INTRODUCTION
A mid-market private equity fund wanted to invest in a US-based digital lending fintech company. Historically, the PE fund relied upon traditional, manual, and defensive methods of due diligence. The fund wanted to get deeper data-driven due diligence insights on the company’s products, market positioning, customer beliefs, and organizational culture. JMI's data analytics team was roped in to leverage the JMI data analytics platform to get actionable insights on four broad areas, which included:
- Product Reviews: We scraped the data from community websites, aggregated internal KPIs, and used machine learning techniques to construct deep learning to curate sentiments on trends and key imperatives.
- Customer Lifetime Value: Used a probabilistic model to predict the future count of the transaction and its monetary value for each customer, potential churns, and primary market drivers that could improve top-line growth.
- Customer Acquisition Cost: Analyzed customer-level data to observe the acquisition cost across different channels and regions with diverse demographic compositions across market segments.
- Organizational Culture: Deployed a dashboard that provides insights on company attrition rate and analysis based on employee experience, gender, educational background, etc. Also, the web scrapped data from review websites to get insights on employees' beliefs about company culture.
JMI's proprietary models and data aggregation platform produced unique and powerful insights on business revenue and margin performances based on raw transaction-level data along with core business capabilities and market drivers such as production capacity, sales and distribution KPIs, cash flow, and potential trends in the competitive market, etc.
The JMI data analytics platform set up during diligence for investment evaluation was further extended to capture the company’s everyday business intelligence in order to retain the key insights and data sources that underpinned the deal thesis and value creation plan over the investment cycle.
Objective 1: Customers’ Perception of Company Products
The PE fund wanted to understand the customers’ perceptions of the various company products and their competitive positioning in the market. They specifically wanted to leverage the JMI data analytics platform to gain deeper insights into customer views for various products using social networking websites and understand product positioning relative to competitors.
JMI Implementation
Step 1: Data Ingestion
- Extracted data from the company webpage or community sites on customer reviews, which typically contain a comment, rating, usefulness, and username.
- Web scraped data from other sources that included Google reviews, Facebook, Quora, Pinterest and Twitter.
- Aggregated key business performance data to gain first-hand information on the health of the products.
Step 2: Data Pre-Processing
- Converted the unstructured data extracted from different sources into structured formats.
- Applied stemming to reduce the size of the vocabulary, remove the junk words and extract the key signals.
Step 3: Train Datasets & Build Models
- Build hybrid models combining lexicon analysis and Machine Learning Techniques.
- Used machine learning techniques to train the dataset and construct a classifier model that can identify text that expresses sentiments and patterns.
- Classified and scored the text based on positive, negative, and neutral expressions.
- Derived predictive insights on specific trends and affinity scores to access the future potential.
Step 4: Evaluation
- Ran the model with the extracted dataset on different products and analyzed the pattern.
- Used evaluation metrics such as Precision, Recall, F-Score, etc. to compare the product with competitor companies and understand its market positioning.
Step 5: Provide Insights
- Identified that 58% of customers were using a Loan Product of 60 days with 98% repayment.
- 74% of customers had positive reviews of its products and customer service, and 30% of new applicants faced difficulties with initial onboarding.
- Identified that its products were uniquely positioned to acquire novice customers relative to its competitors.
- Found that only 9% of customers opted for a 90-day loan product and had high NPAs.
Insights
- Customer Perception on each loan product.
- Areas with negative reviews and needs improvement.
- Product Positioning relative to competitor.
- Product Performance.
Objective 2: Due Diligence on Customer Acquisition Cost for the target company
JMI analyzed the cost of customer acquisition across different marketing channels of the fintech company and provided specific insights on.
- Marketing-mix across different channels with geographies and age groups.
- Quantify the Customer Acquisition Cost (CAC) and benchmark it with competitor companies.
JMI Implementation
Step 1: Data Ingestion
- Ingested the large volume of data for the last 5 years on each product from different marketing channels and aggregated this into the JMI platform.
- Scanned reports of competitor companies on keywords like customer acquisition cost, marketing spends, marketing mix etc.
Step 2: Data Cleaning
- JMI team cleaned the data and ensured that it was in a usable form and did not carry any discrepancies.
