Retail

Retail has never been under more extreme pressure. This tension comes from the increased expectations of the empowered consumer and the need to have accurate forecast and inventory to deliver goods at a time and location that is convenient for the consumer while delivering operational efficiency.

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AI and Retail

The empowered consumer is more connected and informed than ever before, and they demand that retailers understand their ever-changing shopping habits to provide excellent customer service. At the same time, it has never been more critical to optimize the range of stock and inventory in order to deliver to customers the products they want at the time and place where they want them. All of this needs to be executed by identifying opportunities to decrease costs, increase operational efficiencies, and ensure a frictionless customer experience. AI has the power to help by leveraging your own data about your customers to better understand how their needs have evolved and how your organization can adapt to a changing marketplace.

Empowered Consumer

Predict next customer CRM state (e.g. activation, regular, high value, decliner, dormant, churn) to inform the strategy of future marketing comms
Customer satisfaction: using surveys and reviews data, predict sentiment for the entire customer base
Based upon prior purchase history, predict the number of days to next order
Identify the most effective product to present to each customer to influence their buying decisions based on historical data (purchases, web searches, etc.)
Attribute the value of conversion across digital channels to ensure digital marketing spend is being used in the right channels/campaigns

Product Assortment and Supply

AI-Driven Demand Forecasting: using a range of historic data sources to inform the level of future demand
Forecast returns: Predict the probability of return for every item purchased through all channels
On-shelf availability: for each SKU by actively detecting or inferring potential lost sales situations at the earliest opportunity to drive corrective action
Promotions Optimization: Identifying the best SKUs and best promotion strategy (e.g. rebate, discount, BOGO, etc.) to achieve targeted revenue or volume
Price Optimization: Identification of optimal price points influenced by multiple factors such as Item, brand, sub-category, category, location, product affinity, competitive and demographic

Operational Efficiency

Identify best sites to open, expand, reduce, or close stores based on strategic goals without cannibalization of existing store sales
Predict staffing levels for fulfilling orders, customer service, shipping as demand changes
Minimize time to deliver the shipment
Predict channel volumes: e.g. call center or in-store footfall. Helps to predict staff resources required for any given trading day
Identify store foot traffic to predict staff resources required for any given trading day

Empowered Consumer
- Predict next customer CRM state (e.g. activation, regular, high value, decliner, dormant, churn) to inform the strategy of future marketing comms
- Customer satisfaction: using surveys and reviews data, predict sentiment for the entire customer base
- Based upon prior purchase history, predict the number of days to next order
- Identify the most effective product to present to each customer to influence their buying decisions based on historical data (purchases, web searches, etc.)
- Attribute the value of conversion across digital channels to ensure digital marketing spend is being used in the right channels/campaigns
Product Assortment and Supply
- AI-Driven Demand Forecasting: using a range of historic data sources to inform the level of future demand
- Forecast returns: Predict the probability of return for every item purchased through all channels
- On-shelf availability: for each SKU by actively detecting or inferring potential lost sales situations at the earliest opportunity to drive corrective action
- Promotions Optimization: Identifying the best SKUs and best promotion strategy (e.g. rebate, discount, BOGO, etc.) to achieve targeted revenue or volume
- Price Optimization: Identification of optimal price points influenced by multiple factors such as Item, brand, sub-category, category, location, product affinity, competitive and demographic
Operational Efficiency
- Identify best sites to open, expand, reduce, or close stores based on strategic goals without cannibalization of existing store sales
- Predict staffing levels for fulfilling orders, customer service, shipping as demand changes
- Minimize time to deliver the shipment
- Predict channel volumes: e.g. call center or in-store footfall. Helps to predict staff resources required for any given trading day
- Identify store foot traffic to predict staff resources required for any given trading day

Use Cases for Retail

AI-driven retailers seize every opportunity to use data to analyze customer-changing behavior and shopping habits across multi-channels to align the right product assortment, promotions, and personalized communications. At the same time, they want to have the right product available with the right site selection to ship to customers who want to purchase certain products and have them delivered on their terms.

Customer Behavior

In a world where the consumer is empowered to shop anywhere, anytime, on any channel, retailers are using machine learning to detect critical changes in patterns of behavior. They use these models to predict incremental spend potential from existing customers when shopping in-store, online or via mobile. By using customer purchase history to come up with the most effective offers, retailers hope to influence their buying decisions and anticipate which customers are at risk to churn.
Product Assortment

Product assortment is as critical today as it has always been. But now, due to the explosion of choice and accessibility created by the digitization of retail, it is a matter of survival. Retailers are using models to predict what products to stock in which store, what items to rationalize to introduce new products, and what items are being returned by which customers.
Supply Chain

One of the biggest challenges retailers face is determining how much product inventory—and what types of products—they need to have on hand to meet the expected demand from consumers. Automated machine learning can help retailers improve their ability to predict consumer demand for goods.
Operational Efficiencies

Today’s multi-channel retail environment is more complex than ever before. And yet, the goal continues to be to identify opportunities to decrease costs and, at the same time, increase the operational efficiencies to ensure a frictionless customer experience. Retailers are using models to determine where to open new stores based upon sales revenue and how to forecast the correct amount of staffing to operate a retail distribution or store environment.

DataRobot Helps You:

Execute decisions based on AI

DataRobot can significantly increase the number of AI machine learning systems that are deployed inside your retail business to help you gain a strategic focus on the retail challenges with the largest returns.
Increase AI skills using existing headcount

With the lack of data scientists available for hire, DataRobot can help you transform your skilled business analysts into citizen data scientists. Guardrails built within the DataRobot platform ensure you can democratize AI across your organization - safely.
Transform your business from descriptive to predictive

DataRobot helps transform your business from one that makes decisions based on past events to one that makes strategic decisions based on what will happen in the future.
Be more personalized with customer engagement

With models that continuously learn from past behavior, DataRobot can help easily predict the next best offer for your customers.
Increase accuracy for the demand of your product

One of the biggest challenges retailers face is determining how much product inventory—and what types of products—they need to have on hand in order to meet the expected demand from consumers. DataRobot helps retailers improve their ability to predict consumer demand for goods using automated machine learning.
Predict sales revenue based upon store site selection

With many retailers closing stores or reducing their store footprint due to online competition, retailers are more aware than ever of how important it is to locate stores where the customer has convenient access. DataRobot can use your data to help you identify the best sites for opening new stores, as well as where to expand, reduce, or close stores without cannibalizing the sales of existing stores.

Brandon Greenwell

Data Scientist

By integrating aspects of machine learning across all of our daily processes, we’re changing the way tests and studies are carried out and embracing the 21st century way of analysis with new data sources, new technologies, and consequently, new ways of working.

Julien Boulenger
Director of Innovation & Data, Carrefour