Customer Intelligence

Leaders in customer intelligence combine rigorous research design with innovative thinking. The ideas, how-tos and case studies you find here will fill your toolbox with best practices on how to engage with your customers. Read our blog posts to get a glimpse into the future of customer research and to discover how to maximize the impact of customer intelligence in your enterprise.

More data, more problems: Improving customer relationships in the noise The collection and use of personal data excites marketers but scares the public. As a recent report by Gartner noted, companies must ensure that their use of Big Data does not cross the line and upset customers.

A little over a decade ago, when the term Big Data began to grow in popularity, companies dove head first into all of its possibilities. With massive amounts of customer data on actions including purchase history and online behaviors, businesses thought they had customers figured out. Years later, the gathering of this amount of data is not in question, it’s the analysis and deployment of those insights that’s the challenge.

According to a recent EMC report, Big Data is doubling in size every two years. By 2020, the data created and copied annually will reach 44 zettabytes, or 44 trillion gigabytes. For some perspective, one zettabyte alone is equal to 4,919,131,752,989,213 books that are 200 pages in length, or 240,000 characters. It’s no wonder that only one percent of the world’s data is being analyzed.

It’s tempting, in the midst of all of this data, for companies to think of customers as just numbers, despite the myriad of warnings advising against the approach. What businesses need to keep in mind is that customers are increasingly mindful of the credit card wake they leave behind, and want to make sure their personal information isn’t exploited.

Don’t miss the mark with customer data.

The Gartner post mentioned above notes that an angry public does not care whether or not an organization is compliant with the law if sensitive information about them falls into the wrong hands. Companies that misuse people’s data will experience the wrath of today’s empowered customers who have more influence than ever before to drive a brand’s success or failure.

The risk of miring a customer relationship can even happen without any personal brand interaction. David Baker, senior manager at the management consulting firm Navigate was quoted in Forbes not long ago, stating that, “As tools mature and increase the capability to capture historical data from social media platforms, profiling could take place without personal interaction.” A world where companies attempt to predict customer preferences without ever actually communicating with them can be a dangerous place.

Treat customer like you would your friends.

If you were to ask your friends for a movie recommendation, you wouldn’t ask for cinema receipts to find patterns in what they watch. Instead, you’d have a quick conversation with them to ID why they may prefer one movie or the other. Companies need to interact with their customers with the same kind of human touch, yet few make the effort.

I don’t mean that businesses like Target or Macy’s need to treat their millions of customers like personal friends. Companies that simply care about customer needs, keep their promises and respect customer opinions are ones that win. There is a reason why Trader Joe’s ranks highly in customer service year over year. The atmosphere, courteous staff and friendly environment make a significant impact on the shopping experience. The company treats its customers as people with individual needs, not data points, and it pays off.

Give data a voice to improve business outcomes.

Big Data is daunting, especially when there is so much of it. It’s easy to “blame the data” and the technology used to process it, but who is really at fault if a company doesn’t understand why its customers do the things they do? Mining all of the transactional and customer relationship management information on hand can tell a business when customers make a purchases, how much they bought and how often they buy, but that data will never explain why customers prefer one brand over another, why they like specific patterns or styles or why customers are infrequent shoppers.