Every business seeks to develop a relationship with each of its customers in order to guide more personalized service that will result in repeat business. To an extent, large enterprises rely on predictive analytics software to achieve these ends. Now, small businesses can do the same.


Thanks to the easy availability of cloud-based web services, application programming interfaces (APIs) and predictive model markup language (an XML-based language that enables the definition and sharing of predictive models between applications), businesses of all sizes can mine their mountains of data for golden nuggets of insightful information for decision-making purposes.


Christer Johnson, a principal in the data and analytics practice at management consultancy EY (formerly Ernst & Young), is an expert on how business owners can leverage predictive analytics to get a deeper understanding of customer sales and service trends. The upshot: Big data has big value for the smallest of companies.


What are predictive analytics and how can they help with customer retention?


Johnson: Predictive analytics helps companies of all sizes identify repeat customers to create a lifetime-value model, where you’re accessing and evaluating all the information about former and current customers to discover signals or events that will help you retain those customers. For example, a signal based on the analytics may be that a particular customer no longer does business with you or is doing less and less business with you over time. Knowing this gives you the heads-up about what is likely to happen in the near future—lose the customer—and gives you the opportunity to take specific actions to try to prevent that outcome.


Does the value of predictive analytics outweigh the expense of investing in the tools?


Johnson: Certainly, there are costs in buying a predictive analytics solution. But a more overlooked expense is the need for people on staff that know how to use the tools—building models and then operationalizing them. Some, but not all, tools make it easier to do that. If done right, there’s substantial value in deploying these solutions, because they can help with repeat sales that drive the bottom line.


Would a small business have to hire someone with expertise to get the most from the software?


Johnson: You can do that, or you can retain a consultant to train someone on staff in the use of the tool. Your staff member then becomes the organization’s “analytics guy.” He or she can create graphs, tables and charts that are easy for business users and other non-analytical people to see where problems may reside. For example, using colors, a heat map can indicate the average customer-retention rate for every ZIP code served by the business.


There are open source predictive analytics solutions available at no cost to a business. Are these a viable alternative?


Johnson: There are plenty of free open source predictive analytics tools out there that will help keep costs low. For owners, they’re a great starting point before reaching a decision to purchase a solution from a provider. But to go back to what I said earlier, you will need someone on staff who is able to use the tool and deploy it.


What is the first step that owners should take to implement predictive analytics in their businesses?


Johnson: It’s most important to be clear about your primary objectives before you do  due diligence exploring providers. Owners think they go down the path of analytics first and then develop the business case—a mistake. You need to know, upfront, what you want from predictive analytics: Is it to make the business more efficient, to increase retention, to drive revenue per customer or decrease the cost to serve each customer? Only after you have some answers should you seek out vendors to see how their solutions correlate with your goals.


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