They say data is the new oil. But do you know data can let you observe what the future holds for you?
Measuring growth is essential to track and address the needs of the business. However, you often need some upsetting metrics that serve as an early warning.
Predicting the churn rate is one of them. The data might not give you the reason for a treat, but it does offer prospective benefits.
Let’s face the truth: not all consumers are loyal.
Thanks to a blisteringly growing marketplace. They’re volatile and always move for unique features, better experiences, and relevant price tags.
Predicting churn rates, therefore, adds a competitive advantage and a potential revenue source if executed properly. It’s a ‘make or break’ opportunity where you identify your vulnerable customers and act before it’s too late.
In this article, you’re going to learn how important Churn rate prediction is for any business and what it takes to make a successful analysis. For your further understanding, I’ve listed five on-demand analytics tools per different sizes of the business.
Predicting Churn Rate:
In simple terms, predicting churn rates means sneaking-peek into the future and sense the possibilities of loss.
The whole process is an amalgam of artificial and human intelligence.
Nothing moves here without data.
And when you have data, the churn prediction tools undergo three analytics processes:
1 Diagnostic Analytics: You get to know customer pain points and reasons behind their dissatisfaction.
2 Predictive Analytics: Helps you to pinpoint a segment of users going to churn sooner or later.
3 Prescriptive Analytics: Helps you with actionable solutions or decisions in minimizing the churn rate.
With the prediction numbers in hand, marketing and sales managers can make calculated decisions to reduce churn and build brand loyalty.
Moreover, these numbers alone won’t solve your forthcoming problems. But they do show you signs of user dissatisfaction before they completely abandon your service.
One of the great examples is a subscription-based service. Let’s talk about the OTT platform.
With the number of OTT platforms rising, users have now more options to move. This paved the companies to optimize their services focused on key performance indicators such as Churn Rate, Lifetime Transaction Value, Customer Acquisition Cost etc.
But why are some companies on the top of the game, whereas the rest of them endlessly struggle to keep subscribers?
Because most of them follow old and rigorous ways of acquiring new subscribers only. Whereas, the top players focus majorly on churn reduction strategies. And regarding implementation and outcome, Netflix is a clear winner.
According to a recent report, Netflix’s churn rate is just 3%.
It’s low because they always have fingers on the pulse of their subscribers. With the help of predictive analysis tools, they figure out their pain points, reach out to them, and take some extra care. Their subscriber models for both satisfied and at-risk customers go together.
However, note that predicting the churn rate doesn’t provide you with a ready-made solution.
Each company has different business strategies, prediction factors, and workflows. Tools aren’t magic wands and work differently in each case. Marketers need to customize and tailor the models to the specifics of their customer goals.
Though there are some underlying principles for everyone. For example, to carry out effective analysis, marketers should probe the churn probability months before their renewal – say, 8 to 10 months for annual subscribers.
This helps them collect their sentiment analysis data such as behavior, usage patterns, watch times, and overall experience with the product.
The way Netflix creates user personas is far more precise than conventional marketing methods. With a good mix of personalized recommendations, feedback systems, and attractive offers, they’re able to get into the heads of users.
How to Predict the Churn Rate?
An effective churn rate prediction allows you to seal the leak in your consumer bucket. Not only does it help you track your consumer’s journey, but also to compare them with the retained consumers to figure out what went wrong.
This all begins with a huge data collection which is then used in automation systems and CRM tools. Although you work with robust software and data science, that doesn’t make for a smooth ride.
Moreover, there is no one way to investigate the churn rate forecast.
So let’s figure out how companies are using the best industry practices for churn prediction:
1. Problem definition:
Wear the customer’s shoes. That’s the very first step you need to take to define a problem. Ask the right questions. This helps the team to prepare a customized model for machine learning and figure out the anomalies.
For example, some common questions should sound like:
Will this customer segment renew the subscription or leave our service?
What are some uncommon signs of user behavior?
What is the probability estimate (in numbers) of the churn?
