Case Studies
We are proud to work with clients across a diverse range of industries. Where there is data and repetitive processes, we see opportunities for impactful digital value creation. Our case studies demonstrate the broad range of our value adding capabilities.
Case Study
Data-Driven Deal Sourcing for Private Equity
Brief
An east-coast Private Equity firm established an investment vehicle to consolidate specialty real estate properties across the United States. With more than 15,000 potential targets, the PE firm needed a systematic way to source, diligence, and prioritize acquisition candidates.
Approach
Autokatalyst developed a proprietary database of the entire population of US-based properties and further enriched it with quantitative and qualitative data on each target. With over 1 million data points collected in the system, Autokatalyst then developed proprietary algorithms to identify, rank, and segment these assets based on the PE firm's investment criteria.
Results
With a data-driven sourcing system that tracked property attributes in near real-time, the investment team was able to streamline deal sourcing, outreach, and early-stage diligence, leading to significant efficiencies in deal origination. Given the scale of data gathered, the firm also gained competitive market intelligence on the broader real estate market.
Dealmakers that harness technology and take a data-driven approach in their deal sourcing efforts transact 55% more and generate internal rates of return (IRR) 8.3% percentage points higher than their peers.
SourceScrub 2023
Case Study
Pricing for B2B Services
Brief
Waste hauling firms operate in a fragment market where billion-dollar publicly-traded companies and small mom-and-pop firms compete for local waste management services. Our client, a waste management firm, had collected a large set of data but lacked internal resources to aggregate, organize, and draw out key business insights on pricing, demand, and local competition from this data.
Approach
Autokatalyst built a data-driven pricing engine for the client to determine accurate pricing for waste service contracts anywhere in the US. This pricing engine incorporated machine learning models that were built using historical data provided by the client, industry-specific data influencing business cost drivers, additional proprietary datasets identified during research, and other factors used in estimating cost and demand for commercial waste management services.
Results
The client utilized the pricing engine to assess bids and quotes from waste vendors, ensuring their own customers received quality service and competitive pricing. Additionally, Autokatalyst built a custom API so that real-time price estimation could be leveraged during sales meetings. Showcasing thier data-driven approach, pricing transparency, and speed in quote estimation, the client was able to demonstrate significant differentiation in the market.
Price management initiatives can increase a company’s margins by 2 to 7 percent in 12 months—yielding an ROI between 200 and 350 percent.
Deloitte 2012
Case Study
Predictive Maintenance for Industrial Services Firm
Brief
Our client, an industrial services firm, had developed Internet-of-Things (IoT) sensors and software applications in-house to monitor industrial equipment operations for their customers. They had collected over 18,000 hours of data from these sensors across the country, and the quantity of data continued to increase hourly as the industrial equipment was operating. The client was struggling with sensor and machine data and unable to operationalize it. They needed to increase the efficacy of detecting specific patterns of behavior with a very high degree of accuracy.
Approach
Autokatalyst performed analysis of the data and ascertained if it was possible to automate the detection of certain behaviors using machine learning. Autokatalyst then developed a cutting-edge solution for detecting and classifying specific patterns of behavior the industrial equipment were performing.
Results
The client incorporated the prediction system into their software application, which enabled additional value-add services to be provided to their customers. The client’s end customers were empowered to review valuable operational data from their industrial equipment remotely, enabling the following capabilities: accurately classified machine behavior, predictive maintenance, and moving their third-party service agreements from schedule-based contracts to on-demand contracts by leveraging the real-time data from the IoT sensors and the prediction system incorporated within the software.
Better predictive maintenance using IoT can
reduce equipment downtime by up to 50 percent and
reduce equipment capital investment by 3 to 5 percent...
In manufacturing, these savings have a
potential economic impact of nearly
$630 billion per year in 2025.
McKinsey Global Institute 2015
Case Study
Marketing Automation for Real Estate Investment Firm
Brief
A real estate development and property management firm with over 150 buildings and 8000+ units was spending considerable resources on marketing their apartments online. This client was exploring ways to streamline workflows and reduce repetitive tasks so that their marketing team could focus on initiatives with higher strategic value.
Approach
Autokatalyst deployed a Robotic Process Automation (RPA) Bot using it's OfficeBots platform to fully automate the client's entire online marketing workflow on a large social media platform. Additionally, Autokatalyst's unique solution leveraged third-party APIs and Generative AI content to enhance the effectiveness of this workflow.
Results
The client's workflow on this social media platform was completely automated, eliminating many hours of tedious repetitive work per week for their marketing team. The RPA Bot runs marketing tasks autonomously on schedule and provides task reports and statistics. Bot maintenance is also minimal as new property additions and apartment availability changes are automatically detected.
Marketing automation returns on average $5.44 for every dollar spent
Nucleus Research 2021
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