Combine your data sources to get the best chance of reducing churn.

Combine your data sources to get the best chance of reducing churn.

Key Findings:

  • A customer may churn for a variety of reasons. In most cases the issues that show up tend to overlap, meaning you must understand the relationship between them and fully understand why your customer does not wish to continue with their original subscription.
  • Combining customer feedback data from multiple sources and then putting them through your analysis is essential to putting together a complete picture.
  • Finding the key reason(s) customers churn in a dataset of thousands can increase your company's revenue, and this can all be achieved in minutes, instead of days, or weeks using the Ipiphany solution from Touchpoint Group.

If you are dealing with a customer churn issue then you are not alone. Among those surveyed in HubSpot's 2021 State of Inbound report*, more than half mention increasing customer satisfaction and generating leads are top marketing priorities in the coming year, a trend that's remained constant for years.

Churn, or the rate at which customers stop doing business with a company, is known as customer churn. This is commonly expressed as the percentage of subscribers who discontinue their subscriptions within a certain period of time.

A high churn rate requires a company to compete with the stress and difficulties of pulling in enough new customers to fill the gaps left behind by customers that churned. Even seemingly minor increases in the churn rate can eventually have a significant negative impact on the capacity of your company to expand. Identifying the key drivers behind your customer’s behaviours quickly and accurately can contribute towards a lower churn rate.

Last year, US companies saw an average churn rate of 21%, with the highest being Financial and Cable companies at 25%**.

A customer churns for a variety of reasons, however. In most cases, the issues that show up tend to overlap, meaning you must understand the relationship between them and fully understand why your customer does not wish to continue with their original subscription. These relationships between key issues aren’t always easily visible and can require substantial digging if data is being manually analysed.

The Touchpoint Group Customer Life Cycle Analysis provides a framework that takes these relationships into account, allowing you to identify the root cause of those issues that lead to churn in your organisation. Read more about how we can assist you to improve retention in this case study.

An effective churn analysis requires assessing both the data offered from your review score and the comments. This is critical, as the statistics quantify the problem, and the comments include granular data that will lead to identifying possible solutions.

Combining data from multiple sources and then putting them through your analysis is essential to putting together a complete picture. As a result, all available data sources can be used for segmentation, allowing you to draw unique connections between customer behaviours.

One problem with doing this type of analysis is the time required to consolidate all your data from every source. Most companies analyse this data only once or twice a year, missing out on prime opportunities to understand the behaviour of their customers and alter outcomes, which results in the loss of a greater number of them than they expected.

The Solution

Finding the key reason customers are churning in a dataset of thousands can increase your company's revenue, and this can all be achieved in minutes, instead of days, or weeks. Using Touchpoint Group's AI Customer Analytics Tool, Ipiphany, understand engagement on all platforms, interaction types, demographics, or any other segment to define issues or areas of improvement specific to that segment. Use this knowledge to prioritise improvements based on those that will have the biggest impact on your business and its stakeholders.

The process of analysing customer churn data involves looking for relationships, patterns, trends, etc. It may be necessary to run a statistical procedure on your data to understand what sorts of relationships are present between variables and to what extent you can rely on those findings. The main objective of an analysis is to gain an accurate assessment of the data to better understand the reasons behind the actions of your customers. More often than not, statistical methods fall short of providing holistic insights into the driving forces behind the customer’s decisions and feedback. Contact one of our dedicated staff today to start analysing your churn rate and taking the steps necessary to prevent it.

Source:
*https://www.hubspot.com/state-of-marketing
**https://www.statista.com/statistics/816735/customer-churn-rate-by-industry-us/

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Contact one of our dedicated staff today to start analysing your churn rate and taking the steps necessary to prevent it.