The future and next generation of shopping

Artificial intelligence can turn your one-and-dones into customers for life

It’s been two decades since recommendation engines turned shopping on its head. You know how they work: If you bought this book, other people who made the same purchase bought these books. Or if you watched this movie, here are similar genres. Or if you searched for this armchair, here’s others that look just like it! These are all recommendations we probably take for granted at this point, but they’ve driven us to expect more from our shopping experience. 

Notably, less than 1-in-10 times have I received some personalized expertise, where I feel the merchant is saying: “Don't worry, Michelle, I’m going to make sure you look great in this skirt.” Or maybe say: “This is the right trim for the fittings that you have in your bathroom from Kohler and it's going to coordinate with the chrome on the vanity that you bought.” 

Most of the time, I’m completely on my own. This sentiment is shared. Some 80% of shoppers say they feel they’re not receiving a personalized shopping experience even though a survey in 2018 found that two-thirds of consumers were interested in personalized recommendations and more than half were willing to share data to get that extra help.  

There’s an obvious disconnect between what shoppers want and what they’re getting. One problem is that the definition of “personalization” is unclear or inconsistent. Often the interpretation leaves much to be desired as shoppers are simply shown more of the discrete products they’re likely to buy. There is no coaching; no expertise; no learning to be a better version of yourself or upping your game. What consumers want is to know what they need. Needs are so much deeper than just “this is the product out of 1000 from the same category that’s most likely going to suit you.”

The consumer actually needs to take that product and operationalize it in their life. For a piece of clothing, it means pairing it with other items to wear for specific functions or occasions. For beauty products, it means knowing how to apply in a particular order to achieve a certain goal. For a vacuum cleaner, it means knowing what replacement items or add-ons are needed. The selection of the right vacuum is just the first step. It can be exceedingly frustrating to go down a rabbit hole trying to search for the filter bags and attachments that fit your chosen model of vacuum. You aren’t a vacuum cleaner expert. But the store should be. So why isn’t that expertise getting to you?  

Automating a manual process

The reason is that many retailers and brands don’t have the resources, or the manpower, to leverage their data to get their unique expertise to the consumer. Merchandising teams, store associates, and personal shoppers spend much of their time deeply understanding the function of products to help their customers make decisions and succeed with the products they’re buying, but there’s not enough of those product & brand experts. Top brands often have hundreds of millions of customers who couldn’t possibly get that one-on-one attention. Importantly, “attention” isn’t created equal: new store associates can’t possibly know everything about every product or just aren’t suited to be a stylist even though they’re a really good salesperson. To get highly-curated, expert guidance into the hands of hundreds of millions of consumers isn’t just a matter of quantity, quality matters too.  

Adidas sold 448 million pairs of shoes in 2019 alone. As one of our clients, we helped give their millions of customers that tailored experience by helping them match their shoes with the rest of their outfit. To achieve what we did through the hiring of expert personal shoppers would not have been feasible, let alone profitable.

Even if they hired those people, the process would still be inefficient because the data generated from each customer experience and engagement wouldn't be shared across the different touch points. For instance, in physical stores, customer interactions and related communication (e.g., emails) are often handled within that store. 

Also, creative assets are often static. If the advertised product goes out of stock, or a higher-margin product comes in, ideally the ad should change in real time otherwise there's a lost opportunity to convert customers. So not only do we help with increasing conversion and the average order volume, but gross margins as well. 

FindMine’s solution applies machine learning and artificial intelligence to the human creative process. Our technology runs predictive intelligence on the output a human expert in the brand would create so that a brand or retailer can produce volumes of continuously updated content that can deliver more customers branded, real-time, personalized expertise.

Creating brand loyalty

Generating repeat purchases in retail is no small feat. Most customers are “one and dones,” meaning they buy one time and never return, largely due to the plethora of options. Think about the websites you visit and where you buy from: do you care more about which place you’re buying from or about which site gives you the best deal? Everyone wants to save money, but a trusted brand with a service-oriented mission will keep the customer coming back.   

Giving customers that guided experience is a way to add that value. I’m proud of my team’s ability to help our retailers and brands have the advantage to create loyalty, which is the biggest driver of overall revenue. This is something we’ve already been able to demonstrate: one client saw a 4.5% increase in repeat purchase rate in a 30-day period, and another saw an 8% increase over just two weeks. That may not seem like much but if most of your customers are “one-and-dones” getting 8% more of them to come back just two weeks after they purchased has a huge compounding effect. In fact, retailers see anywhere from 20 to a whopping 95% increase in profitability for every 5% improvement in repeat purchase. So, even that small increase can actually make a huge difference to their bottom line. 

The success rate of selling to an existing customer is between 60 and 70% versus the success rate of selling to a new customer, which is between 5 and 20%. Acquiring a new customer can cost five times more than retaining an existing customer. Any way you slice, it’s in a brand’s best interest to keep their customers for as long as they can, and that means giving them a reason to come back. 

The future of the retail experience 

Many brands still often delineate the shopping experience into different silos: retail, wholesale, online, offline, social and email, even though they may aspire to have consistent data-gathering across all channels to create real-time marketing campaigns across all channels. They know this will work if only it wasn’t so expensive. 

Through the automation of this data gathering to create continuously updated content, the cost isn’t as daunting especially when compared to the return.

(image source: socialbakers.com)