How consumer data analytics help reduce churn
There are various ways by which companies deal with consumers who stopped doing business with them, namely with those who churned. Customer churn is always a headache for businesses, and after they employ churn data analysis and measure what happened, they begin examining why it happened and then focus on efforts that would help reduce customer churn.
But as the customer churn rate is relatively easy to calculate, the reasons for it happening and the means to analyze customer behavior can be, and often are, a tough nut to crack. Sometimes it is the product that customers complain about, other times it is the company's service that is the cause they churn, and oftentimes it's the competition that comes up with a better offer. And as they say, the customer knows best.
Connecting data analytics with customer churn
In recent times, data analytics tools help companies deal better with customer churn. When they choose to employ consumer data analysis, companies begin to better understand how to run their churn prediction, reduce customer churn and increase customer retention.
Data analytics helps both understand the problems as well as create possible insights and solutions. Many companies use data-driven methods to figure out why customers are churning and even make an effort to discover those who are likely to churn. Using advanced analytics, artificial intelligence, and machine learning algorithms, helps them better understand customers and perform a more accurate churn analysis.
By figuring out the data about what the consumers think, need, and want, businesses can improve their retention rates and reduce churn. Data analysis of a customer journey and customer experience can assist a company to identify the segment and problem areas, leading them to plan how to fix them.
Customer data, whether relating to a product or service, must be tracked and analyzed for a better long-term focus from companies on true value solutions. But since the data quantities are enormous nowadays, the employment of machine learning and AI-powered solutions can bring about better business models, insights, services, and solutions.
There are three methods of calculating churn, and companies usually use any one of them or a combination of all three (an integrative approach). These quantitative methods explain "what happened" in the world of churn rate.
As churn prediction is always on the table, companies are trying to employ whatever formulas and methods they can use in order to better prepare to deal with it. With volatile and very competitive markets in which companies find themselves dealing, each of the following calculations can expand companies' understanding of the churn aspects.
1. Subscription churn
This is simply the calculation of the number of customers that quit over the total number of customers over a period of time. This method serves for a basic understanding of customer churn rate and gives an overall description of how many customers are churning.
The first step to understanding the churn of customers usually begins with this formula. The calculation can be done over pre-defined time periods (month, quarter, annually, or year-to-year) and it answers the question about the size of the problem.
2. Non-subscription churn
Churn can further be figured out by measuring the number of customers that have dropped below the threshold and are considered inactive, within a time period. It is up to the company to determine the exact threshold, as it can change from product to product or over time, but it still serves as the quota that must be met and obviously didn't.
Customer attrition measurements are done by the above two methods, but there is another quantifiable way to figure out churn rate and customers who stopped doing business with the company.
2. Revenue churn
Revenue calculations in relation to churn rates can be tallied by the amount of recurring revenue lost over the total amount of recurring revenue at the start of the period. This is yet another method to figure out just how much a business is hurting.
Using those numbers' methods is always the first step towards understanding customers and churn better. Other than the obvious numbers, a thorough qualitative data analytics examination is required, in order to find out why customers churn. Then a company can offer customers value propositions in order to decrease customer churn.
The importance of reducing churn
With customer churn as a top priority on managers' minds, they have multiple reasons to take proactive steps to reduce it as much as they can. Some of them use churn prediction methods to try and calculate in advance how many customers are likely to churn vs. those who are less likely to churn.
But whether a manager is able to predict churn or not, once he uses the above-mentioned methods to calculate the actual numbers of customers stopping doing business with his company, he must measure the existing losses created.
The reasons companies do all that they can to reduce churn vary. Apparently, customer churn creates several problems for every business, and reducing churn becomes a top priority company must focus on resolving.
Avoid revenue loss
Business revenue loss is the number one problem for every organization. As profits and growth signify the aim of every business, when companies lose revenue because fewer customers bought their products and services, the brand must be hurting.
The next best thing in that type of situation is to measure the damage done and then figure out and have insights into the reasons for such a customer churn. Something along the customer journey isn't working, so the company knows that it must find out the causes for such a problem.
Reduce customer acquisition costs
Churn by customers means also lost revenue of the money spent trying to acquire new customers. Every business model includes various marketing efforts, and once customers churn, such amounts are lost too.
So every customer churned means not only lost revenues but also customer acquisition costs are gone. That is why many companies invest lots of energy, and money, in retention programs and other customer retention efforts.
Lower marketing and sales costs
Another key reason why organizations make every effort possible to reduce customer churn is that they try to decrease marketing and sales costs. Since such costs appear on the expenses side of every budget, they become more problematic when more customers churn.