2. Data collection and survey
As I mentioned at the beginning, data is the new oil. For predicting the churn rate, you need lots and lots of historical data.
Generally, businesses rely on reliable sources of data: Google Analytics, AWStats, Salesforce, Microsoft Dynamics, comments from social media or forums, and on-demand surveys from companies.
3. Preparing data for processing
The data collected is in raw and unstructured form. It needs to be customized and documented according to the needs of the business. For example, historical data is generally saved to a CSV file before uploading to the modeling process.
Furthermore, data preparation needs to be classified into customer health and red flag indicators. The process is called cohort analysis that focuses on retention efforts but also figures out customer inactivity.
Customer health indicator:
Tracking the user data from the point of purchase to their level of engagement, usage time and satisfaction. The data helps to understand the health status of users, especially those who are likely to stay.
Red flags indicator:
It involves tracking the risks of customer friction, change in the business (merger, market change, etc.), lower adoption rate and overall engagement with the service.
4. Quick & dirty Probability calculator:
Steven H. Noble of Shopify defined the equation.
What’s using this equation?
For instance, you have a subscriber-based service. You’re facing churn issues but also receiving many new users in a good number. Seems like a race, but who’s going to win?
You’ll have less churn if the number of new users exceeds the churn rate. Does that bring you an advantage?
No!
Here’s is one single big problem. Where is the retention?
The day a customer decides not to leave the service, the better you see the chances of retention. This is also called User Day.
Example calculation to find monthly churn rate probability:
No. of churned customers in April = 500
Net no. of new users:
Customers at the month start = 700
Customers at the month-end = 800
Gain in new customers = 100
As a result, the monthly churn rate probability = 0.6%
5. Net Promoter Score (NPS) method:
NPS is a computational method for identifying the types of customers that could churn in a given timeframe. The score ranges from 0 to 10 and is classified into 3 parts:
Promoters (9-10): Happy customers
Passives (7-8): Neutral customers who may switch to better options
Detractors (0-6): Unhappy customers
NPS is simply the difference between % of Promoters and Detractors. For example, if the number of your Detractors is more than Promoters, this indicates churn. So to reduce the churn rate, companies must reduce the proportion of Detractors and Passives.
6. Predictive modeling:
Here, the data is ready to be trained on the software. They are fed into predictive models that are tuned and tested to deliver relevant results. The model is nothing but a well-designed algorithm such as a decision tree, regression, etc.
For instance, on a decision tree-based model, your analysis starts on the root node and ends on the leaf.
You have multiple nodes that associate with your questions which further spreads to the branches. These branches denote the number of possible solutions which then ends up on leaves where you finally get predicted values.
Eventually, the results help you in predicting the churn rate and determine whether your customers are satisfied or at the risk of churn.
Benefits of Predicting Churn Rate:
1. Know risks ahead of time:
Predictive risk intelligence and data visuals provide you with an awareness of the dangers lurking in the future. It helps to assess existing plans and procedures with the prediction report and gives you a chance to reconsider it.
2. Gain insights on at-risk customers:
It helps you uncover patterns by presenting current customer insights. By comparing the metrics, you can monitor the results and figure out users who aren’t generating any value or showing signs of dissatisfaction.
3. Pinpoint the target market:
With predictive segmentation, you can effectively target your potential customers. It makes use of machine learning technology to create a solid buyer persona. This helps you to connect with a relevant audience that can ease the conversion and save you tons of time and money.
4. Mitigate financial loss ahead of time
Improves visibility through predictive sales analytics that helps to curb financial loss because of factors such as market manipulation. The tool may also help to recover operational losses and minimize overhead costs.
5. Productive calculated decisions
Calculated decisions might not always yield the right solutions. But with the help of big data analytics and machine learning models, you can capture the variables from past events along with your calculations to predict a productive outcome.
6. Build brand loyalty and reputation
It helps to track brand strategy if commitment towards their audience is being delivered rightfully. Businesses also get the advantage of understanding key recommendations from their users. They can analyze whether the quality of the products is being improved as per feedbacks and reviews.
