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GLOBAL FINANCIAL & INVESTMENT RESEARCH

PROVIDING ANALYTICAL SUPPORT TO FINANCIAL FIRMS AND CORPORATES

HEDGE FUND

|
Client Testimonials

1500+EMPLOYEES

Across Global offices with Corporate HQ in New York City

1997ESTABLISHED

20+ years of track record

400+CLIENTS SERVED

In US, Europe and Asia including Fortune 500

$2BILLION TRANSACTIONS SUPPORTED

Globally across multiple sectors


SERVICE OFFERINGS

INVESTMENT BANKING

We are working with investment banking firms to help improve productivity,
reduce costs, and become more agile. Supporting them with CIMs, transaction
research, financial modeling & valuations, due diligence, and building pitch
decks.

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PRIVATE EQUITY

Our comprehensive suite of value-added services spans the entire private capital
markets spectrum, encompassing deal origination, thorough evaluation, and
diligent portfolio monitoring, all of which empower investment professionals to
stay at the forefront of the industry.

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HEDGE FUNDS

We provide support to hedge funds by offering specialized services that enhance
operational efficiency and strategic decision-making. These services include
comprehensive risk analysis, market research, and financial modeling, enabling
hedge funds to make informed investment decisions.

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VENTURE CAPITAL

JMI play a pivotal role in supporting venture capital firms by offering
specialized services such as market research, deal sourcing, financial modeling,
due diligence, valuation, and portfolio monitoring. Our expertise enables VCs to
make informed investment decisions, identify promising opportunities, and
optimize the performance of their portfolios.

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FIXED INCOME & CREDIT RESEARCH

Our comprehensive suite of value-added services by leveraging expertise in
credit analysis, risk management, and strategic investment to fuel growth and
maximize returns to empower your financial endeavours.

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DATA ANALYTICS

We help firms navigate in today's world of complex data. JMI provides
custom-made solutions by leveraging our AI, ML, NLP, and Visualization
Expertise.

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REAL ESTATE INVESTMENTS

We help firms navigate in today's world of complex data. JMI provides
custom-made solutions by leveraging our AI, ML, NLP, and Visualization
Expertise.

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FAMILY OFFICES

We specialize in tailored financial solutions designed specifically for family
offices, supporting the preservation and growth of generational wealth. Our
expertise spans administrative services, asset management, corporate finance,
and regulatory compliance, catering to the diverse needs of family offices.

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VIEW OUR INSIGHTS

 1. 
 2. 

INVESTMENT BANKING

FINANCIAL MODELLING AND BUSINESS VALUATION – MERGER ANALYSIS

Preparing a quarterly model with separate financial statements for each of
the...

Learn More


HEDGE FUND

FINANCIAL MODELS BUILDOUT & MAINTENANCE

Tracking TMT sector and identifying key economic and market-driven factors that
affect...

Learn More


PRIVATE EQUITY

INVESTMENT SCREENING

Preparing a comprehensive database of all mid and large corporates across the
sectors...

Learn More


VENTURE CAPITAL

EXIT VALUATION FOR A PORTFOLIO COMPANY



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PRIVATE EQUITY

INVESTMENT RESEARCH & DUE DILIGENCE USING AI

Combine Alternative data into financial modeling to provide a clearer picture...

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PRIVATE EQUITY

DEAL ORIGINATION

Identify Potential Investment Opportunities

Learn More


HEDGE FUND

QUANTITATIVE STATISTICAL ANALYSIS OF US EQUITY INDICES



Learn More


REAL ESTATE INVESTMENT

INVESTOR PRESENTATION



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GET A MORE COMPREHENSIVE VIEW WITH OUR INSIGHTS

View all Our Insights


CLIENT TESTIMONIALS

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ANDREW CARMAN

Chief Executive Officer,SQN Capital


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JEFF JULIANE



Founder, Juliane Advisors

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RAYMOND PACINI



Chief Financial Officer, Modiv Inc.

