SetLogik Blog Post: Putting the Pieces Together: Good Data and Predictive Analytics
Paul Nolan’s cover story, “Customer Data: What to Get and How to Make Sense Of It,” in the July/August edition of Sales and Marketing Management, provides real-life examples of Big Data usefulness in understanding and reaching customers more accurately. The article illustrates how predictive analytics is changing the way B2C companies are capturing potentially profitable market segments with intelligent use of transactional data. Nolan points out that B2C companies are effectively applying analytics and technology to increase loyalty and sales while B2B companies are often struggling to determine which data is relevant. SetLogik’s Enterprise Edition provides B2B firms the start-to-finish, cloud-based Big Data solution they need to turn their enterprise-wide data into a powerful, sales engine.
To better understand predictive analytics; let’s examine the process that transforms B2B Big Data into a robust tool for discovering opportunities within the customer’s enterprise.
Within the B2B environment, data is derived from many sources, which we described in our blog post “Understanding B2B Big Data”. This data must be understood or synthesized in order to build the predictive model. Data synthesis requires the greatest investment in time. Data experts will tell you that 90% of the time spent to build a predictive model is spent on data synthesis; the remaining 10% is spent actually building the model. SetLogik Express continually and automatically assimilates the comprehensive “best-of-breed” contact, company, and transaction records from multiple, enterprise-wide data sources. It is this “campaign and sales ready” data that powers the predictive models. Without high-quality data, the Garbage-in, Garbage-out axiom comes into play, making the predictive results simply unreliable.
Once the data is cleansed, the final piece of the puzzle, lifting the percentage of won opportunities with predictive analytics, is ready to be put into place. To start, sample sets of sales-ready data are used to build the model. The model is “trained” to ensure that it is processing the data correctly; yielding accurate results. Extensive testing ensures the model’s ability to accurately fulfill all of its requirements. SetLogik Enterprise leverages the data synthesis from SetLogik Express to build predictive lead-rank and account-rank while integrating the results into your marketing automation systems (e.g. Eloqua, Marketo etc.) and Salesforce automation systems (e.g. Salesforce.com). As evident by the 2012 award winners at the SiriusDecisions Summit, the predictive models do a better job at quantifying true lead qualification as opposed to time-consuming, expensive mostly gut-driven, point-based lead-scoring model.
SetLogik’s cloud-solution solves two difficult problems for B2Bs: 1.) taming B2B big data and 2.) enabling marketing and sales professionals to leverage the Big Data to drive revenue. You have made major investments in collecting prospect, customer and partner data. Why not put the data to good use?