..And On-Demand Tools to Predict Churn Rate:
1. SAP analytics cloud (recommended tool for planning & collaboration)
SAP Analytics Cloud is one of the most widely used software for market analysis. It is a robust analytics platform packed with Business Intelligence, Predictive Analytics, and Planning.
The cloud-based system converts static spreadsheets into action-oriented information that lets marketers discover insights. The software was released in February 2015.
Deployment platforms:
- Cloud, SaaS, Web-Based
- Mobile – iPhone
- Mobile – iPad
Key Features:
1. Cloud system: It is powered by an in-memory database called SAP-HANA that boosts real-time decisions.
2. Augmented Analytics: A next-gen analysis feature run by machine learning algorithms and NLPs that guides marketers even without data science skills.
3. Business Intelligence: Like a human mind but way more agile, the system’s cognitive ability filters and categorizes data, and alerts about the errors.
4. Enterprise planning: Create and link strategies, operations, financial transactions, and versions within a single cloud interface for hassle-free and collaborative workflow.
5. Sophisticated UI: A rich user-interactive tool to plan, customize, and manage the contents, templates, data models, and widgets.
Pros:
- On-cloud analysis operations
- Scenario planning features
- Cool widgets and templates
- High security and access controls
- Timely updates
Cons:
- Fewer chart options while using analytics story
- Autosave feature
- Data retrieval delays from live sources
- Still in the development stage and not all features are full-fledged
Pricing:
- 90-day trial with limited features
- Business Intelligence plan (limited planning model): ~ $35 per month
- Complete plan (with all planning models): Ask for a quote.
2. KNIME (recommended tool for large enterprises)
It is the best data science software for large enterprises. KNIME offers two types of software: KNIME Analytics Platform and Commercial KNIME Server.
KNIME Analytics Platform is an open-source tool for data mining and predictive analysis. Whereas, KNIME Server is an enterprise edition tool that includes everything from Analytics Platform plus features for automation, collaboration, management, and deployment.
Both the tools can be hosted on AWS or Microsoft Azure. The software was released in July 2006.
Deployment platforms:
- Cloud, SaaS, Web-Based
- On-Premise – Windows
- On-Premise – Linux
Key features:
1. Scalability: Intelligent automatic data caching to handle large amounts of data.
2. User Interface: Intuitive UI feature for efficient workflow and data exploration.
3. Algorithm support: Supports a wide range of enterprise-grade data mining algorithms (decision tree, clustering, neural networks, etc.)
4. Data Visualization: Advanced mathematical and statistical features and customization options.
5. High-performance server: Reduces data loading time and aids seamless collaboration on the cloud.
Pros:
- Made to handle large amounts of data
- Robust set of algorithms, mathematical and statistical functions
- Supports many web reports
Cons:
- A crowded user interface might seem confusing
- Too expensive for SMEs
- Quite complex for individuals to learn
- Uses a large chunk of computer memory (may affect efficiency)
Pricing:
- KNIME Analytics Platform is free of cost
- KNIME Server has 3 levels of pricing (for 5 users & 8 cores):
Small: $14,500
Medium: $29,000
Large: $52,000
3. RapidMiner Studio (recommended tool for researchers & startups)
RapidMiner tool offers depth in machine learning and data modeling operations. Recognized by top market research firms (Gartner, Forrester), there is no second thought on why this tool is data scientists’ favorite.
The software has two versions: RapidMiner Go and RapidMiner Studio.
Incorporated by both large enterprises and startups, this tool is suitable for all experience levels. RapidMiner was founded in 2007.
Deployment platforms:
- Cloud, SaaS, Web-Based
- Desktop – Mac
- Desktop – Windows
- On-Premise – Windows
- On-Premise – Linux
Key features:
1. Rich algorithm: Library of 1500+ machine learning algorithms to support data science operations.
2. Can add new functionality: Using Python and R languages, researchers can add new functions by reusing existing code.
3. Education and training: One of the best tools for students or interns to understand the application of data science
4. Multi-industry use case: Applicable to almost every sector: SaaS business, automotive, e-commerce, etc.
5. Seamless data analysis: Strong machine learning mechanism prevents loading delays and delivers results in a short time.
