Average Revenue Per Active User (ARPU) | Pecan AI

Average Revenue Per Active User (ARPU)

How Predictive Analytics Supports ARPU Goals

ARPU is a metric that measures the average revenue per active user over a given period. Originally used primarily within the telco industry, other leading industries have recently adopted the average revenue per user metric. These include mobile apps, mobile games, SaaS companies, streaming services, and other businesses offering subscription plans.

Due to its simplicity, ARPU can also serve as an LTV metric. It’s a great gauge of each user’s value for your business. ARPU measures this value within a given time period. In contrast, you would calculate LTV over the customer’s entire relationship with a brand.

Why Is ARPU an Important Financial Metric?

Over the last 10 years, this metric has become influential among acquisition managers, growth directors, and RevOps teams. Knowing the average revenue for each user provides insight into growth potential on a per-customer basis. In addition, it allows teams to calculate acquisition targets needed to reach revenue goals.

ARPU is also an excellent metric to support pricing strategies. ARPU allows RevOps managers to understand how a product is priced within the market. Low ARPU may indicate that the product is priced too low. Alternatively, it might mean the sales and marketing team needs to target different audiences in their acquisition initiatives. In either case, this metric helps steer teams in the right direction to drive higher value for the organization.

How Do You Calculate ARPU?

ARPU is calculated by dividing your revenue by the number of users.
average revenue per user formula

ARPU should consistently increase over time for your user base. An increase in ARPU indicates that you’re reaching the right customers with value-aligned pricing.

A few factors drive change in this metric:

  1. Process Improvement: As your sales and marketing processes improve, your business should naturally become more efficient and better at closing sales opportunities.
  2. Shifting Upmarket: As your company grows, you will likely work with higher-spending clients or larger accounts. You’ll increase your average revenue per active customer by winning and retaining higher-spending clients for the long term.
  3. Product Diversity: Adding new features and functions to a product offering will naturally increase the amount of revenue per user. In addition to improved pricing, this also presents an opportunity for upsell and cross-sell promotion.
  4. Pricing Changes: As products become more sophisticated with new features, product pricing will naturally increase. Pricing models may also change. In either case, there will be an effect on this metric.

How Does Predictive Analytics Support ARPU Goals?

Tracking and analyzing ARPU is a good way to know your customer and the effects of your sales motion. Higher ARPU typically signals internal process efficiencies and aligned go-to-market strategies across sales, marketing, and customer success.

But that doesn’t mean you won’t have room for improvement. Many brands are turning to AI-powered predictive analytics to optimize user revenue and drive future value. Below are three ways predictive analytics can support your goals for boosting this metric:

  1. Increase your customer base: The best way predictive analytics supports ARPU goals is by supporting acquisition strategies and driving new paying customers. Conversion rate modeling powered by predictive analytics allows businesses to build audience look-alike segments and score leads. These models typically result in higher conversion rates and more qualified prospects engaging with your sales team.
  2. Prioritize upsell/cross-sell opportunities: Predictive analytics for cross-sell/upsell is one of the best models for ARPU. For example, you can predict when a customer will likely buy a complementary product or upgrade their service. Taking action on this prediction will improve your ARPU, CAC payback periods, and loyalty initiatives.
  3. Predicting customer churn: Predicting and intervening in customer churn can prevent ARPU declines. Predictive churn software can detect 85% of customer churn and improve retention rates by as much as 35%.

If you’d like to learn more about how predictive analytics can support your revenue generation goals, we created a helpful guide to selecting the right implementation strategy for your team.

If you are interested in how Pecan AI can support your business, feel free to contact us or schedule a demo.

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