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Propensity Models Provide A Novel Way to Target
Your Best Audience by Brand, Attitude, In-Market, and More

Propensity Models – What Sets Us Apart

Until recently, propensity models have been available for just a few types of behaviors. The earliest models were to rate a household’s likelihood to be in market for a new car.

Over the past few years several top U.S. consumer database compilers have started to offer propensity models. The good news is that Inbound Insight has access to thousands of models from multiple sources, for both data enhancement and targeted postal and email lists.

What is an Audience Propensity Model?

Propensity models start with known information about tens of thousands to millions or households or people. This raw data may be sourced from credit card company transaction data, automobile dealer sales, retail sales, surveys or other sources. Based on deep demographic knowledge of essentially every U.S. household, regression analyses are performed to determine which known demographic and other characteristics are key factors in predicting a household’s propensities and a model is created. This model is then applied to the tens of millions of households for which raw data is not available in the form of a score, typically 1 to 10, 1 to 20 or 1 to 100. Then this model is back tested against the raw data to validate its predictive value.

The propensity score can now be applied as a data enhancement element to existing name and address data to be used by a company’s marketers and data scientists, or it can be used to select a target audience for postal or email lists.

Propensity Model Examples

Propensity Models cover a wide range of propensities, behaviors and attitudes, including:

  • In-Market Propensity
  • Attitudes and Behaviors
  • Brand Propensity
  • Media Usage Propensity
  • Purchase Channel Propensity
  • Product Propensity
  • Spending Propensity
Propensity Models

Here are just some of the thousands of examples of in-market predictive / propensity models:

  • In Market for Furniture – likelihood to spend $2,500 plus on furniture in next 90 days
  • In Market to Remodel – likelihood to spend $1,000 plus on renovation in next 90 days
  • In Market for Cosmetic Procedure – likelihood to have a procedure in next 12 months
  • In Market to Get Engaged – likelihood to get engaged in next 12 months
  • In Market to Have a Baby – likelihood to have a baby in next 12 months
  • In Market to Retire – likelihood to retire in next 12 months
  • In Market to Sell a Business – likelihood to sell a business in next 12 months
  • In Market to Buy a New Vehicle – likelihood to be in market for a new vehicle
  • In Market to Build or Buy a Home – likelihood to build/buy a home in next 12 months
  • In Market to Buy a Watch/Jewelry – likelihood to buy for $2,500 plus in next 12 months

Attitudes and Behavior models also go far beyond what one would typically expect from third-party demographic targeting/enhancement data elements, for example:

  • Highly Likely Investor – top 10% of households likely to be investors
  • Price Sensitive Penny Pinchers – likelihood to be price sensitive
  • Large Contributor – likelihood to have Contributed More than $500 in past 12 months
  • Luxury Fashionista – interested in high-end designer clothing and couture
  • Likely to Switch Insurance Provider – likelihood to switch insurance provider
  • Internet Investors – likelihood to have used internet for investments past 12 months
  • Sleep Quality – predicts the likelihood of sleep quality
  • Diet Lifestyle – predicts the likelihood of diet being followed
  • Affluent Tech Early Adopter – likely to be an affluent tech early adopter
Propensity Models

Key Benefits

  • Ability to reach your target audience in using completely new dimensions not reachable using traditional demographic elements.  This is a huge breakthrough and key benefit!
  • With numerical scoring and high match rates, these models lend themselves well to data science and analytics project

Propensity Models – Use Cases

  • Gain insight into clients or prospects using new dimensions versus traditional demographics

  • Use propensity model score ranges (e.g., deciles)  to evaluate how customers differ in terms of:
    • Propensity to Convert
    • Lifetime Value
    • Propensity to Churn
    • Propensity to Buy
    • Up-sell/Cross-sell/Next-sell Recommendations
  • Refine existing analytic / data science models with new, predictive data points
  • Reach people in your target audience via direct mail or email in a privacy-compliant way (many models are FCRA and RegB compliant)

Propensity Models – Case Study

An electricity retailer wanted to know which prospects to prioritize as it expanded into a new state.  Inbound Insight appended Energy Consumer Dynamics (ECD) clusters to 10,000 existing customers.  From this we were able to determine the absolute number of customers that fell into each of the 13 ECD clusters.  We also created the Market Penetration Index by comparing their customer share by cluster to the cluster share of households in the markets they currently served.

The result was a clear set of winning top-priority clusters that they were then able to use to determine the best target audience for their direct mail prospecting efforts as they expand to new territories.  We provided the direct mail list based on this targeting, and also included key demographic data such as age and income, along with the Personicx cluster of each household.  This provided the client with multiple frameworks to analyze results of their initial campaign and determine the best way to refine their target audience in the future.

Propensity Models – Documentation

For more information about Propensity Models download our brochure.