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Uplift Modeling: Predictive Analytics can't Optimize Marketing Decisions without It

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Customer responses and conversions can be increased and churn decreased through predictive analytics which optimizes website behavior and marketing campaigns. Business intelligence becomes actionable when the predictive score of each customer is taken into consideration, these scores gives the information about the action that should be taken with the customer.

An organization or a company needs to get rid of inefficiencies and make improvements in offer relevancy for competing in this new competitive environment. Uplift modeling is more powerful than traditional modeling. This is because the former one is a more powerful tool for predicting the company's ability to influence and to change customer's behavior. All these information helps the company to focus on those customers who will stay and will react positively and the ones who will not react in a positive way i.e, the customers who will not stay are extracted.

Uplift Modeling

Standard Predictive Analytics Optimization

The wrong thing is optimized by standard predictive analytics. To predict customer behavior predictive analytics is not sufficient and this is a disadvantage of it. Through the customer's predicted scores marketing decisions can't be made in an optimal way. The impact of every decision cannot be predicted through the scores. So, on the basis of the predicted marketing influence which is the influence on customer's behavior in the future a business decision is optimized.

Uplift Modeling

An action that makes to the customer's behavior is performed through uplift modeling. It is used to predict the level of risk, attrition probability, alteration in purchase probability, which occurs when marketing actions like making a call, sending a mail, an making changes in services are made. The fact is that uplift model and a normal model knows the same thing about the change in future behavior, but the uplift model can predict it. This is done by considering two groups of people, the first one is the experimental group i.e, the subject to the marketing decision and the second one is the control group. Similar to the standard measurement for examining the overall difference in the purchase rate of the control group and the treated group , uplift modeling also models such difference of the behavior between the two groups and also finds patterns of such variation.

Standard Predictive Analytics

By means of uplift modeling through controlled experimentation the incremental impact on customers of a marketing campaign is predicted. The variation in the difference of control and treated group is measured with the help of it and dividing the customer into the following groups:

  • Customers who would buy if they are treated.

  • Customers who would buy or not regardless of the fact that they were treated or not.

  • Customers who don't buy when treated and buy when not treated.

The return on investment of marketing spend and alternative goals are optimized by targeting the customers ,and thus uplift modeling permits the business to do so. The following are the benefits from the areas of customer retention and demand generation:

  • The number of customers that are required for achieving a given level of business stimulation is reduced and so costs are also decreased.

  • Given a level of spend the level of business generation is increased.

  • The level of negatively received material is decreased and thus customer dissatisfaction is lowered.

  • In business of the effectiveness of different types of marketing spend understanding is enhanced.

  • Most of the negative effects that are associated with mis-targeted campaigns are eliminated.

  • Customer retention is increased.

Attrition Probability

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