PREDICTIVE SCORESINSIGHTS

INSIGHTS OVERVIEW PREDICTIVE SCORES

Zuora Insights helps companies move into the next phase of maturity in the Subscription Economy where they can apply data and analytics to maximize growth and reduce churn. By unifying financial, behavioral and demographic data sources, Zuora enables companies to improve relationships with their subscribers by analyzing usage patterns, subscriber preferences and financial metrics to uncover opportunities to proactively engage, upsell, cross-sell and retain customers. Predictive Scoring is a key capability of Insights, designed to enable companies to proactively make strategic business decisions based on what is likely to happen as opposed to what has already happened.

Predictive scores rank accounts on their propensity for a future outcome such as renewal or churn. Insights includes a set of predictive scores for each active account as a standard feature. Using a proprietary machine learning process, Insights analyzes each business’ unique data sets to tailor fit Predictive Scores to specific business needs.

THE ENGAGEMENT

SCORE

The Engagement Score is an easy-to-use summary of account health. Zuora Insights analyzes data from usage and account actions to determine how well they predict renewal and upsell as opposed to downsell or churn. After the predictive model is created, every account is ranked on a simple 1 - 100 scale. The formula is designed so that account(s) with the lowest predicted probability to have a desirable outcome (e.g. renewal, conversion) have scores of 1, and those with the highest predicted probability to have the desirable outcome have scores of 100.

THE CHURN SCORE

Similarly to the Engagement Score, the Churn Score also ranks every account in order from best to worst. But, unlike the Engagement Score, the Churn Score is an actual probability forecast where the score can be anywhere between 0% - 100%. The Churn Score corresponds directly to previously observed churn rates.

SCORE DECOMPOSITION

Zuora Insights not only provides a ranking of accounts, it also provides visibility into the reasons behind the prediction. To this end, the Engagement Score is decomposed into a contribution from all metrics that were used in the analysis.

Consider the following example as illustrated below for a fictitious video streaming service.

On the left, the Engagement Score is measured with just 3 metrics: Mobile logins, videos published, and social shares. Suppose the Engagement score is 85, it is decomposed into points awarded for each metric: 54 for mobile logins, 3 for videos published, and 28 for social shares. Next to each component score is an indicator to denote whether the score is low, normal or high.

On the right is the Churn Score which is the actual likelihood of churn. The underlying metrics that lead to the Churn and Engagement Score are also detailed below.

The ability to decompose predictive scores provides insight into each account’s relative strengths and weaknesses and tells you exactly what actions to take and how to prioritize them - Not all accounts are the same.

SCORE CALCULATION

SCORE VERIFICATION

Zuora Insights uses each customer’s unique data sets to generate Engagement and Churn scores tailor fit to your business needs. The analysis combines your financial data in Zuora related to a particular account with your behavioral and demographic data from your other applications. All available usage and actions are analysed for how well they predict outcomes like renewal, upsell and trial conversion in the historical data.

Each time an account has renewed in the past, a snapshot of its metrics is made at a time shortly prior to the renewal. The same applies to any

other outcome of interest such as upsells, cross-sells or conversions. Typically, the snapshot is made either 30 or 90 days before the renewal or other outcome events. Making the snapshots prior to the date of the event(s) ensures that the statistical model makes an accurate forward-looking prediction that does not rely on outcomes that can only be observed after the fact. The complete set of account snapshots and resulting events forms the sample set for Zuora’s proprietary statistical and machine-learning algorithms that predict the various outcomes of your accounts.

The accuracy of Zuora’s predictive model is tested through a rigorous historical simulation process called Backtesting. Backtesting provides an estimate of the future predictive accuracy of the model based on how well the model would have done in the past.

In the simulation, the model is re-calculated as if the outcome event dates were in the past. For example, only renewals and churns prior to the set date are used to create the model. Then, the model is tested to predict renewals and churns that actually occurred shortly after the simulation set date (only events that were not used to create the model). The simulation is then reset forward in time, typically by either a month or a quarter, and the test is repeated giving an accurate picture of model performance. Insights uses the procedure to iterate model versions until an accurate prediction is achieved.

INSIGHTS FOR FINANCE

Churn can have a negative impact on cash collections and the ability to spend. Knowing how much churn to expect allows finance teams to implement the right financial strategies, proactively plan forecasts, and avoid risk.

Consider the example as illustrated below. Insights allows you to identify all accounts that are showing signs of churn risk. In this case, accounts that have a churn probability greater

than 20%, or their churn probability has increased by 5% over the last 3 months, and their subscription contract ends in the next 90 days.

All of the accounts that fit this criteria are also listed below. For each individual account that applies, you can also see its exact churn probability, account owner, contract end date, product name and impacted revenue referenced.

INSIGHTS FOR SALES

The ability to predict which accounts are most likely to renew allows sales teams to strategically target the best fit accounts for upsell opportunities.

Consider the example as illustrated below. Insights allows you to identify all accounts that are most likely to renew and upgrade to a higher volume plan. In this case, accounts that have reached an overage usage charge, have an

engagement score of greater than 75, and usage intensity that has increased by at least 10% over the last year.

All of the accounts that fit this criteria are also listed below. For each individual account that applies, you can also see its respective engagement score, usage intensity, contract end date, product name and overage charges incurred over the last 30 days.

INSIGHTS FOR CUSTOMER SUCCESS

An account’s churn probability can be influenced by many factors. Knowing which accounts are showing signs of churn risk allows customer success teams to proactively take the right actions (e.g., offer training, invite to webinar) to save churn before it happens.

Consider the example as illustrated below. Insights allows you to identify all accounts that need the most help realizing value from the product. In this case, accounts

that have a churn probability greater than or equal to 25%, engagement score that has decreased by at least 10% over the last 3 months, and the contract end date is within the next 90 days.

All of the accounts that fit this criteria are also listed below. You can drill into each account to see the decomposition of scores that tells you exactly what actions to take and how to prioritize them.

THE ZUORA INSIGHTS DIFFERENCE

ZUORA’S DATA SCIENTIST

Zuora Insights is the first solution that turns your financial, behavioral and demographic data into actionable insights. Zuora has developed a state-of-the-art, machine-learning algorithm that ranks accounts based on their propensity to renew and probability of churn. Not only does Zuora provide

the predictive ranking of accounts, Zuora also provides visibility into the underlying subscriber behaviors that drive those predictions. Zuora Insight’s predictive scoring is what enables you to make strategic business decisions that maximize your growth potential and reduce churn.

Carl Gold holds a PhD in Computation & Neural Systems from the California Institute of Technology. He has first authored publications in leading Machine Learning and Neuroscience journals. Gold has spent most of his career as a quantitative analyst on Wall Street and is inspired by discovering the world using mathematical and scientific methods.

For more information: https://www.zuora.com/product/analytics

“Companies with less data tend to think they need to get more data but that doesn’t necessarily make sense. B2B companies naturally have less data than B2C companies because they have fewer customers but each customer of a B2B company is worth more. There are smart ways to reach useful conclusions even if you don’t have big data. As long as you have enough data to draw out actionable insights, companies should focus on what they can learn from their data.”