously. When you log on, you see only what you want and exactly what you want, every time.

We are not—I repeat, NOT—going back to the old normal

Where has grocery been all this time? I’m calling out every CEO, CTO, and CMO in the grocery industry. Ladies and gentlemen, our time has come! Those memes on the internet that say “if you’re waiting for a sign, this is it”…those are for you. Don’t be the ones who say “everything will go back to normal soon.” It won’t. We are not—I repeat, NOT—going back to the old normal.

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Not this quarter, not this year, not this century. The future has arrived. Get on board now, or be left behind!They tell me I only get about 1,000 words to speak my piece in these articles. And the discussion of what grocers should do in these strange and interesting times requires more than the eight minutes it takes to read a post like this. But I don’t want to be that guy who points out all the problems and then drops the mic without offering any solutions, so let me break it down to something relatively simple:If you are a grocer today and you are looking to make the right moves for the future, the one thing I want to suggest is that you take control of your customer relationships today. How? From this moment forward, I want you to focus a great deal of your energy, talent, and budget on personalizing your customer experience. When a shopper returns to your site or your scan-and-go app, you need to know their preferences as well as you currently know your own merchandise. Are they vegetarian? Do they shop for themselves, or for a family of five? Are they open to personalized recommendations of imported foodstuffs, or do they strictly buy the same shopping list every two weeks? If the latter, what is it? What do you do when their favorite items are out of stock? How can you help them find what they need more conveniently? And how can you help them discover the delicious foods that they didn’t even know you carried?  

Your advantage is data.


What it comes down to is this: You have one chance—ladies and gentlemen, that chance is right now—to convince your customers that you are the right grocery shopping choice for them. In the year 2020, there is simply no excuse not to know your customer as well as if you were a corner bodega shopkeeper in a tiny neighborhood. You need to establish the digital equivalent of the neighborhood relationship that existed between seller and buyer 50 or 100 years ago, or else they will go and buy from someone who does that better.Your advantage is data. You are sitting on a goldmine, which is waiting to be used for the benefit of both grocer and shopper. If you don’t throw resources into this now, you will never have the chance to do so again. I don’t mean to be alarmist. There is still time. But I do firmly believe that history will look back at this moment as the turning point in grocery shopping. And I want you to be one of those who saw it, took advantage of it, and made it all work for you. It seems my 1,000 words is up. We established Halla to help grocers use food shopping data better. Please feel free to contact me if this spoke to you.

Digital transformation sure did a number on impulse shopping. Even before COVID-19 drove customers online en masse, the impulse-friendly 18-34 age group was already making fewer trips to the physical store, and for fewer items. Now, more customers buy online and in-store clientele are spending less time roaming the aisles. The result is more “mission-driven” shoppers who are less likely to make unplanned purchases.

Online shopping is not the only technology that is putting pressure on impulse. For those buyers who do make it into stores, the growing penetration of self-checkoutsmart carts, and smartphone scan-and-pay technology means less time waiting in line (and therefore less temptation to buy fizzy drinks, sugary and salty snacks, gum, and tabloid news magazines).As a result, the impulse category has been flat for years, with some segments like snacks and magazine sales experiencing double digit annual declines. It’s time for a re-think of impulse both on and offline. Here are a few ways of approaching this:

1. In-store, cater to customer needs more than desires

Go to just about any grocery store in America and you’ll see the same kinds of items at check-out—those driven by visceral desire, rather than genuine need. It makes sense, really. Impulse purchases are unplanned because they are unneeded. Less time in line means less opportunity to cave in to desires, and that is hurting in-store impulse sales. 

What would happen if grocers took some of the real estate allotted to “nice to haves” like candy and magazines, and reassigned it to things that customers really need, but may have forgotten to buy?

It’s an unconventional way to think about “impulse,” but when you think about it, it’s quite easy to say no to that bag of M&Ms when you are quickly breezing through a self checkout line. But wet wipes, hand sanitizer, and toothpaste are household “must haves” these days. Perhaps fast-moving, mission-driven shoppers will be more likely to top up their carts when they are offered reminders of things they really should buy anyway.

