7 Steps Subscription Brands Can Take to Reduce Churn
The subscription e-commerce market continues to see rapid growth, with McKinsey’s latest figures suggesting it now totals between US$12 billion and US$15 billion in the US alone.
Alongside the big global players, such as Hello Fresh, the Dollar Shave Club, and Naked Wines, are an increasing number of smaller, more localized brands such as LookFantastic, Pint Society and Japanese furniture and interior goods provider Muji.
Despite the growing popularity of brands such as these, subscription companies typically face high churn as they struggle to keep customers over time. So, what can subscription brands do to prevent and reduce churn?
What Is Churn?
Churn is the percentage of customers who stop using your products or services within a specific time frame. According to Recurly, a global subscription billing management company, the overall churn rate for subscription brands is 5.6 percent with subscription box and consumer goods running slightly higher.
Customers churn for several reasons. A big one is to reduce expenses – an issue that has become more prominent since COVID-19 as people have become more conservative with their spending. Other reasons include less frequent use of a product or service, regular travel, and the seasonal nature of some subscriptions.
According to McKinsey, 40 percent of people who subscribe to a product or service go on to cancel it. In addition, more than a third cancel in three months or fewer. As churn can greatly affect profitability, minimizing it is key to long-term success and growth.
How to Minimize Subscription Churn
To reduce churn, subscription brands need to continually connect and demonstrate value to keep customers paying up each month, quarter, or year – something achievable with the right tactics and the use of massive data and AI-driven technologies.
1. Predict potential churners to rebuild a connection
Any customer who has signed up for subscriptions can potentially cancel at any time, but some customers are more likely to cancel than others. If you can figure out who these customers are, you can work harder to make them stay.
Leverage predictive machine learning (ML) models to analyze usage and contextual data from your existing customers. Usage data shows how much a customer used your product or service before they left, while contextual data adds more context to the former. ML can strategically predict which customers are high-risk based on their actions. For example, people who recently downgraded their subscription or who have been inactive. You can then engage them with regular, relevant communications to reignite that connection.
2. Ask customers why they want to cancel
Understanding why people want to cancel a subscription is crucial. It helps you create a strategy to avoid similar future losses. For example, if people are leaving because your service is too expensive or they aren’t using your products, you could look to adjust your prices or rethink your offering.
You can collect this data via a simple online survey, by email, or over the phone at the point of cancellation. Make it easy by giving a few multiple-choice options, plus give them the opportunity for open feedback. At the same time, tackle those pain points by suggesting they shift to a lower tier or pause their subscription temporarily.
3. Develop real-time customer profiles to stay relevant
One reason customers cancel subscriptions is that they no longer have the same level of need or interest in that particular product or service. This can be avoided by adopting a data-driven approach.
By leveraging legally-compliant third-party data on customer behavior and interests outside your owned channels, you can use AI tools to develop more accurate customer profiles in real time. This will provide you with a better understanding of your customers’ changing needs and preferences, so you can take actions to ensure your products or services remain useful and relevant.
4. Bring back lost customers based on lifetime value
Even if customers cancel their subscription, it is never too late to bring them back. By targeting the customers most likely to re-subscribe with engaging communications and relevant offers, you can see better success and increase your ROI.
To identify these high-value segments, you can use deep learning tools to define specific segments based on the audience’s massive behaviors and interests. You can then use predictive deep learning models to combine the best segments based on predefined rules and rank the segment combinations based on their value or likelihood of re-subscribe.
5. Re-engage through the right channel at the right time
Preventing churn is all about keeping your subscribers sticky – and to ensure that, you want to stay visible, connected, and continually deliver great experiences.
Using AI-based marketing automation, you can better understand your subscribers’ preferred channels of communication (email, app push, in-app messages, SMS, messengers, etc.), their top times of day for engagement, and what types of creative formats they respond best to. You can then send personalized, interest-based offers to re-engage.
