Effect of analytics on churn
Industries worldwide continue to face pressure when it comes to competitive pricing, as the ability to tell differences in quality by eye has reduced over time. Instead, more companies and their products are available than ever, and the ability to offer the most competitive pricing, along with product quality and customer service, has become mission critical.
These dynamics throughout industries, particularly the telecom industry, have led to churn management becoming a top priority for many companies. Churn reduction is possible for these companies in many ways, but one of the most important is through an analytics-driven approach.
Critical Steps for Approaching Analytics in Your Business
Developing practices required to reduce customer churn and increase customer satisfaction may take an organizational overhaul from the very top. For example, an organization’s structure utilizing analytics must be built from the ground up, ensuring that a company has the business processes, infrastructure, and support to analyze customer behavior and increase customer satisfaction through different methods of analyzing customer churn and calculating customer churn.
Customer churn metrics can be difficult to manage at first, but there are critical steps that must be taken to apply customer churn analytics and allow for the building of loyal customers compared to losing customers.
Initially, a business should define the role of analytics in their company and establish its necessity, create an analytics center of excellence to measure customer churn and move from functional silos to cross-functional teams.
An analytics-led approach for a company to solve business problems, such as analyzing customer churn, requires a top team that consistently finds data to support analytics and continuously runs tests to make sure that strategies are working successfully. For example, at one leading high-tech player, the senior team demands that data analytics support every decision.
In doing so, the company actively seeks to test new use cases for data and ensure that strategies and findings discovered through data are supported through multiple tests over time. By having a top team that sets the standard for analytics, agility, and quick responses, companies can set the guidelines for their company to master customer churn analysis basics and ensure that new customers find value in the product or service.
Additionally, organizations that place importance on customer loyalty, analyzing churn, and retaining existing customers should establish a center of excellence for analytics that is maintained by qualified data scientists and data engineers. This center can help in bringing forth cross-functional teams and ensures a hub of interconnectivity that allows for unilateral decision-making made due to data analytics to affect all aspects of the company.
For companies focused on churn analysis, preventing poor customer service, average customer lifetime, etc., data centers should be focused on those key aspects, and data for such key metrics must continually be collected. For most companies, this can be accomplished by working with an external data analytics provider that utilizes an important churn analytics tool to measure churn metrics and allows for revenue growth and company success.
Finally, increasing the rate of experimentation in data analytics within an organization helps guide the significant changes to existing business operations. Instituting rapid test-and-learn processes like those required for customer churn analytics and analyzing churn metrics and churn data requires truly cross-functional teams that include members of multiple departments of a company, such as marketing, finance, operations, IT, and legal. Each team should have the results for different segments of data and operations and be empowered to identify and implement new strategies that focus on demonstrating product or service value to more customers and getting customer feedback on their tactics.
All of these steps are critical in order for businesses to successfully transition into analytics-driven organization that use data to analyze and determine why customers churn in order to help reduce this rate.
Strategies to Reduce Churn Using Data
Customer churn can be difficult to predict or control organically, but by directly leveraging data as part of reduction strategies, organizations can make improvements that can really pay off when it comes to customer retention.
Revise Internal Processes
The first set of factors that businesses need to take into account when forming strategies to reduce churn is internal processes. These processes can manifest in a variety of effective methods aimed at reducing the customer churn rate.
One step is to develop a coherent data roadmap and stick to it. It should include automated and scalable KPIs that you can use to measure progress and create targeted solutions, as well as prioritize tasks that will help you address churn efficiently.
Ensure that any changes you make are based on the data available, rather than simply relying on guesswork or intuition. It’s also important to differentiate between data-related issues and systemic problems – either may be causing your churn, but your chosen solutions need to be tailored specifically to each one if they’re going to have any real effect.
After a data roadmap has been constructed, the next step is to follow through on the roadmap by using data analytics and machine learning to create predictive models.
Analyzing customers to determine which users drop, where voluntary churn occurs, and other customer churn analysis metrics can help to create an idea of impending customer churn. Based on this, companies can utilize machine learning to generate predictive models that will improve the customer journey, customer experience, and other business processes that directly tie to the customer churn rate.
Find Your Target Customers
While businesses can orient themselves internally to prevent customer attrition as much as possible throughout the entire customer journey, these churn reduction strategies can fail if the right customer segments and markets are not targeted.