2. Make impulse purchases all about personalized “food discovery”

Most people will tell you that the sensory experience and irresistible temptation of impulse purchases is impossible to replicate online. That’s fine—you shouldn’t be offering those kinds of impulse items online anyway. Nobody thinks that websites should be perfect digital reproductions of brick-and-mortar stores.A concept which works much better online is what we at Halla like to call “personalized food discovery.” Here’s an example: Imagine that a shopper has already placed ground beef and bell peppers in his or her online shopping cart. Those could be part of a recipe or perhaps perfectly unrelated purchases. Either way, you can help your shopper imagine delicious uses for those ingredients and you can suggest complementary items that perhaps they never considered. Propose exotic spices and a box of couscous to make Moroccan stuffed peppers, or suggest taco shells and guacamole to create a Mexican fiesta at home.This does not need to be limited to recipe suggestions though. You can also suggest items that match your shopper’s lifestyle preferences or current state of mind. Say they have fair trade coffee and free-range eggs in their cart, for instance. Ok, so you can offer products with a social mission, like Ben & Jerry’s ice cream or KIND bars. If they added chicken breasts and whey protein powder, you can suggest beef jerky sticks and protein bars.This may seem like a very unconventional way to think about “impulse” purchasing. But the kinds of unplanned purchase items that work in-store are selected for their universal appeal—prime store real-estate is too valuable to cater to individual preferences. Online, you don’t have that problem. Personalized recommendations eliminate the need to cater to the lowest common denominator in the same way that Netflix and Spotify killed “one size fits all” radio and television. Personalization also increases basket size by as much as 40% and improves shopping experience. This works just as well with in-store smart carts and shopping apps.

3. Remember that customers are now “always online”

It used to be that the checkout aisle is where most unplanned purchasing happened. Where is the home of impulse today? It’s in every shopper’s pocket. If you have a shopping app, you don’t have to wait for customers to come to you. Impulse buys are driven as much by sheer boredom as by any other factor.

Even if they are not waiting in line at your store, shoppers still have to wait in line at the post office, on the subway platform, and at the department of motor vehicles. It’s a great time to take care of the grocery shopping— and add in a few tempting treats.

You can also think about your customers’ “virtual pantry” across digital shopping channels. Online purchase data can inform your impulse ideas for in-store shopping apps and smart carts, and vice-versa. Allow shoppers to set up standing orders for the products they need delivered regularly, and keep track of when they last purchased them in any channel. This allows you to help manage the shopping process even when customers are not thinking about it. When the time comes for a restock, present creative, personal offers for items that complement those in their regular cooking arsenal. And if a shopper has made any unplanned purchase recently, you can even suggest adding them to the faithful reorder list.On the surface it may look like the lucrative impulse purchasing category is doomed. In reality, the digital channels that have disrupted impulse purchasing also hold the power to rejuvenate it.Ultimately, each shopper should have a personalized, omni-channel “store” of their own, with offers tailored to their unique purchase history and taste preferences. Grocery shopping is changing both in-store and online. This presents savvy grocers with a great opportunity to observe shoppers’ new habits and redefine the model of impulse buying.

Remember shopping online for groceries before COVID? The answer to this question for many would-be “no”. But with the stay-at-home orders and people sheltering in place most people have now become intimately familiar with buying their groceries online. In fact, 70% of consumers said they’re more likely to continue shopping online for groceries because of the pandemic (source: zdnet.com). We’re reaching a new normal and online grocery, which used to be second in command to shopping in-store, is catching up. Check out the stats below that we collected from ZDNet.com, PYMTS.com, Nielsen, & Forbes that show just how much the demand for online grocery has grown and is forecasted to grow in the next year. With all this demand, grocers are feeling the pressure to deliver an experience online that will keep customers coming back and not switching stores.

A friend of mine was recently waxing poetic about his butcher at his local grocer. He missed being able to point at a cut of meat in the case and ask a question about it. And simple things like being able to say how thick he wanted his steak cut seems like a luxury now. He also mentioned the helpful produce associate who was always sharing tips about how to tell if an avocado was ready or which peaches were perfectly ripe.

Now when my friend shops online he is swimming in a sea of SKUs and unknowns. We all are hoping that we can go back to the way things were before the pandemic, but the reality is that the shopping experience (both in-store and online) is going to be much different than it was before COVID. How do we bring the best parts of shopping in real life, that personal connection and attention, to shopping online?

Grocers need to meet people where they are and provide an experience that takes into account each individual shopper’s unique tastes, habits, dietary preferences, household, etc.

With Halla the online shopping experience is a lot like shopping in-store with a guide that recognizes your individuality, pointing you towards your favorite items, offering personalized substitutions, and suggesting products you never knew you’d love.

