Hyper-target your customers with Personalized Promotions (e.g., coupons, discounts, offers) that would maximize profitability based on historical purchasing patterns, campaigns, location, time, and more
Understand the Use-case under 5 minutes
Video (1 minutes)
Capgemini case study with a major F&B retailer, implementing an AI-based predictive model to forecast campaign success. ROI includes more than 2X additional sales for each $1 promotion
Watch
Article (5 minutes)
Introduces 7 business benefits Machine Learning can bring to Price and Promotion Optimization
Read
Get to know more Business and Technical details about the use-case (15-30 minutes)
More detailed introduction covering business and technical aspects
Video (10 minutes)
How AI can optimize every Promotion, by predicting all the effects it would have on individual customer behavior - taking potential cannibalization into consideration
Watch
Article (14 minutes)
Why does your business need Personalized Promotions? Also Expands on the Steps and Times of launching a Personalized Promotion Campaign, and the Channels for implementing it
ReadCase studies, Organizational Aspects, Return on Investment examples
Article + Case Study (23 minutes)
Comprehensive explanation for how AI can help Optimize Retail Promotions by Predictive Customer Lifetime Value Effects. Includes a case study with ROI figures
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White Paper (20 minutes)
A collaboration between Oracle Retail Science and MIT Sloan School of Management researchers use machine learning, and counter-intuitive promotions, to improve profitability more than nine percent.
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Case Study (12 minutes)
OW Labs applied machine learning to determine for a large multinational retailer how given products would sell based on its print promotions, achieving 8% increase in forecasting accuracy
VisitMore details on the technical aspects of the use-case
Video (36 minutes)
Explaining how MANGO has used ML to predict the demand for each item and the ideal price point to be used during each week of the clearance season
Watch
Video (35 minutes)
See how AI helps retailers leverage customer basket and loyalty data to deliver smart, automated promotional decisions at scale across millions of customers. Watch till 35:00
Watch
Technical resources that will help you implement the use-case (notebooks, tutorials..)
Github Repo
How multi-armed bandits can help Starbucks send personalized offers to its customers. The article links to a Github Repo that includes data from Starbucks Rewards App + Code
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Article (50 minutes)
American Express launched a contest to predict the likelihood of Coupon Redemption per user. You can read through the submitted notebooks by clicking the links under “Participant’s approach”
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Solution Documentation
Microsoft Azure provides advanced analytics tools - data ingestion, data storage, data processing, and advanced analytics components - all of the essential elements for building a personalized offer solution.
VisitGithub Repo
This solution combines several Azure services to provide powerful advantages in making personalized offers to the customer. Event Hubs collects real-time consumption data. Stream Analytics aggregates the streaming data
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Data Sets you can use to build Demos, POCs, or test Algorithms
Dataset for Predicting Markdown Effect on Sales. Markdown and Sales data for 45 stores. These markdowns precede prominent holidays, the four largest of which are the Super Bowl, Labor Day, Thanksgiving, and Christmas
Details of a sample of campaigns and coupons (18 campaigns). Attributes include: User Demographic Details, Campaign and coupon Details, Product details, and Previous transactions
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20%+ Profits/Promotion
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