Retail Use Cases
From omni-channel dynamics to recommendation engines and analyzing in-store behaviour, the application of accurate analytics has the capability to identify trends and increase sales in the retail industry.
- Shopper Segmentations
- Omni-channel Shopper Segments
- Loyalty Program Performance
- Omni-channel dynamics
- Store Segmentations
- Price Optimization Models
- Intelligent Customer Journeys
- Location Based Promotions
- Optimize Marketing Content
- Dealer Performance Analysis
The most successful business in retail execute integrated, omni-channel, customer experiences in product, marketing, and customer service, in some cases involving multiple complimentary brands.
Delivering consistent and seamless messaging and brand objective across multiple touch-points takes deep analytical capabilities. Recognising a consumer and their behaviour from channel to channel and presenting them with the logical next step in their customer journey and interpreting what complimentary service or product a sister brand could offer requires extensive analytical processing and customer identification capabilities across channels and brands.
The extensive processing and matching of customer data across channels and brands for analysis purposes requires the specific and informed consent of users. Retail businesses historically may not have collected this data or experience, particularly from their bricks and mortar businesses. And low consent rates from online customer acquisition can limit a business’s ability to maximize the potential value in its data.
Using the Trūata Anonymization Platform, clients can retain persistent identifiers over enriched data over long periods of time to create richer insights, enabling improved cross-channel modelling and analysis.
Identifying and targeting key customer segments is a critical retail strategy, particularly for high value customers. Intervention programs can be executed to ensure that retailers engage and converse with specific customers as they are considering making high value purchases and ensure that they do not lose that valuable customer to the competition.
Predictive modelling on customer data allows retailers to build analytical solutions that will identify when their customers are likely to make a high value purchase and build their engagement strategies accordingly.
Privacy laws limit the ability of retailers to utilise customer data for these purposes without specific and informed consent. In addition, high value purchases may be infrequent, meaning long term views of data may be required to create accurate models, conflicting with data minimization regulations. Retailers with a low percentage of high value consent rates may find that they have too little data for effective analysis.
By using the Trūata Anonymization Platform, retailers can perform longitudinal analysis and predictive modelling across their entire customer base without the need for specific and informed consent. This provides the ability to create accurate strategies and protect high value customers and investments.
Follow the links below to learn more about the use cases for the Trūata Anonymization Solution.