Editor’s Note: As a hub for customer-centric marketing news, research and information, we pay close attention to thinking emanating from academia – particularly from notable business schools such as Wharton – and in this case, Google. The following offers solid guidance for those organizations re-focusing their efforts toward a more data-first / data-informed strategy.
It is hard to imagine a company that does not claim to use data to make better and smarter decisions. It is equally true, though, that many of them make big mistakes, sometimes again and again. How can they improve their ability to build a better data-first strategy and get better at measurement? That is the question that Neil Hoyne answers in this opinion piece, which was originally published by Think with Google. Hoyne is the global head of customer analytics at Google. In addition, he is a senior fellow at Wharton Customer Analytics.
By Neil Hoyne
Time and again, I see companies making crushingly common mistakes with data, and refusing to give themselves the room to experiment and to fail.
Data empowers marketers to make better decisions and take smarter risks, but sometimes the best intentions lead to the wrong solutions. Interpreting data isn’t always easy, and I’ve seen marketers come up short by not allowing themselves the space to learn, grow, fail and improve from their collective experiences.
A campaign that falls short of its goal can teach just as much as one that succeeds. And marketers who wish to do the right thing well can learn from how they do the right thing poorly.
What I’ve noticed is that marketers have become experts at doing the wrong thing, because they’re grounded by the past and the “way we’ve always done it.” Their organizations expect them to succeed, even if that success is dependent on the wrong technique or marketing channel, or in pursuit of customers who are detrimental to the company’s long-term growth. But I’ve also seen what companies can do when they allow themselves to take steps in the right direction, even if they fall flat at first.
Becoming a Data-first Organization
Here are a few examples of how successful data-first organizations think, and how you can apply them to your business.
Look at your metrics as part of a story, not the whole picture
One of the biggest mistakes that a marketer can make is to look at their data in isolation. If you oversimplify your data, you’ll lose out on the magic that’s happening around you.
One thing that I’ve observed from successful companies is that they don’t capture metrics for the sake of it. For every metric they set and optimize toward, they go a level deeper by asking themselves some key questions:
- Do I know what this metric truly means? Let’s look at conversions, for example. They are central to everyone’s business, but not all conversions are created equal. When considering cross-device users, online-to-offline users, and view-through conversions, we start to see real differences emerge between platforms. And those differences could determine how you interpret performance measurement and future spend. It’s important to understand the specifics and the distinction when calculating for each metric.
- What could influence this metric, and how? The late Andy Grove, former CEO of Intel, said, “For every metric, there should be another ‘paired’ metric that addresses the adverse consequences of the first.” I suggest taking his advice to heart. If you are optimizing toward using deals or coupons that are driving a ton of customers to your store, are you doing so to the detriment of profit margins and customer retention?
- Am I limiting what I can learn from my metrics? Don’t focus your attention solely on what’s underperforming. Think about what you can learn from what you’re doing well, but could do even better. If you’re a shoe company and you’ve successfully incentivized people to buy multiple pairs of your shoes online, that’s a great win. But why did that happen? Do you even know? Can you replicate the findings to other marketing activities?
I’ve seen that successful companies don’t just look at their metrics as numbers. They look at their metrics as opportunities to ask more questions: Where is the market headed? What should we be aware of? That way, a single metric becomes part of a larger story, not the whole picture.
“Marketers who wish to do the right thing well can learn from how they do the right thing poorly.”
Expect human behavior
Machine learning is growing fast and teaching us a lot. But people are not machines, and as such, they’re not always rational, efficient bidding and buying engines. They don’t necessarily respond in the way you’d think they might. As a marketer, you have to plan for that by gaining a better understanding of the human story behind your data — because it’s those behaviors that may drive your business forward.
Here are some examples of what I mean from recent research on behavioral economics.
- Slow doesn’t always mean no. It’s hard to deny that, when it comes to site speed, faster is far better. The longer people wait for your site to load, the more you’ll lose. But a study by Harvard Business School found that, due to what it called the “operational transparency,” people can tolerate — and, in some cases, prefer — websites with longer waits as long as there is an understanding of the work being performed. If you demonstrate that your site is exerting effort on a customer’s behalf (consider Domino’s Pizza tracker, which keeps you updated every step of the way in the journey to getting your pizza), it can contribute to having a stronger sense of loyalty and reciprocity toward your company.
- The perils of proactivity. Facing the challenge of increased customer attrition, many service companies will start to recommend lower-cost pricing plans to their customers in an effort to demonstrate care for their customers and the greater benefits they can provide. Researchers from Columbia, Wharton, and IAE Business Schoolsfound this tactic had the opposite effect: Encouraging customers to switch to cost-minimizing plans can actually increase churn. In some cases, it inspires customers to be less inert about making a change, and they start to look at other service providers.
I’m not suggesting you throw your conventional wisdom out the window with these varied examples of counterintuitive consumer behavior. But I think it’s important to know that successful companies know that you can’t predict every single element of the customer journey. No matter how much you measure, you’re not going to capture everything. If there are no perfect humans, there’s no perfect data.
“Successful companies know that you can’t predict every single element of the customer journey.”
Fall in love with failing
When we work with smaller businesses or startups, we tend to see some incredibly miserable attempts at marketing. That’s all part of growing, right? But we can learn so much from how these companies tend to respond to those failures: They look inward. They consider that perhaps their brand isn’t strong enough yet, or that they haven’t properly optimized their campaigns in these early stages. What they don’t do is look for something or somewhere else to place blame.
Here’s what I see over and over in larger organizations: They’ll test something, and if it fails, they’ll pivot immediately to a strategy with which they can win, arguing the customers simply aren’t there or that the channel doesn’t work for their business.
This is where doing the right thing poorly needs to become your new manifesto, no matter what size your organization is. There are multiple components to performance measurement, and failure is one of them.
Give yourself and your teams the ability to fail, with the understanding that it’s the first step to growth. And that growth can only come if you’re using your failures — and your successes — to ask questions, the key one being: What is the right thing for me to be doing?
Even if you can’t immediately take action on the answer, acknowledging it to yourself is the first step to doing the right thing well.