Table of Contents
- Introduction
- Use Case 1: Using AI for Better Lead Generation
2.1 The Opportunity
2.2 How AI Improves Lead Generation
2.3 Complexity and Ease of Deployment
2.4 ROI Potential - Use Case 2: Content Enrichment
3.1 The Opportunity
3.2 How AI Enriches Content
3.3 Complexity and Ease of Deployment
3.4 ROI Potential - Use Case 3: Sophisticated Marketing Analytics
4.1 The Opportunity
4.2 How AI Improves Marketing Analytics
4.3 Complexity and Ease of Deployment
4.4 ROI Potential - Conclusion
If you spend any time at all on LinkedIn or reading B2B tech content, you already know AI has the potential to deliver powerful, actionable insights that can supercharge your marketing efforts. But actually applying those insights into real-life real estate use cases? That’s a whole other story.
Use Case 1: Use AI for Better Lead Generation
The opportunity
Consider your current lead list. Whether you’re a realtor or mortgage broker, it’s probably filled with hundreds of names—people whose business cards you’ve collected over the years, online visitors who’ve clicked on your ad or filled out a contact form, or perhaps a lead list you bought or subscribe to on an ongoing basis.
According to the National Association of Realtors, the average conversion rate from lead to client clocks in at a measly 0.4 to 1.2%. Based on this, if you’re reaching out to 10 people each day, it could take as long as 25 days to convert just one new client.
Now imagine starting with a list of people you know who are actively looking for a new home or to renew their mortgage before you ever pick up the phone or send an email.
When it comes to generating the best returns from your marketing efforts, starting with high-quality leads has always been key. By some estimates, zeroing your efforts in on potential leads who are qualified based on need or intent can boost conversion rates as high as 25%. Not only does this reduce the time, money, and effort required to land a new client—it also frees you up to move on to the next prospect faster.
How AI improves lead generation
Discovery calls, sending out questionnaires, complicated lead scoring systems—while qualifying your lead list might improve your conversion rates, the work associated with doing so manually is incredibly time consuming. Once you factor in all the work involved, it may not ultimately help you realize any meaningful improvements.
AI changes the game. Whether you’re using a single dataset or—even better—connecting ATTOM’s robust property, ownership, and mortgage datasets (including up-to-date home equity, pre-foreclosure status, and recent sales activity), AI can zero in on qualified leads nearly instantly. For example, as a listing agent, you could cut through all those contacts who aren’t currently interested in selling using ATTOM’s propensity to default, vacancy, pre-foreclosure or mortgage release data to find your most highly motivated prospects.
Not only can AI help pinpoint prospects with need and intent, it also gives you a richer, more sophisticated understanding of your target customer. This can inform how you prioritize outreach and personalize messaging, and help improve conversion rates even further.
Examples of the types of leads you can use AI to develop include:
- Potential property purchasers
- Borrowing and refinancing candidates
- Construction projects requiring financing
- HELOC and reverse mortgage candidates
- Insurance sales and upselling
Complexity/ease of deployment
Easy, so long as your AI platform can directly access your datasets.
The global consulting firm Bain identifies lead generation as a top use case for AI. But they also emphasize that “clean, connected data” is key to realizing better leads. This means whatever your sales goal, for actionable, accurate targeting, you need robust, accurate, and timely data that exist in formats which can be readily accessed and utilized by your AI solution.
Because real estate datasets are typically quite large, data delivery matters too. Barriers such as long upload times or onerous technical requirements such as file conversions stand in the way of ready access to actionable marketing insights.
To readily tap into better lead generation for any purpose, look for unified, well-structured data files that ideally can be accessed and utilized instantly within enterprise platforms such as Snowflake. An instantly deployable combination such as ATTOM and Snowflake together means you can integrate your data and AI with no ETL pipelines, file transfers, or waiting for conversions or uploads, improving the ease of deployment and getting you those qualified leads faster.
High ROI
Considering how much time is wasted chasing poor-quality leads, there’s no question building a sustainable high-quality lead engine has the potential to offer outsized returns. Leveraging the most up-to-date information about potential clients is key for zeroing in on strong leads, which is where a data solution such as ATTOM comes in.
But if you’re simply trading the work of manually qualifying leads for ongoing technical work to access AI insights, you’re not gaining an actual advantage. That’s why ROI from AI-enhanced lead generation largely hinges upon sustainable, low-lift integration that enables ready access to that real estate data.
