How Predictive Analytics Supports ACV Goals

Annual contract value (ACV) is a standard revenue metric for SaaS companies and other subscription-based businesses. It refers to the annualized contract value per customer contract. ACV breaks down the total contract value into an average value per year over the length of the contract.

How Do You Calculate ACV?

Similar to MRR and ARR, ACV calculations typically don’t include one-time set-up or onboarding fees.

ACV is generally a very straightforward calculation. The annualized revenue per contract equals the total contract value (TCV) divided by the years set in multi-year contract terms.

annual contract value formula

Why Is ACV a Critical Metric?

ACV allows you to analyze the financial performance of your business in three fundamental ways:

  1. ACV reveals the average monetary value of each customer.
  2. ACV informs customer acquisition and growth strategies that support broader business objectives.
  3. ACV shows how long it will take to recoup associated customer acquisition costs.


With ACV and customer acquisition cost (CAC) calculations, businesses can forecast customer growth plans, keeping in mind the time required to pay back these acquisition strategies. For example, the below formula shows how long it will take to recoup $10,000 of CAC for a customer with an ACV of $4,000. In terms of CAC, it will take 2.5 years for this customer to become profitable.

cac over acv formula

How Predictive Analytics Supports ACV Goals

ACV growth is a common goal at many B2B companies. Due to the nature of many B2B contracts, ACV becomes a consistent financial metric. With AI and machine learning, companies can create predictive models that support ACV goals, inform business decisions, and drive long-term customer value.

The most common uses cases for predictive analytics in supporting ACV goals are:

  1. Conversion Rate Modeling: The best way predictive analytics supports ACV goals is by supporting acquisition strategies and driving new subscription contracts. Conversion rate modeling powered by predictive analytics allows businesses to forecast and improve conversion rates. Predictive lead scoring scores the likelihood of inbound leads turning into closed-won deals. Predicting the quality of inbound leads typically results in higher-qualified prospects engaging with your sales team.
  2. Look-alike Modeling: Look-alike modeling is another excellent way to support ACV goals with predictive analytics. With predictive analytics, you can create audience segments that look like your top-performing customers. This modeling will help lower your customer acquisition cost in finding new customers.
  3. Upsell/Cross-Sell: Predictive analytics for upsell/cross-sell allows you to predict when a customer will likely upgrade their service with your business or purchase complementary services. For a SaaS business, this might mean moving from a lower to a higher tier of your service or purchasing add-on features. Either way, these sales boost the revenue generated.
  4. Customer Lifetime Value: It’s now possible to predict customer lifetime value at the start of any new customer relationship. Predictive LTV software allows you to deepen customer relationships for exclusive offers and special incentives.


If you want to learn more about how predictive analytics can support your ACV goals, we created a helpful guide to choosing a predictive analytics strategy for your team.

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

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