Taking Aim with Consumer Cellular Sample

How Consumer Cellular Sample Can Give You a More Accurate Geographic Fit of your Target Population and Improve Coverage

Geo-targeting. We all know what it means, but for the sake of this article, let’s get at the essence of the concept. Geo-targeting is a way to pinpoint an audience based on location. Accuracy is everything. Geography is the fundamental basis for every sample frame – be it individual streets, Census geography or Postal geography.

Certain sample frames such as Cellular RDD tend to be difficult to target geographically due to inward and outward migration of individuals who retain their cell phone numbers.  It’s important to be aware of these limitations when using a Cellular RDD sample, especially when targeting small geographies.

Here’s how you can miss the target: a cellular RDD sample will include people who have moved outside your target geography (a.k.a. outward migration). Additionally, respondents might live in the area of interest, but do not have an opportunity to be included in the RDD frame (inward migration). They have a cell number that corresponds to an entirely different geography. These people wouldn’t  be included in a traditional cellular RDD frame. The result? Under-coverage due to inward migration and increased data collection costs due to outward migration

So how can we account for the under-coverage and take better aim? One option is to supplement from a relatively new convenience frame called Consumer Cell. This frame is based on a multi-source model comprised of consumer databases linked to publically available cellular telephone numbers. It is updated monthly.

The Consumer Cell  database is built from literally hundreds of sources including

  • Public records
  • Census data
  • Consumer surveys
  • Telephone directories
  • Real estate information (deed & tax assessor)
  • Voter registration
  • Magazine subscription
  • Surveys responses
  • E-commerce
  • Proprietary sources

Geographic targeting using consumer cell can be highly accurate, zooming in on small geographies such as census blocks or even the household level.  Further stratification can be done for numerous person and household level demographic variables.

One limitation to the database is that it is a convenience frame (non-probability compilation of households). It does not offer the same coverage as an RDD frame. It is probably best utilized as a supplement to sample respondents who live in a targeted geography. One of the benefits is that you now include respondents who otherwise would not have been sampled.

If your area of interest is at the state or local level, you should consider where we can address under-coverage issues with RDD cell sample.

Split-Frame Sampling

Oftentimes, researchers are faced with the challenging task of targeting rare domains in a population while maintaining the probability-based nature of the employed sample.  For instance, in a national RDD sample it might be necessary to oversample households with small children or those with even less prevalent attributes.  While an epsem sampling design, whereby all numbers have the same chance of selection, will provide the most efficient sample with respect to the precision of survey estimates, from a cost perspective such a design can be completely prohibitive due to the required level of screening for reaching eligible households.  This is where a cleverly designed stratified sampling alternative that employs disproportional allocation can prove highly valuable.

In practice, an optimal sample allocation scheme takes into account the unit cost per interview in each sampling stratum.  As such, a stratum with a high incidence of reaching members of the target population will receive a higher allocation as compared to other strata.  This disproportionate sample allocation should be exercised while providing a non-zero chance of selection for all telephone numbers to ensure a probability-based sample.

The objective of this stratification is to provide a means for over sampling the target populations by segregating higher incidence households into distinct sampling strata.  This is done by matching all numbers against commercial databases, which contain household and individual level demographic data, and identifying the numbers that meet the specified target.

With access to all the top commercial databases Marketing Systems Group can provide cost-effective solutions for sample surveys that aim to target rare domains.  By placing such telephone numbers in the “top” or high incidence stratum and the remaining telephone numbers covering the geography of interest in another, you can create a complete sampling frame.  Subsequently, using an optimization procedure a higher sampling fraction will be determined for the top stratum cognizant of the design effect that will result from a disproportional sample allocation and will need to be adjusted for when weighting.