High churn is a big problem for both online and traditional retailers, and with the costs of acquiring a new customer ranging between 5 and 25 times more than retaining an existing customer, businesses need to focus on customer retention rather than acquisition.
Customer retention is a byproduct of customer satisfaction, which means you need to keep your customer happy to prevent them from leaving. But how do you do that? By solving their problems more efficiently and offering personalized products and services. This is where artificial intelligence comes in. This technology offers a whole new way to get to know your customers and anticipate their problems before they even arise.
Good customer service = Happy Customers
One of the most frequently asked question in any business is “Why do our customers leave us?”. While competitive prices and high-quality products or services are important, what customers want the most; is to be heard and taken care of. If they are not, they leave. According to a survey done by RightNow Technologies, bad customer service drives customers away twice as often as price and quality, especially when customers are experiencing problems with the service or product.
To improve customer service and make your customers feel taken care of, you need:
Active communication with your customers
Increased efficiency of problem resolution
Create a more satisfying shopping experience.
These actions might have been simple 5-10 years ago, but the high demand for personalization has changed that. Customers expect content and offers tailored to their needs and preferences, which transcend beyond simple demographics. Today, retailers and e-commerce need to get to know their customers on an entirely different level, and this is there AI and predictive analytics become necessary.
Demand for better customer segmentation
Simply put, predictive analytics is about gaining a deeper understanding of customers’ needs and preferences. It is a set of AI tools that conduct advanced analysis of your customer data, and ultimately help you anticipate who will leave and when. This may sound like magic, but it’s not. AI tools compare customers to those who came before them. The data from your current customer is matched to similar customer personas, which lets you figure out what questions people are going to ask and whether they’re likely to leave.
To achieve this, customer data is key, and there is a lot more information outside of basic demographics that you should use:
campaigns the customers came from
source of the customer
discounts they’ve used
time between purchases
items bundled together
shopping cart behaviour
social media mentions
...and many more.
The difference between the old ways of segmenting clients and the analysis done by the AI tools are the details you learn about your clients. AI tools segment customers into micro-segments, which contain much richer information about a type of customer than before. An example of a micro-segment is “Men who are between 35-40, from New York City, who typically buy dress shirts and shoes, and purchase twice a year, in-store, spending $200 each time, and have been customers for two years or more”.
New ways of handling churn
Traditionally, most businesses take a retroactive approach to addressing customer churn where changes are made, and their effects are measured after the churn has occurred. The problem is - with the reactive approach you are testing one or two changes at a time, measuring success, and selecting the best option based on limited data. This process is slow and ineffective. With AI tools, on the other hand, churn can be addressed proactively. Once you know who your clients are, you can analyse how different micro-segments behave, what incentives they need to make a purchase, or what makes them churn. This way you can forecast churn and take actions to prevent it, which is something AI tools can also assist with. Some AI tools can automate certain interactions with clients, thereby making them more efficient. They can also suggest appropriate customer communication. A generic “we’ve missed you” message might work for a certain category of customers, but a more targeted “free shipping on item X” might be appropriate for a different group. Furthermore, AI tools can prioritize a frustrated caller who has been identified by an AI system as a high churn probability customer and then recommends actions that will better address their needs.
AI is a competitive advantage, but not for long
We’ve only touched upon the basics of what AI tools can do to reduce churn. The potential of these tools is only expected to grow, especially with the rapid development of better, more advanced technologies. Implementing AI tools in your retail or e-commerce business might soon become a necessity rather than a competitive advantage.
Wondering where to start? Schedule a meeting with our team to see if AI tools is the right fit for your business.