Retail executives have work to do if they want to take a bigger bite of the digital apple.
This year, much of that apple is already spoken for: Amazon will account for nearly 40% of US retail ecommerce sales in 2022. That’s two of every five dollars spent online. As for the remaining three dollars, competition is fierce, with the top 14 retailers accounting for 30% of it combined.
Even for the Walmarts, Apples, and Best Buys of the world, there’s not much breathing room.
As to how these and other top ecommerce companies win, “be data-driven” is the common refrain. But there’s more to it, at least according to Neil Hoyne, Chief Measurement Strategist at Google and author of Converted.
Hoyne recently sat down with Nicolas Darveau-Garneau, Chief Growth Officer at Coveo (and former Google Chief Evangelist), to discuss what he sees as the ecommerce differentiators moving forward.
1. Focus On What Really Matters: Customer Experience
The pandemic proved many retail heads wrong. “Consumer preferences are more durable than we thought,” says Hoyne. According to a recent piece in the Wall Street Journal, ecommerce adoption is now returning to pre-pandemic levels.
As it turns out, in-store experiences aren’t going anywhere.
With this in mind, Hoyne suggests companies refocus on the larger, longer-term changes that will shape retail and ecommerce. He points to the lack of continuity in the customer experiences as a lasting issue facing brands.
“Isn’t it concerning when you go to a retail store, buy a product, then go online and that brand has no idea what you purchased?” he asks.
The truth is, people look at brands as brands, not the organizational units that comprise them. According to Hoyne, the winning ecommerce brands will be the ones that deliver on this expectation.
Will this require monetization strategies, automation, and additional investments? Certainly, but with one caveat. “The goal is to be 1% higher than your company is today,” says Hoyne. “Are these customers 1% more valuable today than the ones you acquired previously? […] And to do it in a thoughtful and productive way that’s good for consumers and business.”
2. Optimize for Lifetime Value (LTV)
The shopping cart experience provides a useful window into short-term optimizations vs. lifetime value. Take the customer that adds items to a shopping cart, leaves, then comes back and removes one (but not all) items.
A short-term optimization might be to tinker with some part of the experience before and while customers interact with their cart to reduce abandonment rate, right?
Through the lens of LTV-oriented optimization, on the other hand, a retailer might see this behavior as a customer’s commitment to exploring and purchasing the right mix of products—they’re curating their cart and getting ready to make the purchase. This commitment might indicate a more valuable customer in the long term.
In his work with the world’s top retail brands, Hoyne likes to pose a simple question. “I ask, ‘do you believe that some customers are more valuable than other customers?’
“When they say yes, I ask why they’re investing in all customers equally.”
A successful LTV optimization strategy for ecommerce will focus on what makes the high-value customers so unique. “It’s all in the transaction data,” says Hoyne. “It’s what they’re doing on your site and allowing the data to show you the behaviors of your high value customers.”
That’s part one. Part two is how to act on these insights. How can you promote the right products to high-value customers, or engage with them differently? On the flip side, what about the experience is actually detracting from LTV?
3. Apply the Data (No, Really)
“Take 100 decisions that you may make in a day and six that you make will be made based on data. More than half will be made based on intuition,” says Hoyne. He’s alluding to the idea that retailers are perhaps less data-driven than they think—and that even a small step in the right direction can have terrific outcomes.
To get there, ecommerce executives need to double down on first-party data.
“It’s understanding your customer—information about their expectations, needs, and interests—so you can personalize that experience,” says Hoyne. All it takes is knowing just a little bit more about customers than the competition.
The good news is that machine learning (ML) can observe a lot of these behaviors. The key is applying that data through automation and ML in next-generation ways. This often requires a human touch.
Sure, you can recommend batteries to somebody buying a toy online. But what about the products they’re unlikely to discover on their own, but that increase the chances of an incremental purchase?
4. Test More and Learn From the Results
It’s remarkable how many organizations don’t test enough because their people don’t have enough time. People have hypotheses about what drives value long term, sure, but they’re too heads-down on their day-to-day work to act on them.
Yet Hoyne argues that gaining the edge in ecommerce depends on how fast you can test and how many tests you can run. In his view, experimentation can’t be haphazard, nor wishful thinking—it needs to be an embedded process.
From their teams, executives should ask for (and incentivize) more than just hypotheses. Ask for and collect:
- The hypothesis
- The observed data
- How to test the hypothesis
- What the company would do differently if the hypothesis is true
From there, says Hoyne, executives have a list of viable hypotheses to work with. At which point it’s their responsibility to find and alleviate the bottlenecks preventing these tests from being carried out.
“Most retailers run three to four tests a quarter,” he says. “If you can find a way to generate six, you’ve doubled the rate of innovation.”
Beyond increasing their volume of quality tests, the top ecommerce companies do two other important things:
- Run tests side by side
- Go back and learn from the tests, even those that fail
5. Choose Advertising Partners Wisely
Most of the top retailers weave advertising through their ecommerce experiences. Says Nicolas Darveau-Garneau, the best blend the most relevant ads in with non-ad results.
“Most retailers can’t do all this and rely on a third party to provide the ad business — a bolt-on piece — that’s sometimes okay, sometimes extraordinarily disruptive.”
Like Garneau, Hoyne urges us to treat these interactions as sacred and valuable. Ads are reflective of a brand, and sometimes what’s best for profitability isn’t best for customer experience.
“If you’re going to let other people join in that experience,” he says, “you better make sure they meet the standards of what you’re doing. And that standard shouldn’t just be to poke the customer to get them to spend more money.”
6. Finally, Leverage Your Machine Learning with Purpose
It’s easy for executives to see an ecommerce search results page driven by thousands of rules, then ask how many analysts they need to optimize those results. The alternative is a dynamic recommendation engine that takes what’s already there and makes it more efficient—automatically.
“It’s the whole idea of optimizing the exact right product recommendation and search for one individual,” says Garneau, “but also making sure we show stuff that makes us money for that one individual transaction. One of the reasons people don’t make money in ecommerce is they’re not showing the right stuff.”
Even in the era of data privacy, where you might know very little about customers from the outset, customers do send signals. “Take the signals even anonymous customers send—where they came from, the products they’re looking at, and start building that history.”
In exchange for a personalized experience, many customers will provide more data.
“It’s like going to your favorite restaurant,” says Hoyne. “They know what you like and even though they might make more money, the exchange is mutual, as opposed to being handed a menu each time you show up.”
Both Garneau and Hoyne agree that ML and automation shouldn’t be viewed as a cost-cutting initiative, but an efficiency to create more hours for growth. Too many companies get caught in the cycle of more rules, manual optimizations, and new-hires to sustain it all. Not every component needs to be done manually.
It can be automated under the right conditions — and to the customer’s delight.
Hitting a ceiling with the OOTB capabilities of your current commerce platform? In this demo, you’ll get a firsthand demonstration of Coveo’s AI-powered search, personalization, and recommendation solutions.
What are the three most important things retailers must do to stay competitive? Tune in to Episode 2: The Next Generation of Retail Ecommerce of our podcast, The Ecom Edge!
How should larger, more established brands respond to the rise of digital? Tune in to Episode 5: Is Physical Retail Dead? of our podcast, The Ecom Edge!