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About Us Case Studies Our Customers Contact Us Career
© Jean Martin Inc. 2024 | All Rights Reserved.


EXIT VALUATION FOR A PORTFOLIO COMPANY

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SITUATION

 * US-based VC firm requested JMI to help its Partners in analyzing exit
   valuation for their renewable energy portfolio company.
 * JMI was asked to provide the outline of the specifics to be reviewed for the
   valuation exercise, including details of the portfolio company.

SOLUTION

 * Performed a screening for public and private entities in the renewable energy
   space and compiled a list based on their offerings.
 * Filtered and reviewed renewable energy companies with a detailed analysis and
   history of all the investments and acquisitions in the sector.
 * Computed their current and forward trading multiples and arrived at a range
   of exit multiples.
 * Provided the range of valuation multiples based on the analysis and helped
   identify KPI enabling premium valuation.



OUTCOME

 * Provided a detailed review and synopsis of deals and exit valuations within
   the US renewable energy space and the exit valuation range.
 * Created a deal enhancer document emphasizing the strategic advantage of the
   portfolio company.
 * Created a list of potential strategic (competitors, value chain etc.) and
   financial buyers (sale to PE fund) for the portfolio company.
 * Helped the VC firm take an informed decision regarding exit opportunities,
   enabling them to realize optimum return on investment.


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INVESTOR PRESENTATION

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SITUATION

A United-States based Real Estate Fund wanted to raise money for acquisition of
one property.

 * They wanted JMI to prepare an Investor Deck which highlight all the major
   details about the property.
 * The Project involved creating a buy side case which include detailed
   community and deal overview, market opportunity, location - area highlights,
   Cash flows, return analysis, business plan and risk analysis.

SOLUTION

 * The JMI Team through extensive research and market analysis found that
   properties like that were in high demand and low supply which made it a
   lucrative opportunity.
 * The JMI team collated aerial drone shots and ground photos to present them in
   the deck to help better understand the surrounding locality.
 * Carried out extensive market research of the surrounding area and found out
   the median income, largest employers, demographics of the locality.
 * Analyzed the surrounding area and found out the key centers such as big
   retailers, shopping malls and other points of interest in and around the
   area.
 * Evaluated comparable apartments in the area and analyzed the rents and prices
   of such properties to gauge the fair value of the portfolio property.
 * Highlighted the Business plan of the property and listed the key business
   risks.



OUTCOME

 * JMI helped the client in preparing strategic presentation for the
   acquisition.
 * JMI team delivered a 20-25 pages investment deck which was used by the client
   to showcase it to potential investors.
 * JMI also helped the client in reaching out to potential buyers.

SAMPLE WORKS


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DEAL ORIGINATION

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SITUATION

 * Our client, a private equity firm that investments in early stage growth
   companies wanted to adopt a data driven deal origination process.
 * The client wanted to build a platform to identify potential targets for deal
   origination based on specific criteria and investment strategies.

JMI IMPLEMENTATION

JMI’s solution comprised of the following three-phased approach:

 * Data Extraction: Extracting data through web scrapping tool based on specific
   criteria such as company brand value, customer sentiment, product portfolio,
   social media posts, customer review, management team and other unstructured
   data.
 * Predictive Analytics: By looking at relevant data patterns and trends, model
   can conduct early detection of the companies with high growth opportunities
   and decide whether it’s an opportunity worth pursuing or not.
 * Deal Sourcing: JMI also helped the PE firm to identify the target companies &
   assisted in engaging company management.



RESULTS

 * Increase in deal origination: 30%
 * Savings in overall processing time: 40%


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QUANTITATIVE STATISTICAL ANALYSIS OF US EQUITY INDICES

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S&P 500 (SPX): JUST 5 %TO 7% SHY OF CRITICAL 4150 WHICH MAY INITIATE LONG-TERM
SELLING.



