In order to successfully grow your business, you need to know as much as you possibly can about your customers to enable you to provide them with the products and services they want and need. This is becoming ever more critical in most industries, as barriers to entry for competitors are often minimal and customers are not as loyal as years ago. In fact, whereas in the past retailers and others could look at inherent customer lifetime values of many years, today’s customers are much more subject to seeking products from competitors at a whim, according to leading retail marketing expert, Jay Dunn of Chief Outsiders.
Jay also offered that businesses, particularly retailers, might be wise to consider “lifetime value” as “annual contribution” instead since the concept of loyalty exists more in the mind of the retailer than it does in the mind of customer. That change of mindset ensures marketing efforts are concentrated on driving as many transactions as possible within a one-year window as opposed to subsequent purchases over a longer period of time.
Based on these observations, it is imperative that businesses do whatever they can to actively engage with customers as quickly as they are onboarded. You need to be prepared to address their needs at each and every juncture where they interact with you. More importantly, you need to create additional intersections by utilizing customer intelligence to provide them with offers which will entice them to buy other products during their brief tenure as a full-fledged customer before they decide to take their business elsewhere.
As a starting point in developing a framework for managing customer intelligence, it is best to consider what I, as well as Eric Flamm, a leading Atlanta-based authority on business intelligence and member of the Board of Directors of the Technology Association of Georgia Data Science and Analytics Society, feel are some of the top 5 trends in customer intelligence based upon earlier discussions. These include:
1) A Customer-Centric Mindset
Historically, there has been a mindset of accumulating and aggregating data for the benefit of satisfying some internal metrics dictated by management. Today’s ultra-competitive environment, however, requires that data be utilized to interpret consumer behavior, enhance the customer experience, provide valuable insights, and guide effective decision making to enable revenue growth and cost savings. This will not only continue, but accelerate as more and more companies recognize the impact that such a focus can provide.
In fact, at a recent conference I attended, I learned of one company that actually sets aside an empty chair that represents the customer at every meeting that they hold. This ensures that a customer-centric mentality is always employed and that the customer is never overlooked.
2) Big Data
The term big data has been around for years. Data has been present in one form or another forever. From the punch-card days and before, to today’s cloud-based solutions, it has been accessible in one form or another.
There has always been an issue with how to manage big data and to best address the Four V’s Of Big Data, a term coined by IBM. These include:
- a) Volume-Scale of Data
- b) Variety-Different Forms of Data
- c) Velocity-Analysis of Streaming Data
- d) Veracity-Uncertainty of Data
Technology has made dealing with the 4 V’s much more manageable and will continue to do so. The ability to integrate this within the framework of a customer intelligence system will only accelerate.
Unfortunately, even with these developments, management has not utilized big data as much as possible as part of a data-driven decision making process, but has instead relied on intuition for most decisions. This is changing, as the amount of data available regarding customers has grown exponentially and the ability to access, aggregate, manage, evaluate, and act on it has provided management with new tools to more effectively interact and service customers. Data is now cleaner and more reliable, there are fewer duplicate records, and integration among disparate data sets is much easier. This will continue to enable a more robust customer intelligence program to exist within companies.
The ability to glean customer intelligence via mobile platforms will continue to accelerate as we move more and more to a mobile-centric world. Effective integration with other channels will be critical, as will be the need for mobile-friendly dashboards that will allow the user to readily review the data, interpret it, and act on it when required.
4) Internet of Things
There will be further acceleration in the accumulation of customer data and the accompanying intelligence that can be gleaned from it via the ever-increasing deployment of the Internet of Things. The real question for organizations will be how to manage and act on this data once they’ve aggregated it. With the continued decrease in data storage costs, accumulation of data from each conceivable data transmission point will become the norm. Although it might not be readily utilized, it will be stored. As ways are conceived that enable the use of it for decision making to support revenue growth and/or cost savings, or to enhance the customer experience, it will be accessed, analyzed, and used.
Analytics will become an ever more important part of customer intelligence. It’s nice to have all of the big data from various sources as described above, but how it is utilized and analyzed for decision-making processes will become ever more critical. There will be increasing improvements in analytic tools, and the ability for non-data scientists to interpret what they represent. In looking at the four major categories, as emphasized by Gartner, each one will see continued innovations.
Descriptive analytics – You will continue to have a need in understanding what happened as part of your overall customer intelligence program.
Diagnostic – Why something happened will always be part of the equation. What was the root cause? Why did sales drop? What happened to profits? These will be important considerations moving forward, as they have been historically.
Predictive analytics – By analyzing unusual business trends and integrating it with data subsets, there will be the ability for organizations to more accurately forecast business and interpret consumer preferences and needs even before a company might have a product available, further contributing to customer-centric breakthroughs. Furthermore, the ability to more accurately predict the impact of pricing decisions on sales volumes and profits will be commonplace.
Prescriptive – By utilizing findings and data from descriptive and predictive analytics, and combining it with algorithms and models that have been created, companies can develop more effective future strategies and programs. These can be used to take greater advantage of opportunities as they emerge or to mitigate risk.