How Artificial Intelligence Can Improve Your Customer Retention
Sales professionals tend to fall into a trap where they become so focused on closing deals that they make a sale and then move on to the next prospect. However, anyone in sales is likely familiar with the cost comparison of customer acquisition and customer retention. Depending on the industry, it can be anywhere from five to 25 times more expensive to acquire a new customer compared to retaining an existing one. Not to mention research from Bain & Company found increasing retention by just five percent can increase profits by 25 to 95 percent. Customer churn cannot be ignored, but too many sales reps think customer success is not part of their job description. Sales reps need to care about losing customers. Luckily, emerging technology can help.
As more companies look to the future of artificial intelligence (AI) for help in their sales, they are starting to see how AI can be used to improve customer retention. Companies looking to get a better handle on customer churn and retention should be looking to leverage the power of AI. Before you can begin your journey of using AI to improve retention, there are two foundational items to address.
1. Determine the quantity and quality. You have to determine the cost of customer churn to your business, which involves looking at three separate elements: (1) lost revenue; (2) lost opportunities to upsell customers—in other words, lost potential additional revenue; and (3) customer acquisition costs. Adding up these three components gives you a picture of customer churn cost. This is key, as it allows you to then assess the quality of churn.
Not all customer attrition is regrettable for a business. Have you assessed your churn to determine what an acceptable level of churn is? Before AI can be implemented, you need to use basic analytics to set a benchmark for your acceptable churn. Consider customers with a high cost-to-serve and low margins. If they leave, is that regrettable? As long as you are acquiring net-new customers at an appropriate velocity and volume to replace the lost business, it is not. Determining the cost and quality of churn is part of laying the foundation for AI.
2. Move away from churn. Companies will often invest their time and resources in preventing customer churn. This is problematic, as churn is a reactive metric. If you are focused on just churn, often you are going to be too late to address the issue. The indicators of a customer likely to churn begin appearing further back in the customer life cycle, during the acquisition and on-boarding phase. If you are facing high customer attrition, it might be due to poor customer acquisition rather than poor customer service. This is why you must move away from focusing on just churn and examine overall customer success. You need to begin to differentiate leading and lagging indicators. For example, a lagging indicator might be order cadence, as it reveals a problem that previously manifested.
To begin implementing AI, you are going to have to consider the entirety of a customer life cycle. Think about the variables of on-boarding. Were you in the midst of a product launch? Did your manufacturing process change? What was the time between the closed deal and the customer actually using your product or service? To truly understand your churn, you must assess the acquisition period. We all know first impressions are hard to shake, and the business world is not immune. The selling and on-boarding processes create expectations for the customer that they will hold throughout the duration of the relationship.
Becoming More Intelligent
Once you determine the quantity and quality of customer churn and assess your customer success, you can begin creating an intelligent experience. When implementing AI, it is not enough to gather insights. You have to be able to pair insights with actions, which is what is known as the intelligent experience. In other words, you cannot simply use AI to determine a score measuring how likely a customer is to churn. You must provide your team with these insights in their existing workflow, as well as give next best steps.
Here is how using AI to improve customer retention actually works.
- Logistic regression models: A logistic regression model is trained on historical data and then can make predictions on the likelihood of a customer to churn. It will look at examples of both customers that have churned and ones that have been retained. These situations will teach the model and allow it to develop a score for each customer. In addition, it will provide various actions to help prevent the customer from leaving.
- Natural language processing: Discerning a customer’s sentiment is another way to attack churn. Through natural language processing models, you can look at large amounts of unstructured data such as call recordings and web chats to find themes. From these datasets, customers can be classified based on sentiment.
- Putting it together: The classification of a customer can be placed in a logistic regression-based churn model. Now you are putting together models that will isolate the customers likely to churn. The goal is to find the customers and act before something happens. This is the true power of predictive analytics. You are able to be proactive in retaining customers rather than be reactive to customers leaving.
To pair insights with action, you might consider setting up a retention desk. This would be a specialized team of sales reps focused on the customers identified by your AI models as having a high propensity to churn. You can set these reps up to have a call list generated by an AI model in their CRM dashboard. The model will surface and rank customers based on the likelihood of churn. The rep can see the factors leading to the high score for each customer and provide the next best steps to prevent them from leaving. The system can be set up to re-score customers in real time as reps update their CRM. This will allow your team to ensure you are continually offering the best customer service to increase customer retention.
Moving forward, companies that want to lead their industry will have to turn to AI, and creating an intelligent experience is the key to success in implementing it. Your sales team can be set up for success by leveraging your existing data to create this intelligent experience, thereby using the power of AI to improve your customer retention.
As an account director at Atrium, Nicholas Christ works with customers to leverage their data to unlock its analytic and predictive potential. For more than 20 years, Nicholas has been a sales and service leader transforming organizational processes and CRM capabilities. He is also a Salesforce Certified Administrator. Nicholas graduated from Loyola University Maryland where he earned his B.A. in business administration and management. Nicholas lives in Maryland with his wife and two teenage boys.