Business leaders in retail acknowledge the need for machine learning (ML) to accelerate and drive digital transformation. The problem they face is how to get started. At ElectrifAi, we understand this challenge. We’ve worked with top-name retailers for nearly two decades on some of the most challenging ML use cases.
If you want to apply artificial intelligence (Ai) to improve customer experience, increase process efficiencies, or achieve other important business goals, we can help. Our Machine Learning Model Factory has produced thousands of ML models, with amazing results.
How do you get started on your ML journey? ElectrifAi’s Machine Learning Model Factory has you covered with many pre-built options to fit your specific business requirements. We offer pre-trained, pre-structured, and brand new machine learning models.
Get the benefits of ML quickly with pre-trained models that use your data to fine-tune what’s already built. Pre-structured models do require some modest modification and more specific tuning to fit your precise business objectives. But even these models can get you insights within weeks.
But brand new models you were told would take months or years to get up and running? With ElectrifAi’s vast library of machine learning models, we can take work we’ve already created and use that to make a new model in only one or two months.
That’s the benefit of our Machine Learning Model Factory. Fast, accurate, and proven results that deliver insights you need to make strategic business decisions.
Here are a few ways we’ve helped retailers overcome business challenges with our pre-built machine learning models:
This model identifies customers who are likely to churn (cease doing business with you). Mitigate that risk by offering customers more appropriate marketing, products, or promotions that truly interest them.
A machine learning model for churn mitigation can lower the risk of a customer disengaging and improve the likelihood of retention. This model provides the information you need to change your outreach.
This model solves fluctuating supply and demand problems by changing the price of an item or service to meet consumer demand. Machine learning can help you predict the base sale price that can be adjusted when the variables change (e.g., seasonality, trending items).
Setting the right price at the right time is the key to making sales. Getting the buyer to actually place items in the cart (either in person or online) is the hardest part. Increase your top-line revenue by identifying the right price point that will convert a person who is merely window-shopping or browsing into a buyer.
This model clusters your customers into groups depending on your targeting strategy by analyzing thousands of customer attributes and behaviors (i.e., demographics, spend and trip patterns). Customer segmentation is based on predetermined business goals.
Truly understanding your business goals ahead of time is a crucial component to this machine learning model’s success. And we are very good investigators.
Customer Segmentation often is a precursor to the other machine learning models we’re discussing as it helps to optimize marketing efforts. By bundling customers into groups or micro-groups, you enable highly targeted messages, products, or promotions to be sent to the right people.
This model makes it possible to precisely target customers and get them to purchase more items. How? By predicting which products they would be more likely to buy after considering what they have previously purchased. The Cross-Sell machine learning model produces a cross-sell propensity score for each customer.
What is the benefit? You can increase customer lifetime values, average order values (AOV), and repeat customer purchases. You always want to inspire customer loyalty to your brand, and machine learning is the best way to achieve this goal.
Encouraging shoppers to become buyers is crucial to success. The Purchase Propensity machine learning model outputs a score that identifies those customers most likely to purchase a specific product or group of products.
Creating hyper-targeted campaigns based on the most appropriate action given your goals can be difficult. It can feel like a guessing game, requiring you to fine-tune as each campaign is completed. What worked and what didn’t work?
Imagine not having to guess. Machines are incredibly accurate because they can process thousands of data points with precise results. This machine learning model increases your marketing team’s ingenuity and helps to convert more sales, faster.
This model predicts a customer’s remaining lifetime value (in months) based on their status in the customer lifecycle. Knowing your customers’ lifetime value makes it easy for you to choose the next best action you can take to maximize a customer’s experience with and value to your brand.
Knowing the customers who are likely to bring in the most money helps you understand which ones are most worth your effort to retain. ElectrifAi’s Customer Lifetime Value Optimization machine learning model predicts the customer’s remaining lifetime monetary value given each action they’ve taken in the past.
Using historical data, you can measure a customer’s brand loyalty and the estimated timeframe they will disengage as a customer. When you align your marketing message, products, and promotions with your customers’ predicted value, you are more likely to retain the customer’s business.
Our data scientists and ML experts have the experience and domain knowledge necessary to accelerate your ML project. From proof-of-concept to production, we help you focus on more strategic work. Put your organization’s valuable data to good use with strategic deployment of machine learning.