Interpreting Your Data to Reduce Customer Churn

Customer churn is costing you money. Most times it’s costing you a lot of money! Fortunately, increasing your customer retention by only five percent can increase your profits by 25 to 95 percent. So, you might be wondering how you can convince a customer to stick around. Well, it all starts with analyzing your customer relationship management (CRM) data.

CRM systems have transformative potential when it comes to reducing churn. However, very few businesses understand how to maximize their systems to improve customer success. If this sounds like your organization, you should focus on three areas when you track and analyze churn. After you’ve finished reading this article, you will understand how to identify problem areas and monitor change as you implement strategic solutions.

Analyzing Customer Behavior

Have you ever noticed that some of your customers behave the same way—especially if they have similar characteristics? Particular groups of your customers do in fact exhibit common behaviors.

If you can learn the common behaviors associated with particular groups, you can predict how similar customers will behave under similar circumstances. For example, you can estimate what a customer’s reaction will be to a future marketing action or an outreach moment from a team member.

Luckily, building customer behavior models is fairly simple with the help of a CRM tool. The confusing part is often translating the data into all-in-one visualizations. Many companies work with a data visualization, like we do Visual Cue, to better understand their reports.

Image #1 (1)Another aspect of customer behavior analysis that can challenge companies is deciding how specific the models should be. Businesses that already use software designed to track user behavior can generally make their reports very granular. After all, one of the advantages of a CRM software is that it collects and organizes a plethora of specific user data.

Businesses that don’t have a user behavior software in place may need to work with their engineering team to see if they can provide the data or create a tool that will collect it moving forward. Overall, the more specific you can get with customer behavior data, the better. An in-depth report will take time to produce, but it’ll provide you with the best picture of what behaviors lead to customer success.

Considering Customer Age

Another effective way to analyze customer churn is to look at churn by age. Here, age is calculated by how long customers have been with your company. In other words, one age group could be “first month,” and another could be “twelfth month.” After all of your users are sorted by the amount of time they’ve been customers, you can start to analyze customer abandonment rates.

Viewing data in this way will help you grasp customer information in a simple way. It will also allow you to know your customers’ behaviors as they age. Chances are you will notice some similarities that you can try and address.

Companies that have high churn rates within the first few months can and should be working on improving their onboarding process. On the other hand, businesses that notice increases in churn several months in might find that their rates increase when customers need to renew their contracts. If spikes occur during specific time frames for various reasons, and addressing these reasons can make all the difference.

Risk_Pg11Once you implement solutions to problem areas, take the lessons learned there and apply it to another problem area in your business. Step by step you will be able to conquer all the issues your company faces.

Churn By Time Frame

In some ways, analyzing churn rates by time frame is similar to analyzing churn rates by age. The main difference is that time frame data can be harder to track and analyze—especially if you’ve been in business a few years. Again, this is why many companies work with a data visualization tool or software. It’s all too easy to become confused by a chart that has hundreds of lines.

To group customers by time frame, you must first define your parameters. There are several common ways to do this, but one of the most popular is to group customers together my month. Under this parameter, all customers who purchased in January of 2017 would be grouped, all customer who purchased in February of 2017 would form another group, so on and so forth.

When you analyze data based on time frame, you’ll notice two benefits right off the bat. First, you’ll find that your numbers aren’t influenced by customer acquisition. You’ll have clean, clear data that speak only to your customer abandonment rates. The second benefit is that you’ll see clear patterns for various groups.

After you create this report, analyze patterns and look for causes of customer churn. If you find that a lot of customers joined your company in November but abandoned shortly after that, seasonality might be to blame. With this speculation in mind, you can begin to look into what happened in the month November to learn why that time frame group had a high churn rate. As soon as you figure out what led to the increase in abandonment, you can create a plan to improve your retention for the future.

This type of report may also provide you with insights on actions that improved customer retention. For example, if individuals that joined in April have lower attrition rates, chances are you did something right to influence them to stay with your organization.

Once you dig into the month, you can begin to determine the elements that incentivized customers to stay active. Then, you can work on reducing churn by creating similar conditions for your entire customer base.

The Impact of Reducing Customer Churn

There are many ways to make reports, but every single one you create should inform strategic action. Overall, the easiest way to interpret data is a clear and accurate visualization that will help you gain insight into your customer base.

Equipped with strategies to collect and represent your data, you’ll be able create plans designed to influence churn. You might instruct your marketing team to run campaigns that share similarities with past successful campaigns. Maybe you take the time to work with your call center employees and teach them about certain behaviors they can perform to reduce churn.