We call it “Taste Intelligence, but you can call it your “secret ingredient”.

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For a while now, grocery shopping came in two flavors: online and offline. Offline was the $678 billion, heavyweight champion and online—a plucky underdog to be sure—but not nearly ready for prime-time.

That way of looking at things made perfect sense right up until about three months ago. Then there was COVID-19. The omnichannel retail world of the distant future suddenly came careering into the present day, stunning all that were unprepared for it (which is to say—everybody.)

We planned for a world where there would be two distinct grocery shopping channels. Each would be favored by a unique demographic, with a unique set of expectations. Brick and mortar stores would hold steady or decline slightly, online would gradually rise, and the two would only rub elbows at the fringes. Instead, we find ourselves in a world where there is nothing gradual about the way online grocery shopping is expanding. What’s a grocer to do?

Supply-chain management must work online as well as it does in-store.

New shopping modes, familiar expectations:Online grocers are tickled to suddenly be a main event contender, after years of struggling on the undercard. But online grocery’s newfound fame as the first option for many grocery shoppers comes along with some baggage—shoppers expect online performance to deliver exactly what they can get in-store. This usually shows up in two areas:

1. INVENTORY:
The problem: Supply-chain management must work online as well as it does in-store. In-store, what-you-see-is what you get. If an item is out-of-stock, customers can’t put in their cart. But online, it is all-too-common that shoppers are informed of order shortages at the point of check-out or delivery, if at all! When online grocers try to offer substitutes, they often fail, sometimes comically.

The solution: Online grocers need robust mechanisms to monitor the quantity and quality of their stock to make sure that customers will be served well. If a product is dangerously low in stock, it should not be possible to put in a cart in the first place. Tweak your presentation algorithm to favor items that you know you can deliver without fail. But when a delivery failure can’t be avoided, offering smart substitutions with A.I. is a great way to build customer trust and satisfaction.

“For items that truly are unavailable, let user intent guide your substitutions,” wrote Halla’s own Henry Michaelson in a recent article for Total Retail, “How does a shopper plan to use those organic, free-range eggs that just ran out? Does her selection of ham and grated cheddar tell you that these eggs are for high-protein breakfasts? Then maybe turkey sausages would be a good substitution. Or does gluten-free flour and sugar-free chocolate suggest that she’s baking a healthy cake? In that case, offer a plant-based egg substitute.”

Either way—why not automatically add the customer’s originally desired item to a personal “wish list” in their account, so that it is easy for them to buy next time. You can even go one step further and send them a text message or email with one-click order capability when the product is back in stock!

Imagine a store with 3,000 skus that are perfectly tailored to your tastes, and yours alone.

2. EXPERIENCE:
The problem: We talk a lot about the distinction between shopping and buying. In a physical store, this is all about presentation—the sights, smells, and textures of the fresh section, neatly organized bottles of wine declaring their national heritage, endcaps of exciting products on promotion, and endless aisles of choices, presented in bottles, boxes, bags, cans, and containers. In-store shopping is as much about discovering new ideas and inspiring tasty new cooking opportunities as it is about merely stocking up. That’s hard for online grocery shopping to compete with.

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The solution: Imagine a store with 3,000 skus that are perfectly tailored to your tastes, and yours alone. It would be easy—exhilarating even—to shop there. Every time you entered, you would encounter delightful new products that matched your tastes, dietary habits, budget, and family needs. You would feel…understood. Frankly, why would you shop anywhere else?

Technology empowers grocers to build such a store for each and every one of your customers. Online, you don’t need to replicate the endless aisles and tens of thousands of SKUs that you carry in store. You just need to learn about your shoppers, and to have A.I. that understands food and how different types of food interact with each other.

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For example, let’s say that a shopper rarely if ever buys any meat items, but never fails to put tofu in their cart. It’s an easy guess that his or her household are vegetarians or vegans, right? Well, there are many more interesting protein sources which are neither meat nor tofu. The shopper’s “personal store” can now be filled with products and ingredients for vegetarian dishes. You can even offer recipes, seasonal selections, wine pairings, and brand-sponsored content that is custom-tailored for this shopper’s personal store.

The challenge is clear. To maintain its momentum after the initial shock of COVID has died down, online grocery needs to up “up its game” fast. Online grocers can keep up their winning streak with the help of technology. Make sure that delivery of both product and shopping experience are superior, and customers will wonder how they ever shopped without you.

