> For the complete documentation index, see [llms.txt](https://docs.talkfurther.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.talkfurther.com/products/web-data/move-in-prediction.md). # Move In Prediction Digital customer acquisition for senior living communities can be challenging, and one of the biggest problems is finding the right leads to focus on. This causes wasted effort by sales teams and a lower return on investment for marketing departments. At Further, we understand this issue and have developed a solution using machine learning and AI called the Prospect Move-In Prediction Score. ![Dynamic Flows Chat Editor](/files/nKjtE9JTZydBXf0bv7vq) ## How will this help you? * **Focus on the Best Prospects**: We accurately identify prospects who are most likely to move in, enabling your sales team to prioritize their efforts and maximize their chances of success, saving time and resources. * **Curating Higher Quality Prospects**: By creating a smaller, but more targeted set of leads, we increase the chances of your sales team’s success. Sales teams experience higher engagement and motivation as they work with leads that have a higher chance of converting, resulting in improved overall team performance. * **Automated Prospect Management**: We intelligently hold back prospects that have a lower likelihood of moving in and leverage SMS and email automation to nurture and engage with them. This allows your sales team to focus on the highest quality leads while ensuring no potential opportunities slip through the cracks to maximize move-ins. * **Real-Time Insights to Improve your Marketing**: Our system provides fast and actionable feedback to your marketing team, allowing them to identify which strategies are working and which ones aren't. With a long lead time for move-ins in the senior living space, our quick feedback empowers marketing to adapt and optimize copy, campaigns, and creative elements so that you can increase move-in rates. ## How it works The Prospect Move-In Prediction Score leverages machine learning and AI algorithms to predict the likelihood of a prospect moving into a senior living community based on their digital journey. We analyze a vast amount of data, including marketing, behavioral, website engagement, conversational data, and move-ins, to train our models. * We can predict how likely a prospect is to move into a senior living community with 80% accuracy based on their digital journey alone. * 75% of move-ins come from just 18% of prospects. * The bottom 55% of leads represent only 3% of move-ins. We include this incredibly valuable information in key parts of the product such as in the new prospect email, Further’s Conversation, and Analytics Dashboards so that your sales team can prioritize the highest quality leads. Take a look at the image below to see how the scoring methodology performed with predicting move ins for over 800,000 leads! ![Machine Learning](/files/Bhp7kPc2U5OLa9YeInLh) ## Marketing teams Armed with the power of the Prospect Move-In Prediction Score, marketing teams can delve into granular insights, examining performance at the keyword, traffic source, and campaign levels in the Analytics Dashboard. While move ins are the ultimate measure marketing teams want to be evaluated by, having an intermediate metric that is instant, can give them faster feedback loops to help make positive impact more quickly. By leveraging this score, teams can make data-driven decisions which will allow them to quickly improve move-in rates and ultimately drive higher community occupancy. ![Keywords And Move In Scores](/files/acHpYaNkqJyXrySkEYqT) ## Sales teams For sales teams, time is precious. Allocating equal effort to the 62% of prospects that make up 7% of all move-ins is not the most efficient of your sales teams time. You can block low quality leads from being sent to the CRM and use automation instead to help move them down the sales funnel. You can test out blocking leads based on score and see what impact it would have had on the last 12 months of data in your further dashboard. ![Lead Quality Filter](/files/o5tY3UMbbfWfWAUtpGW3) Additionally, to ensure that top-quality leads don’t slip through the cracks, we prominently display the Move-In Prediction Score for qualified leads throughout the sales team’s workflow. This ensures that prioritizing leads becomes second nature. Our primary touchpoint is the “New Lead” email, where we emphasize prospects with a higher likelihood of moving in. ![Lead Score In Email](/files/3Ftsk5g2eLB2mCsonwHO) The beauty of the Prospect Move-In Prediction Score is its dynamism. As prospects engage more — for example by revisiting the website or delving deeper into community details — the score adjusts, enabling sales teams to recalibrate prospect prioritization. We've ensured that the Prospect Move-In Prediction Score is prominently displayed on the Further Conversations Dashboard and in your CRM, keeping it at the forefront during the entire sales journey. Regional sales managers and corporate managers can view the Analytics Dashboard, which displays the Prospect Move-In Prediction Score for keywords, traffic sources, and campaigns. This data empowers them to optimize their sales strategies and mentor their teams effectively to improve outcomes. ![Analytics](/files/dCyXXEkgG8fUhPnlAZ9X) ## In closing The Prospect Move-In Prediction Score transforms the way senior living communities approach the digital lead sales process. By accurately identifying the best prospects, our solution helps improve sales and marketing performance and ultimately increases occupancy rates. Contact your Futher Customer Success Manager today to start leveraging the Prospect Move-In Prediction Score. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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