If you’ve heard of Speedeon, then you know we know a thing or two about new mover marketing. It’s our thing. If you haven’t heard of us, well, we work with some of the largest financial, insurance, D2C and retail brands in the US on helping them leverage new mover triggers to enhance their acquisition and retention programs. We’ve invested years building the largest, most accurate database of new movers in the industry. The strategies enabled by the new mover data also run the spectrum – customer acquisition, cross-sell and upsell strategies, win-back, and reducing churn. We’ve been around the block more than once and have the new mover play book to prove it.
Over the course of the next few blog posts, we’re going to show you different new mover strategies deployed by our clients and give you insider tips on the best way to leverage this data. When the right strategy and the right data come together, good things happen.
In our first post, we are going to highlight a regional furniture brand who wanted to be more relevant to consumers who lived in their footprint. They realized they needed to be more competitive in an incredibly saturated market and looked to Speedeon to help them leverage pre-mover and new mover data to gain a competitive edge. Happy reading!
Part #1 – Modeled New Mover Data Drives Lift in Response & Spend
As previously mentioned, our client is a regional furniture brand who we help support by providing turnkey new mover direct marketing programs for. The program targets two groups of movers with direct mail and email communications, pre-movers at contract and new movers. Both groups of movers are further segmented based on household income and home value.
While our client has been pleased with overall program results, we identified additional opportunities to improve program performance by modeling responders to the program. By using historical response data from prior campaigns, our data science team trained response models, one for each mover type. This process included enriching the responder records with geo-demographics and applying machine learning algorithms to identify key attributes among the two groups to predict the likelihood of responding to a targeted mover message.
The modeling development process analyzed how features like dwelling type, household composition, debt management, home value and population density (urban / suburban / rural) impacted responders vs non-responders.
Once the model was built, it was used to score which movers were most likely to engage with the brand as a result of receiving a direct mail and/or email communication. Highest scoring movers were selected based upon which model decile (deciles represent a 1/10th distribution of the model and are commonly used to simplify the selection process) and lower scoring deciles were divested to optimize the marketing spend with minimal impact to response.
The outcome…in market, the models performed.
- New Movers: .4% lift in response and an $83 increase in spend.
- Pre Movers: 2.2% lift in response and a $75 increase in spend.
New mover data on its own performs well for many brands but with modeling, it’s a game changer. It’s marrying intent and likeliness of responding. Most providers can provide the trigger, Speedeon takes it a step further by providing the analytics to further enhance campaign performance.
As one client who was running a new mover program at a competitor said, “It was generally ok, but we could tell it wasn’t their thing. We can tell this is your thing.”