The subscription economy spans nearly every industry you can think of in both B2C and B2B settings. Consumers and businesses are paying monthly subscription fees for products and services. Access to cloud-based software and delivery of subscription boxes are two standard business models. But no matter what the subscription product is, one critical consideration is accurate data in subscription services.
What Are Subscription Services?
A subscription is when a customer—consumer or business—pays a periodic membership fee to access services or delivered goods. This fee can be weekly, monthly, or yearly. However, research has indicated that longer biller periods result in long-term engagement.
Weekly subscriptions have an average retention rate of just 3% after one year, which is very low. Monthly subscriptions have a retention rate of 11%, which is not bad. But annual subscriptions have an average retention rate of 28%, which is more than twice (what seems to be) the more normative billing model (monthly).
The size of a business also impacts retention rates. Businesses with more than 50,000 customers see a slight decrease in retention year over year, while businesses with less than 50,000 saw a slight increase. These numbers could indicate several things—for instance, that lasting customer loyalty is easier to build for small and midsize businesses.
What Industries See The Most Success in Subscriptions?
Some subscriptions are seasonally successful. Some research has shown beauty and personal care merchants having the lowest long-term churn rates, while pet care merchants see the most substantial year-over-year decrease, probably because guinea pigs don’t live forever.
Fashion and apparel merchants see wide fluctuations in their churn rate, ranging from 6.5% in November to 9% in January. Home goods merchants had the most success in the study, with 51% of subscribers staying on board after 12 months.
Retention also varies by company. Amazon Prime has a reported retention rate of 90%. Fitbit has a retention rate of 82%. Netflix has a retention rate of 72% over six months. But over the same period, Hulu will retain 67% of its subscribers, while Disney will retain 78%.
As Netflix, Hulu, and Disney are all in the same industry (streaming services), this tells you that the success of a subscription model is about more than the industry… it’s about the company itself.
Staples, Discretionaries, and Data in Subscription Services
Of course, some things are just necessities, while others are not. Home goods can include intriguing, hand-picked, decorative accent pieces. But it can also include consumer staples like cleaning supplies, diapers, and toilet paper.
People will always need to eat but may not always need gourmet food. And some people will find a way to keep that Netflix subscription even if they have to sacrifice something else.
As you can see, it’s an extremely nuanced picture. Even with all the stats and numbers, it’s still challenging to get a clear picture of subscriptions and subscribers in the United States, let alone the global market.
With data in subscription services, decision-making is complete guesswork. And guesswork does not always go according to plan if Columbus taught us anything. Sure he discovered the Bahamas. But he was looking for India, wasn’t he?
In any case, data is crucial for assessing things like customer experience, product value, and marketing impact. The more data you can collect and the better the data quality, the better your business intelligence. And as we will see, the best subscription-based businesses collect lots of data.
How To Sell A Subscription Service
Getting customers and keeping customers is the story of every business. Subscription businesses are no different. They have an arguable advantage over non-subscription business models because their captive base generates consistent cash flow. Generally speaking, acquiring new customers is more expensive than keeping existing ones.
Many estimates have suggested that keeping a customer is five times more cost-effective than acquiring a new one. Even improving customer retention by just 5% can yield up to 95% profit increases, which is incredible. And the success rate of selling to an extant customer is between 60% and 70%, while the success rate of selling to new customers rarely exceeds 20%.
So that said, there are two angles to selling a subscription service: selling to new customers and selling to existing customers. Some subscription industries have a truly captive audience.
Insurance is one example. Life insurance customers are locked into 10, 15, 20, 30-year, or even lifetime contracts. While it’s true that they can look for another insurer, there’s a risk that they might not qualify. Then there’s car insurance, which is legally required in most states. And health insurance. And homeowners insurance.
But even these models are only partially guaranteed for the business. The subscriber can look for better coverage elsewhere if they choose. Our point is that even the most seemingly stable subscription services (in this case, viewing insurance as a “subscription”) have the potential for churn—all the more so for retail and discretionary subscriptions in general.
The Cost of Acquiring New Customers
This means that selling a subscription service is more than attracting new customers. It’s also about keeping your existing ones. But before going into that, let’s look at some typical customer acquisition costs by industry.
According to one study, travel had a CAC of just $7 (pictures of beaches go a long way). Retail, in general, had a CAC of $10. Manufacturing was $83, marketing $141, financial services $175, real estate $213, and software $395.
Of course, this is just one study; other studies have yielded different results. But overall, B2C CAC is much lower than B2B CAC, which makes sense. Businesses require more expensive products and solutions, and CAC is often proportionate to cost in some measure.
