Machine Learning Use Cases in Marketing

simMachines supports use cases for marketing spanning customer lifecycle predictions, dynamic predictive customer segmentation, customer experience management, sales forecasting, trending and analysis.

Churn Prevention

Large international telecommunications provider needed to reduce churn for pre-paid phone cards.

simMachines deployed dynamic predictive segmentation to enable proactive churn prevention.

  1. simMachines created predictions of who was likely to defect based on past defectors. Every prediction’s Why factors are different.
  2. We then created clusters of similar predictions. Each cluster was assigned a corresponding action.
  3. In one case of this example, the prediction is based on a group of customers with a family plan who were recently divorced with children no longer in the household.
  4. The corresponding action for this prediction is: “Offer these customers an individual plan.”

30% reduction in churn amongst contacted customers

Sales Forecasting and Customer Lifetime Value

Specialty retailer needed to calculate the future sales and lifetime value of customers.

Client wanted a technology solution that could learn and adjust over time, as well as accurately predict and classify customers based on limited data.

The solution predicted future purchase volume and net income, as well as classified the customer base into groups based on value and the associated “Why” factors that define them.

Predictions with 95% accuracy compared to ground truth demonstrate that the algorithm can provide an automated calculation of a new or existing customer’s future purchase volume, net profit and LTV.

Machine Learning Fraud Detection

Top 3 financial institution wanted to speed up ability to implement ML fraud detection solutions for e-commerce clients, enable continuous learning, and expose the factors driving the fraud.

ML methods did not continuously learn or expose the Why factors. Increased competition was creating a need to upgrade speed and service value to client.

Ensemble approach using gradient boosting and similarity-based machine learning.

70% increase over current fraud detection performance. Time to implement reduced from 6 weeks to 2 weeks.  Continuous learning enabled and the Why factors reported/available behind every prediction.

Shopper Reactivation

Leading specialty apparel brand retailer needed to improve precision of targeting prior customers for reactivation to improve campaign efficiency and sales.

Client needed a modeling tool that could produce models more accurately and quickly than their existing solution.

Propensity modeling application generated grid of multiple models in days and ran selected highest performing algorithm in minutes on full US population producing predictive segmented outputs for campaign execution.

50%+ improvement over existing model, and the WHY allows for heightened understanding of key drivers to affect treatment and offer.

Dynamic Predictive Customer Segmentation

E-commerce pet supplement provider wants to shift purchase volume from Amazon to direct, online channels.

Client had limited analytic resources and need to generate look-a-like prospecting segments based on existing customers that could be activated in a direct to consumer campaign quickly and effectively.

The solution generated dynamic predictive prospect segments on a 3rd party file based on existing customers, generating hundreds of segments that were narrowed to five representing 65% of the target population within 2 weeks.  Weighted predictive buying factors associated with each segment enabled tailored creative, messaging and offers through email and digital channels.

Dynamic predictive segments immediately achieved higher than industry average click through rates in first DTC campaign ever run and is foundational to future DTC strategy for two brands.

Digital Identity Resolution

Large scale DMP needed to improve linkages across devices associated with single users.

Client had millions of unconnected devices it needed to accurately connect for improving campaign reach and frequency objectives.

The solution developed used similarity search to analyze a high volume of records quickly to pair two or more devices together with a high degree of accuracy quickly.

The solution was able to comb through over a quintillion pairing combinations in 48 hours and pair over 800,000 out of 1 million device records together with ~95% accuracy compared to known ground truth.  Daily updates can be run on a growing volume of records in under 2 hours.

One-to-One AB Testing Measurement

Large scale agency interested in more efficiently matching test and control groups with greater precision and understand ad effect at a one to one vs. stratified sample level for client measurement projects.

Current statistical approaches result in higher level aggregations of test vs. control comparisons to generate conclusions regarding test vs. control ad effect at a group level.

The solution applies similarity-based machine learning methods to match test and control populations enabling campaign drop outs from the test group to be automatically matched and removed from the control group.   For analysis, individual one to one twin pairs enable the clustering of positive vs. negative ad effect, with the most predictive weighted factors associated with cluster revealed in weighted order of importance.  This customer centric approach supports the automated selection of similar audiences to receive the same successful ad treatment.

The solution provides a simple user interface to create and test pairing accuracy and then generate one to one ad effect cluster analysis to inform campaign adjustments in under 8 hours per project with a high degree of accuracy.  Look-a-like audiences can be identified and output in minutes.

B-to-B Intelligent Lead Modeling

Large scale telecom provider that offering multiple services to businesses needed new solution that could quickly predict likely to purchase / upgrade with detailed explanations to inform sales reps as to Why.

No current state solution existed to accomplish this with the required precision and explainability.

The solution leveraged explainable machine learning methods to predict likely to purchase and upgrade business prospects and customers as well as reveal the weighted factors associated with the predictive purchase behavior.  The top factors can be pushed into lead sheet applications used by sales reps in the field.

Predictive explainable machine learning model performs with ~70% precision compared to ground truth, while providing Why factors in rank order of importance to inform the sales team’s messaging and offers.

Automated Online Product Purchase Classification

Large scale business intelligence platform provider wanted to be able to automate the classification of millions of products sold online to pre-set categories.

The client was manually assigning products to categories since the products had a wide range of naming conventions ranging from 2 to over 25 words including numbers.

The solution initially trained a classifier on known ground truth and was able to automate product labeling.  Then for never before seen products the solution leveraged unsupervised clustering to group similar products together and allow users to assign labels.  The classifier was then re-trained with the update product category assignments.  This process enables the client to work through over 100 million un-categorized products for reporting.

96% accuracy in precision was achieved for the classification model within 1 week.  Unsupervised clustering was created in an additional 1 week’s time enabling the model to be updated and assign product category’s to products at a high rate of speed.

Automated Contextual Content Segmentation

Large scale digital ad tech provider needed to generate contextual-content based segments from web pages faster on a global basis in multiple languages to ingest into audience segmentation products.

The client was limited in their current rules based system in terms of scale and was looking for machine learning technologies that could handle their scale, speed, content clustering and labeling needs globally.

The solution applied unsupervised clustering on a sample of bid stream optimization data that ingests tens of thousands of online bid events per second.  The clustering was able to group like URL pages together for easy labeling and the level of granularity desired, which can be adjusted by the user.  A simply user friendly interface optimizes the cluster labeling.  A classifier was built to automate the assignment of URL pages to known content categories.

Clustering was able to support completely accurate assignments quickly for content segment labeling within two weeks in multiple languages.  The classifier is able to achieve ~95% accuracy in assigning pages to content categories to eliminate personnel time.  The solution increases the value of audience segmentation products the client sells to advertisers.

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