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Predictive marketing analytics can save companies money by honing marketing efforts and eliminating campaigns that don’t resonate with shoppers. Analytics can increase conversions with personalized, well-targeted marketing that turns shoppers into buyers. Predictive marketing analytics are particularly helpful in B2B sales, where customer acquisition is often more costly than in B2C. Predictive analytics can sharpen a company’s cross-sell, upsell, or renewal options.
Large ecommerce companies like Amazon and Netflix use predictive marketing analytics and recommendation engines to offer customers suggestions for additional purchases. Personalized product recommendations can only be ascertained using predictive analytics. Data scientists are now able to generate machine-learning algorithms that provide real-time, personalized offers for different customers. With the introduction of affordable SaaS solutions, smaller ecommerce companies can now use such tools.
The popularity of using social media and location-based messaging for business means that companies have more sources of information on leads and customer preferences. Putting all the sources together allows marketers to gather valuable insights. This translates into more sophisticated segmentation, which in turn sharpens the marketing message. As a result, campaigns are more successful at conversions, and budget and resources are focused on who in the market will buy.
Algorithms can predict a shopper’s response to a marketing communication, the impact on customer behavior, and any incremental impact of multiple messages. These algorithms allow marketers to choose the best campaign message for each individual and determine the best mix of marketing communications..
Knowing what techniques and what marketing channels work best for each customer lets companies carefully target communications rather than randomly bombarding prospects. With predictive analytics, marketers can determine where to advertise and how to improve email and direct mail campaigns. This results in fewer wasted marketing dollars.
For B2B marketers, better lead scoring is probably the most prominent use of predictive analytics. Lead scoring is a methodology that ranks prospects on a scale that represents the perceived value of each lead to the organization. To determine lead scores, a company gathers information about prospects to estimate their likelihood of taking a desired action — usually purchase intent.