$10BN AUM VC

Request: Confirm veracity of customer base before IPO listing
  • An education/e-learning company within the portfolio was in the process of drafting their prospectus for an IPO.
  • While all the due-diligence and checks were done, A wanted to run another round of checks to make sure that there wouldn’t be any surprises from the regulator or the markets before the stock eventually was listed.
  • Using a proprietary set of algorithms and rule engines, LightRay was able to conclusively determine that more than 35% of the customer base reported by the organization were manufactured by a third-party marketing agency on behalf of the customer.
  • Our work included analysis of more than 500GB of unstructured social media content to identify data patterns through integrated stochastic forecasting methodologies.

Texas Multi-Billion AUM PE Fund

Request: In-depth analysis of historical performance of 9 years of investment transactions.
  • Made an investment into a real estate fund with an AUM of $500M with operations in US, Europe, and Asia Pacific.
  • As a part of their annual LP reports received by their portfolio management team, there were concerns raised about the gross NAV's reported for one of the funds under management and deeper intelligence was needed.
  • Using a proprietary set of algorithms and rule engines, LightRay was able to conclusively determine that more than 35% of the customer base reported by the organization were manufactured by a third-party marketing agency on behalf of the customer.
  • Our work included analysis of more than 500GB of unstructured social media content to identify data patterns through integrated stochastic forecasting methodologies.
Request: a data-driven strategy with insight into traffic data, demographic data, competitor site selection, and more.
  • Starbucks wanted a data-driven strategy for determining site selection for their coffee shops.
  • They had a peculiar problem with respect to non-US/non-EU locations as there was a lack of credible data to help them make the decisions.
  • In the absence of data, there were instances of abuse of power by the local managements recommending sites with ulterior motives for financial gain etc.
  • We provided a comprehensive data-driven framework that aggregated data categories across 17 disparate topics for countries across emerging markets.
  • We then normalized and refined the data to triangulate optimum locations for site selection.
Request: Capture the voice of the customer through social media
  • Heineken wanted to harvest the huge social media feedback that was being provided by their consumers through Twitter, Instagram, Facebook, TikTok, Snapchat, etc. across multiple countries and languages to influence their marketing, product, logistics, packaging, and sales teams across the world.
  • Capturing the voice of their customer became critical input for their business strategy.
  • LightRay was able to aggregate and isolate historical and real-time feedback posted by product users across 78 global social media sites.
  • We created a custom sentiment analysis algorithm that distilled content into actionable nuggets of information and package them across functional categories including marketing, packaging, taste, flavor profile, product availability issues, pricing etc.
  • This information was then processed by the client's functional teams as actionable strategic insights.
Request: Enhance business listing products for emerging markets using a proprietary set of algorithms and rule engines.
  • Yext wanted to significantly enhance their abilities to list products for emerging markets - E. Europe, Russia, China, Middle East and LatAm but needed to be able to better rely on the data available in these markets.
  • Given their positioning and licensing frameworks, they weren't able to sell subscriptions to global customers unless they could fulfill this data set for their product.
  • Using a proprietary set of algorithms and rule engines, LightRay was able to first determine the priority set of industry categories for these regions.
  • Having defined these categories, we then aggregated datasets across multiple sources, formats, and languages building a contact directory service.
  • We delivered a database of 700K validated rows of business listings across these regions, allowing the client to significantly enhance their product portfolio.
Request: provide insights into IT landscapes of top 5000 companies across 12 countries.
  • ServiceNow wanted to understand the existing IT investments of their top 5,000 accounts across 12 countries with profiles of the key decision makers within the companies identified.
  • They also wanted to understand the market share of competing products by identifying the companies to which the products were sold. The need was time critical as historically Q2 was their strongest quarter for business and the data needed to be current as well within a specific timeline for it to be used for maximum impact from their sales and marketing teams.
  • LightRay combed through more than 100 GB of structured and unstructured data and ensured that the integrity of the data tallied with the ground information through primary data and sales information.

International Regulatory Agency - Financial Analysis and Research WIng

Request: identify data patterns and rule engines.
  • Wanted to identify data patterns and rule engines which could help their member organizations to: 1) predict riots and crowd disturbances in advance. 2) Identify how fissionable material is smuggled within countries. C) How to strengthen AML and KYC for nationals specifically from Azerbaijan and Barbados
  • LightRay data analysis and research engines analyzed data across 135 categories of unique, discrete and disparate data sets across 112 countries to arrive at the following rule engines that were subsequently applied as advisories by them to all their member countries.
  • A unnatural increase in the sales of pre-paid sim-cards and data plans around sensitive dates and occasions usually meant the possibility of imminent riots and crowd disturbances taking place.
  • An exodus of people with criminal records to their home-towns/ parents houses usually meant there was a big event being planned.
  • Unnatural reduction in social media chatter within the dark web was another pattern we identified.
  • For fissionable material, we uncovered a unique data pattern which was to monitor the non-radioactive inventories of the cancer wings of hospital and diagnostic centers.

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