A careful customer journey analysis and insights are called for in order to better plan future marketing and sales expenses. Evaluation of the existing customer base and more data analytics regarding how to better match expenses vs. results are also required if a company wishes to reduce such expenses.
Improve customer lifetime value
The customer lifetime value (LTV) refers to the measurement of the average customer's revenue generated throughout their entire relationship with a specific company. So for existing customers who decide to churn, businesses can figure out the income loss from their stoppage of purchasing.
In order to reduce customer churn, companies go out of their way to make sure customers continue buying from them. The next best offer, club memberships, discounts, and a variety of other exclusive deals are offered to customers in order to increase retention and reduce customer churn.
Upgrade quality of customer service
Customer support serves as another key element in business-customer relationships. Customers value the service they get from organizations as they evaluate just how much those businesses care about them. So the whole process of handling complaints and then using customer comments to ensure quality service means a lot to businesses.
Since there are ways to measure customer satisfaction from the service they receive, a company can compare its score from period to period and figure out how to further improve this aspect of its business. Sentiment analysis also contains how a customer views customer support, so every business can receive a pretty good idea of how it is performing here too.
Increase revenue options from existing customers
Up-sells and cross-sells mean getting more income out of a company's existing customers. This can be done by offering them extra products and services and building upon the trust they have for the business which is based on previous deals.
By getting more revenue from existing users, a business reduces the risk of losing income from customers that churned. So a customer loss may give the company somewhat less pain if the business manages to cover such loss by getting more value and making more money out of a regular customer.
Maintain brand reputation
The final key in the importance of reducing churn relates to keeping the positive sentiment and brand reputation of the entire business. After all, customers do not like to continue doing transactions with a company that suffers from a large churn due to the underperformance of a product or service.
A sentiment analysis, when done on a regular basis, can give a company a glimpse of what the overall reaction customers carry for its brand. Data analytics, when calculated properly, can help businesses identify their current position as well as compare such brand reputation scores to previous time frames.
The types of quantitative churn analytics
Businesses use customer data and other analytics to calculate churn. There are predictive models and post-churn models, as organizations try to figure out and analyze their data regarding churn risk.
While churn can be difficult to predict, since many factors must be considered for such a calculation, it still can give businesses ball-park figures regarding possible revenue losses.
While the data gathered from the numbers' methods is not sufficient to comprehend the full situation, and a consumer's voice and opinion data analysis is required for comprehensive knowledge, there are a few analytics models and tools which organizations use to try and measure their churn risk.
When businesses wish to predict the future outcomes of their sales picture, they use predictive models, statistics, and modeling techniques. Predictive analytics takes current and historical customers' purchasing data patterns, in order to identify if such patterns are likely to repeat themselves.
Data insights derived from predictive analytics make companies use a form of personalized marketing called "Next best offer". They offer customers products based on their habits and past behavior. In doing so, businesses predict their consumer needs and want, and later analyze whether such efforts materialized.
Predictive analytics is an interesting method of figuring out the customer experience and trying to repeat it. Insights from previous purchasing patterns are used to analyze, create and predict future customer buying moves. it also falls under the customer retention efforts made by organizations, intended to also reduce customer churn.
Yet another category of predictive analytics, this analysis model combines machine learning algorithms and artificial intelligence to simulate several approaches to possible customer behavior patterns. Eventually, such a model suggests the best action to take in order to optimize practices.
This type of data analytics puts the focus on “what should happen.” It differs from the predictive format by diving deeper into the customer journey and employing much more data and a broader perspective.
This type of data analytics relates to understanding "what we know" and collecting all current known customer data information. It often serves as the basis for the above-mentioned two predictive models.
As soon as describing what an organization knows regarding its customer's journey and current buying habits, such data can serve multiple functions such as predictive models, churn prediction efforts as well as customer retention analytics.
The process of how data insights are leveraged within an organization is termed "Consumption analytics". Such a process follows the recommendations to act, and then the actions an organization takes.
Later there is a measurement of those actions, leading managers to a "feedback loop" of repeating those steps over and over again. As customers keep buying and then commenting online, there is more data to track and analyze constantly.
A similar term is "decision Supply Chain", describing this process of acting and measuring those actions. This applies to customer churn and customer retention actions too, which are first taken, then measured, bringing about yet more actions and further measurements, etc. The loop never stops.