EuroShop 2020, the world’s #1 retail trade show, drew to a close last week—and boy, it was a whopper! 94,000 attendees from 142 countries came to Düsseldorf, Germany, including representatives of European grocery titans like Ahold Delhaize, Aldi, and Metro.

Halla joined the conference for the first time this year, exhibiting alongside 2,300 other retail suppliers offering the latest competitive retail solutions. And then there were the presentations—eight stages in all, offering a steady stream of ideas from nearly 400 industry leaders tackling the latest trends and developments in the world of retail.

For those of you who did not make it to the show, here are the three big takeaways that stuck out for us:

1. Digital and in-store are converging, and fast

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If the convergence of offline and online retail worlds was ever in doubt, this was the conference that put the uncertainty to rest. From digital price tags to in-store mobile assistants to “smart floors” and other in-store analytics tools, conventional brick-and-mortar stores are taking their cues from e-commerce to boost supply chain efficiency, foster customer loyalty, and optimize sales.

Michael Gerling, Chairman of the EuroShop Advisory Board and CEO of the EHI Retail Institute Cologne, summarized this key theme, saying that ”Retail digitalization is booming. It enables retailers to offer their shoppers even more services and link online and offline channels, on the one hand, while simplifying process flows, logistics and lots more, on the other.”

In grocery, as well as other areas of retail, combining the “emotional discovery” strengths of in-store retail with the superior personalization and analytical strengths of online is going to be the key competitive driver in the coming years, a task which McKinsey just this month predicted will deliver up to a 15% increase in revenue across channels.

2. To successfully adopt omnichannel, retailers need technology vendors.

The pace of technological change is just too fast for any single organization to keep up with. But retail is especially complex. There are at least eight dimensions of retail, and no single organization can be an expert in all of them. When it comes to the latest and greatest opportunities in signage, in-store marketing, mobile solutions, e-commerce, payment systems, delivery services, checkout trends, lighting, display, fixtures, logistics, and energy management—”DIY” is not an option. And grocery has it tougher than most, contending also with refrigeration, in-store catering and other complexities.

Keeping up with all the change is hard enough as a management and a capital investment challenge. But beyond that, retailers just don’t have the people to do it on their own. “Large tech companies and hot start-ups continue to have the first pick of data scientists, tech engineers, robotics and artificial-intelligence experts,” said another McKinsey report“Digital talent may be the single most important determinant of a company’s likelihood to succeed in the grocery market in the next few years.”

Sourcing top-notch technology vendors who can help them gain and maintain their competitive advantages at scale is key to the successful implementation of the omnichannel imperative.

3. Unplanned purchasing is low-hanging-fruit for grocery transformation

Digital transformation is changing one aspect of omnichannel grocery shopping more than any other—impulse purchasing. Self-checkout is enjoying a rebirth. Smartphone scan-and-pay technology, like FutureProof Retail, is reducing wait times at checkout. And with millennials driving online grocery shopping towards $145 billion in sales by 2025, people are just spending less time in-store than before.

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Hardest hit by this shift are unplanned purchases like snacks, which have seen sharp declines during in-store visits. According to Euromonitor International’s industry manager of food and nutrition, “E-commerce is really a threat to these unplanned store purchases because of declining trip frequency. People are just going to the store less often.”

With these trends reaching critical mass, it is high time for physical retailers to learn a few things from their online counterparts about unplanned purchases. Providing timely, AI-powered personalized recommendations through in-store shopping apps and optimizing unused checkout potential are good ways to begin. Grocers with online stores are sitting on a goldmine of shopper data that can be repurposed to make smart merchandising and display changes.

Until fairly recently, even the most progressive retailers have tended to look at online and in-store as two separate channels. If we took one thing away from Euroshop 2020, it was that these false boundaries are well on their way to being dissolved permanently. Success in the next round depends on forward-thinking retailers teaming up with great technology vendors to bring the best of both worlds together.

At Halla, we talk a lot about the bad recommendations that shoppers are served by online grocery recommendation algorithms poorly suited to food. Avoiding those pitfalls is a challenge that every online grocer must reckon with. But rather than dwelling on what’s wrong with most grocery recommendations these days, why don’t we talk about doing it right? Instead of merely avoiding the embarrassment and ineffectuality of making bad recommendations, how can online grocers successfully make “precisely the right recommendation?

