Anticipate to their needs: the key of Predictive Analytics

2018/05/21 06:11

Predictive analytics is a science that predicts future events based on historical data. It leverages a variety of statistical techniques for data mining, pattern recognition, statistical modeling, and machine learning.

Predictive analytics, predictive modeling and the technology it powers has turned out to be one of the most exciting techniques in the marketing toolbox. According to a recent study, 32% of US marketers plan to use their data to predict customer trends in the next five years.

  What are the types of predictive analytics?

Predictive analytics creates a range of models, such as:

  • Predictive Models

A predictive model is a statistical technique that helps predict future behavior. It is used to analyze past performance in order to “predict” how probable is for a customer is to show a specific behavior in the future.

Predicting customer behavior and preferences are the hallmark of companies like Amazon and eBay

  • Acquisition models

This model predicts the possibility of a prospect purchasing your company’s product or service. You can also understand how certain factors affect the purchase decision of the buyer.

  • Cross-sell model

Through this model, your company can get a better idea of the possibility for an existing customer to purchase an additional product or service

  • Up-sell model

It predicts the possibility of an existing customer making complimentary purchases or service upgrades. Up-selling strategies are effective ways of strengthening the distribution channel.

  • Attribution model

This model predicts the possibility of a customer to give up on your company’s products or services. Benefits of Predictive Analytics in your Marketing strategies

According to a study performed by Forrester, organizations that use predictive analytics are 2.9 times more likely to have a revenue growth at a rate that is higher than the industry average.

Even though predictive analytics has been around for several decades; it now has become mainstream as it allows companies to predict trends and achieve better business performance.

Better marketing campaigns

In today’s competitive market companies are under greater pressure to improve efficiency. Predictive analytics can help your company acquire new customers, ensure that you’re getting the right customers the right offer at the right time, improve the retention rate, as well as increase your overall customer lifetime value.

Marketing departments use predictive analytics, to better identify potential customers. Through the use of predictive analytics, you can look at consumer data for specific campaigns and see what could be working or not, and what you can do to cross-sell, up-sell and increase your revenue.

  • 81% of marketers estimate that email marketing is the best method to acquire customers
  • 80% of marketers consider that emails are the best way to keeping customers on board
  • Email marketing generates an average of $44 for every $1 spent on each specific marketing campaign

Here are the main reasons why predictive analytics is being used by businesses in industries of all kinds.

Identify which prospects will become your best customers

Conversion is harder and more expensive when marketing efforts are wasted on prospects uninterested in becoming customers. Predictive analytics helps companies identify those prospects that are more likely to convert. This can be done by evaluating their interests, values and personalities through the content they read and write. Furthermore, an in-depth analysis can even point you to the topics, messages and keywords that will make them convert.

Send marketing campaigns to customers who are most likely to buy

The main goal of your marketing campaigns is to bring in new customers. When potential customers sign up for your newsletter, you can send them several welcome emails to encourage interaction with your brand.

By analyzing behavioral data in response to these emails, such as the products that the customer choose to click on or offers claimed, you can generate subsequent interactions that have higher success in converting the lead to a customer

Implement a Personalized Recommendation System

Product recommendations are one of the most important ways that predictive analytics can help to engage and retain customers.

Amazon is a leader in using predictive analytics. But how, is it possible for Amazon to predict what you want? That’s where the algorithm comes in. They use a forecasting model that analyzes your prior Amazon activity, such as time spent on site, duration of views, links clicked, shopping cart activity and products added to your wish list. If available, the algorithm also takes into consideration information retrieved during customer telephone inquiries and responses to marketing materials. Amazon uses all this data as well as external datasets, such as census data for gathering demographic details to build up a “360-degree view” of you as an individual customer.

This information is used to recommend additional products that other customers bought when purchasing those same products.

Predicting Likelihood to Buy for Repeat Buyers

Based on a customer’s propensity to purchase, you can predict the likelihood to buy for first-time buyers, as well as the probability to purchase for repeat buyers. There are two main uses for likelihood to buy predictions: you can establish which customers to focus on and how much money, including discounts, your company should spend on each customer.

Determine the customers who are most likely to leave

By using predictive analytics companies can now predict when a particular customer is at a high risk of churning. By spotting this on time your company will have a huge additional potential revenue source.

One of the biggest disadvantages online companies are facing is not knowing the intentions of their customers. Silent customers, do not reach out to companies to let them know if they are not satisfied with their products or services. They will just stay away from the company and cancel services altogether.

If these customers are identified at an early stage, organizations can take a full suite of steps to influence the customer’s decision to leave.