Real estate: tailor and improve your sales proposal’s relevance though AI
AI can help boost sales teams' customer insights! It's what a key player in the French real estate market uses to help push more sustainability services onto the market for its customers. Ekimetrics has developed a powerful tool for analyzing prospects' social and environmental commitments, enabling property developers and asset managers to better position their offers and build their sales pitch.
What we did
Challenge
- Facilitate sales teams’ access to in-depth analyses of their prospects' CSR commitments and developments
- Identify the most relevant service offers for each prospect based on their priorities
- Identify new customers potentially interested in these sustainable real estate offers
Our approach
- Compile business requirements to build the use cases through various workshops
- Gather public CSR data and structure it for the use cases
- Identify each customer's CSR commitments and develop an engine to make sustainable real estate offer recommendations
Outcome
- Over 90 organizations analyzed from a CSR perspective using the customer insights tool
- 12 targeted sustainable real estate offers
- A relevant tool and interface used by 4 different types of professionals
Challenge
A business wishing to take up a new commercial field swiftly must enable its teams to quickly develop their skills in this area. But gathering and synthesizing customer data to build a sales pitch and a tailored, relevant service offering is laborious and time-consuming.A French real estate heavyweight seeking to support its customers in the sustainable transition of their real estate assets decided to use artificial intelligence to boost the CSR analysis capabilities of its sales teams. The aim was to make it easier for them to grasp sensitive topics for their customers/prospects such as carbon neutrality, reducing their energy consumption, preserving biodiversity, and the well-being of their employees in the workplace. And, above all, to build offers matching their customers’ "green” requirements. This new commercial approach was driven by the imperative to reduce the carbon emissions of services sold (Scope 3), the need for transparency on "green" revenues (in line with the European taxonomy) and the strategy for transforming their real estate business.
Our approach
To meet this demand, Ekimetrics developed an advanced and user-friendly ESG analysis tool. Its design and deployment were sequenced into four major phases:
- Phase 1: compiling the requirements of the sales teams to develop the targeted in-depth analysis grid (based on an initial scope of some one hundred identified priority businesses).
- Phase 2:gathering ESG data from the websites of current and potential customers (business and CSR reports, values and statement of purpose, press releases, etc.)
- Phase 3: querying the gathered data to identify the prospect's CSR commitments in connection with the group's sustainable real estate offers, using generative AI models
- Phase 4:structuring and deploying the tool with a mobile interface for on-the-go access, incorporating business language in the formulation of analyses and allowing integration and display of CRM data.
Outcome
In just four weeks, Ekimetrics created the tool's industrialization platform, developing the first use cases and a sales recommendation engine for a dozen offers.The tool analyzed over 90 business listed in the Group's CRM according to ESG criteria, characterizing their business models and ESG strategies. This analysis included areas of interest to the customer, such as carbon footprint, energy, biodiversity and the circular economy. Over 1,300 files from public data were also gathered and analyzed, including documents rarely included in conventional analyses.Moreover, the tool made it easier to match company priorities to offers promoted by the sales teams, such as energy and water management service offerings, building greening and the installation of soft mobility infrastructure.Large-scale deployment of the product is in progress, in order to:
- Extend analysis to virtually any business or organization in near-real time
- Integrate other types of offers into the recommendation engine (e.g.buildings with specific environmental characteristics)
- Improve response quality and tailoring
- Make the interface more user-friendly and business-oriented, and promote its adoption