An important factor to include in churn strategies is to focus on high-quality leads. Using data to determine crucial information about a business’s customer base is key. Having an idea of how many customers engage with the business, customer lifetime value, and other historical customer data and churn data is essential to being able to predict churn and improve customer retention. Consolidating, analyzing, and applying this data in the form of algorithms can help identify which customers leave and generate a churn prediction for prospective customers.
Using this algorithm to shape the business model and limiting which customers are being targeted can reduce the churn rate and decrease initial customer acquisition costs.
A similar but distinct customer-facing strategy is to segment the market to focus on retaining the right customers. Distinguishing between groups based on how customers engage with a business, such as via RFM metrics, is another vital step in creating customer churn strategies.
Specialized churn analyses based around these groups can enhance all your metrics and make them more tailored and applicable to specific customers. By using data and product analytics tool sets in this more specialized manner, businesses can tailor each customer’s life cycle and improve the customer experience and reduce churn rates.
Specific Considerations and Best Practices for Customer Retention
Now that it is understood that the churn rate can be heavily reduced with the use of data analytics, the specific considerations and best practices of using analytics to minimize churn must be evaluated. The graph below shows that companies most commonly use analytics to modify business processes like churn reduction. Like any step in reducing customer churn, incorporating data analytics into a business’s churn reduction strategy must be done the right way.
Best Practices for Customer Retention Analytics
Gather Multiple Data Points to Make Relevant Recommendations
Decisions should not be made based on one data point or trend. In order to fully understand the metric a business is measuring, the business must analyze multiple data points and trends and not make assumptions around only one piece of data. An example can be someone in New York, USA buying a surfboard. Even though this purchase is a data point, it does not mean the customer should be shown advertisements for surfboards. They could be purchasing the surfboard for a friend or family member in California, USA. Therefore, it is vital to analyze consumer trends over time to generate actionable insights.
Leverage Social Proof Where You Can
Oftentimes, customers that aren’t responding well to certain product recommendations need to be reminded that others who share similar interests to them are benefiting from using those specific products. A business must proactively identify customers that are content with a certain product and then acquire positive testimonials from social media comments and surveys to use as a key selling point.
High Quality Data Is a Must
High-quality data turns into high-quality results. Before engaging in churn analysis using data analytics, a company must first test methods to strengthen the data it collects. Many a time, all a company is lacking is the improvement of its internal data collection measures. In other cases, a business should look into the methods they use to collect external data and experiment with new products.
Improving Internal Data Collection
For many organizations, the way to noticing key performance indicators and generating effective data analytics is improving internal data collection. Therefore, this section of the article will highlight the most important pieces of advice that a company can use to improve this area. Below you can find a list of the methods that are the most effective:
Be Particular When Asking Questions
Being particular when asking questions means not asking the wrong questions, not asking for the consumer ratings of a specific product or service, and not being too vague. Basically, an organization should strike a healthy balance between specific and vague questions.
Don’t Be Impersonal
It is easy to make a survey or other data collection method sound impersonal or like an organization does not care about their customer. Consumer surveys should be treated like any other aspect of customer service. Questions should be worded carefully and encourage the consumer to give their honest opinion.
Don’t Send Long or Complicated Surveys
Long and complicated surveys discourage customers from giving feedback, even if they really want to. Surveys should be easy to access and not take more than five minutes. In fact, there is no reason a survey should be longer than five minutes because the purpose of the survey is to simply create actionable insights.
Mention How Much Customer’s Feedback is Valued
This point is similar to the point about not being impersonal. The consumer is doing the business a favor by filling out a survey. After all, the opinion of existing customers and new customers is vital to deciphering key performance indicators.
Don’t Just Focus on Detractors
It is easy to try to get the customer’s view on every single problem within a company. However, resolving minor problems that the customers have will only benefit a business in the short term. With that being said, it is important to get customers’ views on minor problems every so often; however, the survey should primarily attempt to get a strategic view of what is going well and what is not going well within the organization.
Reducing churn is becoming an increasingly difficult task in a digital era where competitors can be found anywhere and at any time within a digital environment, and companies must quickly shift their tactics if they wish to succeed.
While there are multiple methods that exist to reduce customer churn, enhancing business practices to include analytics and data will allow businesses to quickly combat a rising churn rate, even if implementation may take some time.