4 Subscription Metrics to Track for AI Analysis

4 Subscription Metrics to Track for AI Analysis

A variety of artificial intelligence (AI) techniques are driving bottom line growth of subscription-based businesses. Recommendation systems are improving cross-selling and up-selling, clustering techniques are identifying customer segments for targeted marketing, and classifiers are identifying customers at risk of churning.

In fact, AI is helping to generate a treasure trove of customer information collected by subscription based businesses. This data is a key for applying AI techniques to drive revenue.

To maximize the benefit of these AI techniques, you should be tracking four metrics: trials and trial conversion rate, site metrics and conversion rate, average revenue per user, and monthly recurring revenue.

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Metric 1: Tracking Trials and Conversions

Offering a trial use of a service is a standard way to build your customer base. AI can help identify characteristics of customers who convert and those that don't. From there, you can use that information to decide how to engage with likely converters and increase the rate of conversion — both really good outcomes.

Of course, you will need to monitor how well different engagement techniques are working , and for that you will want to start with some trials that enable you to compare conversion metrics.

The good news is this step is usually free and easy. Although not exactly a free lunch. Trials may be opt-in free trials or opt-out free trials. You can usually start an opt-in trial without a credit card, with the expectation that you'll provide billing information when you finish the trial and, if the vendor is lucky, sign-up for subscription. The opt-out option usually requires a credit card to start the trial. If your company uses both approaches, then be sure to track conversion rates for each.

Metric 2: Site Metrics and Conversion Rates

Site metrics and conversion rates provide insights into how many people are reaching your site and the percentage of those people who become paying customers. Not only can clustering help identify important segments — such as new site visitors that do not convert, they can identify the underlying commonalities among those that convert.

AI can also be used to analyze how customers use your service during the free trail period. This can help you identify patterns associated with free trial users who convert to paying customers. For example, you may find that free trial users who use a particular feature of your platform are six times more likely to convert. If that's the case, then you can focus your conversion strategies on exposing more free trial users to that feature, which seems to be a factor in the decision to convert.

Metric 3: Average Revenue per User

The average revenue per user is simply the amount of money a company can expect to generate from one customer. This is a useful measure for assessing the effectiveness of personalized recommendations.

A recommendation system that combines data from your product catalog with customer behavior data can help you identify products frequently bought together, as well as suggest higher-revenue products as substitutes for lower-revenue items already in the shopping cart. If cross- and up-selling recommendations are effective, you should see an increase in the average revenue per user.

The formula for this metric is simply the total revenue of the company divided by the number of customers. When a company sells multiple products, such as different subscription levels, you may want to calculate average revenue per user for each product.

Metric 4: Monthly Recurring Revenue

The fourth key metric is monthly recurring revenue, which is the amount of money you can expect each month from customers. This is particularly important for subscription businesses since this measure ties closely to the bottom line. Successful subscription businesses will see this metric grow over time. If this metric starts to drop, however, that may be an indication of a fundamental problem.

Metrics Guide: How to Use AI

Your AI-driven metrics can paint a picture of how your business is doing. What's more AI-powered analytics can be applied to areas that need work. For example, if monthly recurring income is declining — and there is a corresponding drop in the average revenue per customer — then the business should focus on up-selling and cross-selling. A drop in monthly recurring revenue could be caused by customer churn. In that case, AI can be used to identify customers likely to churn by using classification models.

AI can help you understand outcomes and can help you to better target the right customers with additional contacts, offers, and coupons in order to increase your conversion rates. In the final analysis, the business insights you gain from using AI should translate into measurable results on your bottom line.

Learn more about how AI tools, like Keyance's KI, can help you optimize your subscription service.

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