Today, we’re excited to share a case study highlighting our recent work with LPG Systems, a leader in connective tissue manipulation and cellular stimulation. LPG Systems faced challenges with outdated prospect data, hindering their marketing and sales efforts. We partnered with them to revitalize their data, applying advanced AI techniques to clean, enrich, and rank their prospects. This project not only enhanced their targeting capabilities but also laid the groundwork for potential expansion across other subsidiaries. We’re thrilled to have contributed to LPG Systems’ success and look forward to continuing our collaboration. Below, you’ll find our press release detailing the project.
About LPG Systems
LPG Systems, a pioneer in connective tissue manipulation and cellular stimulation, has established a strong presence in the aesthetic and medical industries. For decades, they have led the way in non-invasive treatments, particularly with their patented CELLU M6® technology, which uses mechanized rollers and controlled aspiration to stimulate and revitalize tissues. Their approach, grounded in scientific research and clinical validation, goes beyond cosmetic enhancement to address therapeutic needs such as lymphatic drainage, scar treatment, and rehabilitation.
LPG’s commitment to innovation is reflected in their continuous development of technologies that advance both beauty and wellness, promoting natural physiological responses within the body. With a global distribution network and a loyal community of practitioners and patients, LPG Systems stands out for its holistic approach to care, emphasizing the body’s ability to heal and regenerate. This philosophy, combined with their dedication to rigorous scientific research, has solidified their reputation as a leader in the field. Through constant refinement of their technology and expansion of its applications, LPG Systems remains at the forefront of non-invasive cellular stimulation, meeting the growing demand for effective, natural, and sustainable solutions in both aesthetic and therapeutic fields.
The Problem
LPG Systems supplies technology to businesses in the beauty, health, and wellness industries. Their treatments are used by beauty therapists, physiotherapists, and other health professionals to provide treatments for slimming, anti-aging, and therapeutic purposes such as treating scars, oedema, and burns. LPG had a list of its top target customers, but some of the data was old, out-of-date and inaccurate.
The Project
The primary users for this project were LPG UK’s marketing and sales teams, who rely on the ranked prospect list for better-targeted outreach and strategic decision-making. Secondary users included decision-makers at other LPG subsidiaries, who may adopt this system in the future based on this project’s success.
The project involved the following:
- Data cleaning (subset of 3,000 prospects): verification of active businesses listed in an excel file.
- Enrichment – finding missing relevant info such as e-mails, addresses, phone number, key contacts on remaining prospects;#
- Prospect ranking: using advanced AI techniques to rank prospects according to attractiveness (potential and odds of success).
- Additional prospect identification: identifying new prospects in the market using third-party databases
- Key account – using advanced AI to identify Key Accounts (KA) eg. chains of more than two institutes/salons etc (same owner).
- CRM Integration: syncing the updated and ranked data with LPG UK’s Odoo CRM system.
- Monitoring and reporting: tracking project progress, data quality, and providing reports to LPG UK.
- Potential expansion: scaling the project to the remaining database of 17,000 records and potentially across other LPG subsidiaries.
Charles Delelis-Fanien, General Manager of LPG Systems UK said “We were very impressed with the results of this project. NDI’s expertise in applying advanced AI-techniques to the dataset was very impressive and resulted in valuable, actionable business data. We are now looking at seeing the results of these updated data and potentially extend our work with them.”