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Top 10 use cases to improve Customer Experience with ML and AI during COVID-19

March 20, 2020 | Mariale Mendoza

Blog / Top 10 use cases to improve Customer Experience with ML and AI during COVID-19

Top 10 Use Cases to Improve Customer Experiences with ML and AI during COVID-19

These are times to make incremental efforts to reduce business disruptions and to learn how to handle similar situations better in the future. An unexpected crisis like COVID-19 can create completely opposite situations when it comes to growth. While some industries like CPG witness an all-time high demand, others industries are highly affected and disrupted.
The new market landscape has redefined innovation and digital transformation. Companies have no choice but to leverage technologies that can help manage disruptions efficiently, mitigate risk, and predict what is next.
Regardless of your industry, these are the times to introduce innovation to drive sustainable growth and stability. Implementing AI to tackle these use cases could be your first step: 
  • Understand how your customers feel about your products

Sentiment ML solution leverages AI to quickly analyze your customers’ reviews, ratings, and comments posted across your online channels. It empowers companies to create unique customer experiences and build products that customers love.

  • Churn risk prediction
Focus on maintaining the revenue stream coming from your the existing customers. It is important to enable analytics that can help analyze and predict customer churn. This type of data will provide your sales teams with the right information they need to enable new communications channels with those loyal customers. We built Sales ML on Google Cloud to empower sales and customers success teams, it unleashes the power of your pipeline data and sales data to increase customer retention.
  • Demand planning for top products or services 
Forecasting the appropriate future sales for top products based on previous history and similar sales patterns will help the company predict their demand more accurately while fulfilling the current customer’s demand on time. Our Demand ML solution leverages AI to increase forecast accuracy up to 50%. 
  • Contact Center AI to optimize customer service 
To make digital sales more successful, few aspects like quick customer service, immediate issue resolution, and personalized support can be automated with the Google Contact Center AI. This bot has conversational powers to identify customer intent and determine what to say and do next. Discover how the University of New Mexico and Project ECHO enabled a personalized Contact Center AI to serve local communities better.
  • Dynamic pricing for online sales
Automated, flexible pricing mechanisms can be built around various product categories. This feature helps in setting appropriate price for the products based on the market situation, type of inventory run (slow moving item or fast moving item), cross products, and demand for the respective product in the market. 
  • Customer segmentation + Content personalization 
Based on the transaction history the customers can be clustered into high or low value customers. They can be segregated into different demographic regions to further refine them. This new type of segmentation can be used to strategically market products and personalize content.
  • Customer Lifetime Value around different product categories
Considering the number of purchases, purchase value, and purchase frequency of each customer, the customer lifetime value can be predicted. AI can be leveraged to help marketing teams retain old customers by enabling real-time personalized recommendations.
  • Purchase prediction + Personalized recommendations 
The customer behavior pattern, purchase history, and search details can be used to build machine learning model that can predict which customer will purchase. Also, these models can be connected to other data points, such as in-store transaction history, to predict whether the customer will buy online or in store. 
  • Manage Inventory while maintaining supply balance 
To fulfill all these customers’ demands on time and not to go over stock or under stock, proper inventory calculation has to be made along with demand planning. Properties like varying market conditions and new product releases should also be considered in this calculation. Supply ML and Inventory ML are AI-Driven solutions that can help your team manage inventory and supply seamlessly. 
  • Student success reimagined: 
From seamless video conferencing to real time video analytics on student engagement, Google AI solutions for Education helps universities improve student success. Education ML enables immersive learning experiences for deeper exploration and engagement. It unleashes the power of Google Cloud and AI to redefine how your students learn, interact, and research.  
How to use AI in hitech
At Pluto7, this question makes our day. Where you can go from here and what you should be doing after this reading article is limitless. Contact us to discover how our solutions can help you drive digital transformation at scale, faster than ever before.