Telecom giant uses generative AI service to save 3x more on market insights reporting

At a glance

This case study showcases how a leading Australian telecommunications company transformed their market insights reporting process using generative AI and intelligent automation. Our innovative solution delivered the following:

  • 3x cost savings compared to legacy insight software products
  • Dramatically improved employee satisfaction within the marketing insights team
  • Enhanced ability to identify market threats and opportunities earlier
  • Fully automated report generation and distribution to over 4,000 users.

The client

A leading Australian telecommunications and technology company.

The challenge

The client’s market insights reporting process had five employees reviewing, summarising and categorising 600 to 700 industry and competitor-relevant online articles daily. Once gathered, the data was compiled into a comprehensive report and distributed to relevant users.

The client wanted a solution that would automatically scan feeds, collate and send a finalised report to users.

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Our solution

Innovior’s solution used a combination of intelligent automation and generative artificial intelligence (AI) to deliver a fully automated report that could be sent out to over 4,000 users. Using a mix of Power Automate, Python, MySQL and Open AI, the articles could be automatically extracted from the feeds and summarised efficiently. The large language model could also identify the industry, company and market sentiment from the articles - an important element for the report.

Once collated, the information was delivered in a PDF format and included links to the source articles allowing easy drill down by users. The automation solution was delivered as a service to the client, which drastically reduced infrastructure or internal IT costs. The service included future scope to add new feeds and enhance the possible insights as the information database grows.

The results

By implementing Innovior’s fully automated marketing insights reporting process:

The service is two to three times more cost-effective than legacy insight software products.
The service is two to three times more cost-effective than legacy insight software products.
Employee satisfaction within the marketing insights team improved dramatically with the removal of manual and repetitive tasks.
Employee satisfaction within the marketing insights team improved dramatically with the removal of manual and repetitive tasks.
The insights generated from the report have been used to successfully identify market and competitor threats earlier than previously possible, while also suggesting opportunities to differentiate.
The insights generated from the report have been used to successfully identify market and competitor threats earlier than previously possible, while also suggesting opportunities to differentiate.

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Frequently asked questions

Automated report generation is the process of using software and artificial intelligence to collect, analyse and compile data into structured reports without manual intervention. This technology can significantly reduce the time and effort required to create regular business reports.

To automate reports, you can use a combination of tools such as:

  • Data collection software (e.g. web scraping tools)
  • Data analysis platforms (e.g. Python, R, or SQL)
  • Artificial intelligence and machine learning algorithms
  • Report generation tools (e.g. Power BI, Tableau)
  • Workflow automation software (e.g. Power Automate).
Yes, both SQL and Python are powerful tools for automating reports. SQL is excellent for querying databases and generating structured data, while Python offers extensive libraries for data analysis, machine learning and report generation. In this case study, we used a combination of Python and SQL as part of our automated reporting solution.

When selecting a report automation tool, consider the following factors:

  • Your specific reporting needs and data sources
  • Integration capabilities with your existing systems
  • Scalability and flexibility of the solution
  • Ease of use and learning curve for your team
  • Cost-effectiveness compared to manual processes
  • Support for advanced features like AI and machine learning.