Let’s first start off with the most basic definition of predictive analytics as it relates to CRM. Rarely do we refer to Wikipedia for reference material in any published article on our CRM Insights Blog. However, given the nature of our topic this week and the great example referencing CRM, Wiki offers an excellent prelude into our topic of interest: predictive analytics.
According to Wikipedia, “CRM uses predictive analysis in applications for marketing campaigns, sales, and customer services to name a few. These tools are required in order for a company to posture and focus their efforts effectively across the breadth of their customer base. They must analyze and understand the products in demand or have the potential for high demand, predict customers’ buying habits in order to promote relevant products at multiple touch points, and proactively identify and mitigate issues that have the potential to lose customers or reduce their ability to gain new ones.”
Without attempting to leverage this blog as a follow-up to our last blog regarding Customer Loyalty, it seems fitting to emphasize that the practice of predictive analytics correlates directly to successful customer relationship maintenance. Let’s digress. We have a lot to discuss from Wiki’s assertion (referenced above). Wiki’s example definition references three mission critical departments that benefit from predictive analytics – customer service, marketing and sales. In addition, it’s important to also look at how predictive analytics can impact your business’ core product or service offering, too.
Predictive Analytics and Customer Service
Documenting each interaction between your customers and your business is a crucial part of managing successful relationships. However, predictive analytics adds another layer of intelligence towards anticipating future customer behavior as it focuses the anticipation of what your customers will do rather than the more common practice of simply leveraging historical data to manage these relationships. It’s equally important to collect “historical” customer data to determine customer behavior trends, as it is important to transform this data into extrapolated, meaningful information will not only make your CEO happy, it will improve your relationships with your customers. If your business could raise the probability of acquiring new customers, on top of extending the longevity of your more loyal base by anticipating future trends, then spending the time and effort on a predictive analytics layer is a no brainer.
Predictive Analytics and Marketing
There are two categories of marketing professionals: the creative marketer and the analytical marketer. If you can secure a marketer for your business that encompasses both qualities, you’re in luck. Predictive analytics also comes in two forms: (1) data is interpreted by humans or (2) it’s analyzed numerically by a “number cruncher.” Let’s use Baseball as our example here. Have you heard of the movie “Moneyball” with Brad Pitt? The entire movie was based on one man’s desire to compile a successful baseball team solely based on numbers and stats of the individual players. In theory, the numbers never lie. However, since we are dealing with human beings, being able to precisely predict their behavior is challenging because humans are, well, unpredictable. Even more, each individual human is different and we must be able to understand that categorizing our entire customer base as one defined demographic is archaic at best.
Predictive Analytics and Sales
The Big Kahuna. The entire reason we strive to understand or predict what our customers will do before they act. Phrases like “big data” are heard everywhere. We collect information about our customers to assist sales reps and their goal to close. But, it’s difficult to look at the past solely when the long-term vitality of an organization is in the balance. Forecasting months, even years, in advance keeps business’ hopeful and in a position to make decisions for their business on a more intelligent and informed level. Predictive analytics emphasizes the practice of looking forward while still considering the lessons and trends learned from the past. And, if your business is real savvy, taking the time to build a three legged stool (past, present and future), can make tremendous differences between growing and dying.
Predictive Analytics and Product/Service Refinement
Let’s say your business sells affordable ladies shoes. You set up an eCommerce site, maybe even some social profiles and you get the buzz rolling about your products in no time. Women around are flocking to your site and everything seems to be going fine. Then, after about 30 days, you begin to see more returns coming back and you wonder, “why aren’t they satisfied?” Enter predictive analytics. Perhaps the return trend is due to an outlying factor, or perhaps there is an opportunity to improve your product (or customer service process) to strengthen the customer experience. One way or another, assuming that your product or service is always satisfactory without understanding the customer reaction is an oversight that could become a larger loss over time for your business. Predictive analytics can help your business segment, refine, and fashion unique experiences for customers in the future based on lessons learned from the past.
Does your business need Predictive Analytics?
Depends. Size, customer volume, competitive advantage – there are a variety of factors that can help your business determine if investing in analytical employees or an analytical technology is something that will help your business be successful moving forward. Typically, medium to large-sized companies benefit best from a predictive analytics program that deepens their understanding of what’s to come, not to mention help their business make meaningful sense of the tremendous amounts of data collected daily about their customers.
If you business deploys Salesforce, there is a feature to export the necessary data that then can be imported into a predictive analytics tool not native to the Salesforce software. Technology absolutely makes it more efficient to crunch the raw data numbers rather than having someone do it manually. However, it’s still important to be sure whoever interprets the data can draw meaningful conclusions that will result in good decisions for your business.