Customer churn is losing you money: 3 proven ways ecommerce brands can reduce churn

• The importance of customer retention

• Reducing customer churn

1. Clearer focus on maintaining the right customers

2. Focus on generating data-driven insights

3. Use predictive machine learning tools

• Benefits of integrating a customer retention plan

1. Build better customer relationships

2. Reduce customer acquisition cost

3. Increase revenue and growth

• Closing thought

Attracting a strong, returning customer base is vital to business success. Companies who focus on retention efforts build better customer relationships and substantially increase revenue. Research conducted by 

found a 5% increase in customer retention can increase profits by over 25%. 

Any company that has customers, has a need for churn prevention efforts. Creating measurable strategies to ensure customer retention, and taking data-driven actions to prevent churn, is key to thriving in the ever-growing, competitive e-commerce market. 

Churn measures the rate at which businesses lose customers. At some level, all businesses experience churn; yet, not all businesses take steps to prevent it. Instances of churn can be: unsubscribing from a service, shopping with a competitor, or not renewing a contract. 

A LinkedIn study shows that 96% of unhappy customers don’t complain, and 91% of those will leave without giving any reason why. A churn prediction plan can help to identify customers' pain points, and act to solve them before churn occurs. 

Any company with an online presence has access to thousands of data points about their customers. Most companies don’t make good use of their data, and in-turn, are missing out on extremely valuable information that could be driving more revenue. 

Mike Black, chief marketing officer at Profiero, cited that brands who use data analytics outperform their overall industry by as much as 70%, however, only about 20% of all brands use such analytics. Data provides a map to the inner-workings of your product. Not using it correctly leaves a large gap for potential company knowledge that could be used to drive higher revenue and smarter decision-making. 

Product managers who use data to direct decision-making are more successful than those who don’t. Data reveals patterns in customer behavior, allowing product managers to see the commonalities of customers who churn, and of those of loyal customers. 

Gaining access to this information is only the first step in generating data-driven results. Shaping decision-making around data-driven insights is what makes for a successful company in the digital age. 

One of the most useful metrics that can be derived from data-insights are from your customers. Learning the behavior of customers who churn, and that of customers who stay, can be extremely beneficial for the longevity of your business. 

Identifying the profile metrics of high-value customers (annual spend, industry, job title, etc.) enables product managers to guide decision-making to target these specific customers. The cost of obtaining a new customer is 5 times that of building efforts to keep existing ones. Therefore, it’s extremely beneficial to integrate customer retention efforts to target high-value customers. 

Customers today expect to feel special, with successful brands integrating personalization across the entire shopping process. Customers have begun to expect this same level of service across all brands–even small to medium sized ecommerce businesses. 

By first identifying what the high-value customers want to see, product developers will be more equipped to personalize their web pages around the customer needs. For example, highlighting and/or personalizing product listings to match the buying habits of high-value customers. 

Customizing your churn strategy to focus on high value customers allows product managers to focus on the niche of customers that are most relevant to a business and most likely not to churn. 

Machine learning can be used to predict which customers are most likely to churn. Instead, product managers can take initiative to create strategies that target at-risk customers to prevent churn. 

One of the biggest barriers when working with data is the inability to translate customer data into business operations. Machine learning handles the data science steps needed to process and draw conclusions from customer data; building predictive models off of these conclusions. Any company that has access to data has the ability to use machine learning technology. 

For its power in generating accurate predictive insights, machine learning is one of the fastest growing technologies in the field.

reports that 56% of companies surveyed reported the use of AI in at least one way. 

One hurdle preventing further integration of machine learning is the high-level data science and programming skills required of teams to implement. Using AI/ML-based SaaS tools can be a solution for integrating machine learning into your business processes. 

Companies who have a high retention rate will get to know more about their customers than those with high churn. Whether it be a machine learning model, or simple data analysis, more data garners the best insights. 

Companies who implement successful strategies to keep customers from churning understand much more about their existing customers. In turn, they are able to offer much more personalized marketing materials, which has shown to boost revenue and sales.

The cost to market to, and onboard new customers, is extremely high for any brand. Reiterating a fact from earlier in this blog, the cost to obtain a new customer is 5 times that of keeping an existing one. 

Having strategies in place to reduce churn can lower the overall cost of customer acquisition, as maintaining relationships with current customers is much more cost effective. 

Having a large base of loyal customers is also incredibly valuable data for decision-making. With these customer insights, product managers can discover why these customers are satisfied with the product and use this information to guide further outreach efforts. 

It’s no secret that less churn results in more revenue and growth. But surprisingly, even a small percentage increase in retention can greatly improve profit. According to 

., increasing customer retention by 5% was shown to increase profits by 25% to 95%.  

Having strategies in place, and using data analysis, machine learning, and insightful targeting are all ways ecommerce businesses can boost their retention rate. 

Creating a plan to increase retention and reduce churn is imperative for ecommerce brands who want to stay competitive in today’s market. Leveraging data-driven insights and machine learning offer the most insightful metrics to shape a retention strategy off of. 

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