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BlueVenn integrate R programming language to enhance its predictive analytics capabilities

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The BlueVenn Customer Data Platform has opened the world of predictive analytics to its users, with the ingestion of the R modeling language.

BlueVenn platform users can create powerful predictive models through simple-to-use wizards, to make real-time decisions within a customer journey, that ensure the right campaigns and promotions are delivered to the right audience.

As an open source programming language, made available through the GNU Project initiative, R is considered best in its class, built with statistics and data in mind.

Embedded in BlueVenn and overlaid with a predictive modeling wizard, this enables everyday marketers to start using predictive analytics to analyze, segment, profile and model data for more targeted campaigns.

An example in BlueVenn is using Logistic Regression models to find customers who are likely to purchase a particular product based on their historical behavior with you. Without any manual analysis, Marketers simply define a target audience and let BlueVenn segment and analyze the data.

The resulting model, once trained and tested for its robustness and effectiveness, can then be inserted as a step into your customer journey workflows, as well as made repeatable. This means that the modeling process can be scheduled to run on new data as and when it arrives.

As confidence in the platform grows, Users are then able to iteratively adjust variables and refine the models. In the future there will also be opportunity for marketers to create scripts for their own models, to further take advantage of the power of predictive analytics and modeling for marketing.

BlueVenn COO Mark Jameson said:

“Predictive analytics with BlueVenn can be used to create models that accurately predict the occurrence of an event and forecast an outcome. For example, to increase sales of a specific product. Given a set of people who have already bought the product, predictive analytics can be used to analyze the characteristics of previous purchasers, and then find other people with the same or similar characteristics who are more likely to purchase the same product.

"Knowing your ‘best prospects’, combined with a compelling offer, means using BlueVenn's predictive tools could help significantly increase the likelihood of selling your product.”

A Guide to Predictive AnalyticsA Guide to Predictive Analytics eBook

From the importance of good data as a foundation for your strategies, through to building your own predictive model, A Guide to Predictive Analytics look at how a range of applications can both reinforce customer loyalty and increase the probability of purchase. This informative eBook covers:

  • What you should consider in order to prepare your data before using it for predictive analytics
  • How using predictive analytics can benefit your business
  • The different ways you can use predictive strategies
  •  Creating a predictive model in BlueVenn
  • How predictive analytics can improve customer journeys and customer satisfaction 

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