Q&A with Neil Hoyne, Google’s Chief Measurement Strategist and Author of Converted
When the world’s leading brands want to sharpen their marketing strategy, they call Neil Hoyne, Google’s Chief Measurement Strategist and Senior Fellow at the Wharton School. In his recent book, Converted: The Data-Driven Way to Win Customers’ Hearts, Neil shares a playbook that any marketer or brand can use to develop stronger, long-term customer relationships.
In the Q&A below, Professor Eric Bradlow, Vice Dean, Analytics at Wharton and GBK Collective Co-Founder, interviews Neil on best practices for applying better data to understand target customers and high value segments, how to ask the right questions to anticipate customer needs, and how to effectively measure the impact of your marketing efforts.
Q: To kick off, tell us more about your background and what led you to write Converted: The Data-Driven Way to Win Customers’ Hearts.
As Google’s Chief Measurement Strategist, I help companies apply data to make more effective decisions. This has included thousands of engagements with many of the world’s leading brands, helping them increase revenue through the better acquisition, development and retention of customers.
I wrote Converted to help anyone to understand how they can apply data to win their customers’ hearts, build stronger relationships and improve loyalty, rather than interacting purely in a transactional way. The goal was to have a book that could be enjoyable, widely-accessible and leave readers with a sense of confidence in pursuing these types of data-driven opportunities within their organizations.
Q: What problems did you aim to solve for readers with the book? Or to ask another way, what are some of the challenges or common gaps you see within organizations that hold them back from being able to effectively apply data?
One of the greatest challenges marketers face is proving the value of their work. In digital marketing, an ad generates a click that, hopefully, generates an immediate action. But this fails to consider the longer-term engagement with the customer, the relationship. Everything is just in that moment. It’s almost as if you had a real relationship with someone for years and then every time you meet the person, say "all right, who are you again? And how do I know you? And, who cares about what you did for me yesterday. What about doing this for me today" So much is lost at that moment.
Q: Exactly. I've always heard the expression that people tend to confuse what can be measured well with what's important. You can measure clicks, assuming they can be tied back to the individual customer, but that doesn’t tell the whole story. So in your opinion, have people confused accurate measurement with importance?
Absolutely. The first days of digital analytics came from engineers as a way to simply summarize raw data. Those early technology developments continue to define how companies measure today. And an entire industry of dashboards came forth selling the value of that story without taking a step back to say “What is meaningful for us?” instead of “How do we get this particular number to go up?”
Q: I want to go back to an earlier point you made, which is once someone buys something, the process starts all over again for many digital marketers. That seems totally antithetical when it comes to measuring Customer Lifetime Value (CLV). How do firms justify starting a process all over again when CLV is about the continuous transaction stream in a relationship with the customer? Aren't those antithetical concepts? And if you agree with me, does that mean that CLV hasn't made the inroads it needs to yet?
Companies often struggle with how KPIs align with their decisions. Companies will claim to be customer-focused, and that they understand how expensive acquiring new customers is versus keeping existing ones. But, they act on the selected KPIs. So there is a separation between short-term KPIs (or click-based attribution models), and what the company needs to grow. These issues aren’t being discussed widely enough at the executive level.
Q: That brings up another interesting question or point. Do you consider it a data book, or do you consider it a strategy book for people in the C-suite?
It’s a strategy book that's backed by data.
“I wrote Converted to help anyone to understand how they can apply data to win their customers’ hearts, build stronger relationships and improve loyalty, rather than interacting purely in a transactional way.”
Converted acts a guidebook for leaders at all levels to better understand the value of customer relationships and engage in ongoing conversations with their best customers. How do you ask the right questions to anticipate customer needs, measure for specific outcomes including CLV, and find more great customers?
Q: This is a question I get all the time, but I want to hear your thoughts on it. I'll give an example. Prior to starting at Wharton School, I worked for DuPont, one of the most product-centric companies in the world. As a matter of fact, there is no data sharing, or didn't used to be, across product lines at DuPont.
What advice do you give to companies that want to become more customer-centric that have traditionally been organized in a product-centric way?
I’m mindful of offering advice to companies on culture, because it’s hard to clearly understand how decisions are being made from the outside. What I will say is there are ongoing debates within many companies on how they could organize their digital resources more effectively.
The challenge is how to execute on those changes. Whenever you realign priorities, there may be growth at the company level, but individually you see leaders, teams, and existing projects that may be on the losing end of that transformation, meaning their work is now seen in a less favorable light. And, now you have opponents to the effort.
“Start with simply educating the teams on what the new perspective or metrics are showing without any immediate consequences. Let them see that CLV is real and measurable.”
The ideal approach is more hands-off, more organic. Start with simply educating the teams on what the new perspective or metrics are showing without any immediate consequences. Let them see that CLV is real and measurable, and make it a first-class citizen in reporting.
If teams are looking at their marketing campaign performance, they can keep looking at and bidding to their CPA targets. But I also want them to start to consider the CLV. Why do some efforts bring in better or worse customers? How would they make different decisions? Once those conversations start, companies will start to adapt. You’re not going to create change over night, but at a mindful, iterative pace.