DESCRIPTION

Created in 1957, the S&P 500 was the first US market cap-weighted stock market
index. The index includes 500 leading companies and covers approximately 80% of
available market capitalization. Today, it’s the basis of many listed and
over-the-counter investment instruments.

INDEX CHARACTERISTICS

The index is a capitalization-weighted index and the 10 largest companies in the
index account for 28.1% of the market capitalization of the index.

Number of Constituents 505 Constituent Market cap (USD Mn) Mean Total Market Cap
65,445 Largest Total Market cap 2,243,557 Smallest Total Market Cap 3,299 Median
Total Market Cap 25,919 Weight largest Constituent (%) 6.7 6.7 Weight Top 10
Constituents (%) 28.1

SECTOR BREAKDOWN

IT sector companies constitute 27.8% of total market cap followed by consumer
discretionary and financials companies. The 10 largest companies in the index,
in order of weighting, are Apple Inc., Microsoft Corp., Amazon.com. Facebook
Inc, Tesla Inc, Alphabet Inc (class A&C), Berkshire Hathaway, J&J, and JP Morgan
Chase & Co.



HISTORICAL TREND



STATISTICAL ANALYSIS



INSIGHTS ON S&P 500

Based on quantitative analysis of last 100 years of S&P 500 data, we found that
S&P 500 trades

 * 66% of the time 1 SD (standard deviation) above its 10 years mean, 28% of
   time 2SD above its 10 years mean and only 2% of time 3SD above its 10 years
   mean.
 * Historically SPX touches 3SD after a gap of 10-20 years and it normally
   results in 30% correction eventually in the next 2-3 years.
 * Currently, S&P 500 is trading at around 3,850 which is just 7% shy of 3SD
   event at 4150. The last instances when the SPX crossed 3SD were in 1988 and
   1987 and in both the events, it eventually resulted into 30% correction in
   the next 2 to 3 years.
 * The current SPX set up is unforeseen in the last 20 years and hence not
   captured empirically by financial research houses.

We believe that SPX may move towards a maximum of 4150 however risk-reward is
not much in favor and hence eventfully may correct to 3000 levels in the next
2-3 years.

RISK & RETURN

The index has highest annualized return of 18.6% in last 3 years with annualized
risk of 11.0%.

Annualized Risk Annualized Return 3 Years 18.6% 11.0% 5 Years 15.0% 15.5% 10
Years 13.5% 12.8%

Risk is defined as standard deviation calculated based on total returns using
monthly values. All information as on January 30th, 2021

NASDAQ: SHOWING SIMILAR PATTERNS JUST BEFORE 2000 DOTCOM BURST



DESCRIPTION

The Nasdaq Composite Index measures all Nasdaq domestic and international-based
common stocks listed on the Nasdaq Stock Market. The index is a large market
cap-weighted index of more than 2,500 stocks, ADRs, and real estate investment
trusts. The composition of the Nasdaq composite is heavily weighted towards
companies in the Information Technology Sector.

SECTOR BREAKDOWN

As of December 30th, 2020, the industry weights of the Nasdaq composite Index’s
individual securities are Technology at 48.1%, Consumer services at 19.5%,
Health Care at 10.1%, Consumer Goods at 8%, Industrials at 5.9% and Financials
at 5.4%.

HISTORICAL TREND



STATISTICAL ANALYSIS



INSIGHTS ON NASDAQ

Based on quantitative analysis of the last 35 years Nasdaq data, we found that
Nasdaq trades.

 * 62% of the time 1 SD (standard deviation) above its 10 years mean, 52% of
   time 2SD above its 10 years mean, 11% of time 3SD above its 10 years mean and
   only 1% of time 4SD above its 10 years mean.
 * Currently, Nasdaq is trading around 13,500 and has crossed 3SD event at 13000
   and is 15% shy from 4SD event which is at 15500. Nasdaq touched 4SD only once
   in its history just before 2000 dotcom crash which resulted into its losing
   resulted in 83% erosion of value. Nasdaq has crossed 3SD recently only after
   a gap of 20 years since 2000.

