Major US Supermarket Drives Data-driven Marketing Personalization
Retail
The Results
The Client
The company, established in 1936, is among the largest privately-owned retail chains in New England. The grocer is listed in Forbes Top 500 Private Companies. In addition to grocery, the company covers pharmacy, prepared foods, and fresh foods.
The Challenge
The company was experiencing poor customer engagement and low conversions from their marketing campaigns due to the lack of insights-driven personalized, omnichannel marketing. They aimed to:
- Create a unified view of customers across online and offline channels
- Create granular customer segments and gain a deeper understanding of customer journeys
- Drive personalized marketing — curated to each customer’s preferences, transactional behavior, lifecycle stage, and promotional activity — across touchpoints
The Solutions
1 Real-time Customer Profiles
Use Algonomy CDP to capture behavioral data in real-time for both known and anonymous customers. Create dynamic segments for activation at scale.
2 Predictive Customer Analytics
Leverage actionable algorithms to create granular micro-segments. Perform look-alike and propensity analyses to drive next-best actions, and measure response with campaign and journey analytics.
3 Omnichannel Marketing Campaigns
Leverage machine learning algorithms, advanced analytics, and micro-segmentation tools to automatically orchestrate, test, and optimize personalized campaigns across the entire customer journey.
4 Personalized Customer Engagement
Leverage data to send the right promotions and content to shoppers, at an individual level. Auto-optimize and eliminate tedious manual A/B tests.
5 Predictive Customer Analytics
Localize assortments based on sales forecast, seasonal trends, purchase patterns and weather.
Analyze attributes like shelf duration, weight and date to optimize fresh produce inventory and sales.
6 Personalized Customer Engagement
Strike a balance between manual and automated merchandising. Assist Merchandisers with faster replenishment using real-time auto-optimization and custom strategies created manually.
Predict out of stock and prevent lost sales by atleast 5-10%
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