The types of qualitative churn analytics
Lots of machine learning, artificial intelligence, and natural language processing (the ability of a computer program to understand human languages as they are spoken and written) are employed by managers in order to complete the other side of data, namely the qualitative segment. This side complements the quantitative side of data analytics.
Also called diagnostic analytics, this advanced analytics format tries to answer the question of "why something happened". So the "what happened" (the numbers side) gains the much desired "why it did". By having data from both sides of the equation, managers are now able to take fewer risk decisions and more data-driven actions.
The way to figure out "why something happened is by tracking and analyzing the customer feedback and voice. It is the task of every team within an organization to figure out the consumer’s voice and take proactive steps to improve their products and services as well as ensure churn reduction.
The responsibility of each organization's team can be described in the following examples. By taking care of their tasks and actions, each team also contributes to the overall organizational effort to increase customer retention and reduce churn rates.
Product people can contribute to the churn rate reduction efforts by analyzing their customer feedback and improving the designs, features, and contents of the products they are working on. Insights from customer experience tell product people what works and what does not work, with the ability to break it down by category or segment, for a specific fix or improvement.
Once analytics of the customer feedback is delivered to the product department, it enables this team to better focus and takes actions based on such insights for an improved customer journey. Such improvements can later be measured too, as customers react to them, and then the product team can further learn how its fixes were perceived by the users.
By improving products, organizations help increase customer retention and decrease customer churn. By tracking, monitoring, and analyzing feedback from customers, product people can rely less on guesswork and focus their efforts on data-driven analytics for more accurate decisions and actions.
Reducing churn rates is always on the minds of marketing teams, as they consider which steps to take. Brand awareness efforts, public relations initiatives, and paid advertising are some of the actions taken in order to drive in more new customers and prevent existing ones from churning.
but as with any effort, all considerations must be based on data and analytics. As customers react to various organic and paid campaigns, either on social media or on special forums, managers can gather lots of insights and learn what customers are thinking about such actions.
And after a manager is able to identify what worked and what didn't, and what the organization's various customer segments want and need, he can have a better focus when trying to create future campaigns.
3. Customer success
A customer's journey does not end with the purchase of a product or a service, and as it is the responsibility of the customer service team to handle customer requests and complaints, this team serves a key role in the organization's overall effort to avoid customer churn and increase their engagement.
There are various machine learning algorithms and AI means to measure customer feedback regarding companies' customer success efforts so that the team can figure out how customers are perceiving the treatments they are getting. Such data and analytics, based on tracking and analyzing huge amounts of customer conversations, figure out the churn prediction and conclusions that an organization evaluates.
4. The management
Executives dealing with increasing customer retention and churn avoidance must view each team's effort as well as the total brand strategy and goals in order to maintain growth and profits. They too rely on machine learning analytics and usually take a bird's eye view in order to improve overall performance.
Once the churn rate is realized, the actionable insights from the data tracked apply to each team's effort separately but also contribute to the entire organizational churn rate handling and resolving. Customer churn is then met with specific actions, relying on customer feedback data analysis and intended for long-term better customer journey and improved customer retention.
Affogata consumer data analytics helps reduce churn
Affogata tracks and monitors feedback data from customers, as found all over the open web, as well as internal sources. The real-time data collected is then used to analyze a variety of topics and data segments that assists companies to make actionable insights with regard to their brand, product, or service.
The "why" completes the "what"
Analytics of data can apply to each department specifically, as the platform is cross-organizational, and each manager can take advantage of it for his own purposes. But the common denominator for each analytics piece is that it always covers the "why" aspects in support of the "what happened" (number figures) in relation to the product.
AI-powered data tools, including machine learning and natural language processing, manage to track real-time huge volumes of feedback from customers. Such unstructured data then goes through the process of analysis and structuring, so that companies can figure out what customers converse about regarding the product and brand.
As the customers make comments on many online platforms, including review apps, forums, or social media outlets, their voice forms opinions about the different aspects of a product. As the data is analyzed and processed, the platform is able to create reports and summaries of every key segment and category which is relevant to companies.
Such qualitative analysis, or the data explaining the "why it happened", helps companies greatly to figure out the "what happened". They can now evaluate their previous decisions and plan their next steps, based on accurate feedback data from their customers.
More tools to figure out churn
Dealing with churn is always a delicate and difficult matter. Using the AI-powered platform to figure out why customers are not happy and what led them to churn, is the first step toward fixing the problem.
The digital tools promise fast collection and analysis of the customers' voice, and that puts companies in a position to fix a product's category, feature, or segment almost immediately. Ultimately, they can handle churn better and minimize its damages.