As you roll out your solutions, make sure you understand how you will measure their impact. A sound monitoring system should be in place you so you can promote the aspects of your customer churn reduction plan that are working.


together solving churn

Making BETTER-Informed Business Decisions

As a business leader, you’ve probably heard hundreds of iterations of the phrase “Challenge the status quo.” Management experts and industry leaders have been told to tackle conventional thoughts and systems head-on for as long as they’ve been around. The only issue with this phrase, though? It’s become so common that it’s become the status quo!

It’s all too easy to settle for a technique that works, even if there are better options out there. Companies often rely on antiquated processes because they’re familiar with them. They fail to explore new ways to make better-informed business decisions in the name of comfort. If you’re ready to break away from this norm and make a change, this article is for you.

Reducing Churn With Real-Time Data

Although it’s been a trending topic for some time, there is still a lot of confusion about what big data collection can be used for. Simply put, a collection of extremely large data sets can be analyzed to reveal correlations, trends and patterns. These trends often hold powerful insights, that if acted on quickly, can provide awesome results by way of reducing customer churn, increasing customer satisfaction, and will usually result in an increase in company revenue.

Getting data in real-time has many advantages and possibilities, but it’s gaining traction in the business sector because it allows companies to make better-informed business decisions. Historically, organizations had to look at the past to create business insights. For example, they would gather information from the prior day, month, quarter or year and then create reports, projections and working plans from said information. Today, businesses that want to make strategic moves can rely on real-time analytics to inform action. Instead of looking back, decision makers can visualize current data to make time-relevant decisions when they matter most…now!

In the past, a simple question like “How many customers are about to leave our service?” could take hours or days for teams to understand. More complicated questions often took weeks or months to be analyzed. With real-time data being used, answering complex questions is no longer a burden that takes time. In seconds, you can receive a robust answer to your most complex business queries.

The opportunities of real-time data are endless—as long as you know how to access them. For most companies, this technology seems foreign, complicated and time-consuming. Luckily, this couldn’t be further from the case. With a small amount of know-how, every business— regardless of size—can start to use data to make better business decisions.

Knowing The Customer

Experts suggest that companies first use data to better understand their customers. It seems today, people only stay with a company if they have a very good reason to do so. There’s plenty of competition and similar offerings in almost every market, which makes it very hard to gain loyal customers.

As a result, businesses must work harder than ever before to avoid churn and increase customer success. Real-time data can help you understand exactly why and when your customers jump ship. With this information in mind, you can create a plan that keeps customers coming back time and time again.

6a00e54ee3905b8833019aff835edf970Long gone is the need to select and study small samples of customers to try to guess who will be leaving your business portfolio next month. With real-time data, you can understand virtually every one of your customers at any given time. Can you imagine sending fresh customer feedback to your employees instantaneously so they can improve a customer’s experience in real-time? More importantly, can you picture using this technology to turn a bad customer experience into a positive one?

The Goal? Understanding the Customer

With a clear goal in mind, real-time data improves venerable business models. Without a clear goal in mind, this information can do more harm than good. Companies that fail to define what they want will waste their time and money trying to analyze countless sheets of information. Time and money are two of your most valuable resources, so don’t waste either of them for a second. You must create a goal early on and visit it often if you want to use your data effectively. infographic-the-power-of-a-positive-customer-experience-1-638

One major roadblock for decision makers is understanding the picture that real-time data paints. Typically, businesses organize their findings in various charts and graphs—which can tell different stories depending on how the information is enterprited. Fortunately, there’s a new workaround for this issue. Instead of creating pages of visualizations that showcase findings from Big Data, organizations can use a data visualization tool that does it all for them.

This tool creates easy-to-understand visual representations of data. They are designed in a intuitive way, so everyone in an organization can understand them with little to no training. With the help of these visualizations, companies are able to scale up their entire organization’s ability at once.

Industry experts know it’s important to challenge the status quo. However, many of them fail to use this knowledge to drive action. If you’re ready to take your business to the next level, you need to move away from outdated processes. It’s time to stop relying on antiquated business intelligence procedures and transition from capturing data to making it useful.

Departing from the tools and practices that your business knows might feel uncomfortable at first, but it will be well worth it. By 2020, your customer experience will be more important than any brand differentiator. If you learn how to reduce churn rates and improve your customer experience now, imagine the success you’ll have in the future.