We believe that upside in Nasdaq is limited to maximum 10-15% from here while
downside could be very high as it is moving into bubble zone not seen in the
recent times.

DOW JONES INDUSTRIAL AVERAGE (DJIA): OFFERS BEST RISK-REWARD AMONGST THE LARGER
INDICES



DESCRIPTION

The Dow Jones Industrial Average is a price-weighted measure of 30 US blue chip
companies. The index covers all industries except transportation and utilities.

SECTOR BREAKDOWN

IT sector constitute 22% of its weight followed by 17.9% for healthcare and
16.4% for industrial sectors.



HISTORICAL TREND



STATISTICAL ANALYSIS



INSIGHTS ON DJIA

Based on quantitative analysis of last 30 years, we found that DJI trades

 * 66% of time 1 SD (standard deviation) above its 10 years mean, 31% of time 2
   SD above its 10 years mean and 0% of the time 3 SD above its 10 years mean.
 * Currently, DJI has crossed 30,200 level which is 2 SD above its 10 years
   mean. Dow has historically been comfortable at 2SD and hence we expect Dow
   jones to move up further from these levels as 3SD event is 20% away from the
   current levels.

On comparing DJI index with other indexes, we believe that DJI can offer better
risk-reward in the near future compared to NASDAQ, SPX and RUT.

RISK & RETURN

DJIA has highest annualized return in last 5 years with annualized risk of 15.5%

Annualized Risk Annualized Return 3 Years 18.8% 6.3% 5 Years 15.5% 14.6% 10
Years 13.6% 11.6%

Risk is defined as standard deviation calculated based on total returns using
monthly values. All information as on January 30th, 2021.

RUSSELL 2000 (RUT): HAS MOVED INTO UNCHARTERED TERRITORIES



DESCRIPTION

The Russell 2000 Index measures the performance of the small-cap segment of the
US equity universe. The Russell 2000 Index is a subset of the Russell 3000 Index
representing approximately 10% of the total market capitalization of that index.
As of January 31st , 2021, the weighted average market capitalization for a
company in the index is around $3.8 billion, the median market cap is $922
million. The market cap of the largest company in the index is $28.65 billion.

SECTOR BREAKDOWN

As of December 31st, 2020, the sector with the largest weight in the index is
Health Care sector which accounts for 21.1% followed by Industrials and
Financials, each account for 15.3%. The smallest contribution is by the energy
sector.

HISTORICAL TREND



STATISTICAL ANALYSIS



INSIGHTS ON RUT

Based on quantitative analysis of the last 33 years data, we found that RUT
trades

 * 79% of time 1 SD (standard deviation) above its 10 years mean, 32% of time 2
   SD above its 10 years mean and only 1% of time 3 SD above its 10 years mean.
 * Currently, RUT is trading at 2,150 and has crossed 3SD event. Prior to 2021,
   RUT crossed 3SD event only 2 times in the last 30 years, in 1997 and 2013-14.
   In both cases RUT fell by 30% in the next 2-3 years.

We believe that there is no major upside left in RUT and risk-reward is not at
all in the favor of any long trades in RUT. We expect RUT to fall to 1500 levels
in the next 2-3 years.

RISK & RETURN

Russell 2000 has highest annualized return of 16.5% in last 5 years with
annualized risk of c.21%.

Annualized Risk Annualized Return 3 Years 25.3% 11.1% 5 Years 20.9% 16.5% 10
Years 18.8% 11.7%

All information as on January 31st, 2021

CONCLUSION

 * Russell 2000 is into unchartered territories and investors should be
   cautious.
 * Amongst all the 4 indices, DJIA looks the best from risk-reward perspective
   for the next 3-5 years.
 * SPX is just 5-7% shy of the critical 3 Std Deviation event which happened
   just before 2000 dotcom burst and 1987 Black Monday Crash.