6. Explore partnerships to provide added value
Partnerships are the new black in retail, and subscription retail is no exception. Teaming up with another brand can not only help your bottom line, but it also allows you to offer subscribers something new and desirable – increasing re-engagement and reducing churn.
For example, an ice cream subscription box (e.g., Pint Society) could partner with a movie streaming service, or a clothing subscription brand (e.g., Her Velvet Vase) could team up with a beauty brand. Instead of simply guessing which partnerships might be valuable for your subscription brand, leverage AI-powered customer insights to explore ones that could provide added value to your subscribers.
7. Reward subscriber customers for their loyalty
The more loyal the customer, the more they value what you offer, and the less likely they will leave you. Because of this, incentivizing and rewarding your best customers, as well as your new customers, is crucial.
To boost loyalty to its brand, food subscription box retailer Hello Fresh offers customers discounts and a free box for referrals. Birchbox does the same by offering members 50 points for each referral. You could also incentivize other activities, such as engaging on social or watching videos.
While a certain amount of subscription churn is always going to happen, you can keep it down and profits up by using data and the right tools to better understand what your subscribers want and why they come and go, and work hard to stay connected and deliver value.
* Learn more about how machine learning and marketing automation can help you reduce subscription churn. Contact our team today to arrange an exclusive consultation.
WE ARE HERE TO HELP
YOU MIGHT ALSO LIKE
Seventy-five percent of marketers currently use at least one type of marketing automation tool, and spending on marketing automation is expected to hit US$25.1 billion by 2023. However, despite its widespread use, this type of software is not being used to its full potential. Lack of a clear strategy, using too many or too few tools, and not testing and tracking your efforts are some of the obvious pitfalls. On top of these, here are some less apparent marketing automation mistakes you might be making – and how to fix them. Mistake #1: Not Using Your Valuable Data Data is vital in any marketing automation strategy. However, you can’t use it to drive your efforts if you aren’t collecting and using all your valuable data. The Fix for You What is your valuable data? It includes all demographic, behavioral, transactional, and qualitative data from your e-commerce sites, web forms, official websites, apps, CRM, social media channels, customer services and more. Marketing automation platforms can collate this data in real time for a complete customer view. Such insight allows you to segment your audience in order to create more relevant, personalized content. Ninety-one percent of customers say they are more
Harvard is where our startup journey started. From idea to the real business, we have experienced 8 pivots in the first few years…see how Appier failed fast but pivoted faster in this infographic! Harvard is where our startup journey started. From idea to business, Appier experienced 8 pivots before finding success. The lesson we learnt was to fail fast but pivot faster! Follow that journey in this infographic. From idea to business About Appier Appier is a technology company which aims to provide artificial intelligence (AI) platforms to help enterprises solve their most challenging business problems. Appier was established in 2012 by a passionate team of computer scientists and engineers with expertise in AI, data analysis and distributed systems. Appier serves around 1,000 global brands and agencies from offices in 14 markets across Asia, including Taipei, Singapore, Kuala Lumpur, Tokyo, Osaka, Sydney, Ho Chi Minh City, Manila, Hong Kong, Mumbai, New Delhi, Jakarta, Seoul, and Bangkok. For more information please visit www.appier.com.
Author | Charles Ng, Vice President of Enterprise AI, Appier As brands evolve to become more sophisticated with their data, there is increasing pressure to add data scientists to their teams. But not every marketer may need to rush to hire. We are living in the age of data, and all savvy marketers know that leveraging data effectively is key to better understanding current customers and attracting new ones, driving towards long-term relationships and ultimately staying competitive. The amount of data available to marketers is vast and will only increase. At some point or another, marketing leaders are likely to have thought about whether they need a data scientist on the team to help them unify, manage and analyse data. Making this decision isn’t- or shouldn’t be- straightforward. There are several considerations for marketers before they seek out data science talent- something that, to begin with, is in short supply in many markets around the world. The decision to hire a data scientist is likely the result of an evolution. The first step might be to hire analysts, whose job is to make sense of the data, identify patterns and generate basic insights. Next, you may look at hiring analysts