Step 3: Data Analysis
- Using high performance ML models, JMI performed multi-variate analysis to link the CAC with different marketing mix, age group and region.
- JMI team also benchmarked the CAC extracted through web scrapped reports of competitor companies to get insights on the actual CAC of the industry.
Step 4: Provide Insights
- JMI team identified that the CAC for the target company was 26% higher compared to average CAC of industry.
- Observed that digital channel was not efficient and had high turnaround time.
- 70% of customer acquisition was through broker and the cost associated with it was 30% higher than digital channel and 20% higher compared to industry average.
Step 5: Cost Optimization Strategy
- JMI suggested that the company can efficiently manage its marketing expense if they focus on acquiring customers through a digital channel within the age group of 25 to 32 years and in Tier 1 cities.
Insights
- Customer acquisition cost on different channels.
- Benchmarking CAC with competitor companies.
- Right marketing mix for cost optimization.
- Designing Marketing Campaign for efficient customer acquisition.
Objective 3: Customer Lifetime Value (CLTV) Modeling
The client wanted to understand the potential contributions made by a customer to company’s revenue across the years to estimate the customer lifetime value and gain deeper insights on customer mix which could contribute to higher revenues in the future.
JMI Implementation
Step 1: Data Ingestion
- Aggregated the customer and transaction data for the last 5 years.
- Distributed data into a quarterly transaction done by each customer.
- Scanned reports of competitor companies on keywords like churn rate, CLTV etc.
Step 2: Data Cleaning
- Removed missing values and inconsistencies in the upper- and lower-case data.
- Removed duplicate categorization and streamlined the data.
Step 3: Probabilistic Model
- Deployed statistical methods to model the probability distribution.
- Using Probabilistic models using statistical and AI techniques, the future count of transaction and monetary value of transaction was estimated.
- Correlated the company CLTV with competitor companies.
Step 4: Provide Insights
- Identified that Tier-1 city customers had higher lifetime value and 70% of them were recurring.
- Found that customers with an age range between 25-30 years had higher contribution i.e., 35% of revenue and 63% of them were frequent user.
- Identified that CLTV for target company was in line with the industry average.
- Predicted the churn rate of c.17% annually.
Step 5: Cost Optimization Strategy
- JMI team analyzed that customer acquisition cost had a major impact on CLTV.
- JMI team suggested the PE client to lower the CAC and focus on acquiring. customers within the age group of 25 - 30 years through digital channel.
Insights
- Contribution of each customer on revenue.
- Monetary value and future count of transaction.
- Churn rate.
- Predict CLTV with customer type, region and product.
- Potential impact on KPIs to access business performance in both short and long term.
Objective 4: Understanding Organization’s Culture
The PE fund was facing difficulties in getting insights into the organization’s culture. They wanted to get a clear picture on employee beliefs towards the target company and predict employee attrition rate. They further wanted to analyze the correlation between attrition rate vs employee experience, salary and education background, etc.
JMI Implementation
Step 1: Data Gathering and Ingestion
- Gathered data of employees from company MIS.
- Web scrapped data on employees’ reviews from LinkedIn, Glassdoor, Indeed and other sources.
Step 2: Data Pre-Processing
- Classified the data on employees with employee id, designation, education and experience.
- Removed the anomalies, punctuations, and junk words from scrapped data.
Step 3: Build ML Model
- Used machine learning technique to train the dataset and construct classifier model that can identify text that expresses sentiments.
- Classified and scored the employee reviews based on positive, negative and neutral sentiments.
Step 4: Data Analysis
- Used AI techniques on employees’ data to get insights on talent pool.
- Analyzed the correlation between attrition rate vs overtime, experience and salary.
Step 5: Dashboard for Visualization
- Connected BI dashboards to the dataset and created a dashboard.
- Dashboard included analysis on percentage of predicted attrition, analysis by gender, business travel, department and salary hike etc.
Step 5: Provide Insights
- Predicted attrition rate of c.17%
- Out of 17% of turnover, 40% of employees wanted to leave because of low salary.
- Employee mix constituted 70% with finance background and 20% with engineering background.
- 65% of employees were male within 3 years work experience.
Insights
- Attrition rate and reason for attrition.
- Employee mix based on gender, designation, experience and education.
- Employee beliefs on company culture.