There are two classic approaches: content-based systems and collaborative filtering-based systems.

It turns out that making the right recommendation is harder than avoiding making the wrong recommendation by an order of magnitude. And the reason why has a lot to do with the state-of-the-art in recommendation algorithms today.

There are two approaches to making recommendations

The obstacle to making good recommendations lies within the way that data scientists build recommendation algorithms in the first place. There are two classic approaches: content-based systems and collaborative filtering-based systems.

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1. Content based systems rely upon knowledge that programmers can input, like SKU data, broad food categories like “fruit” or “dairy,” and perhaps even rudimentary food pairings like “peanut butter and jelly.” The problem with this approach is that rudimentary data and broad heuristics can not take into account the infinitely possible interactions between food ingredients and the people who eat them. Programmers can easily insert the notion that “ham goes with cheese,” but most shoppers would recoil at the proposal of Paneer to go with that Prosciutto in your shopping cart. Likewise, limited content-based heuristics will have a very hard time with special dietary preferences ranging from vegan to halal.

2. Collaborative filtering, on the other hand, is derived from actual user data. Instead of relying upon blind content assumptions that are often wrong, collaborative filtering goes strictly by past user behavior and expressed preferences to build a model of how shoppers really buy. Collaborative filtering works better and better the more data is available, and is the foundation for personalized machine learning. It is no surprise, then, that collaborative filtering is the “weapon of choice” for most personalized recommendation engines today.

But  collaborative filtering has its problems too. The patterns it picks up are often uninteresting or untrue. They offer no real insight into the personal preferences of an individual shopper, especially those who are new to the platform. Moreover, they just don’t get to the problem of understanding what a buyer is really looking for. To do that would require a level of depth and understanding that is far beyond any collaborative filtering method that is out there today.

Good recommendations come when you combine multiple techniques

In order to at least make a decent suggestion, recommendation algorithms need to combine the two approaches of collaborative filtering and content, with solid content knowledge input, to get the best of both worlds. Start with a strong database of heuristic and historical knowledge to create a framework of what “should” make for good recommendations. Then augment and edit that using real-life data that you capture “from the wild.” The two, combined, make for a powerful combination of the expected and the actual.

But to truly predict and inspire the tastes of consumers, recommendation algorithms have one more arrow in the quiver that they can access: Natural Language Processing (NLP).

However, for truly the best recommendation possible, you can go yet one step further. Combining logical assumptions and a strong supporting cast of recipes, menu items, and other sources of food knowledge with versatile and dynamic behavioral data from actual shopping sessions makes for a potent pairing. But to truly predict and inspire the tastes of consumers, recommendation algorithms have one more arrow in the quiver that they can access: Natural Language Processing (NLP).

3. Natural Language Processing (What’s in a word? A lot.)

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NLP is one of the most resilient and pure areas of Artificial Intelligence, first proposed in 1950 by Alan Turing, (of the famous “Turing Test” which determines the efficacy of AI systems) and it remains a state-of-the-art technique in machine learning technology today. NLP combines linguistics, computer science, and other disciplines in order to analyze and utilize language to the benefit of AI.

What does that mean for online grocery recommendations?

Well, NLP enables leveraging of unstructured contextual food content such as cookbooks, menus, and ingredient databases to derive a very “human” understanding of food shopping choices. For example, it isn’t “four SKUs” in your cart, it is a fresh pear, a log of goats’ cheese, a packet of toasted walnuts, and a bundle of arugula. Recognizing these items by name allows the algorithm to cross-reference them with recipes and discover that this particular combination matches a salad recipe.

Using natural language processing in the recommendations engine not only lets you know that a recommendation for salad dressing is appropriate in this case. It also tells you that a sweet balsamic dressing would be an amazing recommendation! It would even let you suggest beets when pears are out of season—tapping into the recipes with NLP tells the algorithm that the beets go well with the same co-ingredients. That is something you could not get from human heuristics nor from collaborative filtering. It is this extra layer of analysis that gets recommendation engines dangerously close to “precisely the right recommendation.”

In a world where consumers now fully expect personalized recommendations when shopping online,  using content systems or collaborative filtering alone is better than making no recommendations, and may drive some incremental revenue, but it will not get you near “making precisely the right recommendation.” To maximize the potential of recommendations, you need to have an engine that leverages deep content systems together with collaborative filtering and NLP, simultaneously.