Four challenges to successful predictive analytics models

Analyzing customer interactions to create a predictive analytics model isn't foolproof. Expert David Loshin shares four factors that could stymie your efforts.

The availability of low-cost business analytics tools has motivated many organizations to roll out customer profiling and predictive analytics applications in the hopes of revitalizing their marketing and sales initiatives. However, while there is no doubt that the right predictive analytics models can add significant value to customer outreach efforts when properly designed and deployed, there are some situations in which they might not meet user expectations.

First, let's consider the typical analytics framework, which includes customer profiles and collections of historical transactions. Organizations collect customer data from among the different internal systems and consolidate that data into a unified customer database. Those customer records may be augmented with demographic and psychographic data from multiple sources -- such as third-party data providers or online resources -- to create customer profiles.

The second part of the framework requires accumulating different sets of customer transactions. These will include, but are not limited to, records from marketing, sales, finance, credit, fulfillment and distribution, customer support, and legal.

It's useful to maintain some healthy skepticism about the precision and accuracy of predictive analytics models.

The goal is to collect all the interactions with customers so they can be subject to direct analysis that looks for sentinel patterns that precede desired outcomes. A good example is identifying sequences of actions that a customer performs that result in a product sale.

Many tools have become sophisticated enough to analyze transactions from the different business functions to find complex combinations of event sequences that are presumed to predict desired outcomes.

However, it's useful to maintain some healthy skepticism about the precision and accuracy of predictive analytics models, as there may be situations in which they inaccurately predict situations or attempt to influence behaviors that aren't characteristic of the individuals involved. The result is reliance on models that may have limited predictive power. Some causes for this may include:

  • Incompleteness. The accuracy of predictive analytics models is limited by the completeness and accuracy of the data being used. Because the analytical algorithms attempt to build models based on the available data, deficiencies in the data may lead to deficiencies in the model. Correspondingly, the developed model might not encompass enough information to be able to recognize enough sentinel predictive patterns to be of any value. For example, a customer retention model might be built using customer service event histories and transactions, but the most accurate models might require sales and returns transactions to provide the best predictive patterns.
  • Data myopia. Customer profiles are engineered using guidelines based on people's expectations, but limitations on the range of different demographic variables may force customers to be classified in ways that are too limited. As an example, individuals might be classified using salary averages calculated within the boundaries of defined census tracts. However, certain urban areas may have census tract regions in which there are multiple discrete micro-communities with significantly different salary demographics. Refining the size of the area of focus for average salary will improve the precision of the customer classification model.
  • Narrowization. This is a term that suggests that the reliance on predictive analytics models to guide business processes to influence customer behavior may create artificial boundaries that narrow the range of a customer's anticipated behaviors. In this case, there may be business opportunities -- such as product bundling or up-sells -- that are not even considered because the analytics-driven business process does not expect those opportunities to arise.
  • Spookiness. For a long time, automated systems have been capable of simple ad tracking in which sites drop cookies that provide information that can be accessed by partners within an ad network. Systems are becoming increasingly capable of scanning customer actions within a hierarchical semantic context to provide increased information about customer interests. A person's search terms coupled with product page visits may provide enough information to make inferences about what the customer is really looking for. However, as these bits of information are being employed to present advertisements and product placements, customers are becoming unnerved by automated systems attempting to anticipate their intent and influence their activities.

In essence, organizations must strike a balance between three different facets of exploiting predictive analytics models: accumulating the right data to build accurate models, ensuring that the models are complete and accurate, and using the models at the right time and place.

Review how business analytics applications are configured, utilized and put into production to determine the best way to overcome the challenges that may impede optimal use.

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