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,’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.

Analytic Applications (Part Three): Operational Analytics

This is the third post in a series on analytic applications organized by how analytic work product is used within the enterprise.

  • The first post, linked here, covers Strategic Analytics (defined as analytics for the C-Suite)
  • The second post, linked here, covers Managerial Analytics (defined as analytics to measure and optimize the performance of value-delivering units such as programs, products, stores or factories).

This post covers Operational Analytics, defined as analytics that improve the efficiency or effectiveness of a business process.  The distinction between Managerial and Operational analytics can be subtle, and generally boils down to the level of aggregation and frequency of the analysis.  For example, the CMO is interested in understanding the performance and ROI of all Marketing programs, but is unlikely to be interested in the operational details of any one program.  The manager of that program, however, may be intensely interested in its operational details, but have no interest in the performance of other programs.

Differences in level of aggregation and frequency lead to qualitative differences in the types of analytics that are pertinent.  A CMO’s interest in Marketing programs is typically at a level of “keep or kill”;  continue funding the program if its effective, kill it if it is not.  This kind of problem is well-suited to dashboard-style BI combined with solid revenue attribution, activity based costing and ROI metrics.  The Program Manager, on the other hand, is intensely interested in a range of metrics that shed insight not simply on how well the program is performing, but why it is performing as it is and how to improve it.  Moreover, the Program Manager in this example will be deeply involved in operational decisions such as selecting the target audience, determining which offers to assign, handling response exceptions and managing delivery to schedule and budget.  This is the realm of Operational Analytics.

While any BI package can handle different levels of aggregation and cadence, the problem is made more challenging due to the very diverse nature of operational detail across business processes.   A social media marketing program relies on data sources and operational systems that are entirely different from web media or email marketing programs; preapproved and non-pre-approved credit card acquisition programs do not use the same systems to assign credit lines; some or all of these processes may be outsourced.  Few enterprises have successfully rationalized all of their operational data into a single enterprise data store (nor is it likely they will ever do so).  As a result, it is very rare that a common BI system comprehensively supports both Managerial and Operational analytic needs.  More typically, one system supports Managerial Analytics (for one or more disciplines), while diverse systems and ad hoc analysis support Operational Analytics.

At this level, questions tend to be domain-specific and analysts highly specialized in that domain.  Hence, an analyst who is an expert in search engine optimization will typically not be seen as qualified to perform credit risk analysis.  This has little to do with the analytic methods used, which tend to be similar across business disciplines, and more to do with the language and lingo used in the discipline as well as domain-specific technology and regulatory issues.  A biostatistician must understand common health care data formats and HIPAA regulations; a consumer credit risk analysis must understand FICO scores, FISERV formats and FCRA.  In both cases, the analyst must have or develop a deep understanding of the organization’s business processes, because this is essential to recognizing opportunities for improvement and prioritizing analytic projects.

While there is a plethora of different ways that analytics improve business processes, most applications fall in to one of three categories:

(1) Applied decision systems supporting business processes such as customer-requested line increases or credit card transaction authorizations.  These applications improve the business process by applying consistent data-driven rules designed to balance risks and rewards.  Analytics embedded in such systems help the organization optimize the tradeoff between “loose” and “tight” criteria, and ensure that decision criteria reflect actual experience.  An analytics-driven decisioning system performs in a faster and more consistent way than systems based on human decisions, and can take more information into account than a human decision-maker.

(2) Targeting and routing systems (such as a text-processing system that reads incoming email and routes it to a customer service specialist).  While applied decision systems in the first category tend to recommend categorical yes/no, approve/decline decisions in a stream of transactions, a targeting system selects from a larger pool of candidates, and may make qualitative decisions among a large number of alternate routings.   The business benefit from this kind of system is improved productivity and reduced processing time as, for example, the organization no longer requires a team to read every email and route it to the appropriate specialist.  Applied analytics make these systems possible.

(3) Operational forecasting (such as a system that uses projected store traffic to determine staffing levels).   These systems enable to organization to operate more efficiently through better alignment of operations to customer demand.  Again, applied analytics make such systems possible; while it is theoretically possible to build such a system without an analytic forecasting component, it is inconceivable that any management would risk the serious customer service issues that would be created without one.  Unlike the first two applications, forecasting systems often work with aggregate data rather than atomic data.

For analytic reporting, the ability to flexibly ingest data from operational data sources (internal and external) is critical, as is the ability to publish reports into a broad-based reporting and BI presentation system.

Deployability is the key requirement for predictive analytics; the analyst must be able to publish a predictive model as a PMML (Predictive Model Markup Language) document or as executable code in a choice of programming languages.

In the next post, I will cover the most powerful and disruptive form of analytics, what I call Customer-Enabling Analytics: analytics that differentiate your products and services and deliver value to the customer.