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The closer you get to your customer, the bigger the gains. A new report by McKinsey & Company on the future of personalization in marketing shows how companies that excel at personalization generate 40% more revenue than those that don’t. Recently, the study’s author—McKinsey Partner Eli Stein—sat down with us to discuss how marketers maintain loyalty and drive growth through personalized customer experiences.
The way Stein sees it—and as his research has clearly shown—personalization is now both widely expected and easier to do than ever before.
“71% of consumers we surveyed say they expect businesses to recognize them as individuals and know their interests,” he said, adding that increased consumer-generated data is making customers easier to know. “While the bar is rising, the tools at the disposal of companies have been increasing too. It really is the perfect time for disruptive companies to explore personalization to match what consumers now expect.”
In fact, the study showed that an even higher percentage of customers—76%—said they feel frustrated when companies don’t personalize and demonstrate that they know them.
“Personalization is effective at driving repeat engagement and loyalty over time,” Stein explains. ”It’s a virtuous cycle because those repeat transactions generate more data that companies can use to personalize.”
These consumer expectations go beyond the transactional. As Stein illustrated with helpful examples, particular forms of personalization—like delivering convenience when consumers want it most and offering personal assistance—demonstrate an interest in getting to know the consumer, not just the next transaction.
For a deeper dive into these examples, invaluable insights on data and analytics in predicting consumer behavior and driving the consumer conversation—as well as advice on when, where, and how to use data technology— tune in to the full episode here.
The Experience Optimization Pocket Guide
Peggy: Personalization is the topic because personalisation matters more than ever. And the closer you get to your customer, the bigger the gains. In this session, we’re going to explore research that shows how brands that excel at personalisation actually generate faster, higher rates of revenue growth. I’m Peggy Anne Salz, a Forbes senior writer, mobile analyst, and founder of MobileGroove. And my guest today is Eli Stein. He is a partner at McKinsey and Company and an author of the milestone report I just mentioned. So Eli, first of all, great to have you here with us.
Eli: Thank you, Peggy. It’s great to be here with you.
Peggy: Well, we are big fans of McKinsey Research here, and excited to discuss your most recent report on the future of personalisation. But before we get into that, and these really astounding findings about that revenue growth, I just want you to share a little bit about yourself and your role at McKinsey.
Eli: Of course, I’m a McKinsey partner in our marketing and sales practice. I’m based out of our San Francisco location. I spend all of my time thinking about how consumer companies can better interact with their customers. Oftentimes, it means using better data, better technology, and better analytics. Outside of the office, I’m an avid rock climber. I’m trying to personalize my rock climbing too.
Peggy: I love that, striving at both levels. One is to set goals for personalisation, the other is your personal growth, I might say, Eli.
Eli: I love it.
Peggy: I love it. Let’s set the stage here. We’re looking at personalisation, what type of change and disruption are marketers experiencing right now that puts this near the top, if not the top of the business agenda? How does personalisation fit into this?
Eli: During this period of change and transformation, personalisation really became one of the strongest pillars in driving customer loyalty. We just released a big piece of research called “Next in personalisation.” And it showed that customers that interact with companies that excel at personalisation, not only are more satisfied with their interactions with those companies, but they feel more intimately related to those companies. And the companies ultimately generate revenue far more quickly than companies that don’t excel at personalisation. You know, consumers nowadays really expect it. Seventy-one percent of consumers we surveyed say they expect businesses to recognize them as individuals and know their interests. At the same time, while the bar has been rising from the consumer perspective, there’s a lot more data that consumers generate as they interact with customers, even if you look, you know, two years ago pre-pandemic. So while the bar is rising, the tools at the disposal of companies have been increasing too. And it really is the perfect time for disruptive companies to explore personalisation to match what consumers now know by and large expect.
Peggy: Let’s talk about that linkage, you said it yourself between personalisation and brand loyalty and the timing plays a role here as well. And you were saying that companies need to tap into this. Can you give me an example of the approaches to personalisation that maybe makes sense in these times?
Eli: Yeah. You’re exactly right, Peggy. There’s really no way around it. The link between personalisation and brand loyalty is absolutely there. Customers expect companies to personalize and customers get frustrated when companies don’t. Seventy-six percent of customers we surveyed say they feel frustrated when companies don’t personalize and demonstrate that they know them. Personalisation is effective at driving repeat engagement and loyalty over time. And it’s a virtuous cycle because those repeat transactions generate more data that companies can use to personalize. You know, at the same time, customers expect that personalisation is not just transactional. Personalisation of I bought X, therefore, you should buy Y is by and large expected and is needed to meet the table stakes, but isn’t really what sets companies apart anymore.