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FINANCIAL MODELS BUILDOUT & MAINTENANCE

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PROBLEM STATEMENT

Our Client, a US-based hedge fund, wanted us to track the Technology, Media and
Telecom sector and identify key economic factors that affect the revenue growth,
operating cost, cost of capital etc. of portfolio companies and help them in
updating the financial model with latest market updates.

JMI IMPLEMENTATION

 * JMI team conducted deep research on Technology, Media and Telecom sector and
   identified the key factors including Government and Regulatory Changes, GDP
   growth, Inflation and Sectoral revenue growth etc.
 * Tracked the market-driven factors like liquidity, momentum and fund flow for
   each stock and analyzed the co-variance in relation with mean return,
   industry growth etc.
 * Conducted detailed fundamental research and analyzed the financial model of
   firms such as Intel, Apple, Sony, ASML, Live Nation etc. and figured out key
   metrics for each company.
 * JMI team tracked & maintained the latest financials and key parameters for
   each company and updated the model accordingly
 * Models were updated on a daily basis and any significant impact was
   highlighted to the client immediately.



In-depth Study on Sector to identify key metrics



Tracked the market-driven factors like liquidity, momentum and fund flow



Conducted Fundamental Research on Portfolio Company



Keep track on latest financials and key parameter for each company



Updated the model with key parameters on daily basis

RESULTS

 * JMI team kept track of the latest financials and identified the key economic
   and market-driven factors that have an impact on the TMT sector.
 * JMI team delivered the updated financial model with the latest market updates
   on a daily basis.


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FINANCIAL MODELLING AND BUSINESS VALUATION – MERGER ANALYSIS

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SITUATION

 * A US-headquartered IB firm wanted support on valuing a US-based packaged
   Juice manufacturing company.
 * The Project involved doing a merger analysis of two entities followed by
   valuation across various methodologies.

JMI IMPLEMENTATION

 * Understood the business structure of two entities from the management to
   create a business model.
 * Forecasted a 5 year quarterly model with separate financial statements for
   each of the two entities and subsequently merged the financials to form a
   merger model.
 * Calculated the synergies and impact on the capital structure of the merger
   entity.
 * JMI team examined that the merger would result in saving operational cost of
   c.$4M and also bring monopoly in the market.
 * Valued the company with DCF method and market based approach including
   company comparable (EV/EBITDA, EV/Adj. EBITDA and P/E multiples) and
   precedent transaction.



VALUE DELIVERED

 * The output involved a financial model with different scenarios (Base, upside
   and downside), Sensitivity and football field analysis.
 * Figured out the post-merger valuation of company at EV/Adj. EBITDA multiple
   of 4.2x through Football field analysis.


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INVESTMENT SCREENING

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SITUATION

JMI worked closely with senior team members in creating an entry strategy for
European Markets.

 * Phase I - Construct a comprehensive database of all mid and large cap
   companies across sectors.
 * Phase II - Create multiple filters to shorten the focus on particular sectors
   and companies.

JMI IMPLEMENTATION

 * Began the search by classifying 10,000+ companies.
 * Acquired the data list from multiple sources such as Bloomberg, industry
   associations, and trade journals.
 * Filtered and cleaned the list down to 500 companies based on size and
   profitability.
 * As per client’s request, focused on 5 industry segments to understand the
   dynamics.
 * Profiled 66 companies with a special focus on management and their
   affiliations, company financials, operations, and valuation ratios.



VALUE DELIVERED

 * Submitted a comprehensive database of all mid and large sized European
   corporates.
 * Profiled identified companies on key operating and performance metrics.
 * JMI also organized management meetings with 15 European companies shortlisted
   on the basis of Company’s analysis.


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INVESTMENT RESEARCH & DUE DILIGENCE USING AI

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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.


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