Q: One of the things we’ve talked about, and I’ve presented on many times in my career at Wharton, is the concept of better data, not big data. But in your opinion, how has technology changed the way companies apply data and measure for CLV?
In other words, if you had written the book 10 years ago, or 10 years in the future, what would be different?
It would probably be framing. Business tends to go through cycles where certain technologies are placed on a pedestal as the “future of growth”. Big data, cloud computing, machine learning, artificial intelligence, blockchain, NFTs, etc. My concern is when these technologies are seen as a substitute for effective strategies - as opposed to their rightful place as a complement. Machine learning can improve your understanding of customer relationships. But machine learning isn’t going to drive more value for your firm on its own and, from that lens, perhaps there are even better substitutes.
“When I ask CMOs what value does big data deliver to your business?" The answer is often, ‘well, we leave that to the data scientists.’ And I think that that's the problem..”
I think within a lot of enterprises, what they felt was that a particular initiative was needed without knowing how the value was going to be captured. Almost that it was somebody else's problem. And I joke about this, but it's really a sad story.
Whenever I talk to a CMO and ask them to explain their strategy around big data, they'll be able to describe all the systems they’ve integrated and the touchpoints they can measure. But when I ask "what value does that deliver to your business?" The answer is often, "well, we leave that to the data scientists." And I think that that's the problem, because when you talk to the data scientists, they'll tell you, "we don't know what anybody wants in the organization." Or how to translate that back into the strategy. That falls on the leaders.
Q: As I’ve often said, you don’t need more data or more data scientists. What you need is to find people who know how to ask the right questions of the data and how to translate that data and improve conversions and customer relationships.
On that point, where should companies get started? A lot of firms don't know where to begin using the concept of customer lifetime value or how to build it into their business. If your business is just getting started, that already has the data assets, but doesn't yet have the way to translate that into business value, what would you say to them?
The first step is to prove to yourself that you can predict customer lifetime value. After you have that, you need to ask yourself: Who are these people? Why are they valuable? What behaviors of theirs are unique when compared to other groups of customers? And then the third question, this is where the testing begins. And I say prove to yourself that you can acquire more of these people.
I'm simply asking companies to demonstrate to themselves that they can build this into an iterative process, that they can have curiosity about who those people are, and that they can take action on it. When we get to that third step, and you're not taking action, then we have a deeper organizational discussion and say, I just showed you a path to value, and you're not doing anything on it. Let’s talk about where those limits are to the transformation. It’s not just the model.
Q: That three-step process is powerful and similar to the lecture at Wharton I’ve given many times on better data, not big data. Once you’ve developed a model to predict customer lifetime value, the next steps are to understand those customers and their heterogeneity, and then go find millions more people like them, and you've got a very profitable business. It's very simple.
Exactly. But I’ve met companies who spend years trying to construct flawless models before taking action on any of the insights. It’s just not an effective process. Take what you have, experiment, apply and refine. That’s it. Skip perfection and simply try to acquire a mix of better customers today than what you had yesterday. That's it. That’s what success looks like.
“Skip perfection and simply try to acquire a mix of better customers today than what you had yesterday. That's it. That’s what success looks like.”
And, when it comes to acquisition, just be sure not to get lost in the fantasy of volume over quality. Companies that bring in plenty of poor customers with the delusion that you can mold them into great ones later on. It’s as if your friend comes to you and says, "I've met a wonderful person to marry as soon as I can fix all of these problems about them.” You might wonder, wow, maybe they would be better off finding a person that’s a better fit from the start.
The same thing is true when it comes to lower value customers. They're waving all these red flags. Coupon codes, discounts and loss-leading products. You may think you can turn them into full price, full fare customers when they come to your website. But just like the relationship story, they aren’t worth the effort.
Q: The subhead for your book is "the data-driven way to win customers’ hearts". I've heard a lot of companies say, "I don't care if I win their hearts as long as I win their wallets." For the benefit of our readers, what are some of the ways that you actually measure winning customers’ hearts (beyond the points we’ve already discussed)?
Do they want loyalty? Do they want customers who are willing to pay more, anxiously await their next product or promote their brands to others? Or, are they happy with it being a purely utilitarian exchange?
“When companies don’t apply data to personalize the experience, beyond the functional benefits of the product or service, customers have no reason to stay.”
Companies have to win their customers’ hearts and their wallets. I may spend a lot with a particular airline, but not like them. Even if I’m a very profitable customer, the relationship may feel transactional. When companies don’t apply data to personalize the experience, beyond the functional benefits of the product or service, customers have no reason to stay. There is a very human story unfolding here.
And so that's what companies need to remember at the end of the day. Regardless of the metrics you’re looking at, whether its hits, customer lifetime value, or new customer acquisitions, on the other end, it's still somebody. It's you, me, it's a partner, a colleague, a friend, a Mom. There’s a story behind the data, and a real person on the other end of every transaction. You have to be able to listen, ask the right questions and anticipate your customers’ needs to win in the market. It’s never just an exchange.