Along with the buzz about big data, there has been a lot of hype lately about developing a holistic view of one’s customers and customizing the buyer journey. This has created a focus on the predictive power of data and predictive analytics. These techniques allow one to extract information and patterns from existing data in order to predict future outcomes, understand trends, and make decisions.
Predictive analytics do not actually provide exact views of what will happen, but they do provide probabilities. Used correctly, these techniques can be very helpful in planning scenarios of possible outcomes, in guiding investments, or enabling one to make decisions on what course of action to pursue.
Predicative analytics use customer data to create an end-to-end view of the customer no matter where their information resides and where they might interact with a company. The outcome of these type of analyses are used to focus marketing and sales efforts across the breadth of customer touch points. This way an organization can promote products at relevant points of access, and proactively identify and solve problems that may cause the loss of customers or impact the ability to gain new ones.
These techniques have been used to identify customer lifetime value, cross-sell and upsell opportunities and propensity to quit. By understanding these things, marketers are able to determine which customers should be afforded a personal visit versus who should just be sent an email. Having this type of insight clearly makes a channel strategy more efficient and effective. It help businesses attract, retain and grow the most profitable customers and maximize the return on their marketing spending.
One company that has been very successful using predictive analytics to run their core business is UPS. They recognized that knowing where packages were and where they needed to go and by when, was a mathematical problem they could solve for. This way they could optimize routes and make the process more efficient. Using sophisticated mathematical models they have reduced 85 million miles driven a year. In the past, the UPS drivers would need figure out how to handle difficult situations. Now it’s in a very specific order, optimized with data and analytics.
IBM is using knowledge gained from their own customer engagements to create predictive analytics solutions that their clients can use “out of the box.” These contain pre-built dashboards, although they can be modified as needed. Some examples are:
- Banking – These enable banks to use their customers spending patterns to predict financial and life events and allow them to deliver much more targeted offers at exactly the right times.
- Media & Entertainment – These help media and entertainment companies understand viewing behaviors of micro-segments so that advertisers can promote the things that these customers are most interested in.
- Retail – These solutions allow retailers to understand the potential overall revenue impact of individual products and make better decisions about which things to carry and how to best promote them.
In the future, we can expect to see more sophisticated applications of these techniques, so that offers will be customized to the needs of buyers and marketing communications will reside on the appropriate channel, using media each customer prefers. We have been hearing about the power of understanding customer journeys and one-to-one marketing for a while now. Predictive analytics enables organizations to make this vision a reality.