In any case, applying the 5x figure to these CACs, retaining a financial services customer should cost only $28, while maintaining a software customer should only cost $80 (in a given time period, such as annually). But what exactly goes into these numbers?
Data points in subscription services provide the answer to that question. Data points are pieces of information that are collected from observing customer behaviors. And for subscription businesses, data points are readily available because the future customers are already engaged as present customers.
What is Big Data in Subscription Services?
Data is information. A text message, an email, or a blog post… are all digital data forms. As you can imagine, the vast majority of the world’s data comes from the last few decades, with the explosion of internet usage.
But data is not just comprised of static artifacts. Data can also include behavior…such as things that customers do.
Purchases, abandoned carts, and navigation through a website are all data points that can be analyzed. These moments become talking points companies can use to make more effective decisions. Despite economic uncertainty, nearly 88% of companies increased their investment in data analytics, and 90% will continue to invest through 2023.
Some companies are large enough to have internal departments for analyzing data. Others need to become subscribers to a data subscription service. Data as a service business model is provided by the likes of IBM, Oracle, HP, SAP, and hundreds of other SaaS companies. Big Data analytics will have a global valuation of $745 billion by 2030, growing at 13.5% per year.
Big data in subscription services refers to the generated data with greater variety, volume, and velocity—or what Oracle refers to as the 3 Vs. In layman’s terms, big data is lots and lots of information. It’s a treasure trove of research-backed details on how a business can earn more revenue, operate more efficiently, and make a more significant impact.
How Amazon Uses Big Data
Amazon is not unique among consumer subscription companies. And in fact, it’s not entirely a subscription business. But certain aspects of Amazon’s business model, such as Amazon Prime, certainly are.
Prime gives customers certain advantages, such as free, rapid shipping and access to content. It encourages consumers to use Amazon as their primary retailer. And that, in turn, gives Amazon lots of stuff to consider.
Amazon is a global leader in using CFE or collaborative filtering engine searches. It will analyze purchasing patterns of previous customers, what other items are in their shopping carts, items are in their wishlists, products they’ve viewed, and products they’ve reviewed. It will combine this microcosmic analysis with macrocosmic analysis of more significant search trends to recommend products.
As a result, customers shopping on Amazon will see suggested product bundles—buying a citron candle for your porch? How about also purchasing a pop-up mosquito net and all-natural mosquito spray?
Amazon knows you might be interested because you’ve previously shopped for organic, homemade household cleaning products (hence the all-natural spray). And you’ve also shopped for a folding chair, which suggests you may like to sit outside. Besides, most consumers who purchased a citron candle also purchased bug spray one week later… because you can’t carry a citron candle around.
Who Analyzes All This Data?
Our example is fictitious, but you see how nuanced it can get. And it really can get this nuanced. That’s because Amazon has access to all the data you and millions of other shoppers have provided.
Of course, this data is only helpful if there is some way of analyzing it. And the scope of the data means that it’s too fast and furious to be assessed by humans.
That’s where the robots come in. Artificial intelligence is increasingly used to move through data points and identify patterns. Sometimes this can happen within a matter of seconds.
For instance, Walmart has created a command center called Data Cafe to analyze customer data in real-time. Much of which machine learning and AI facilitates.
Of course, the 99% of small businesses that make up America’s business landscape are not big enough to have their own data analysis department. These companies need to outsource their data analysis to a third party, like a startup SaaS company or one of the more prominent vendors like IBM, SAP, HP, and Oracle.
What Do Companies Do With Big Data in Subscription Services?
Let’s return to Amazon as an example and see what a subscription service can do with data from its subscribers—in this case, the 200 million shoppers with Prime. One thing they do is optimize their supply chain.
Amazon is invested in delivering the best customer service possible. Part of that has become their (industry-disrupting) practice of delivering orders within 24 hours. And that requires a supply chain that is ready for action.
Amazon has almost 1,500 fulfillment centers around the United States. To put that in perspective, there are around 2,000 Target stores and 2,500 Home Depot store locations. There are 4,600 Walmart stores.
These companies use big data in subscription services to fine-tune their supply chain, among other things. But we’ll focus on Amazon as an example.
Amazon uses its massive amount of customer data to optimize its supply chain. If many customers order a specific item in one area, they will be moved to that fulfillment center as their home base (as a simplified example).
When it’s time to deliver orders, Amazon plans routes that will minimize time, shipping expenses, and the amount of quacking noises from Prime trucks in reverse.
Price Optimization and Fraud Prevention
Amazon uses data for price optimization. There must be more than the old trick of knocking a price one penny below the dollar amount you want to charge. Amazon takes your activity, competitor prices, availability, and other ingredients and throws them all into the blender. The resulting smoothie is a price that changes every 10 minutes as Amazon recalibrates its discounts.
