The advancements in recent times around machine learning and artificial intelligence have hugely contributed to the growth of object detection and Image Recognition in retail.
Using an AI-based Machine learning model, there can be a deeper insight into the characteristics of a product or offer that consumers would find interesting. In this case study, we explore about using object recognition and deep retail to enhance consumer experience and reduce acquisition cost.
As Consumers are inundated with irreverent offers that are generic and don’t even meet their specific needs, leading to low marketing conversion rates, poor customer satisfaction and customer churn. Our client desired to build an AI based system to product highly targeted promotions to reduce customer acquisition costs using historical purchase data and customer behavior analytics and Facial recognition.
AI based machine learning models are ideal for offer optimization and generating hyper targeted offers for customers. Using AI, our solution enables the client to determine which customers are likely to be interested in current offers and when to deliver it.
Some highlights of the solution:
- We used AI models to create granular segments of customers using rich data and then to determine the characteristics of products or offers that each group would find most interesting
- Unimportant columns were removed, Missing/Invalid data fixed using quick options, Data from various sources joined together for Self Service data transformation
With AI driven targeted offerings, our client was able to provide personalized experiences for consumers with added benefits such as:
- It helps marketing team to develop content and offers that would best appeal to each customer segment which increases conversion rates
- Data on customers including demographics, assets, credit scores, complaints, accounts, tenure etc. can be identified to predict the right messaging
- Proactive campaigns can be run at regular intervals to retain churn customers before they leave
- Driving growth at category level by uncovering the consumer decision process and optimizing your assortment, price and promotion strategies
- Reducing Out of Stock through sales forecasting and identification of roots causes for supply chain efficiency
- Local Store Optimization: Optimizing commercial levers at store level and finding the best place to open new stores
- Improving loyalty of your shoppers by enriching your loyalty data with market view and analytics
- Shopper Intelligence: Adapting your stores to your shoppers by knowing everything on their purchase behavior in-store
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