I believe making smart, fact-based decisions is absolutely essential to customer-centric leadership. I'm sure many of you reading this will say to yourselves, ‘We're already doing that’.
Sorry, but you're probably not. As you'll learn, many decisions are made out of habit, not conscious thought. And others are influenced by biases we all have. But analytics technology alone is not the answer, either. Not every insight can be put into a spreadsheet, analytic model or computer system.
Here are five ways data analytics can help improve the customer experience, build loyalty and boost company profitability.
BIG IDEA 1: Practice Better Data Science
How can we expect to make rational decisions about customers, when customers themselves are not rational? In his provocative 2008 book Predictably IrrationaI, behavioural economist Dan Ariely argues that people don't always make decisions by rational choice, carefully weighing benefits of a potential action against the costs. Instead, we are easily swayed by our habits, emotions and distractions. Nobel Memorial Prize winner Daniel Kahneman, author of the 2011 book, Thinking, Fast and Slow, found that we humans use two systems for thinking:
- System 1: Fast, automatic, frequent, emotional, stereotypic, subconscious
- System 2: Slow, effortful, infrequent, logical, calculating, conscious
Even in conscious ‘System 2’ thinking people struggle to think statistically. We are easily swayed by a small sample of readily available data that supports something we want to do, and ignore other factors that should be considered. This optimistic bias may explain why a good rule of thumb in IT project planning is to take the most conservative (highest) estimate of time and costs, and then double it. Unfortunately, statistics can be just as misleading as human judgment. As economist Ronald Coase famously said, "If you torture the data long enough, it will confess".
Excellence in Customer Experience (CX) is correlated with business performance. But I haven't seen any studies that show an improvement in CX is followed by improved business performance or that CX is the only factor involved in driving success. In fact, other loyalty studies show that product quality and price/deals continue to have a big influence on customer buying and retention.
This is an example of confusing correlation with causation. Other common mistakes include over extrapolating results to a larger population, drawing conclusions from insignificant differences, and failure to use a control group.
The expanding world of Big Data has elevated the role of Data Scientists, who should have a combination of technical skills (computer science, math, modelling and statistics) along with the business acumen to identify problems worth solving and influence business leaders to take action. That's a tall order. McKinsey estimates the US faces a shortage of 140,000 to 190,000 such analysts.
BIG IDEA 2: Optimise Marketing Spend
Business leaders are turning to analytics to uncover insights in so-called Big Data, an IT industry buzzword to put a spotlight on the increasing volume, velocity, and variety of digital information. And marketing is ground zero for many of the applications. Marketers are charged with figuring out the right combination of products and services that will appeal to customers, and optimising the ROI on marketing spend.
Macy's is a great example of a major retailer competing for the loyalty of omni-channel shoppers – those using multiple channels such as retail stores, web sites, mobile devices and even social media. Five years ago, the company began a shift from product- to customer-focus, led by Julie Bernard, group vice-president of Customer Centricity.
Customer Centricity at Macy's
- CEO sponsorship for the on-going use of customer data
- Data analysed to guide strategic customer focus
- Data organised into customer languages to unify the organisation
- Data leveraged to inform customer insight activations
Source: Macy's (Forrester's Customer Intelligence Forum 2012)
Speaking at a Forrester conference, Bernard said her goal was to “put the customer at the centre of all decisions”. Sounds good, but old habits die hard in a 150-year-old brand where data was organised around products. The retailer used POS data to analyse product sales, but couldn't figure out what individual consumers were doing. By also looking at data from loyalty program, credit cards and other sources, Macy's was able create a more complete understanding of the products, pricing and experiences that move ‘loyals – those consumers already buying regularly.
One of my favourite retailers, Nordstrom is an old company embracing new technologies. At a 2012 analytics conference, James Steck of Nordstom's Advanced Analytics group discussed how the retailer used analytics to understand product and brand relationships. The idea is simple: figure how to promote the right products and brands to the right customers, maximising revenue in the process. Perhaps a simple idea, but not a simple problem to solve when you've got a busy website along with 225 stores doing around US$10 billion in sales annually. Nordstom analysed 2,000 consumers over a one-year period, covering 164 brands. They found 50 per cent of shopper bought brand A. How then, can the retailer find those more likely to buy brand B? Turns out analytics could identify a group of customers spending more than $187 had a greater likelihood to buy brand B. Armed with that info, marketers could make more effective merchandising and promotion decisions to increase sales.