Software can generate raw data analysis. It cannot provide insight -- that takes people. Here's how to translate big data into strong engagement.
In the era of big data, establishing rapport with customers should be easier than ever, right? We have the information, and the technology for capturing, analyzing and disseminating customer communications has made huge advances, driven by the adoption of marketing software and CRM systems and fed by the growth of social media, the proliferation of smartphones and the development of online activities that generate waves of customer data.
The trouble is, linking data with technology isn't a straightforward operation. Some reasons for that, and a few ways to do better, are a focus of GI Insight's 2013 Customer Intimacy Index. The survey, which measures how well 16 industries from ISPs to automakers connect with their customers, queried more than 1,000 consumers in the U.K.; my group contrasted current results with the 2010 index to find the winners and losers in this brave new digital, data-fed world.
In the survey, consumers rate the companies they deal with based on how well these businesses' communications demonstrate customer knowledge and understanding. Respondents were asked to rate their supermarkets, banks, mobile carriers and other businesses with scores representing levels of familiarity ranging from "knows me like a close friend" to "treats me like a total stranger." The results of the survey let us calculate overall and relative scores for each sector.
There were a few surprising -- even shocking -- findings. Some sectors that should have sophisticated data capture capabilities and communication technologies were rated very poor at knowing their customers. Sectors that focus on traditional database marketing fundamentals (think regular customer interactions and strong loyalty schemes) while successfully linking marketing and IT do the best job of building strong customer relationships.
Supermarkets and banks, which traditionally have considerable customer contact, are ranked first and second in the index, respectively, with banks rising from third place in 2010. Companies in more technology-driven sectors -- mobile phone service providers, entertainment providers, smartphone makers and ISPs -- placed just below these industries. These companies are closely aligned with new marketing technologies that play a big role in using data and digital channels to more effectively target consumers and send them tailored messages. So why didn't they do better?
Because it's not just about technology.
At the bottom of the pack: car manufacturers and makers of adult beverages, with computer/tablet manufacturers, charities and DIY/home furnishings companies hovering just above the basement. The reasons some of these sectors struggle to maintain customer relationships are instructive. Where a product is a one-off or infrequent purchase, suppliers may have limited ongoing customer contact. Carmakers, for example, need to come up with innovative ways of building continuing relationships. Similarly, the only direct contact that certain brands have with their customers is through third parties, such as supermarkets, making it difficult to build strong bonds. Charities face the difficult cycle of having short funds to invest in database marketing in the current economic climate, which in turn limits their ability to get new funding.
No matter the sector, a key factor in using data to build customer intimacy is an ability to bridge the gap between marketing and IT. There's a dangerous belief that software can provide all the answers when it comes to executing marketing campaigns informed by big data and that the task of employing this information can simply be turned over to IT. This is far from reality.
Software can analyze data and produce reports. But it can't spot the trends, patterns and meaningful insights that breathe success into a marketing strategy. Only trained people can do that. We always come back to a human element to complement any technological advance.
In today's big data marketing environment, to succeed in building lasting and profitable bonds with customers, companies need what are now referred to as "data scientists" -- what old-school data marketers simply call "analysts." This is a specialist role, whether in-house or via a managed service provider, that combines skills in analysis, database, marketing, interpretation and communication. Data scientists not only have to understand the technology at their disposal but be trained in statistics, analytics and math. What's more, they have to be grounded in the company's marketing challenges and goals and be able to explain to the management team and others how data can be used to further those aims.
It's no coincidence that supermarkets and banks, which have not just the technology but the expertise to utilize their databases effectively, scored the highest when customers were asked how well these companies know them.
In poorly rated sectors, too many companies fail to access customer data, even when the technology to do so is available, and don't make good use of the information they do have. In many cases, that's because they rely too much on technology. In a data-led, digitally connected world, companies that lack the expertise to efficiently reach customers either need to get the right people or outsource this function.
5 Steps To Building Customer Intimacy
1. First -- and most important -- link the customer to the transaction. This may mean implementing a loyalty or CRM program. It's the single most important thing you can do. Without it, your efforts will be diminished by at least 60%. Cisco offers some guidance for retailers looking to mine social and mobile data.
2. Undertake a detailed analysis to identify what data is and isn't important. Only then consider how to change your communications -- don't make any assumptions without examining the related data.
3. Have a single customer view database, either externally or internally. This is not necessarily expensive software, but be wary of "off the shelf" packages -- you may end up compromising your requirements if you try to cram your data into a standard package. Better to invest in some customization. (We discuss where NoSQL makes sense here.)
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