Public domain image from pixabay.com
“Location.” Say that key word three times to emphasize how important physical location for real estate. However, when it comes to potential buyers searching online, it’s necessary to locate them via the Web. That’s what Union Street Media (USM) is all about.
USM is a marketing firm that capitalizes on social media to convert sales to the real estate industry. It has a staff of campaign managers whose job is to expand the reach of their clients’ sites through digital marketing strategies, including demographic analysis and reporting, and ads and communications tailored to their target market.
Where Buyers Are in the Process
Rachel Allard, USM’s VP of Operations, explained that they draw on a variety of data to recognize what stage potential customers are at in their real estate purchase: “Sources include our own user data (listings searched or favorited, price ranges selected, agents worked with, etc), site behavioral data (content viewed), listing data (newly listed properties, sold in the area, etc), analytics/search data (keywords and behaviors), ad campaign data (other sites users have visited, user demographics, psychographics, interests, specific messaging a user interacts with) and more.”
What people search for is a fairly good indicator of whether they are about to buy or sell a home. Those seeking to sell their home are likely to check out market conditions or seek information on enhancing curb appeal and other improvements that maximize returns. Someone seeking to buy likely looks for information on neighborhoods and mortgages.
USM also leverages “our client and platform data, identifying trends, and incorporating those into our marketing strategies.” Specifically, they normally use the following data sources:
- Page user data
- CRM/online registration
- Content engagement (including ads)
- MLS (multiple listings services) data
- Third Party Data such as census data, demographics, market data, weather, offline transactions, and more)
It also seeks to optimize whenever possible by drawing on Facebook audience data “for custom segmentation and retargeting of past customers, at times to find lookalikes.”
Creating Customized, Local Content
All that data provides a fairly comprehensive picture that enable them to deliver content tailored to the interests of a person at that particular stage in the process of a real estate transaction. Knowing not just who, but also what the individuals are doing, allows USM to build authority by creating unique content for client sites that is specifically tailored, rather than generic one-size-fits-all pieces. “We create all of our content in-house and no content is ever used across sites – it’s all original to that specific client site,” Allard says.
While some clients also produce their own site content, USM still offers guidance for “the most effective way to structure content to engage the user and capture attention through search engines.” They find it much more effective to use location-specific content. She explains that incorporating data points like “weather, crime, points of interest,” in the area “Is part of our content creation, measurement and optimization process.”
Measuring Ad Effectiveness
To ascertain the effectiveness of ads, they test them out by comparing responses to different imagery. That’s particular important in the real estate market because so much depend on the visual impact, and social channels allow a way to discover which visual aspect are found most appealing.
Allard reports that their clients’ Facebook ads do perform better than similar ads on other websites.” For example, in the time frame covering most of October, there was a 253% greater click through rate on the Facebook ads than on “display ads on popular websites.”
Other numbers that USM shared included the following:
- 2.3 million total listings processed each hour
- 3 million monthly ad impressions served
- 7.5 million unique visitors per month
- 92% customer retention rate
- over 5 thousand content pages transferred last year
- 17 thousand monthly lead conversions