There’s no crystal ball for customer churn prediction that can show you exactly when your highest-value buyers will exit their respective customer journeys and fail to purchase again.
But you can improve your churn rate (and, subsequently, your retention rate) when you constantly and proactively analyze why former repeat and one-time buyers never return.
By closely examining the behaviors, habits, and attributes of specific individuals and segments in your database, you and your marketing team can learn what exactly drove them away and what could drive other high-priority customers like them away in the future.
Given one-third of subscription-based brands said they’ve lost revenue or experienced reduced profitability due to high customer churn rates, it’s vital to scrutinize your customer data regularly and take the necessary actions to keep buyers coming back for more.
Customer churn prediction and its impact on your brand’s retention efforts
Before you begin your data audit, it’s important to understand how proper customer churn prediction protocols impact short-term marketing goals and long-term brand profitability.
We’ll assume we don’t need to answer the obvious question: “What is customer churn?” However, it’s certainly worth outlining why it’s one of the most important marketing metrics:
- Businesses lose roughly $1.6 trillion annually from customers who churn due to poor customer experiences in the past. — Accenture
- The cost of acquiring new customers as profitable as existing ones can be up to 16 times higher retaining current customers. — MIT
- 59% of consumers will stop buying from brands after several bad customer experiences, while 17% will do so after just one. — PricewaterhouseCoopers
The evidence continues to mount in favor of developing customer churn prediction models you can regularly refine and prioritizing customer retention strategies.
And yet, many executives at B2B and B2C businesses still don’t understand the negative ramifications — both fiscal and reputational — of ignoring these approaches:
- Just 15% of senior leaders at B2B organizations said their teams use customer data to routinely inform business decisions. — Forrester
- Only 30% of executives and board of directors members consider customer satisfaction a top priority for their brands today. — Deloitte
- Improving customer loyalty and retention is primary objective for just 38% of retailers and ecommerce companies. — BRP Consulting
TL;DR: Those who fail to forecast when certain customers will churn or focus on bolstering their retention rates will only continue to see their audiences flock to competitors.
“So, how can I address my company’s churn rate and keep our customers coming back?”
That’s definitely the right Q to ask. The answer is threefold: Understand customer intent, track customer experience, and identify customer trends through routine churn analysis.
Analyzing past customer churn data to help drive down your attrition rate
As you likely suspected already, churn prevention and customer retention go hand in hand:
- Churn prevention focuses on the past: unearthing insights from your databases to identify the potential and likely reasons behind previous buyer abandonment.
- Customer retention focuses on the future: using those findings to inform new and/or revised marketing messaging to retain existing customers for the long run.
What’s essential to remember is the best customer retention strategies are only possible when marketing and data science work together to reduce customer churn across the board.
You can certainly use a customer data platform (CDP) like BlueConic without the aid of a data analyst to leverage easy-to-deploy customer churn prediction models.
If you don’t have a CDP (or simply need assistance with getting going with machine learning models), a data analyst can help you dissect your quantitative customer data from your various sources to see what commonalities exist among premier buying segments, like:
- The number of customers to buy your highest-priced products or services
- The volume of clients to sign a long-term contract within a given time period
- The propensity of subscribers to sign up for paid plans after their free trials
Once these figures have been compiled, sorted, and sent your way (or unified in your CDP after simply integrating with the data sources in question), you can supporting your data-based findings further by conducting qualitative research about customers to glean more purchase and churn insights, like:
- Why they repeatedly bought specific products or services but eventually stopped
- What reasons ultimately led them to end contracts and sign with competing brands
- How they perceive the quality of your brand, products, services, and pricing model
Whether or not you decide to go the extra mile with this kind of qualitative customer data research (which can be accomplished through tactics like NPS surveys you can promote on-site and via email), the next step is the all-important one: developing hypotheses and an action plan based on these audience insights.
How B2B and B2C marketers approach customer churn prediction today
At the end of the day, B2B and B2C marketers want to accomplish similar goals through similar means, like nurturing top prospects and acquiring high-value customers and clients.
Having said that, it’s clear B2B marketing differs greatly from B2C marketing in many respects as well — including and especially regarding their customer retention strategies.