There’s a couple of different forms of personalisation that really add significant value to consumers’ lives. One is delivering convenience when you want it. For example, there’s a pharmacy retailer that I interact with quite a bit as a consumer. And when I’m starting to run out of shampoo, they tell me. And they remind me that it might be time to buy more shampoo. That adds a lot of value to my life. There’s another pharmacy retailer that has mastered using temporal and weather-based signals. So, when there’s going to be allergies in New York because the weather and the season indicates it, they email their customers. They typically buy allergy medications and let them know that now is the time they should be considering stocking up because in a day or two, you know, those allergies might be hidden.
So, convenience is really an important thing that personalisation delivers. Another is assistance. You see this done really well in beauty and apparel. And personalisation not only informs you of interesting products to buy but the editorial content lets you know how to wear it, different ways to wear it, what to combine it with, when to wear it. And it really, you know, engages customers in a way that doesn’t seem quite as transactional, as you bought X, therefore, we think you should buy Y. And consumers are responding very positively when they see that brands demonstrate an interest in the relationship, in getting to know the consumer, not just the next transaction.
Peggy: So we’ve talked about how companies can demonstrate this, the different approaches, the different scenarios, ways to treat a customer as an individual. I want to look back now to the data and analytics that power this because, in a company’s personalisation efforts, it’s about the context, as you pointed out. It’s about the need state, it’s about predictability. But there’s also an element of matching it with the channels.
Eli: Data and analytics serve as the backbone for best in class personalisation. First, personalisation has to start with a clear sense of what are the ways that a company is going to deliver value through personalisation? And it starts with targeting a specific set of customer opportunities or dig sites. Data is the way to find those dig sites. It’s uncovering the behavioural patterns, the emotive patterns, the shopping patterns that indicate where are their customers that want to be delivered value through personalisation. So it all starts with the data define the dig sites to focus personalisation efforts. You know, then data is the firepower for filling content, right? So you have to have something interesting to say, something relevant to say. And the way you figure out what is relevant to tell me versus you is data.
Second, data is how consumers give feedback on how they’re communicating, whether customers are engaging with the content, whether they’re clicking through, whether they’re taking actions afterwards, whether those actions are happening after a week, or after a month, or after six months. Data is that, you know, to the bricks on the two-way street. You know, to your point, all of this is powered by technology. And integrated tech stack is really essential to being able to deploy personalisation in an effective and efficient way. You know, technology is needed to store the data, execute designs, distribute communications, and then, of course, measure it all. It’s also equally important that this technology can be used by the right internal marketers, and you don’t need a PhD or a computer science degree to be able to interact with the technology.
And finally, the link between the data and the technology and the business use cases must be built-in right from the beginning. Companies that start by building a technological monolith and then applying personalisation afterwards, oftentimes stumble because, by the time you build the monolith, it’s too late. And then you realize the monolith isn’t specifically built for the use cases that are going to matter most with your customer. Technology when it’s in the tent right from the beginning, deployed specifically to the first and most important personalisation use cases then becomes, you know, purpose-built and iterative, and then it becomes a meaningful part of the conversation with the consumer.
Peggy: And you said it yourself right there, conversation, that is very different from transaction as you pointed out, it’s the conversation has the impact. Can you share more about the impact on business performance?
Eli: You know, we started this research with the hypothesis that consumers wanted personalisation, and that by delivering personalisation companies would accelerate revenue growth. We weren’t sure whether that equally applied to companies and data-rich and data-poor environments, or whether that revenue growth would be immediate. You know, first, the hypothesis that consumers really craved personalisation turned out to be absolutely true. Seventy-six percent of consumers said that receiving personalised communications was a key factor in prompting their consideration of a brand, 78% said that the content made them more likely to repurchase. So it’s unequivocal that consumers really do want this. You know, we then found that personalisation really did drive revenue lift. The revenue lift for companies was between 5% and 25% versus their like peers when they applied personalisation well. Now, the story was actually fairly different for data-rich and data-poor companies. And I’ll tell you what I mean by that.
