Customer success is the lifeblood of any company.
The best customer success teams are able to deliver a high level of customer satisfaction, a high retention rate, and increased revenue for the company.
Customer success teams are constantly looking for ways to optimize their efforts. They know that every customer is different and that no two customers are alike. That’s why customer success teams need to be able to adapt to changing customer needs.
Customer success teams can use machine learning (ML) to predict which customers are likely to churn and then take action before it happens. ML can also be used to identify which customers are most likely to buy more products or services from the company.
Here are 3 ways:
1. Reduce Churn
Avoidable customer churn is incredibly expensive, costing companies over $35 billion a year, according to conservative estimates by CallMiner.
There is a wide range of factors that may impact customer churn, varying across industries, products, and services. In the telecom industry, for instance, churn may vary depending on whether a customer has dependents, their tenure with the firm, whether they have Internet service and other details of their contract with the telco firm.
Given that there are many complex, interrelated variables impacting churn, it becomes infeasible to manually and accurately predict churn.
This is where machine learning comes in, enabling companies to accurately build models that use a broad range of historical data to predict churn. The key here is having that historical data to fuel AI models, which typically comes from CRM tools like HubSpot or Salesforce.
Traditional machine learning is no walk in the park, however, as companies would spend months and hundreds of thousands of dollars to build and deploy models, using complex tools and frameworks like Python and Google Cloud Platform.
No-code machine learning makes the whole process effortless, enabling companies to build and deploy models in minutes, with no technical expertise or coding ability needed. For example, if you sign up for a free trial to Obviously AI, a leading no-code AI player, you’ll see that it takes seconds to build and deploy a churn prediction model.
If you don’t have your own churn dataset, you’ll find a sample customer churn dataset already uploaded to Obviously AI, or you can directly connect a churn dataset from Kaggle. From there, simply select the column that describes churn (typically a binary value like “Yes” and “No”), and hit “Go.” It’s really become that simple to build AI models.
2. Increase Conversions
Conversion rate is one of the most important customer success metrics. It’s simply the percentage of users that complete the desired action, such as making a purchase, subscribing to a newsletter, or submitting a form.
Regardless of what specific conversion rate you’re targeting, the goal is always to bring users closer to your company, increase revenue, and increase brand loyalty. Using AI is a powerful way to increase conversion rates in any setting.
Just as reducing churn requires a historical churn dataset, increasing conversion rates requires a historical conversion rate dataset. Similarly, this data is commonly found in CRMs like Hubspot or Salesforce, but it really depends on the type of conversion rate you’re looking at and the software that your company uses.
We can also find a sample sales conversion dataset on Kaggle, which you can upload to Obviously AI to build an AI model in seconds. After uploading the data, you could select the column named “approved conversion,” which is the total number of people who bought a given product after seeing the ad, to make a model that predicts conversions.
Once a model is built, you can deploy it through a shareable report link, with Obviously AI’s API, with Zapier, or even just upload data to make new predictions directly.
3. Increase LTV
LTV, or lifetime value, is another highly important customer success metric.
LTV is the expected total revenue you can expect from a given customer throughout the entire relationship with that customer.
In other words, LTV tells you how much a customer is worth, and therefore how much you should invest in retention, as well as which customers you should focus on bringing to your company.
By building LTV prediction models, you can figure out the exact customer profile that leads to the most revenue for your business, and also figure out the LTV of new customers.
If you don’t already have a customer LTV dataset, you can build it by taking the average purchase value and multiplying it by the average customer purchase frequency. Next, multiply the output by your average customer lifespan. Doing this for every row of your customer data will yield a customer LTV dataset that you can use to predict LTV for future customers, simply by uploading it to Obviously AI and selecting the LTV column.
If you don’t have an LTV dataset ready, you can use a sample dataset from Kaggle, such as this LTV dataset.
Customer success and sales teams have to manage a number of complex customer success metrics.
With no-code AI, it’s easy to use a data-driven approach to optimize your metrics and KPIs, without needing any technical expertise.