AI-driven Retail with Agilytic

We automate the data gathering from various sources, then use customized models to boost your forecasting and, ultimately, your sales.

We create customer profiles and look at their behaviors: when and where they prefer to shop, how many items they usually purchase, or which products they buy. We can then complement this data with relevant external data (socio-demographic, social media, weather, competition, mobility).

It then becomes possible to present each profile with a personalized shopping experience, with individualized offers or tailored calls-to-action.

Dynamic pricing can also help you set price rules, monitor competitors and adjust prices in real-time

Forecasting will inevitably also have a positive impact on internal processes, such as avoiding stock-outs, optimize the supply chain, reorders, warehousing.

We can help you grow in your inventory optimization maturity step by step:

1. Start simply, and add the right safety buffer to forecasted demand;
2. Calculate an optimal inventory based on the probability distribution of demand, considering the costs of stock holding, but also the opportunity cost of stock shortages;
3. Go further using deep learning, skipping the demand forecasting, and estimating the inventory directly. The AI will scrape all your detailed sales history and take into account all possible factors, both internal (promotions, locations) and external (holidays, weather).

Returns are one of the biggest challenges in retail, especially with the growing share of e-commerce and associated habits. With each return or exchange, a retailer bears additional supply chain costs, but also likely faces depreciation of the returned product due to damage, wear or obsolescence.

Advanced behavioral analysis of your product returns will allow you to

1. Better understand the real return drivers to act on what you need to prioritize: promotions, sizing charts, descriptions, pictures.
2. Segment your client base to identify "high returners" at risk of becoming unprofitable customers;
3. Predict which product/customer combinations present the highest risk of returns;
4. Identify what actions are the most likely to positively improve returns: personalized messages, promotions, additional charges.

Customer lifetime value (CLV) modeling helps you predict the potential revenue that each new customer can bring to your business given the length of time (s)he will remain a customer, the purchase frequency and the average transaction value. CLV will allow you to better allocate your marketing budget on customers with the highest potential, and also help you incentivize the most appropriate behavior by highlighting the types of interactions most often associated with higher value or more frequent purchases.

Then, recommendation engines allow brick-and-mortar and e-tailers alike to recommend products customers are likely to enjoy based on transaction history, similar customers' interests and offline/online behavior. These recommendations can materialize in the physical world (e.g., personalized folders) and digitally (newsletters, mobile apps).

Some say, "Keep is the new get." Indeed, it's often cheaper to increase revenue from existing customers than to acquire new ones. Our broad experience in churn models and proactive retention can become the cornerstone of your retention strategy.

• Clarify your "churn drivers" by combining loyalty data and customer satisfaction surveys. Are customers leaving you because your products don't meet their expectations or because of a frustrating interaction with the front-line staff?
• Identify customers the most at risk of going to the competition through a "value at risk analysis."
• Determine the optimal retention initiative for each customer to secure a customer's sustained business.