Taking Mover Marketing Programs To the Next Level

As a person’s focus changes from selling an existing home to moving into a new residence, their needs and subsequent purchase behaviors change dramatically. Across a wide range of industries, marketers recognize the importance of engaging movers during periods of elevated spending and increased brand engagement, as well as the unique acquisition and retention opportunities that mover marketing programs provide them. But many marketers are locked into conventional marketing strategies that utilize high-volume, low-cost, “Spray & Pray” mover strategies.

Engaging this lucrative audience to its fullest potential requires a real shift to more value-based thinking, a strong desire for campaign ROI, and recognition that quality data is at the heart of the most effective strategies. 

Key areas to consider when building a more data-driven mover approach include: High-Quality Mover Data, Data Overlays, CRM Matching and Geocoding Analysis, Data Modeling and Omni-Channel Engagement.


The quality of mover data is dependent on speed and accuracy – the speed in which mover data is available for use once a home is listed, sold or newly occupied, and the accuracy of the names, addresses, phone numbers and emails contained within data.  Data accuracy is determined by:

  • Data Sourcing: Data accuracy increases with the number of underlying data sources. High quality contact data is requires thousands of underlying data sources.
  • Update Frequency: Data accuracy increases with update frequency. High quality contact data is typically updated on a daily basis.
  • Data Processing: A large portion of movers are determined by monitoring additions and deletions to directory listings, which on a weekly basis is 10%-35% inaccurate. Data accuracy is improved to virtually 100% when mover data is briefly quarantined and processed on a rolling basis
  • Data Hygiene: Data accuracy is improved through extensive data hygiene, including CASS™ Processing, NCOALink® Processing, and Secondary Address Processing.

Historically, new mover data has been slow to compile with lists typically becoming available 21-28 days following a move date. However, by engaging movers several weeks after relocating — after many have made most of their important purchasing decisions — marketers miss prime engagement opportunities.  Reaching movers early in their purchase cycle is critical, and is often the most influential factor in acquiring a new customer.

Since data costs are typically a small percentage of overall marketing costs (about 10%), cost-driven strategies can sacrifice significant topline revenue and return-on-investment, while achieving relatively small bottom line savings.  Marketers are better off investing in high-quality mover data that is accurate and available in some cases, within 24 hours of moving.  Higher response rates, topline revenue and return-on-investment far outweigh the relatively small cost increases to the bottom line.  Higher quality mover data may cost more upfront, but in the long-run it more than pays for itself with increased response rates, reduced costs and improved ROI.


 A mover’s needs, purchase behaviors and brand affiliations are influenced by a variety of factors, such as: household income, credit history, home value, marriage status, presence of children, and other demographic attributes.   Using data overlays, marketers gain new insight and further refine segmentation strategies. Since timing is critical with mover marketing strategies, Modeled Data is a recommended data source, providing 99% population coverage, up to 96% accuracy, and compliance with privacy and security regulations.


CRM matching enables marketers to develop effective retention and acquisition strategies by comparing customer data and new mover data to identify “matched” customers and “non-matched” prospects in the move process.  Various customer data, including purchase history, and loyalty and affinity programs, are also used to refine current customer segments.

Since move distance or proximity to a store or branch location can also be strategically relevant, CRM matching strategies often include geocoding analyses, which determine customers and prospects moving into, out of, and within specific trade areas. Geocoding algorithms which utilize latitude & longitude data are much more accurate that zip code based geocoding.


Predictive models enable marketers to identify their most responsive, highest spending, or most profitable customers. By combining multivariate regression analysis and new mover data using ZIP+4 data models, predictive models leverage the principle of the “homogeneous neighborhood.” Data models enable marketers to:

  • Identify underlying demographic attributes driving performance
  • Predict high-performing customer segments
  • Increase topline response rates, revenue and ROI, while reducing costs
  • Optimize mover marketing programs on a continuous basis.


Expanding media and engagement channels are changing consumers’ shopping and purchase behaviors, and ultimately, their relationship with brands. Consumers are demanding greater transparency, intimacy, immediacy and, most importantly, greater relevancy. Consumers are also expecting seamless recognition and connectivity, whether online, on a smart phone or in store. The potential and need for marketers to engage the right customers, with the right message, at the right time, and through the right engagement channel has never been greater and is only expected to continue moving forward. 


Given their propensities for high spending and heavy brand switching, movers are a valuable target audience for marketers across a wide-range of industries. Most marketers recognize the importance of engaging movers during periods of elevated spending and increased brand engagement.  In order to engage this lucrative audience to its fullest potential, marketers should consider using the highest-quality mover data available, combined with a more data-driven approach to customer engagement.

This excerpt is taken from The New Mover Life Cycle.  Read the complete white paper.

Related Posts