Last week we discovered a fascinating Harvard Business Review Analytic Services report, ‘Marketing in the Driver’s Seat: Using Analytics to Create Customer Value’ and we found some really interesting food for thought in there as well as a couple of case studies. We decided to summarise some of the best bits for you.
Creating Customer Value from Numbers
According to Peter Fader, Francis and Pei-Yuan Chia, professor of marketing at the Wharton School of the University of Pennsylvania, many companies aren’t getting competitive advantages from analytics because they’re still focusing on product-centric strategies. The strategies tend to focus on developing new products and then boosting profits by seeking economies of scale in marketing and operations: “Today, companies face product commoditisation as well as customers who are smarter and more demanding than ever before. Trying to compete by managing costs isn’t enough anymore.”
Fader compares airlines to hotels in order to illustrate this gap. Airlines were nearly the pioneers of customer analytics. Years ago they had sophisticated analyses to drive dynamic pricing and they introduced loyalty programs to reward their best customers. Since then however, they haven’t really invested much in making the flying experience more valuable. Compare this to hotels, such as the Ritz-Carlton Hotel Company – known for its mission statement of “ladies and gentlemen serving ladies and gentlemen.” – it has invested heavily in everything about the service, from room decor to tracking special requests from customers for their next stay, such as hypo-allergenic pillows.
Few companies seem to be applying formal measurement of customer value to evaluate and drive these changes. Let’s take for example Netflix, in comparison to large cable providers. Large cable companies still have a product-centric mindset and do use sophisticated analytics to sell ever-larger content packages to customers. Netflix on the other hand, meticulously mines customer data to create services and programming that please their most valued subscribers. For example, when Netflix decided to make the series House of Cards, it dug deep into user preferences – all the way down to how often scenes from movies with potential stars for the show were being replayed. House of Cards garnered about 4 million new customers. But the thing is, Netflix isn’t using data to sell more content to people, it’s using the data to create greater value for their best subscribers, hence the greater loyalty.
Case Study: Lenovo
Source: Gadget Helpline
Author of Brand Leadership: Building Assets in an Information Economy and Hidden in Plain Sight: How to Find and Execute Your Company’s Next Big Growth Strategy, says that marketing organisations now have access to the tools they need to integrate data and create a full view of customers, but the problem lies in mastering the use of the tools. Lenovo is a company that is a prime example of a marketing team using analytics to go well beyond analysing market data, to driving the company to create better value for its customers.
Ajit Sivadasan, Vice President and General Manager of global commerce realised that customer data was burgeoning and Lenovo needed to harness it. He established an analytics team in his commerce unit that now analyses and integrates customer and marketing data from more than 60 sources worldwide. His team provides data and analysis to support very specific value-adding activities across the company. Senior Executives for instance, the analytics team helps improve net promoter scores by rolling up data on how well the company performs on the drivers of those scores. The team integrates data from multiple customer touch-points to drive increasing customer loyalty.
Sivadasan has found that there are three main drivers of customer satisfaction that correlate to loyalty:
– The first is ‘the quality of the online experience’, and Sivadasan’s team tracks important variables such as how easy it is to find product information, and whether Lenovo provides sufficient follow up on the status of the order.
– The second driver is meeting commitments such as how often the company misses promised ship dates.
– The third driver is the experience with the product itself. By analysing social media and direct customer feedback, Lenovo’s eCommerce team helps the company improve its products.
Taking the third driver as an example, Lenovo launched its tablet in only an Android version, but after monitoring customer sentiment globally, the analytics team discovered a significant opportunity for a tablet with a Microsoft operating system. “We were able to act on that data.” says Sivadasan. “The Microsoft version was very successful.”
Case Study: HCA Healthcare
Source: Business Wire
This is another example and a notable exception in an industry often criticised for the quality of its customer experiences. HCA’s digital marketing groups provides marketing services to the corp’s 268 hospitals, 115 freestanding surgical clinics and 830 physician clinics in the US. A primary responsibility of the marketing group is to assess the impact of online marketing efforts on key sources of profit such as new patient growth. To do this, they track responses to campaigns, content people consume on the web sites, and which paths drive the greatest number of patients contacting one of its medical centers.
The group also plays a large role in reputation management, which drives improvements to patient experience. For instance, it analyses social media comments and sentiment to identify which areas need improvement – such as physician bedside manner, operations, and the quality of ancillary services such as valet parking. These efforts have led to improved services and interventions when patient complaints aren’t properly handled. The group is also a source of best practices such as guidelines for dealing with angry patients and protecting patient privacy online.
More Data Science: Not Data Scientists
The report summarises that marketing organisations do need a team of analytics professionals who understand data and the technologies that integrate it, but beyond this, executives should place more emphasis on data science than scientists. “Data scientists are technicians who are very good at managing and manipulating data,” says Wharton’s Fader. “Data science is about looking for patterns, coming up with hypotheses, testing them, and acting on the results.” So instead of building data science capabilities, companies often bring on more specialists. Fader calls the result of this a “data firehose” instead of valuable data which answers specific questions. Business leaders struggle to think through the implications of the data since so much is being given to them.
He acknowledges that businesses are acting more quickly today but he points out that companies become analytics legends through a disciplined approach to them when time was definitely not on their side – companies such as Harrah’s Entertainment – now known as Caesars Entertainment. He says “Competitors with deep pockets were handing Harrah’s its lunch, and the company was desperate,” says Fader. “They needed to figure out how to zig where competitors were zagging.” Harrah’s aggressively experimented to find out who their best customers were and what would increase their business with the casinos. For example, their best customers weren’t the high rollers most casinos targeted – they were retired professionals such as doctors and lawyers.
The focus paid off and the loyalty program ended up generating more than 80% of the company’s gaming revenue. McGovern at Accenture Analytics has also found that a lack of talent isn’t the primary challenge to achieving competitive advantage with analytics. Accenture found that the biggest hurdle is moving from data to insight to action. To pursue an effective analytics strategy and overcome this, executives need to clearly define business problems, what the analytics solutions need to accomplish, and how the outcomes will be measured. These can determine what data is needed and when it’s needed. “If you can’t make the rubber hit the road with a disciplined approach to analytics, you will end up with customer experiences that aren’t as effective or engaging as they could be,” he says. “Like any source of information, you need to embed and ingrain analytics into decision-making processes to obtain the desired results.”
Feel like reading the full report? Click here to see the full PDF!