B2B customer churn prediction
Whether you work for a SaaS startup or an established B2B ecommerce enterprise, your company’s customer acquisition costs are likely substantial. That means it’s critical to keep those high-cost customers long term through client churn prevention strategies like these:
- Ensure smooth onboarding and follow up often: Let’s say you offer supply chain software for manufacturers to help them better track all aspects of their business: from marketing and sales to packaging and fulfillment. This can be a complicated solution for any manufacturing executive to not only learn, but master over time. Per Vendasta, B2B clients who engage weekly with B2B brands from whom they buy have a 26% greater retention rate than those who don’t. Develop a successful onboarding program that carefully guides clients through the process and follow up with them over time to ensure they’re engaged, happy, and making the most from the software.
- Build a customer consent management strategy: Consent management strategies help companies show they care about their customers and their privacy by allowing them to opt out of marketing messaging and gain control over their data. The more a customer trusts you, the more loyal they’re likely to be. Therefore, crafting a consent management strategy — one that takes advantage of martech with consent functionality to help comply with GDPR and CCPA, like BlueConic — is how you can win over one-time and recurring customers as well as appeal to prospects who’ve yet to buy from you but express interest in your brand. In the age of data privacy, transparency wins.
- Offer custom discounts to ensure contract renewal: A couple common trends B2B marketers discover during customer churn prediction analysis is customers who cease communications with them altogether or stop using the product fail to renew subscriptions or re-sign contracts. (Their silence and lack of engagement speak volumes.) That’s not to say all these buyers will churn. It could simply mean they need a little nudge to re-up their deals. If you notice one or both of these trends, schedule additional check-ins or offer discounts to the customers in question to see if that’s enough to get them (and their business decision-makers) over the proverbial hump.
Given the considerable cost of obtaining new customers and the relatively inexpensive cost of keeping existing ones, approaches like these are worth it for your marketing team.
B2C customer churn prediction
On the flip side of the customer churn coin is the B2C marketing approach. These methods are akin to those for B2B brands above, but with a more individualized focus:
- Research your competitors’ digital presence: Price isn’t the only factor in customers’ purchasing decisions. Personalized deals, social proof (testimonials, reviews, etc.), and digital experience all affect whether consumers buy from your brand. Thus, it’s wise to invest some resources to investigate just how your competition is perceived online, what their website and other channels look like and entail, and the overall user experience (UX) customers have with those digital properties. Sure, you’ll want to price your products and services accordingly to properly compete with brands in your space, but don’t underestimate the quality of a good CX.
- Individualize offers to potential churn risks: Another proactive approach comes from BlueConic customer Brief Media. The brand sent personalized messages to existing subscribers it identified as potential churn risks in BlueConic to update their subscriptions instantly within the product. By messaging these subscribers right before they could churn on the website or via its mobile app, Brief Media was able to retain countless customers and, in turn, keep recurring subscription revenue coming in the digital door — a big marketing win for the brand.
- Modify existing offerings and add new ones: First-party data access allows you to determine which products and services sell most often, through which channels and mediums customers purchase them, and other details about customers’ buying journeys. With this info, you can see which offerings should remain (top sellers and those trending “up”), which should be fixed (previously popular products that are stagnant or declining sales-wise), and which should be eliminated (not selling at all) — modifications that can decrease churn and increase retention.
There are certainly other customer churn-reducing measures: donating to charities your buyers and subscribers care about, producing products in more eco-friendly manners, conducting feedback surveys to better learn about customers’ wants and needs — the list goes on. Start with churn-rate-lowering tactics first.
Calculating customer lifetime value (CLV) with the right marketing technology
Consistent customer churn prediction and analysis allows you to gradually see what helps you maintain customers (and drive others away) over time.
But to really see the impact of your efforts, you need the calculate the customer lifetime value of your top customers to see if they’re helping your business stay in the black.
The most common CLV calculation (of which there are several variations from one brand to another) is the average purchase value times average purchase frequency.
While this calculation will vary slightly from one company to the next, the formula always factors in expenses, profit margins, and retention rates for different segments.
What smart marketers do to get more granular with their customer lifetime value calculations is build and deploy intricate CLV models using machine learning.
Just as BlueConic customers can build churn prediction models, they can also calculate CLV for different segments with our ready-out-of-the-box models in AI Workbench.
In conjunction with engagement listeners in our platform, BlueConic customers can more closely analyze those with “low” engagement scores to prevent them from churning.
As with the churn models, data scientists can aid with model development and training using Jupyter notebooks. But since our CDP is marketing-owned and -operated, it’s simple for even non-technical marketers to get up and running with customer lifetime value modeling using our solution.
Whatever martech you use to calculate customer value and predict churn, discovering this data is crucial to ensuring your activities prevent lost profits and boost your brand’s fiscal outlook for the foreseeable future.