Consumer preferences and attitudes are changing rapidly. Retailers—from brick-and-mortar shopping centers to online stores—are trying hard to keep pace with the changing dynamics. As the world gears up for another holiday shopping season, it’s an excellent time to delve into how data-driven intelligence can help retailers anticipate what customers want and marshal their resources to meet their demands.
For retail outlets today, knowing their customer's shopping preferences and purchasing habits across different locations over time can be the difference between leading the pack as a market leader or losing billions in revenue. Interestingly, retail outlets already have this information hidden beneath the stockpiles of data they generate daily. But the challenge is—how to extract deep insights to help make fast and effective business decisions.
ElectrifAi’s Consequential Ai helps global retail chains solve high-value, high-ROI, business-critical problems with robust, pre-built Machine Learning (ML) solutions in 6–8 weeks. This means organizations can drive more sales, higher margins, and enhanced efficiency in their retail operations.
Getting the price right, always!
Our pre-built ML solutions help retailers achieve an optimal dynamic pricing strategy using market share, profitability, revenue, inventory, and other data sources. The ability to optimize pricing to meet the demand ensures an increase in top-line and bottom-line for the retailer, along with achieving higher customer satisfaction with fairness in pricing.
Knowing the past to predict the future
Our prediction models powered by AI and ML can forecast demand across an assortment of products that will likely attract customers in a given location for a specific time of the year. The insights we generate can help stores carry the right product for their customers. We power this by drawing insights from socio-demographic factors like wealth, income levels, neighborhood, and education. We also tap into popular trends and other data sources to arrive at the correct assortment of products by location, along with relevant product attributes like color, style, size, and quantity. Factors such as store size, traffic, shelf space, and current weather conditions are applied to bring local context to the insights, resulting in forecasts that help maximize cash flow and eliminate slow-moving products.
Ensuring the taps never run dry
The answer to understocking is never overstocking. On the contrary, overstocking consumes more shelf space and cash, adversely impacting the bottom line. Our replenishment model takes the predicted sales, store and warehouse on-hand quantities, and delivery lead time into consideration and automates the process of generating replenishment orders. This saves time for store personnel, enabling them to focus on providing a better customer experience.
Chalking the best route to be just in time
Our store transfer model leverages the sorting and searching algorithms to figure out the fast- and slow-moving products so that products can flow smoothly through the store’s network to meet any additional demand. Which means analyzing a range of data points such as traffic patterns, weather conditions, and delivery schedules to find the most efficient and cost-effective route to transport goods from one location to another. In addition, the model identifies sluggish inventory to avoid unnecessary stock accumulation.
Know your customers up close and personal
Our customer engagement ML solutions help retailers acquire, retain and grow their customer base through omnichannel touchpoints. Armed with deep insights into customer preferences, retailers can now effectively leverage cross-sell and up-sell opportunities. For example, imagine a jogger walking into a shoe store to buy a pair of sneakers. With the right recommendations offered by the shop assistant, the jogger might also be interested in buying a high viz jacket for the cold weather, a running belt, hydration products and a smartphone armband. Thanks to Consequential Ai, the outlet has a great opportunity to not just cross-sell, but also engage its customers better. Now, that’s a win-win situation for both—the shoe store and the jogger!
ElectrifAi: US’ leading ML solutions provider
ElectrifAi is all about solving high-value business problems for the C-suite at the Last Mile. We call this Consequential Ai, leveraging years of deep domain expertise and pre-built machine learning solutions to quickly drive top-line revenue growth, cost reduction, and operational efficiency. We work with Global 2000 enterprises, including several Fortune 500 companies, in a core set of verticals. Our clients see results in 6-8 weeks, transforming their data into a strategic weapon to drive enterprise value growth and profitability.
Our solution does not require investment in a new platform or infrastructure. Instead, we leverage the data existing in your system to power the ML models to deliver business outcomes.
We are the last-mile solution that sits on the top to solve specific business problems and bring about savings. Contact us to learn more!