/Capstone2_M-A_Customer_Analytics

Using advanced customer analytics to value a company

Primary LanguageJupyter Notebook

Using Advanced Customer Analytics to Value a Company

Contents

  1. Background
  2. Motivation
  3. Wrangling Customer Data
  4. Processing & Analyzing the Data
  5. Model Selection
  6. Train & Evaluate Models
  7. HyperParameter Tuning
  8. Predictions
  9. CBCV Calculations
  10. Valuation Comparisons
  11. Opportunites going forward
  12. Lessons Learned
  13. Appendices
    • Additional Imagery
    • Works cited

Background on Valuing a Business

Company valuation traditionally has been calculated two ways

  1. Discounted Cash Flow (DCF): a frequentist approach to project historical revenues, growth and cashflow numbers forward then discounting them for todays value of money.

  1. Market Multiple: similar to pricing a home:
    • Home Price = Total SQFT * $ per SQFT from comparable homes that recently sold or are on the market
    • Company Valuation = EBITDA * Market Multiple
      • See "Median Deal Multiples from Pepperdine's 2020 Middle Market report:

Now With data science we can better predict revenues, growth and cashflows by segmenting customers into cohorts to:

  • Model churn and retention rates for each cohort
  • Model expected future amount of transactions
  • Model expected average sales per transaction
  • Model number of new customers per year
  • Calculate average profit margin per customer
  • Calculate cash flows from customers aka Customer Life Time Values (CLTV)
  • Calculate sum of CLTVs by period cohorts to provide Future Cash Flows that can be discounted to todays value This advanced analytics approach is called Customer-Based Corporate Valution (CBCV) trademarked to Theta Equity Partners, https://www.thetaequity.com/.

Motivation

Curiousness to see how the advanced analytics CBCV compares to DCF or Market Multiple valuation approach for a lower middle market company.

Financial engineering has long been the hallmark of investment firms but with the combination of Data Science and the Customer-Based Corporate Valuation (CBCV) approach they have the ability to add "Customer engineering" as another distinguishable charecteristic.

  • CBCV approach focuses on the purpose of business, building and retaining customers
  • CBCV approach compared to alternative valuation approaches provides a more useful handover from the deal team to the operations team
  • CBCV segmentation can be implemented into Customer Relationship Management (CRM) and marketing tools
  • CBCV approach emphasis customer service as the long tail customer provide the most value in terms of customer equity and cashflow

Wrangle Customer Data:

Company: "eChalk" is a supplier and installer of smart school equipment such as "smart boards"
Dataset: 6 years of customer transaction history

Report example:


Company Financials:


Key Financials takeaways:

  • $16,279,057 Revenue (trailing twelve months)
  • 26% Gross Profit Margin
  • $2,387,000 EBTIDA (trailing twelve months)
  • 0.25 WACC
  • 0.02 Monthly Discount Rate

Process & Analyze the Data:

To find CLTV we transform sales data to a RFM dataset:

  • Frequency represents the number of repeat purchases the customer has made. This means that it’s one less than the total number of purchases.
  • Recency represents the age of the customer when they made their most recent purchases. This is equal to the duration between a customer’s first purchase and their latest purchase. (Thus if they have made only 1 purchase, the recency is 0.)
  • T represents the age of the customer in whatever time units chosen (daily, in our dataset). This is equal to the duration between a customer’s first purchase and the end of the period under study.
  • Monetary_Value Total amount of Money the Customers has spent

988 unique customers
340 are repeat customers i.e. more than one purchase
34.4% are repeat customers Transactions per year
avg sales per transaction 6 yrs
avg sales per transaction 2020



Model Selection:

Model Goals

A) To predict Frequency (Number of Transactions) & Recency (Prob Alive)
B) Predict Monetary Value (Avg Sales per Transaction)
C) A * B = Sales/yr in order to forecast sales

Hyperparameters & Holdout

  • T = days
  • time = year
  • any additional
  • train on 2015-2019
  • holdout should be 2020

Models:

Model Frequency Recency Monetary_Value Output
DISCRETE
BG/NBD X Pred_Txn
BG/NBD X Prob_Alive
Gamma-Gamma X Exp_Avg_Sales
NON-DISCRETE
Pareto/NBD
POP+POISSON
MBG/NBD
TBD
BG/BB

Train & Evaluate the Model:

use Cross Validation Grid Search train on training data then evaluate on test data

HyperParameter Tuning:

t =
t =
t =
holdout =
holdout =

Predict with the Best Model:


Cohorts | Exp. Avg Value of Sales | * | Exp. Txn| * |Retention | = | LTV | * |Profit Margin | = |CLTV | \ | WACC |= |(PV) CLTV

CBCV Calculations:

Valuation Comparison:

Opportunites going forward:

Lessons Learned:

Einstein's quote on 55 minutes of an hour to solve a problem

Apendices:

Resources:

McCarthy papers
Fader papers
Hardie notes & papers
lifetimes package: https://lifetimes.readthedocs.io/en/latest/index.html Analytics Vidhya CLTV guide: https://www.analyticsvidhya.com/blog/2020/10/a-definitive-guide-for-predicting-customer-lifetime-value-clv/ Modelling CLTV for Non-Contractual Business with Python: https://towardsdatascience.com/whats-a-customer-worth-8daf183f8a4f Cohort Analysis: https://towardsdatascience.com/a-step-by-step-introduction-to-cohort-analysis-in-python-a2cbbd8460ea https://www.kdnuggets.com/2018/05/general-approaches-machine-learning-process.html https://www.tablesgenerator.com/markdown_tables