3 Ways AI Is Set to Revamp Subscription Management

If you’ve been reading tech industry news in recent months, you’ve likely seen stories about challenges in the SaaS niche. Unstable macroeconomic conditions and the VC slowdown have made funding and growth difficult. However, as a whole, SaaS companies have shifted priorities and continue to thrive, with AI playing a growing role in the way they handle revenue.

In today’s efficiency-first environment, retention is driving more growth than ever. ChartMogul’s 2023 SaaS Benchmarks Report shows that while the proportion of ARR gained from new business has fallen to 57.9%, expansion ARR’s contribution grew to 32.3% (up from 28%).

In short, growing SaaS companies are prioritizing subscription management to minimize churn and expand revenue. AI is set to become a key player in this area, given how accessible AI-powered analytics have become. Here’s how subscription management is set to change, as AI makes its presence felt in SaaS revenue teams.

Upsell and cross-sell management

SaaS revenue teams have always valued upselling and cross-selling. Increasing lifetime value from existing customers is far cheaper than investing in new lead generation. However, executing an upsell is challenging.

Get it wrong, and a SaaS customer success (CS) team will come across as pushy, perhaps leading to churn. A lack of quick insights into data can be a significant roadblock for CS teams. While almost every SaaS product team monitors user activity and creates detailed logs for CS to refer to, making sense of that data, and turning it into increased subscriptions, is tough.

For instance, a CS rep might notice that a customer repeatedly clicks on features restricted to higher pricing tiers, suggesting a need and therefore a potential up-sell opportunity. However, the rep has no way of knowing if this need is temporary or if it aligns with the customer’s latest financial priorities.

Given today’s challenging economic atmosphere, CS reps have a minefield to navigate. AI can reduce noise in such instances by assimilating vast sets of data and correlating different trends. For instance, AI might notice that customers who visit pricing pages frequently and explore restricted features might be more willing to upgrade their membership.

In the case of a freemium product, AI can quickly analyze data and spot behavioral patterns that indicate the likelihood of an upgrade. For instance, by correlating the number of hours a user spends on the product with the features they access, AI can present CS reps with a score that indicates the likelihood of an up-sell offer succeeding.

Given these benefits, CS teams can spend more time understanding customer needs and executing up-sell campaigns more efficiently.

Better CX

SaaS companies understand the importance of great CX. Zendesk’s 2023 CX survey revealed that 52% of customers will switch to a competitor after a single negative experience. While companies invest significant resources into improving UX for customers, they often inadvertently neglect the beginning of a buyer journey.

Quoting processes in enterprise SaaS sales are complex due to the complex matrices of features these products offer. Buyers usually need help figuring out what bundle works best for them and need guidance from sales. This situation puts immense pressure on sales reps, since they’re now expected to close a deal and offer smooth CX at once.

For example, a prospect might ask for several custom-priced quotes within a short timeframe. While CPQ software eases the way for sales teams to compile price points based on different functionalities and other parameters, access to AI-powered data insights can help reps put quotes together faster.

AI can also marry customer usage data to CPQ functionality to give CS teams an instant q**** process to assist with upsells. Even if an upsell opportunity doesn’t exist, AI can identify the signs of frustration in customer usage and prompt customer service to proactively reach out and prevent churn.

Instead of a lengthy data log, AI can summarize customer statuses quickly, giving support reps a full context of customer issues. The result is a well-informed customer service cycle that takes care of issues proactively, ensuring great CX.

Accurate revenue forecasts

SaaS companies are under pressure to deliver capital-efficient growth, given the current funding atmosphere. In such an environment, creating accurate revenue forecasts and growth models is critical. Currently, SaaS revenue teams pull data from different sources to create these projections.

However, tool limitations and a lack of granularity in data force them into lengthy analysis cycles that lack insight. For example, revenue teams pull customer usage data but lack insight into features used or correlations to the number of support queries raised. As a result, they estimate the likelihood of churn based on shaky variables.

AI can revamp this process by gathering and processing vast amounts of data. It can present revenue teams with ready-made analysis and answer questions about a dataset instantly. The result is an accurate ad-hoc analysis that charts a company’s path along realistic growth routes.

Combined with reduced churn as discussed previously, SaaS companies can ensure their customer base remains strong no matter how challenging new customer acquisition becomes.

AI is the key to subscription revenue operations

AI development holds plenty of potential for subscription management analytics, and SaaS companies will be among its biggest beneficiaries. This development comes at the right time too, given the macro challenges SaaS faces right now.