Use Case 2: Content Enrichment
The opportunity
Once you’ve identified qualified leads, the next challenge is in the approach. Whether it’s that first phone call or email, your follow-up messaging, or the content you display on your website, the right message at the right time can make all the difference.
AI can help identify opportunities to tailor your content and communications based on what’s happening in the market now or on individual targets’ current situation. Surfacing relevant content highlights your understanding of what’s happening in the market and of your clients’ needs, positioning you as a trusted expert. And when it comes to inbound marketing efforts, insights from AI can also boost the effectiveness of your initiatives—for example, informing content calendars, SEO keyword strategies, social media messaging, and ad buys.
How AI enriches content
AI’s ability to process vast amounts of current and historic data means it can tap into trends and market changes while they are still imperceptible. For example, by integrating ATTOM’s market-level data, such as foreclosure trends, home affordability, and climate risk, AI can help you to discover emerging local patterns that inform the neighborhoods you choose to highlight in your newsletter or other content strategies.
Leveraging robust datasets can also help you pick up on signals pointing to economic shifts or climate concerns that influence buyer sentiment, allowing you to speak to potential clients where they are now, and to address needs they may not yet know they have. Whether it’s flipping reports, real estate valuations or school data, ATTOM’s extensive datasets offer near-endless opportunities to inform your content and messaging. You can also tap into AI’s generative capabilities to use these insights to automate the work of tailoring content or creating new content when opportunities arise.
Complexity/ease of deployment
As with lead generation, a viable strategy to leverage AI to enrich your content requires both robust, current data and accessible underlying technical solutions. Specifically, your AI needs to be able to connect with large, up-to-date datasets without having to repeat onerous manual uploads and other steps every time.
Looking for an AI solution capable of automatically capturing changing sentiments and other shifts into dashboards and visualizations also makes it easy to spot relevant content opportunities.
High ROI
Relevant content creates stronger, faster connections with target audiences, driving better long-term results. According to McKinsey, tailored marketing can cut customer acquisition costs in half, increase marketing ROI by 10 to 30% and boost overall revenues by as much as 15%.
Furthermore, using AI to automate the work of identifying content enrichment opportunities (and the subsequent work of creating that content) reduces costs and other resources associated with content marketing. That said, as with lead generation, to drive real value from AI when it comes to enriching your content, you’ll need to remove inherent technical barriers associated with the ongoing analysis of vast amounts of information. In other words: how your data and AI connect is once again paramount to achieving value.
Use Case 3: Sophisticated Marketing Analytics
The opportunity
Market analytics don’t just inform how you market to individuals. These insights can also identify market-level needs and inform entire strategies, such as what products you launch and where. For example, analyzing climate change and hazard/risk data can provide a picture of shifting or emerging patterns that affect people’s insurance needs.
How AI improves marketing analytics
It can take a long time before weather, demographics, or other changes bubble up to a conscious level. But AI’s ability to leverage the latest signals, such as ATTOM’s nationwide property, transaction, and climate risk data, enables sensitive and sophisticated forecasting and projections.
With AI and these datasets, you can essentially establish an early warning system that keeps you ahead of the curve, helping you to identify emerging and new needs in specific regions or among target demographics.
Complexity/ease of deployment
As with other AI use cases, an automated solution that allows your AI to routinely access multiple, large datasets without technical intervention is important. Once you have identified what datasets to track, dashboards and other visualizations can make it easy to monitor and spot trends and changes early on.
High ROI
Think about the explosions of Zoom and Instacart in 2020—there is no better formula for success than the right product, at the right time. AI improves the discoverability of shifts and other changes that open new marketing opportunities. With the right underlying data solution and tools, this can be achieved with little ongoing effort.
Conclusion
Overwhelmed by the possibilities of AI? Start by identifying simple, yet high-value use cases such as ones in this blog. The ease of deploying these use cases will depend on your underlying data solution—and how easily it connects with your AI. Look for data sources that are uniform, up-to-date and that can easily be connected to your AI (don’t forget, these files are LARGE).
Once you have the right data and a seamless technical solution in place, start with simple use cases, such as using AI to improve the quality of your leads. From there, you can use your existing integrations to develop more sophisticated modeling and insights that can transform all your marketing activities.
