The Top 3 Post-Epidemic Trends Affecting The Subscription Industry

 The Top 3 Post-Epidemic Trends Affecting The Subscription Industry

Voyantis, a UA platform that assists growth teams in activating signals based on LTV data to optimise UA campaigns, is led by Ido Wiesenberg.

It would be an enormous understatement to say that the subscription sector has undergone several significant changes since the pandemic's onset. Numerous elements of the subscription economy have showed resiliency throughout it all.

That is not to argue that there were not challenges encountered by the subscription business sector. In a previous article, I discussed how businesses that performed remarkably well during the pandemic and had previously displayed double- or triple-digit growth started to slow down. Peloton is a good illustration of that.

But in a broader sense, the subscription business has been booming. Metrics are the key to everything. Subscription brands in the post-pandemic era became more data-driven than ever before amid all the changes in consumer behaviour, privacy regulations, and ad network adjustments.

First, relying on data

In my work circles, which are primarily made up of growth marketers and data scientists, there is a cliché that I frequently hear: Data is the new oil. And it's not useless. Data plays a key role in both customer value creation and subscription firms' own growth.

This insight has led to a plethora of fresh partnerships with providers of digital infrastructure to support expansion initiatives for long-term profitability. This also explains why subscription brands started to attract investors' attention. According to a Robeco analysis, due to the nature of their business model, subscription businesses typically offer higher long-term income predictability. "These organisations tend to generate a steady stream of data that can be used to improve service, increase customer satisfaction, and find supplementary business prospects," the research continues. Naturally, this usually results in growth that is comparatively steady over time.

What performance measures have subscription brands been examining, then? There are several. Data on various cohorts and average order value are two of the most prominent ones. Analyzing lifetime value (LTV) data has allowed more savvy brands with access to more big data to advance their strategies. LTV data, as its name suggests, relates to the average revenue generated by each individual customer over the course of their relationship with the company, from acquisition to attrition.

2. Purchasing the most lucrative subscribers

By improving upon their KPIs and investing on the most lucrative customers, subscription businesses were able to preserve their exceptional LTV of 2020. This made it possible for a sizable proportion of customers who made their first subscription purchases in 2020 to demonstrate long-lasting behaviour changes in 2021.

Brands in the beauty and personal care vertical "saw a whopping 120 percent rise in subscribers and a 30 percent gain in LTV in 2020," according to a Recharge research. The fact that these companies have dominated the charts for so long demonstrates their endurance with customers over time.

Building on their KPIs and investing in the most lucrative subscribers allowed subscription businesses to sustain their record-breaking LTV of 2020. This made sure that many customers who bought subscriptions for the first time in 2020 had long-lasting behaviour changes in 2021.

A Recharge research states that in 2020, brands in the beauty and personal care sector "saw a whopping 120 percent rise in subscribers and a 30 percent boost in LTV." These companies' sustained success in the marketplace demonstrates their endurance with customers.

What specifically did the most profitable subscription brands do with their LTV data? They projected using that statistics. The practise of futurespecting is helping subscription brands remain in front of the curve in user growth and marketing.

3. Scalability Futurespecting To Lock In

Futurespection is a result of my first statement, which is that it is data-driven. It is imperative that all significant decisions be based on careful data analysis. But in theory, that would be acting in the past. Making decisions based on what is extremely likely to occur in the future is now possible thanks to AI technology.

It is comparable to approaching corporate growth from the perspective of a master chess player. Since I started a business that specialises in growth marketing, I should disclose that my team and I truly believe that futurespecting should be adopted by online firms, especially subscription brands, since it will likely become the norm over the next five years.

Futurespecting is the use of data to forecast future developments, including the results of campaigns. Naturally, not all businesses have the time or resources of larger businesses to develop their own systems to help them make the most of LTV data for futureshopping. Fortunately, there are several options available that fall under the category of predictive marketing and employ AI and ML to support the growth teams of subscription brands and other businesses that rely on recurring revenue.

Companies of all sizes talked a lot about doing pre-mortem meetings or analyses in the early 2000s. In these meetings, teams would pretend that a project or organisation had failed and work backward to identify the possible causes of the collapse of the project or organisation. Contrarily, when subscription brands futurespect with LTV data, it's more about picturing the best achievable result supported by data than it is about figuring out precisely how to get there in the most effective manner.

Final Reflections

The time is now for growth leaders in the subscription sector to effectively utilise the tools and technology at their disposal to meet their specific demands and ensure that they will be able to maintain their development and momentum for years to come. It will be a huge comfort for internal marketing and data science teams alike that moving to the next stage of development and scalability will be seamless for the teams that are most data-driven by nature.

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