Analytic Applications, Part Four: Enabling Customers
This post is the last in a four-part series covering analytic applications organized according to how enterprises consume analytics.
Part One (here) covered Strategic Analytics, or analytics that address C-suite questions and issues.
Part Two (here) covered Managerial Analytics, which serve to measure the performance of products and programs at a departmental level, and to optimize the allocation of resources across programs.
Part Three (here) covered Operational Analytics, analytics that improve the efficiency or effectiveness of business processes.
All of these applications have one thing in common: they exist to serve internal needs of the enterprise, which retains the value produced by analytics. This is not a bad thing; credit card customers benefit indirectly when an issuer uses analytics to avoid giving credit to customers who subsequently default, but the firm itself is the direct and primary beneficiary of the credit risk analysis.
Customer-enabling analytics turn this logic on its head: the analytics are designed to provide a benefit to customers, while the enterprise benefits indirectly through product differentiation, goodwill or some combination of the two.
There are four distinct categories of Customer-Enabling Analytics:
- Analytic Services
- Prediction Services
- Analytic Applications
- Product-Embedded Analytics
On the surface, Analytic Services provided by consulting firms, marketing service providers and so forth are simply a sourcing alternative for the previously defined Strategic, Managerial or Operational Analytics, but not fundamentally different. In practice, however, analytics delivered by service providers tend to be very different than analytics developed “in-house”. With few barriers to entry, the market for Analytic Services is highly competitive; as a result, successful providers tend to be highly innovative and specialized, offering services that cannot be easily reproduced. Moreover, the relationship between service provider and enterprise consumer (and the visible costs associated with a project) tend to ensure that project goals are well-defined, a step that is often omitted from internally-delivered analytics (to the detriment of all engaged).
For Analytic Services, the “product” sold and delivered is an analysis project, which is typically priced based on the effort required to complete the project and the time-value of resources consumed. For Prediction Services, the product sold and delivered to the customer is a prediction, not a project, and is typically priced on a per-use basis. Credit scores are the best-known example of Prediction Services, but there are many other examples of prediction services for sales, marketing, human resources, insurance underwriting. As with Analytic Services, the end uses to which Prediction Services appear to be the same as in-house delivered Strategic, Managerial and Operational Analytics, but in practice externally developed Prediction Services work in a very different way. Since the development and deployment costs for a predictive model are amortized over a large volume of transactions, Prediction Services enable a broad market of smaller enterprises to benefit from predictive analytics that would not be able to do so otherwise. Prediction Service providers are also able to achieve economies of scale, and often have access to data sources that would not necessarily be available to the enterprise.
Analytic Applications are a natural extension of Analytic Services and Prediction Services. Analytic Applications are business applications that consume data-driven predictions and support all or part of a business process. Examples include:
- Mortgage application decision systems (which consume predictions about the applicant’s propensity to repay the loan)
- Insurance underwriting systems (consume predictions about expected losses from an insurance policy)
- Fraud case management systems (consume predictions about the likelihood that a particular claim or group of claims is fraudulent)
These applications are often sold and delivered by providers under a “razor-and-blade” strategy, where the application itself is delivered under a fixed price and combined with a long-term contract to provide Analytic Services or Prediction Services.
Each of the first three categories of Customer-Enabling Analytics is similar to and competes with “in-house” delivered Strategic, Managerial and Operational Analytics. The fourth category, Product-Embedded Analytics, is potentially the most disruptive and offers enterprises the greatest potential return. Product-Embedded Analytics differentiate the firm’s products in meaningful ways by solving a consumer problem.
If this sounds esoteric, it is because the best examples are often not thought of in the same way we think about other kinds of analytics:
- Consumers have a problem finding information. Google’s search engine solves this problem.
- Consumers have a problem finding a movie they want to watch. Netflix’ recommendation engine solves this problem.
These examples — and many others, including Facebook’s newsfeed engine, Match.com’s matching algorithm — use machine learning technology in ways that directly benefit customers. But the firms that offer these services benefit indirectly, by building site traffic, selling more product or satisfying customers in a manner that cannot be readily reproduced by competitors.