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Customer Retention. Automated

Attribution Modeling: Last Touch, Multi-Touch & Revenue Lift

Posted by Polly Flinch on Apr 9, 2018 10:59:51 AM

One of the biggest barriers to marketing success is being able to quantify the value of your marketing campaigns. At the end of the day, marketers are accountable to ROI and ensuring that the tech stack they have in place is procuring the right numbers and keeping the C-Suite happy. This article will look at 3 attribution models - Last Touch Attribution, Multi-Touch Attribution, Randomized Control Trials (Revenue Lift) - and their strengths and weaknesses.

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Related: Data Science, Best Practices

PCV Model Performance Metrics

Posted by Bryan Shepherd on Dec 5, 2017 5:01:00 PM

Overview

In Q4 of 2016 Windsor Circle released a Predicted Customer Value (PCV) module based on the "Buy Til You Die" approach. At the time, the model was vetted on a subset of historical data. Now that it has been in the field for close to a year, we have an opportunity to use current data to evaluate its real-world effectiveness. To do so, we use predictions generated using data through January 1, 2017 and evaluate how well those predictions held up over the next 6 months, through July 1, 2017.

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Related: Data Science

Windsor Circle Custom Audience Beats Facebook Audience Across the Board

Posted by Polly Flinch on Oct 17, 2017 10:00:00 AM

On April 24th, we launched a native integration to Facebook allowing retailers to create Custom and Lookalike Audiences using product, purchase, customer data, along with predictive data sets, such as predicted gender and predicted next order date. We’ve been proud to stand behind the success our clients, such as NewYorkDress and RightStufAnime.com are seeing with this tool; however, we were curious how our Custom Audiences would stand up in a test against the exact same Facebook Audience.

One online retailer, working with our partner, ROI Revolution, was willing to do such a test. This retailer, whose estimated online sales in 2016 topped $15 million, worked with ROI Revolution to set up the campaign.

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Related: Social Media, Best Practices, Data Science

Front End vs. Back End Data: What's the Difference, and What are the Benefits of Combining the Two?

Posted by Kyle Marion on Sep 6, 2017 7:00:00 AM

In the world of predictive marketing, your messaging is only as powerful as the data that powers it. But where does that data come from?

e-Commerce retailers pull behavioral data about their customers from two different sources: front-end and back-end. Read on to learn what defines these two types of data, what the benefits are of using each, and why it is so beneficial to use them in tandem!

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Related: Data Science, Customer Retention

The Cost of Classification Error in Targeted Marketing and Coupon Optimization

Posted by Bryan Shepherd on Nov 7, 2016 4:01:07 PM

In the last post we covered some useful metrics for evaluating binary classification algorithms. In this post we’ll go into more detail on why they are important and see how ignoring them can affect ROI. We’ll use a contrived example that’s simplified, but not too far from reality.

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Related: Data Science

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