Construction & Property Management

Relevance can help you leverage unstructured data such as text and images to make better decisions & reduce your costs – just like our Publicly Listed construction & property management customer

  • Reduced Construction Costs: Revealed common defects across both commercial and residential building projects.
  • Increased Efficiency: Uncovered key lessons learnt from 1000’s of building reports.
  • Improved customer experience: Reduced the amount of time it took to respond to customer feedback surveys by automating analysis.

Objective

Our publicly listed property management customer wanted to leverage the large amounts of unstructured data they were collecting throughout the construction and property management process, to uncover patterns in common defects as well as lessons learnt.

Problem

The data they are collecting sits across multiple data sources in different formats. This includes:

● Customer surveys archived within their CRM
● Building reports in PDF
● Text descriptions of defects held within their construction app.
● Cost amount and description reports in CSV, SQL Tables & TSV
● Construction defect images in PNG format


This poses a common problem in that standard business intelligence tools are ill equipped to analyse this data.

Solution

By leveraging the Relevance AI platform, they have been able to take their unstructured data, and transform it into a format that can now be analysed using artificial intelligence, without the lengthly task of training models or labelling data. The end result is a selection of analysis dashboards that can be consumed by executives and business stakeholders that are easy to interpret and consume.

Our Data Experimentation process for Construction & Property Developers

Data-Science-Clustering-Experimentation-Flywheel

1 – Experimentation built for Data Teams

Our client’s data team took the data from multiple data sources (PDF, CRM, Excel). With no data labelling and just a few lines of code they leveraged our Python library platform to run ML algorithms to produce a dashboard.

2 – Analysis for Domain Experts

The first iteration of the dashboard was then sent to the various domain experts that really understood the context of the data.
These included Operations, Property and Customer Experience Managers.
They they then used the platforms easy to use features such as filtering and semantic search to produce a dashboard that could be easily consumed by any executive or business stakeholder.

3 – Business Decisions for Executives and Key Stakeholders

The end result was an interpretable and intuitive dashboard that can now be used to find patterns in common defects as well as lessons learnt to reduce construction costs.

“Relevance AI has not only helped uncover the actionable insights from our unstructured data that helps save millions in repeated construction mistakes, but also helped further align the business with the data team to become more data-driven”

Image of Hugh Stephens from Sked Social

GM of Data Science

ASX Listed Construction Company

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