Data-rich companies are companies that have direct access to the consumer. These are retailers, telcos, grocery stores, folks that are interacting directly with the consumer and are getting information back right from the consumer. There’s meaningful parts of the consumer value chain that don’t have a direct relationship with the consumer. Consumer packaged goods companies are just one example, right? They’re selling their goods to others who then sell the goods to consumers. The fact of the matter is that in data-rich companies, personalisation is expected. And if you don’t have at least decent personalisation, you’re falling behind. Personalisation really did make the absolute best of the data-rich companies stand out from the rest of the pack. And we were talking lifts between 15% and 20% that really made the few shiny examples really stand out from the rest.
Now, it’s a really different story among data-poor companies. For data-poor companies, by and large, they’re still getting started with personalisation. They’re learning how they can think about having a direct conversation with the consumer when in many transactions, they don’t talk to the consumer directly and they don’t even know who the consumer is. What we saw here was that the large number of companies that are starting to explore personalisation are really standing head and shoulders among the rest. And that’s where we’re seeing 25% plus increases in revenue growth from the meaningful cadre of companies and data-poor environments that are starting to figure it out.
Peggy: So you’ve given us a lot to consider here, Eli, and a lot to do if you want to reach these results. I mean, they’re staggering. So I’m sure there are many marketers, growth leaders in this room looking at this and saying, “Okay. I’m sold. I want to start this on Monday morning.” What advice would you give teams to get started?
Eli: It’s a great question. You know, so first, personalisation requires a cross-functional team. It requires marketing, product, analytics, technology data. And the most important thing in getting started with personalisation is making sure that all the right constituents, they’re going to be able to solve this problem at the table. Second, it’s important to focus on a couple of key dig sites first. You know, personalisation is a big goal and it can be the top priority for marketing for the next five to 10 years. It can’t all be solved at once. And the value of personalisation dig sites is very discrepant. Pick the one that matters for your company, whether it’s getting people to make a second purchase, whether it’s reducing churn, whether it’s increasing frequency of the customers that buy from you every other week, whether it’s increasing basket size for the folks that are only purchasing one category. Pick a dig site and really focus on that dig site first.
You know, then it’s really a matter of getting started. It’s always intimidating to get started in personalisation. You always need more data, you always need better analytics, you always need more technology, you always need a faster upskill team. You know, the fact of the matter is, you’re never going to be at Nirvana, and that’s okay. You’re always going to be building your capabilities in parallel to deploying personalisation and engaging with the customer. So pick a dig site, get started with what you have and use the learnings in your first efforts at personalisation to inform the capabilities you build first. Now, that’s not an excuse to take a long-term view. In parallel to really getting started, it really helps to take a clear-eyed view of which capabilities you lack, whether it’s on the data side, on the technology side, on the capability side, on the full funnel measurement side, those are all crucial, and you should develop a roadmap of the highest priority capabilities to develop. But that should be in parallel to getting going, proving value and starting to talk to your customers better.
Peggy: And there’s also an element of adapting and learning, and acting in real-time here as well.
Eli: That’s exactly right. You know, personalisation isn’t something that you turn on and then let it run. And there’s two layers of learning in real-time. The first is reading what the individual communications tell you, what consumers are responding to, what’s working, what’s not, what’s taking a long time to get consumer response, what’s taking a short time to get consumer response. You know, marketers should start to view communications not as something that works or doesn’t work, but as a learning experience where they’re wanting to learn from the customer. And on that tactic level, there should be rapid iteration, you know, 10, 15 tests launched in a bi-weekly sprint to understand what is working and what isn’t working. That’s layer one of being adaptive and iterating. Layer two of adaptive and iterating is at the strategic level, so whether a dig site is working, whether investments and a big piece of technology like a decisioning engine are working, you’re going to get some of those wrong. And it’s very important that not only can you learn from individual communications, but you also can learn from what’s working in the personalisation program. And companies that are effective at this pivot quickly from different dig sites, from different significant investments as they learn where consumers are willing to change and where behaviors are a bit more embedded.
Peggy: So thank you so much, Eli, for telling us about the dig sites, what we need to do to have impact, even a blueprint for that, and to keep with the data mining and dig site analogy. Thanks also for uncovering the pure gold in your report. Really appreciate that. Thank you so much.
Eli: Thank you, Peggy. It’s been a pleasure speaking. You couldn’t think of a more important topic for companies to address right in this moment.
Peggy: Perfect. Perfect. Well, thanks again. And of course, you’ve shown how increased personalisation drives higher performance for companies.
Eli: Fantastic. Good. Thank you, Peggy. That was great.