Making Sense of Big Data

Data, in whatever form it takes, is on the forefront of most business plans today. If you are the one responsible of making business decisions based on data, you are going to want to make sure you know how to analyze the information as quickly as possible. Take an e-commerce company for example. Your employees aren’t the only ones contributing to your data. Your customers submit data of their own every time they sign up for your service or purchase a product from you. Overall, the numbers from both sides add up quickly and it often takes a significant amount of effort to analyze. The progression of data creation is exponential, with colossal amounts of data generated every day.

Simply put, Big Data is comprised of extremely large amount of information. This definition is important for business owners to understand, but understanding the scope of Big Data isn’t the be-all or end-all. Business owners need to learn about how Big Data can be analyzed for insights that lead to more strategic business decisions and moves.

Below, you’ll find information that will help you make sense of Big Data. After you finish this article, you’ll understand how data is collected, the types of Big Data and what your business can learn from this this data.

The Collection of Big Data

Data collection methods often differ from organization to organization. Some industries and organizations’ Big Data encompasses information on transactions, while others is comprised of enterprise content. What is more standardized across industries, however, are the steps of data collection.

data collectionThe first step of Big Data collection is gathering information. Some companies use web scraping tools to gather their data, and others rely on their customer resource management tools to capture information. Next, companies need to store the data they collect. Many companies build internal automated processes that allow them to store their data in spreadsheets. Others might take advantage of a storing service that saves the information for them.

The third step is data organization. Even if an organization collects data efficiently, they’re likely to collect extraneous information they don’t need, too. So, every organization needs to sort and clean the information they collect and save. A company will likely also have to reorganize their data after it’s clean, so it’s optimized for further use. Last—but not least—companies need to verify their data. Until companies validate the authenticity of their data, they cannot trust any insights the information produces.

The Types of Big Data

Big Data is made up of a mix of unstructured, structured and multi-structured data. Unstructured data is information that’s not organized or easily interpreted by traditional techniques. A great example of unstructured data is a social media post. In general, standard databases and data models are unable to organize and understand this type of metadata.

structured and unstructured dataStructured data almost always has a defined length and format. Numbers, dates and strings of words are a few examples of structured data. Chances are your company already uses structured data that’s stored in a database to inform your business decisions.

Multi-structured data is derived from interactions between people and machines. One of the best ways to remember multi-structured data is to think of a web browser. As a user works on the browser, a combination of text and visual data is chronicled; the browser will also log structured data, like transactional information, about the user.

Understanding Data Improves Business

The amount of insight businesses can gain from Big Data is somewhat overwhelming. Due to this fact, experts suggest that companies focus on what they want to learn from Big Data, not what they can learn. To take advantage of all that Big Data has to offer, you need to establish a clear plan.

Several prominent companies use Big Data to decrease their expenses, and others use Big Data to improve their internal processes. One of the most popular processes right now is to use Big Data to reduce customer churn.

The Four V’s of Big Data

Industry leaders often use “The Four V’s of Big Data” to frame the Big Data discussion. If you need a quick way to remember what Big Data is and how its massive amounts of data are used, think of the following words—volume, velocity, variety and veracity.

The most obvious characteristic of Big Data is its volume. The amount information taken into consideration for business decisions also grows every year, making volume an essential component of Big Data. With an exponential growth model, Big Data’s velocity must also be addressed. Remember, everything from a text message to a credit card swipe can (and most often is) considered part of the Big Data collection process. As more technological advances become established, Big Data’s velocity will only continue to increase.

Variety is another important characteristic of Big Data. When you think of Big Data’s variety, remember that Big Data is comprised of unstructured, structured and multi-structured data. As discussed in “The Collection of Big Data” section, veracity is another part of understanding Big Data. Without prior data verification, you can’t draw valid insights.

ibm-big-dataThe Bottom Line

To use Big Data as effectively as possible, companies need to understand the ultimate value that Big Data offers their operations. More specifically, businesses leaders need to understand how seemingly countless attributes influence their data collection objects.

Screen-Shot-2015-09-28-at-2.05.01-PMThe high elevation view of Big Data can be overwhelming, but it’s pivotal to business success. Currently, companies that aren’t using Big Data to their advantage are stuck in the past. They’re scrolling through countless spreadsheets and data sources trying to make sense of everything. Then, they’re compiling analysis reports that take either months or years to create. By the time these groups are able to make business decisions, their data is often outdated and irrelevant.

On the other hand, companies that use Big Data are ditching the troubles and limitations of traditional business insight creation. These industry leaders use Big Data’s real-time cycle of analysis to make the most informed business decisions possible when they matter most.

If you want to run your company as efficiently as possible and improve your bottom line accordingly, you need to understand your data.