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Preparing CRM for predictive analytics

16th Oct 2017
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Backyard barbeques are a mainstay of summer.  One of the key ingredients to a successful cookout 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 favourite weather app to make sure the day’s forecast doesn’t call for a rain shower at the same time the burgers will be going on.  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.  According to a recent survey by the Aberdeen Group on analytics and business intelligence, 46% of respondents report that competitive pressures require 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 analyse structured and unstructured data to reveal key trends and correlations while also predicting the likelihood of certain customer behaviors.  Customer Relationship Management (CRM) solutions are an ideal partner for predictive analytics, allowing a company to maximise 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, combined with historical buyer data, to form a clear view of the customer journey.  Combining predictive analytics with social CRM allows even more potential to learn 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.  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 research, 96% of companies suffer from ineffective use of data.  One aspect of predictive analytics that intimidates potential users is 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. 

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 should also integrate data into a unified view of the customer across all systems to increase the accuracy and relevancy of the data to be analysed.  Companies that use analytics are 42% more likely to standardise 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 data allows for a rapid return on investment and incremental expansion into more complex sources, and ensures continued support for a predictive analysis initiative. 

Conclusion  

While CRM solutions already collect massive amounts of information, even deeper insights can be obtained through predictive analytics.  CRM 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 every-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.  

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