Frequently Asked Questions
Scout Analytics™

Scout Analytics is a behavioral analytics solution to increase the top line of recurring revenue businesses as much as 10-15%. Using patent-pending demand rating, Scout Analytics enables a business to measure and monetize previously undetected correlations between behavior and revenue opportunities.

The Scout Analytics platform implements a patent-pending demand rating system that is a ratio of the actual usage vs. investment (i.e., cost, number of licenses, etc.). The demand rating ratio provides a comparable measure of the customer's dependence on a product (i.e., demand).

Because the demand rating is a comparable measure of how dependent a customer is on a product, it can be used to understand if the customer’s dependence is trending up or down. Is the customer’s demand more or less than last quarter? Last year?

A customer's demand rating can be compared to other customers'. Which customers have more demand than others? Which ones have the least demand?

The demand rating can also be aggregated into an index to judge demand in customer segments. Which customer segments have the most demand for a product? Which ones have the least? Which segments show increasing demand? Which ones are declining?

Usage data provides statistics on the number of times a customer either used the system, downloaded content, or conducted a transaction. If a customer performed 100 downloads last month, does that represent good demand or a lack of it? The answer is highly dependent on how many licensed users the customer has (10?, 100?, 1000?) and what they paid for the content ($100?, $100,000?). The demand rating transforms usage data into a comparable ratio by incorporating the customer’s investment.

Demand ranking is a score to indicate the relative strength of a particular customer’s demand compared to that of similar customers. A positive demand rank would indicate that customer is in the upper half of their segment, whereas a negative score would indicate the customer is in the lower half. A rank of +1 puts the customer in the top 20% of demand, whereas a rank of -1 puts the customer in the bottom 20% of demand. Demand ranking provides a quick means to further qualify the demand rating.

Scout Analytics dynamically aggregates a rich set of data about a company - called firmographics - such as number of employees, revenue, industry, locations, and more. Scout Analytics enables a service provider to define customer segments using the firmographics data as parameters. Once segments are defined, demand ranking occurs based on the customers contained within that segment.

The Scout Analytics platform integrates 3 sources of data: usage data, contract data, and firmographic data.

For usage data, the platform can accept a data feed from an existing internal system or web analytics tool. Additionally, the platform has its own collector that is used to aggregate missing information such as device counts, actual user counts, and domain names. For sites that do not have an existing web analytics tool, the platform’s collector can aggregate page and download information as well.

Data from the contract such as type of license purchased, number of licenses, assigned accounts, and purchase amount is collected via a data feed.

The Scout Analytics platform is able to take a data feed from an existing CRM or other systems to get firmographic data regarding a particular customer. The platform also dynamically aggregates firmographic data from external data sources to automatically populate the customer's profile.

Scout Analytics has three types of revenue identification analytics: revenue assurance, revenue expansion, and revenue retention.

Revenue assurance captures revenue by identifying latent demand in the form of unlicensed use. The analytics look for accounts that have clear patterns of being shared among multiple users or unlicensed domains gaining access to a service.

Revenue expansion increases revenue by identifying potential up-sell and cross-sell opportunities. For up-sells, the analytics look for latent demand in the form of customers reaching the limits of their existing rate plans via denials or excessive pay-per-use. For cross-sells, the analytics correlate demand for groups of products from comparable customers to identify recommendations.

Revenue retention protects revenue by identifying a lack of demand that would be indicative of customer churn. The analytics look for new subscribers that are not reaching important adoption levels, existing subscribers that have a drop in their utilization, and customers that have low usage combined with high denial rates.

The algorithms find revenue opportunities by sensing usage patterns of a customer. Each type of opportunity can have its own set of usage patterns. For example, a shared account is a usage pattern representing an up-sell opportunity. High denials for an unlicensed product, is a usage pattern representing a cross-sell opportunity. The Scout Analytics platform has a library of usage patterns it leverages to sense actionable demand. This library is extensible and will be expanded with each update.