Retail executives have work to do if they want to take a bigger bite of the digital apple. Your traditional strategies for eCommerce success will no longer cut it in 2022.
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 in an online store. 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 store achieves business success, “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 drivers for eCommerce success moving forward.
How Can Ecommerce Strategies Be Improved?
- Focus on what really matters – customer experience
- Optimize for Life Time Value (LTV)
- Apply the data captured
- Use the ‘Test & Learn’ approach
- Chose advertising partners wisely
- Leverage AI and machine learning
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 inlfuence online shoppers. He points to the lack of continuity in customer experience as a lasting issue facing brands.
“Isn’t it concerning when you go to a retail store, buy a product, then go to its online store 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. Sometimes, a potential customer expects to be recognized by the eCommerce website after an in-store visit. According to Hoyne, the winning eCommerce business will be the one that delivers 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 user experience and the online 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 user 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 to maximize customer satisfaction. This commitment might indicate a more valuable customer in the long term signaling an opportunity to offer or prompt the customer with a loyalty program.
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 an existing repeat customer (high-value) or engage with them differently? On the flip side, what if the entire customer 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 help them be that success story that they envision themselves to be. To get there, as an executive, one eCommerce business priority should be 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? Knock-knock, that’s customer loyalty at the door.
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 success in ecommerce depends on how fast you can test and how many tests you can run. In his view, experimentation is the ultimate guide. It can’t be haphazard, nor wishful thinking- it needs to be embedded into processes and marketing.
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 this 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 to identify potential success factors to test for in the future
5. Choose Advertising Partners Wisely
Most of the top retailers weave advertising through their eCommerce experiences. Says Nicolas Darveau-Garneau, the best blend in the most relevant ads with the 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 do some search engine optimization. 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 signals from across the customer journey – even anonymous customers send – where they came from, the products they’re looking at, what customer reviews they are looking at and resonate with, 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 as an efficiency to improve productivity. 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!