How to Use Customer Data Insights to Build a Better CX
Letting AI and data guide your strategyAdd bookmark
Marketers are flush with data on consumer behavior - from their search activity on Google to the videos they watch on YouTube to their daily travel patterns tracked by various GPS apps.
In fact, some are even privy to kitchen table conversations overheard by smart home appliances like Amazon’s Alexa, where a brand mentioned in passing appears in banner ads the next time the name-dropper opens their Internet browser. Despite this repository of empirical customer insights, many organizations hesitate to act upon the data. Why? The simple answer is bias.
Either the data doesn’t square with what the brand has already assumed about its customers, or the inertia to manifest those insights is overpowering. But by ignoring what the data reveals about their customers, businesses miss opportunities to deliver market-proven, customer-centric experiences.
At a recent event in New York City, ‘Back to the Future with Digital, Omnichannel Experiences’ hosted by digital transformation consultant Marlabs Inc., CX experts discussed how omnichannel organizations should leverage data to transform the way they reach their customers.
Don’t use data like a drunk uses a lamppost
Some businesses use data to buttress entrenched biases and justify the status quo, said Julie Lyle, an entrepreneur, investor and former CMO who’s consulted for Barnes & Noble, Prudential and Walmart. Lyle likens it to how a drunk person uses a lamppost. “Are you leaning up against [the data] and is it bolstering your argument for what you want to do anyway?” she said. “Or is it truly guiding you forward and helping light and illuminate the decision-making process?”
Marketers have to be opinionated, creative and knowledgeable about their customers, so they are especially prone to confirmation bias. Others are so fixated on pleasing the median customer or felling a competitor that they follow the data blindly by trying to introduce a new product without the requisite inventory or supply chain - or, they push for inauthentic rebrands that alienate their core customer base.
“I see marketers fall into that trap where they want so much to satisfy that customer need,” said Lyle. “We all want to be where they want us to be but we just can’t.”
Like many retailers, Barnes & Noble made the fatal mistake of going above its paygrade and competing with Amazon. By acquiring the inventory and real estate of a big-box retailer, the brand went from being the trusted corner bookstore to a superstore staffed by minimum-wage workers rather than knowledgeable booksellers earning $20-30 per hour. Per-unit profit margins on books are small given their high shipping costs, so a big-box business model is risky for a bookseller.
By the time Lyle was hired to revitalize the brand, there was no turning back. “They couldn’t go back to being the corner bookstore competitor because by then they had the mass infrastructure and they had to continue to support it,” said Lyle.
Sadly, Barnes & Noble may have overestimated Amazon’s threat to its market share. In fact, when Barnes & Noble first launched its e-commerce service in the mid-1990s, Amazon investors believed Amazon was doomed to extinction. Amidst the uproar, founder Jeff Bezos convened all 135 of his employees and made his famous refrain: “Don’t focus on the competitor; focus on the customer.” Just a few years later, the tables turned and Barnes & Noble was (and still is) fighting to recapture market share from Amazon.
Let the data surprise you
Data can reveal unexpected insights into who your customers are, how they use your product, and how they interact with you at numerous touch points throughout the customer journey. One Marlabs. Inc client, a financial services company based in a developing country, wanted to boost profits on its loans and mortgages.
They started by using analytics to understand who purchased the loans and what they used the money for. In a traditionally male-dominated market where men were the primary breadwinners who made financial decisions for the household, the company was surprised to discover that its core customers were in fact middle-class women.
“Women were out in the workforce earning their own money and they wanted to reinvest it in their family for entertainment, holidays and vacations,” said Chris Clegg, Digital CX Lead of Marlabs. “A lot of women were taking out these loans to take their families on trips.”
Armed with this insight, the company changed tack and pulled its banner ads from financial services websites and instead began advertising on the travel, tourism and events websites used by their newly discovered target market. Within two years, the company racked up a 70 percent year over year boost in revenue because it understood who its customers were, why they were buying, and how to target them.
“I think the takeaway from us is don’t go in with a pre-built assumption around what users are doing,” said Clegg. “They had their minds open about what the data could reveal about their customers [...] and by responding to that data in a meaningful way they were able to become more effective at marketing.”
When Lyle was contracted to consult for Prudential’s Asia operations, also in a male-dominated market, data revealed that the primary purchaser of its insurance policies, was, in fact, 45-46 year old men, but the person driving that purchasing decision was his wife. “When the oldest child hit 12 years of age, that was when the wife began the panic of, I have to buy a car, I have to buy a college fund, what if something happens to the primary breadwinner?” said Lyle. “And she began the nag, and the nag drove the head of the household to sign the insurance policy.”
Prudential shifted 40 percent of its marketing budget towards a series of ten, 30-second Prudential-branded music videos on financial literacy targeted at children. The videos were introduced in schools as part of a financial literacy curriculum and aired on TV, and were purposely produced in English for a non English-speaking market. “We knew that the mothers would sit and watch children’s TV with their kids to learn English as a second language,” explained Lyle. “It was a common shared family experience.”
Prudential Asia went from representing just 23 percent of global revenues to 56 percent in two years, and was ranked number 69 in the top 2000 brands in Asia from not ranking at all. “This was at the height of the global financial crisis. And we used AI to do it,” said Lyle. “We used AI to find those true nuggets.”
Using AI to correct for biases
AI-powered analytics tools help businesses not only visualize the customer journey but convert those data points into recommendations for the business to improve its customer experience. In fact, the ability for an AI system to say, Here’s what the data says and here’s what to do about it makes it harder for businesses to justify not changing what they do just because they are biased.
“This is really a shift in cognitive view,” said Alan Hart, podcast host and public speaker at Marketing Today. “And I think it’s interesting because with a lot of analytical tools that have come on board, it’s incumbent on the person doing the analysis to set the search parameters.” Using AI to iterate and reiterate processes using data requires a mindset shift among marketers who use the technology to apply the tools to search for solutions they never even thought of.
A few years ago, Marlabs had a client that was drilling for oil offshore. When an oil rig goes offline, the company loses a quarter of a million dollars in downtime, so the oil company used predictive analytics AI to help its engineers forecast breakdowns and diagnose what caused it. The AI discovered that the engineers were doing preemptive maintenance too often in an effort to suppress the costs of unscheduled maintenance.
“The AI was able to predict when things were going to break and it found that there were correlations that the engineers weren’t aware of,” said Clegg. “So by finding those correlations, they improved the reliability of the system, reduced the downtime for maintenance and their payback was hundreds of millions of dollars.”
The future of omnichannel and customer data
Research shows that customers are willing to share their data if companies are transparent about what they do with it and actually use the data to better meet customer needs. “The true commerce of e-commerce is convenience,” said Lyle. “That’s what you’re selling online.”
Customers don’t think of targeted advertising, predictive analytics and multimodal contact centers as “omnichannel,” - but they have been primed to expect the seamlessness of omnichannel, and to expect the customer experience and value delivery system to be the same whether they’re buying online or at a brick-and-mortar.