Pros:
- Rich algorithm resource for data training
- Huge trust based around the world
- File handling and visualization capabilities
- For all experience levels
Cons:
- An issue with Python integration
- Not very frequent updates on learning materials
- Takes too many system resources if data is big
Pricing:
- For Individuals:
RapidMiner Studio is free
RapidMiner Go: $10/month
- For Enterprises: Request a quote
4. Alteryx (the most versatile analytics tool)
Alteryx offers Analytic Process Automation (APA), an advanced analytics software made to suit almost every industry needs. The software is known for robust automation, quick turnaround time, and fast ROI.
It can perform sentimental analysis on raw data and makes it easy for teams to share insights. Unlike many other analytics tools, Alteryx can handle unstructured data.
Alteryx’s analytics software was launched in 2006.
Deployment platforms:
- Cloud, SaaS, Web-Based
- Desktop – Windows
- On-Premise – Windows
- On-Premise – Linux
Key features:
1. Reusable models: Data engineers can reuse datasets to prepare different models.
2. Most versatile use case: Operated by almost every leading sector: Tech, Retail, Finance, Healthcare, Gas/oil, Government, etc.
3. Ease of collaboration: Users can access models created by others to prevent reinventing the wheel or repetition.
4. Excellent community: Easy to share the templates and ideas to ease workflow and save time.
5. Flexible deployment: Can use existing models created in R or Python without any need for major customizations.
Pros:
- Efficiently handles large data volumes
- Unmatched versatility and speed
- Extremely active and supportive community
- Excellent UI and drag-drop features
Cons:
- Premium-priced licenses
- Occasional issues with Python integration
- Issues while running the workflow while excel sheets are opened
Pricing:
- For individuals: $5,195 per year
- Add-on 3rd party data package (for individuals):
Location intelligence: $11,700 per year
Consumer intelligence: $33,800 per year
- For enterprises (Alteryx server): Request a quote
5. H2O.ai (best open source tool for individual/enterprise)
It’s an open-source and distributed in-memory ML platform that adheres to the mission of democratizing AI. It offers a scalable solution to data professionals with the help of widely used ML algorithms, statistical visualization, and deep learning features.
H2O Wave, a recently added product, is an open-source framework for Python development. The framework is used to develop real-time AI apps according to the needs of the business.
H2O.ai was founded in November 2011.
Deployment platform:
- Desktop – Windows
Key features:
1. Open source: The best open-source tool that enables effective collaboration and community support.
2. H2O Sparkling Water: Lets users manage heavy data processing with the help of Apache Spark integration.
3. H2O4GPU: Built to handle advanced ML models by harnessing GPU’s computing capabilities.
4. H2O Driverless AI: User-friendly GUI and applicable for data engineers of all experience levels to train, deploy models and enable AutoML functions.
5. Algorithm support: Widely used statistical & machine learning algorithms to leverage modeling performance.
Pros:
- A state-of-art open-source analytics tool
- Active and Supportive community
- Easy to use UI design
- Excellent AutoML functionality
Cons:
- Cryptic debugging messages
- Inaccessible on multiple platforms
- No support for data cleaning
- Documentation on Python integration
Pricing:
- 21-day free trial
- Full access: Request a quote
Conclusion:
The churn rate is a never-ending hassle. But when you have the right tools and the right analytical strategies, you’re able to reclaim your share against the odds.
Although there is a bunch of advanced automation and intelligent features, predicting the churn rate is never an easy task. You still need a human perspective to make conscious choices against the churn.
Of course, common sense does play a huge role in making the right predictions. The software you use doesn’t feel the customer’s pain points or have any negotiation ability. But you do.
I hope this extensive report has helped you understand the scope of the churn-rate prediction strategy. The minimum the churn rate is; the maximum is the ROI and ARR.
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