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Prepare Your CRM Now for Predictive Analytics
CRM systems are made even more powerful by adding predictive analytics, but you have to make sure your data is up to the challenge.
Posted Jul 7, 2017
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A mainstay of summer is the backyard barbecue, and one of the key ingredients to a successful one is the weather. Some people glance at a clear sky and light up the grill; others consider aching joints as a signal that rain is coming. Thanks to advances in meteorology, most people open their favorite weather app to make sure the day’s forecast doesn’t call for a rain shower just as the burgers are ready to go. Everyone is happier when they are assured of a positive outcome for their efforts. 

In the past, many companies ran on gut instincts and past experiences; however, today’s competitive markets demand that business decisions be based on facts and figures rather than hunches. In a recent survey by the Aberdeen Group on analytics and business intelligence, 46 percent of respondents reported that competitive pressures required them to become more data-driven. The ability to convert historical trends and real-time data into actionable insight paves the way for companies to drive performance gains.   

Peering into the Future

Access to data is not enough for a company to maintain its competitive edge. Employees at every level must be able to take action based on the available information. Aberdeen defines predictive analytics as a technology allowing firms to analyze structured and unstructured data to reveal key trends and correlations while also predicting the likelihood of certain customer behaviors. CRM solutions are an ideal partner for predictive analytics, allowing an organization to maximize sales opportunities and improve the productivity of its account managers. Making the wrong decision at the wrong time can be costly; the ability to predict “what” and “where” is imperative.   

In addition to improving business relationships and ensuring the delivery of high-quality service, companies must have insight into their customers, buttressed with historical buyer data, to form a clear view of the customer journey. Combining predictive analytics with social CRM creates a even greater potential for learning about current and prospective customers. Information from profiles, posts, and click histories can be used to create richer customer profiles, which leads to more accurate analyses. Deeper, more timely insight into rapidly changing consumer trends allows companies to enrich those relationships and drive for better satisfactions. It amounts to a positive feedback cycle that gives companies a competitive edge. 

Preparing the “Crystal Ball” 

The vast amount of information and the speed at which it flows are two of the biggest challenges many companies face. According to Aberdeen’s research, 96 percent of organizations suffer from ineffectual use of data. One misgiving that potential users may have about predictive analytics is uncertainty over the accuracy of the data on which conclusions are based. In order to provide the best analysis, the data involved must be adequately prepared. This step is so important that some analysts spend more than three quarters of their time simply preparing the data for analysis. Automating data preparation allows users to maintain data governance while reducing stress on IT. Gartner analysts researching predictive analytics recommend that companies begin with clean, accurate, and complete data in their sales force automation solutions prior to implementing analytics. 

But inaccurate data is not the only factor that can sabotage a forecast; sometimes information is scattered in so many locations and in such a wide variety of formats that it cannot be consolidated. Companies must integrate data into a unified view of the customer across all systems to increase the accuracy and relevancy of the data to be analyzed. Companies that utilize analytics are 42 percent more likely to standardize data captured across multiple channels to ensure ease of software integration.

Besides “clean” data, predictive analytics must have access to a wide variety of data sources, as it “learns” with every new data point. At the same time, it is important to avoid incorporating too many sources too quickly. An agile approach that leverages smaller, already consolidated sets of data allows for a rapid return on investment and incremental expansion into more complex sources, and ensures continued support for a predictive analysis initiative. 

While CRM solutions already collect massive amounts of information, even deeper insights can be obtained through predictive analytics. CRM combined with predictive analytics presents real-time, actionable insight that augment the critical decisions that sales, operations, marketing, and executive teams must make every day.  

Most CRM systems are extremely flexible and provide rich data models that are easy to modify or extend. This flexibility ensures that CRM is able to adapt to ever-changing data requirements. Over the years, however, many companies have not enforced data governance. Prepare the way for predictive analytics by embarking on data-cleanup activity today.


Imad Alabed is senior director of Pivotal CRM & Knova KM at Aptean , with 20-plus years of experience in the CRM space. Alabed started his career as a CRM technical consultant and spent more than 10 years implementing and integrating CRM solutions for numerous clients across a variety of industries. During the past five-plus years, he has shifted his focus to CRM product management. As a senior director for CRM, he is responsible for evolving CRM to ensure that it meets the client’s needs. Alabed holds a BA in electrical engineering from Boston University and an MS in management information systems from Concordia University in Montreal, Canada.

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