What Are Customer Retention Analytics (The Data You Need)

Courtney Dercqu
March 18, 2021
Community

customer retention analytics

Regardless of your industry, keeping customers and ensuring they are satisfied with your product is vital to your company’s continuing success. Chances are, you probably already have access to your company’s analytics, so you might be wondering how web scraping can help you with your customer retention analytics. Web scraping can tell you how to improve your company’s retention rate, all the while generating new leads and helping you build more developed customer profiles.

In this article, we will discuss what customer retention analytics are, including the importance of your customer’s lifetime value, what retention modeling is and how web scraping can be an invaluable resource for reducing your customer turnover rate and improving customer satisfaction. Find out how web scraping can give you illuminating data to help keep customers and improve recurring revenue. On top of that, find out how a web scraping API can work for company’s and developers to introduce data directly into that process.

If you have a specific question in mind, please feel free to use our table of contents listed below to jump ahead for some helpful information.

Table of Contents

1. What is a Customer Retention Analysis?

2. Customer Lifetime Value and the Value of Web Scraping

3. Understanding Data Driven Customer Insights

4. What is Retention Modeling

What is Customer Retention Analysis?

customer retention analytics

Customer retention analysis is the process of applying statistical techniques such as retrospective survival analysis and periodic survival analysis to help businesses understand their customer turnover rate. This process is invaluable to gaining insights about your business that would normally take a long time to gather. Below, we look at what these two statistical methods are and how they work.

What is a retrospective survival analysis?

When you use a retrospective survival analysis, you essentially assume that each of your customers is active until they have been inactive for a specific length of time. This method identifies when a customer left. For example, if you started with 10 customers and after 10 days, only seven of them remained active, you could easily pinpoint which days yielded the highest customer turnover rate.

What is periodic survival analysis?

On the other hand, periodic survival analysis measures whether a customer was active or inactive during a certain period of time. Unlike a retrospective survival analysis that takes a relatively short time to measure, this method varies depending on the industry. While social media websites might measure this customer retention rate over the span of several days, other companies such as retail would have to measure this over several weeks, maybe even months.

Both of these methods are useful in identifying your company’s retention curve – a visualization that represents your users’ average retention over a certain period of time.

How web scraping can help your customer retention rate

While your company’s data is important, it can only get you so far. Let’s say, for example, that you increased the price of some of your products. Shortly after raising your prices, you noticed a dip in the number of customers you had. In this type of scenario, you clearly understand how and why your customers left. But what if the information isn’t so clear?

Let’s say your customer Bob was with you for six months before leaving for your competitor. You turn to your company’s analytics to find out why but you still need more information to solve the puzzle. With web scraping, you can instantly gain access to all of your company’s brand mentions – i.e., all the times your company was mentioned in customers’ social media posts and customer reviews. With the data you sourced from web scraping, you find out that Bob had several negative experiences trying to resolve a problem with customer service. After multiple frustrations, Bob decided to leave for another company.

Understanding these types of user sentiments – your customer’s observable emotional response while engaging with your business – provides invaluable insight into the changes you need to make to help maintain your customer retention rate. You may be left scratching your head without this information, trying to figure out why your customer, Bob, left.

Customer Lifetime Value and The Value of Web Scraping

customer retention analysis

Your customer retention rate matters. In fact, companies that see as little as a “5% increase in retention can increase profits by 25%-125%.” Your customer lifetime value is the number of money people will spend while they are an active customer with your brand.

While many companies realize that it costs more money to create a new customer than it does to retain an existing one, having the data you need to build developed customer profiles is a good place to start to get an idea of your buyer’s behavior. These buyer personas can help you figure out who your target audience is and how you should market to them. Understanding your buyer’s behavior will not only help you identify the potential costs involved with creating and retaining customers, but it can help you with lead generation, as well. This is where web scraping can help.

Web scraping can help you pull invaluable data from your competitors, social media channels, and other business directories on the web. Most importantly, by dictating where you want your leads to come from, the more likely they’ll be to engage with your product successfully. When selecting a website to scrape, you want to ask yourself these three important questions:

  • Does this website/platform contain my ideal customer base?
  • Are there any identifiable patterns between the customers’ buying patterns on these websites/platforms and my own?
  • Where do my potential customers hang out, both online and off?

Through web scraping, you can generate leads by gathering names, bios, and contact information (to name a few) to help you develop customer retention strategies and build customized buyer profiles.

Scraping Robot offers 5,000 free scrapes per month

At Scraping Robot, our API can scrape any page with ease. Find out how our modules are designed to get you the data you need quickly and efficiently. We have a variety of demos available, as well. If you want to find out how a scraper API can benefit your business, don’t delay contacting us to set something up. With Scraping Robot, you’ll get the information you need delivered to your fingertips instantly. We offer our clients 5,000 free scrapes per month, as well.

Understanding Data Driven Customer Insights

customer api

Data driven customer insights are those that are derived from both data analysis and interpretation. In other words, this is the process of using customer data to predict their needs and future behaviors. Predicting your buyer’s needs is also known as predictive analytics customer retention, which involves looking into their purchase history to decide future promotions and retention strategies. An example of this would be if a customer took part in a certain sale and used that information to determine their likelihood of participating in a similar type of sale in the future. Understanding your data driven customer insights is important to developing strategies that yield a high return on investment – or ROI.

A data-driven marketing strategy also takes a buyer’s demographics (i.e., age, gender, location, etc.) into account. To obtain this information, you can turn to your company’s historical data, such as payment records, or you can make the tedious choice of trying to obtain this data yourself. Web scraping omits the headache of trying to obtain this self-reported data manually and pulls it all from your website of choice so you can better understand your target audience. Understanding your target audience is the first step in making sure you can do whatever you can to retain them.

What is Retention Modeling?

customer analytics api

Like a predictive analysis, a retention model uses historical data to predict a customer’s future behavior. Retention modeling is based on a customer’s transaction history, demographic and socioeconomic characteristics to determine the likelihood of them canceling their subscription and/or not using your products.

Retention models can be built, not only with the information listed above but with data about consumer engagement, form submissions, and other first-party sources. With web scraping, you can get your hands on this information to help you quickly identify what changes have to be made to reduce your customer turnover rate. Likewise, this information can be used to help you determine what keeps your customers satisfied to continue meeting these expectations.

Final Thoughts

customer analytics

We covered a lot in this article, most importantly, how crucial web scraping can be to understanding your customer retention analytics. With web scraping, you can get your hands on invaluable data that can help you understand what makes a loyal customer and, likewise, which areas your company needs to improve upon to reduce your customer turnover rate. Furthermore, web scraping can help you generate new leads that can turn a one-time customer into a loyal one with a comprehensive retention strategy.

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