Newspaper publishers are always looking for new methods they can use to attract readers and advertisers.

Enter predictive analytics. Retailers often use this tool to offer attractive cross-sell and up-sell recommendations to customers based on past purchases. It’s one of the most cost-effective weapons available, according to analyst firm IDC, which says that predictive analytic projects yield a median ROI of 145 percent.

For newspapers, predictive analytics can help them manage the stiff challenges associated with subscription acquisition and retention.

The explosion of free online news, information and classified ad sites has severely impacted newspapers’ recurring revenues and is forcing publishers to immediately replace traditional revenue sources with new ways to reach an eroding customer base.

Predictive analytics can help.

Automated toolset

Predictive analytics is an automated way to sift through massive amounts of transactional, subscriber, behavioral and geodemographic data. It’s designed to help users identify likely subscribers, determine price elasticity, forecast retention rates, and more.

It is also the modeling engine that drives online behavioral targeting solutions that help increase ad revenue.

Have I got you interested? Good.

Here are six keys to implementing successful predictive analytics:

*Secure commitment across the organization. Key departments - marketing research, circulation, IT - must collaborate to ensure consistency and quality while gathering data, interpreting models, executing marketing campaigns and integrating predictive models with the marketing database.  

*Build a marketing database. Buy-in complete? Now you need a place to store and tag your subscriber and non-subscriber data. A simple marketing database able to refresh the predictive model as new individuals are scored is essential. 

*Evaluate the accuracy of the third-party data appends. To predict non-subscriber interests you need geodemographic data for your marketing area. Virtually every third-party data supplier promises the highest level of accuracy, so test before you buy. Request a sample file you can use to test vendor claims - it’s well worth the extra time.

*Schedule iterative model reviews. Iterative model reviews are crucial. Too often, analysis projects are a recipe for unused shelf-ware: your in-house or outsourced tech guru dispatched to build a complex statistical model and deliver it “complete” to the marketing team. Marketers should be in at the start to define the business questions to be answered, contribute business logic and help determine what variables should drive the model for the most useful predictions. Weekly or bi-weekly model reviews will keep everyone focused and the project on track.

*Test the model. Before you go any further, test the model on a sample from your marketing database. Testing lets you evaluate the model’s performance and identify ways to tweak it for greater accuracy before “going live” with your entire marketing database.

*Automate model updating. Through the marketing database, instruct the predictive model to deploy on a pre-established schedule. The benefits? Minimal maintenance, regularly refreshed behavioral data and updated predictors so that each time you target new prospects, you are taking advantage of the most recent data  - and best intelligence.

Predictive analytics provides publishers with reliable ways to leverage their subscriber databases to identify and target new subscribers. The tools are now available for newspapers - it’s time to take the next step and embrace this technology that will be a key driving force to adapt the newspaper and Web business model.