This results in mark downs on its hottest products and produces higher profits on items that are not as in demand. It’s a seemingly counter-intuitive strategy, assisted by big data, that has boosted Amazon’s revenue by as much as 143%.
Amazon also uses big data to thwart fraudulent purchases. This is something that banks and credit card companies also do. Consumer purchases are entered into the analytic pool to create a comprehensive portrait of each account holder and their normative purchases.
If something is purchased outside that pattern or their typical locations, Amazon may require additional verification, or they may block those outright.
In summary, Amazon uses big data to do more, sell more, and make more. While it’s true that they could glean this data from every shopper, Prime subscribers are engaging the most with Amazon and furnishing them with the most data points on a regular basis. So what does all this mean for your subscription business?
Getting To Know You…
Customer data in subscription services businesses is the cornerstone. At a basic level, it’s necessary. You need to retain customer data like payment information and contact information like email, phone number, and maybe even address.
Of course, retaining this data requires PCI compliance (Payment Card Industry Data Security Standards). The treatment of consumer data, in general, will increasingly be in the spotlight of debate.
It has already emerged that Amazon is using Alexa devices to listen to your conversations. And that Facebook may have sold your data to other companies without your permission. Se la vie!
But returning to data drawn from consumer account information, this data has limitations. It may tell you where the majority demographic of your subscribers are so that you can focus your marketing efforts on the groups you’ll see the most success with.
But you must observe subscribers in their natural habitat to get more out of customer data. You need to see how they engage with your product or service. For a business the size of Amazon or Walmart, this is no problem.
Amazon, in particular, leads the way in using consumer data to fine-tune its sale approach, turning the Bezos-founded empire into a juggernaut dynasty that will last for a thousand years (perhaps hyperbolic, but it sounds epic).
But for most SMBs, Amazon-level analytics is not possible. Thankfully, as mentioned, there are third parties that provide analytic software. This software as a service SaaS companies are themselves subscription-based models.
They sell access to cloud-based suites that can crunch the numbers on your orders. SaaS companies can also map out customer journeys and monitor customer behavior as they interact with content.
Data at the Point of Sale
For most businesses, the point of sale will be the primary gateway for accessing data points. You cannot always get into a customer’s mind as they decide to buy a subscription. But you can analyze their concrete actions, which include purchases.
A payment gateway can furnish data points about your subscribers. It will provide you with statistics like monthly recurring revenue (MRR), annual recurring revenue (ARR), churn rate, renewal rate, average revenue per account (ARPA), and customer lifetime value (CLV). You can then make decisions in response to these data points.
Low ARPA (average revenue per account)? Maybe it’s time to build some upselling into the strategy. Some subscription services do this by offering clients a premium subscription. These premium subscriptions can augment the product or service with additional products or services—like one-on-one coaching or additional goods.
Low CLV (customer lifetime value)? This might be a sign of several things. Bad customer experience. Poor customer experience. A playing field of competitors. Or a product or service that has an inherent shelf life.
Adding novelty and value to the subscription can go a long way here. This is why streaming services like Prime, Hulu, Netflix, and Disney always offer various new shows. And you can bet your bottom dollar that the content of these shows is based on big data, such as how many subscribers are watching what.
Customer Data and Reducing Churn Rate
The average subscription service has a churn rate of around 5%. And once again, customer data in subscription services can help keep that churn rate down. One of the biggest reasons subscribers leave a business is because they are frustrated by customer service.
Data points can help you assess how and when customers engage with support services. You can see what some of the common complaints are. You can see how efficiently these complaints are addressed. And you can test out different offers or incentives to keep subscribers engaged.
These data points can also help develop proactive strategies to preempt churn. If you are getting many complaints about specific features or shortcomings, you can address these by fine-tuning the product or service. Responsiveness is key to building good customer rapport.
SaaS company HubSpot found that in most industries, around five companies lead the way with a 94% retention rate. And almost 90% of companies admitted that customer service was crucial for retention. These companies are using data points to analyze their support-related interactions with subscribers. They see what works and what doesn’t work, doing more of the former and less of the latter.
Data in Subscription Services: A Wrap-Up
Customer data is crucial in the subscription business because it can increase retention and reduce churn. And subscribers already contributing to the cash flow are 5x less expensive than acquiring new customers.
However, consumers will leave a company if the customer care is unsatisfactory. They will leave if they get bored. And they’ll leave if they can find a better price somewhere else.
Addressing all these problems requires a significantly sized social experiment. And this experiment is comprised of the voluminous data points that subscribers provide. For most businesses, integrating a payment gateway to analytic software is how data points can be harnessed.
To learn more about the intersection between data and payment processing in the subscription business, contact ECS Payments today.
