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Better ROIs with Predictive Analytics

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Wading through loads of data trying to come with an analysis on possible risks and opportunities for a particular business can be a mind numbing task. This is especially when the amount of data one refers to runs into hundreds and thousands of pieces. Predictive analytics is the process of creating sophisticated mathematical models that can be used as a basis for predictions. Countless pieces of data related to individuals feed this model. The richer the data fed into the model, the better the results. Usually predictive mathematical models analyze the past performance of a customer. This gives one an idea on what his/her likely behavior is going to be in future.

A research study by the Aberdeen Group says that companies using predictive analytics race past their competition with a significant increase in sales and a better click-through rate from their online mass marketing campaigns.

Here are some positives that arise in sales performances when an organization makes use of predictive analytics.

  • Predictive analytics can precisely segment potential buyers and helps with a deeper understanding of their needs and motivations based on their patterns of use.
  • Through analytics, marketers can now analyze customer behavior and work on optimizing their marketing messages and personalized offerings. This will help them work towards improved ROI.
  • Where EMIs for products are involved companies have to deal with customers that delay or default on payments regularly. The number of people involved in collection increases operational costs. Predictive analytics helps the organization here in two different ways. First, it proactively ensures that the products are offered to the right set of customers who have a good credit history. Second, it optimizes the collection resources based on their past efficiency, contact strategies, legal actions etc.
  • For businesses offering multiple products, predictive analytics can help cross sell products based on their behavior and purchase history. This leads to higher profitability per customer and strengthens relationships.
  • Predictive models are converted to sophisticated computer algorithms, which offer similar or related product suggestions to buyers when they purchase online. Similarly, algorithms can be customized to analyze and suggest products to people who seem to be exhibiting purchase patterns of others who have been analyzed. This means that if customer A buys a product, he will be shown a list of products that customer X, who bought the same product, also looked into. This kind of suggestion is provided to a group of customers having similar purchase patterns.
  • Predictive analytics technologies are becoming easier to use. They promise to deliver faster at lesser costs with greater value. As it enables accurate insights, it has become an attractive option of small and medium businesses to boost their sales and marketing performance.

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