Over the past decade, companies have gone from not knowing enough about their customers to almost knowing too much. They are seeing unprecedented volumes of demographic, behavioral and contextual data entering on a daily basis. While business leaders are dutifully capturing the data few have figured out how to mine it, let alone monetize it.
Data has fundamentally changed the way companies engage with customers. In the past, companies would push information to prospective customers (think email blasts). Today, customers demand the ability to proactively pull the information they want requiring companies to monitor and respond to their actions.
There are many extraordinary examples of digital businesses in action — think Amazon recommendations, Prama analytics at TransUnion, Amex Offers from American Express. They are raising the high water mark on digital transformation. They have become fluent in developing the right type of questions, the right way to ask and the right data to get the answers and make the decisions they face.
It is critical to take a strategic view of data collection and use. Taking a critical look at real data needs is the place to start. Too often we have seen organizations embark of multi-year, cross-functional enterprise-wide data initiatives resulting in a well-orchestrated data warehouse only to find that they can’t get any good intelligence out of it.
Several things need to happen before you can start to pull meaning from raw data. First, the problem needs to be defined. Next, you have to figure out what data is available to inform an analysis. Finally, the information needs to be consolidated from internal and external systems in order to identify patterns in the data to build context and find insights. Only then, can data-driven solutions be proposed.
We suggest that our clients start by asking: “What are the questions that, assuming I had real-time information, could actually improve decision making?” Further we suggest that the examination be focused on impact. Basically, if the answer won’t lead to a bottom-line improvement then ignore it.
It is necessary, but not sufficient, to collect data to identify customer behavior. What is missing is the “why?”:
- Why is the individual behaving that way?
- Why are the carts being abandoned so often?
- Why are products enjoying long page visits but sales don’t follow.
- Why is this our best selling product?
Data just reflects the past behavior. The “why” indicates the consumer’s emotional state, identity and mindset at the particular time.
Our experience shows that when a company focuses on the customer experience and creating wildly satisfied customers, revenue follows.