Predictive Analytics – Predicting and Influencing Behavior - Intelliverse - ContactCenterWorld.com Blog
Marketers and businesses are always looking to better understand their customers and potential customers, seeking to know what influences the purchase decision, and what actions or encouragements will accelerate sales. Organizations have looked more and more to data science to gain valuable information including insights into customer behavior from the growing volumes and types of data – sometimes called “Big Data,” which one author says is merely an ungrammatical way of saying lots of data. In the past few years, there has been more and more interest by organizations in the area of data science known as predictive analytics.
Traditional analytics is generally descriptive, meaning it provides statistics and reporting on historical events or actions that have already occurred. Predictive analytics on the other hand, is an area of data science where models are created using data, statistical algorithms and machine-learning techniques to analyze current and historical facts in order make predictions about future events – the likelihood of a future outcome.
Predictive analytics focuses on actions on an individual level – per person, per campaign, per store and the like. Massive amounts of data are available that encompass behaviors, characteristics and outcomes for all kinds of individuals and activities, representing a vast array of experiences. Predictive analytic models use the computer power of machine learning to analyze the volume and details of all this experience represented in the data in order to discern the predictable rules, patterns, propensities and behaviors, which can then be used to predict the likelihood of certain behaviors, actions or performance and recommend specific actions to influence the outcome.
As the models train using more data representing more historical experience, it leads to more precise predictions. While this area of data science is not absolute in predicting future outcomes, it is more precise than guessing and therefore is of significant value.
A 2014 TDWI report found that the top reasons organizations are using predictive analytics are the following:
• Identify trends.
• Understand customers.
• Improve business performance.
• Drive strategic decision-making.
• Predict behavior.
According to a report by Forrester Research, the most common applications for predictive analytics are cross-selling, upselling, determining customer profitability, promoting customer loyalty and credit scoring.
An enterprise might use predictive analytics to predict purchasing behavior so that it can better target its marketing efforts; predict which sales will be successful to prioritize leads; predict order cancellations to implement ways to improve customer retention and loyalty; or predict a customer’s product choices based on views or past purchases to make product recommendations personalized to that customer.
Frequently cited examples include the following:
- • Target using a customer’s shopping patterns to predict the customer’s pregnancy and then direct product offers specifically useful or necessary to newborns, identifying 30% more prospects
- • Hewlett Packard predicting employees who were likely to leave their jobs so that managers could take actions to retain them or otherwise be prepared
- • Google predicting for a search user which web sites that user would find to be of high quality to show those pages in the search results
- • Amazon using a visitor’s product views to offer personalized recommendations for other products, generating 35% of its sales from these recommendations
Uplift Modeling (sometimes called Persuasion Modeling) is a specific predictive analytics technique that has gained recognition in recent years. The technique is used to find members of a target audience who are “persuadable.”
It gained publicity after the statistical modeling team for the Obama for America 2012 campaign used an uplift modeling program to precisely identify voters who were leaning Republican but were likely to be receptive to the Obama message. The models used demographic, geographic and political data to statistically identify the characteristics of persuadable voters in swing states; and used the models to determine which voters should be targeted with television ads, which with door to door solicitations, calls or mailings.
Uplift modeling has obvious applications in a marketing context. The idea is that an audience of potential customers includes the following:
- • Those who have already decided to purchase a specific product regardless of any contact
- • Those who will absolutely not purchase the product even if contacted
- • Those who would react negatively to being contacted
- • Those who can be convinced or persuaded to purchase with contact.
Clearly, it is more efficient for a marketer to target the group that can be persuaded. Marketing resources are wasted on those who have already decided one way or the other; and counterproductive to expend on those who would react negatively to contact.
Uplift modeling allows marketers direct resources more efficiently and potentially accelerate sales by targeting ads and other efforts at persuadable consumers.
For example, a company undertaking a direct mail campaign might use uplift modeling to predict who on the mailing list are un-persuadable, unlikely to respond to the company’s offer, and remove them from that mailing. The company would save marketing dollars, have a better targeted audience and increase the response or conversion rate.
Predictive analytics is an exciting area of data science with wide applications. For business, it has significant potential to more efficiently allocate resources, accelerate sales and increase revenues. It allows vast amounts of historical data to be analyzed which can be used to predict the likelihood of certain behaviors, and then recommend specific actions to influence the outcome in a desired direction.
Publish Date: January 11, 2016 5:00 AM
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