The year was 2009 – eons ago in today’s fast paced world. A then great vendor called Attensity hired me to write some thought leadership into the budding world of Analytics (budding as in people noticing, not as in just emerging as you well know). They wanted a series of blog posts that talked to the issues about Analytics that most people were not thinking about – or even considering.
Some of those posts did not survive the time (like the one comparing the Sodabowl and the Brandbowl approaches during SuperBowl 2010) and some of them are timeless and well worth the re-read. These are the questions that organizations and vendors are asking today about Analytics.
I’d like to use them to kick off the series again – and update it.
I won’t tell you if it’s a republished one or not – you can figure that out I hope, but I can tell you for sure this is what you need to be thinking about as we embark, again, into the world of analytics.
Let’s start with the idea of getting to know your customers better.
Back in the stone-age of computers, circa 1980s, we did not know much about our customers. We kept contact information and some account information but we did not use it often. The fact that over 30% of data out there is outdated, incorrect, or even not real (most organizations have at least one Mickey Mouse and one Donald Duck among their customers) tells the tale of businesses that didn’t spend too much time caring for their data. Once we realized that data was valuable we set to create profiles of customers, collecting as much demographic information as we could.
We later added transactional data to these databases, and we thought we knew our customers. We knew who they were, where they lived, what car they drove, what credit cards they carried, where they shopped. In some cases, we kept personalized information about their habits and likes-and-dislikes by “analyzing” their use data.
Later, we began to accumulate transactional data from CRM and similar systems, and we sought to learn about our customers by using analytical CRM in all this transactional data. If a single, 36-year-old, male customer who lives in Milwaukee and drives a Cadillac buys our product surely somebody else with the same profile will do it as well – right?
There is a lot more intricate behaviour to segment customers than their demographics; what we called attitudinal (what they are going to do) and behavioural (what they are doing) information appeared from using surveys and was aggregated with existing information inside Enterprise Feedback Management (EFM) systems.
Finally, getting back to this day and age, we found out that customers aren’t truthful in their answers to surveys. The reliance on biased information yielded bad analysis; depending on wrong conclusions to make decisions is never a good idea.
Can we really get to know our customers’ needs and wants?
Enter Customer Familiarity.
We are accumulating massive amounts of data on our customers, their transactions, behaviours, likes-and-dislikes – but we are not using it. Stored data is very similar to fresh fish: after a few hours, not so fresh anymore. After a few days, well – you know. Using this data is where analytical engines come in and they make sense of it, provide value, and actually drive actionable insights. And, as the title for this article explains, where we can get to know more about customers.
Why should you get to know your customers better? Glad you asked…
Better Segmentation, Better Returns – One of the tenets of success in managing relationships with customers is proper segmentation. Once the customer base is segmented, organizations can assign the necessary resources to the segments they want to retain to maximize the returns. The problem with segmentation is that it is usually done by the number of dollars spent, not by a metric that can be managed (customer purchase decisions, with very few exceptions, can only be influenced – not managed) and tracked back to KPIs (Key Performance Indicators). There are more clever segmentation techniques that create smaller, more profitable groups – but they cannot be applied without the proper data and the right analysis. Knowing not only who the customers are, or how much money they spent but also what they want and need provides need-based segmentation, making it easier to properly apply marketing, sales, and even service interactions and to optimize the relationships eventually leading to met expectations, higher satisfaction, and eventually emotional loyalty.
Expectation, Satisfaction, Loyalty –There is a simple path to loyalty: get to know customers’ expectations, meet and exceed them over time to create long-term satisfaction, turn that long-term satisfaction and habit of over-delivery into emotional loyalty. The problem that most organizations have in this equation is the first variable: getting to know their customers’ expectations. Thankfully we collected all this data about their attitudes, behaviours, needs and wants – even opinions on different matters. The massive amounts of data we collected until now (Yahoo, Google, and Sears routinely process databases in excess of a petabyte) can actually be used. Most organizations can use this data to learn about customers’ expectations, but they do not because we are not paying attention to the right thing. They use reports and “analytics” to see if customer satisfaction is up or down, or if loyalty can be built – but not to familiarize themselves with their customers. They seldom take the time to apply he right resources to the right place.
Resource Allocation – All organizations have very limited people, time and even budget to invest in improving relationships with their customers. It should also come as no surprise that some relationships are worth investing in, where others are not so worth it. Using analytics to get to know better what customers want ensures that the allocation of resources is perfected. As Bruce Temkin, Forrester analyst specialized in Customer Experience, said in his Six Laws of Customer Experience:
Given that most people want their company to better serve customers, a clear view of what customers need, want, and dislike can align decisions and actions. If everyone shared a vivid view of the target customers and had visibility into customer feedback, then there would be less disagreement about what to do for them. While it may be difficult to agree on overall priorities and strategies, it’s much easier to agree on the best way to treat customers.
Have I convinced you to start looking at ways to use your data better? What are you doing to learn what you customers want and need? Are you seeing the results of your initiatives? Let me know…
Image via flickr