UCSD MGT 100 Week 09
Einav et al. (2021) analyzed all US Visa CC transaction data
- $1T+ spent in 32B transactions by 428MM cards at 1MM stores from 2016-19
- Assume card~=customer
\(\text{BrandRev.} = \sum spend \equiv \sum \frac{stores}{1} \frac{cards}{stores} \frac{transactions}{cards} \frac{spend}{transactions}\)
How well does each factor explain brand revenue?
Regressed log(rev) on log RHS with merchant & year fixed effects
Market size (\(N\)): # of people who experience the core need in a given time period
- Noisy but helps inform potential returns to investments
- Typical investor's first question:
$100B market is viewed differently than a $100MM market
- How will you know if you got the right answer?
- What happens if you overestimate market size?
“Marketing myopia:” Neglecting nontraditional competitors, e.g. Zoom v. Uber or Carnival v. Whistler
“Top Down” Total Addressable Market (TAM) :
How many people have the core need?
“Bottom up” Served Available Market (TAM):
How many people pay to solve the core need?
Analyst estimates
- Often estimate SAM in $, units or volume
USA population : ~340 million
Assumption : \(TAM\approx SAM\) (why? pros, cons?)
Assumption : Avg mattress lasts 7 years (pros, cons?)
Market size \(\approx\) 47.1 million people annually
Average mattress price : $283, across all bed sizes
Market size \(\approx\) $13.3B/year
Let’s check Grand View Research & ISPA
\(M\) : Market size (we’ll estimate this)
\(t\) : Time periods
\(A(t)\) : Accumulated sales before time \(t\)
- AKA "installed base"
- A(0)=0 by assumption
\(\frac{dA(t)}{dt}\) : number of new adopters in time \(t\)
\(R(t)\) : Remaining customers who have yet to adopt, \(R(t)\equiv M-A(t)\)
\[\frac{dA(t)}{dt}=pR(t)+q\frac{A(t)}{M}R(t)\]
\(p\) : “coefficient of innovation”
\(q\) : “coefficient of imitation”
- p and q assumed constant
\[\frac{dA(t)}{dt}=pR(t)+q\frac{A(t)}{M}R(t)\]
This is a first-order diffEQ with analytic solution
\[A(t)=M\frac{1-e^{-(p+q)t}}{1+\frac{q}{p}e^{-(p+q)t}}\]
If you have sales data by time, you can use Nonlinear Least Squares to estimate \(p\), \(q\) and \(M\), i.e. choose parameters to minimize square errors \((LHS-RHS)^2\)
Or, notice that \(\frac{dA(t)}{dt}=pR(t)+q\frac{A(t)}{M}R(t)\) \(=p(M-A(t)) + q \frac{A(t)}{M}(M-A(t))\) \(=pM + (q-p)A(t)-\frac{q}{M} A(t)^2\)
We can regress \(\frac{dA(t)}{dt}\) on a quadratic in installed base
- Then recover p, q & M from the parameter estimates
- We'll do both
Extensions: Multiple markets, hazard models,
types of “influence”
\[\hat{\beta}=(X'X)^{-1}X'Y\]
\[min_\beta (Y-X\beta)'(Y-X\beta)\]
\[min_\beta [X'(Y-X\beta)]^2\]
In theory, there is no difference between theory and practice. In practice, there is.
Complicated models often offer multiple estimators
Some models have no estimators, some models have many
Subfield that studies estimators' theoretical properties: "econometrics"
Diffusion depends on Relative Advantage, Perceived Complexity, Compatibility, Observability, Risk, Divisibility (aka “Trialability”)
- Summarized 40 years of research, incredibly influential on practice
- Provided diagnostics to interpret Bass (1969)'s predictive analytics
- E.g., a prototype could be evaluated on these 6 dimensions then optimized for diffusion
Heterogeneity might drive adoption timing
- Adoption driven by consumer income, need or risk attitudes?
Markets typically evolve after introduction
- Production becomes more efficient, prices and costs fall
- New features, technology generations, safety improves
- Competitors enter and target unserved customers
- Network effects, e.g. smartphone compatibility with chargers or accessories
- Complementors, e.g. Verizon stores, iFixIt, Genius Bar
- Consumer preferences, e.g. reliability matters more with time
CLV is the most common customer revenue metric used in firms
- Expresses the firm's value of an individual customer relationship as the net present value of expected future customer profits
- Pioneered in catalogue retailers in the 1980s
- Has spread widely, but not yet everywhere
- CLV metrics enable quantification, and hence serious discussion, of novel strategies
Housing First has not solved homelessness
- "Chronic" means unhoused for one year plus
- ~28% of CA homelessness is chronic (2019)
- UT originally claimed 90% reduction, then revised downward
- Reliable efficacy metrics are rare
- Housing First has been implemented haphazardly
- UT built new apartments. CA uses shelters, SROs, vouchers
- Point of contention: require wraparound services or not? E.g. Addiction treatment, job counseling, mental health, etc
I claim: Quantification enables novel policies
- USA adopted Housing First as its preferred approach to homelessness in 2014
- CLV estimates help predict policy effects, enable ex-post evaluations
- We then can use data to refine CLV estimates and policies
Marketing campaigns should be profitable if Avg. Customer Acquisition Cost (CAC) \(<\) CLV
- Caveat: So long as acquired customers have CLV>=avg CLV of existing customers
- Often, managers impose a "fudge factor" as a speedbump
Suppose the tennis club has a chance to pay $20k for a billboard. It will be seen by 100K people with an expected conversion rate of .1%. Should we do it?
Similar “break-even” calculations possible for
- partnerships, opening new stores, price promotions, etc. Anything that requires an upfront outlay to potentially acquire new customers, increase current customer retention, or develop current customer spending
CLV popularity rose alongside CRM data systems
- e.g. Oracle, SAP, Salesforce
- Services and retailers used CLV to set customer experiences: high-CLV flyers got upgraded, high-CLV lodgers got better rooms, high-CLV shoppers got sterling return service and attention, high-CLV callers got shorter wait times and more consideration
In the 00s, consulting firms published claims that 20-30% of customers were unprofitable. Many firms tried to “fire” unprofitable customers
- American Express offered some cardholders $300 to cancel their cards. Best Buy stopped notifying some shoppers about upcoming promotions. Banks used minimum balances and teller fees to drive away some accountholders
Customers talk to each other; firing customers is a brand risk
CRM data may be incomplete, disconnected, error prone
- E.g., can you connect customer identity across different credit card #s?
- Think about measurement error in retention rate
CLV models are typically misspecified & uncertain
- CLV-based policies may be self-fulfilling
- Treat someone as unprofitable, they may act that way
Guiding Principle
- The firm and the customer share an interest in creating an optimum level of customer value. Using CLV metrics for value creation benefits everyone
- The firm and the customer have opposing interests in price. Using CLV for margin extraction creates perverse incentives for customers
- Customer dissatisfaction may reveal CLV flaws; requires careful attention
- Hence many firms now measure point-of-sale satisfaction
\(Churn\equiv 1-Retention\).
HBR (2014): “…increasing customer retention rates by 5% increases profits by 25% to 95%.”
- Avg. CAC = 5-25 * Cost of keeping a customer
- Average net margin is about 7-8% overall; or 12-13% in the S&P 500
- Small changes in revenue can have large effect on profits
Hence CLV firms focus intensely on retaining customers
- Implication: Tell the company you're leaving! You may get something
A telco in Portugal ran an unusual field experiment:
- Predicted which households would churn
- At-risk households were randomly assigned to 1 of 4 treatments:
1. Retention call to household ("ego")
2. Retention call to friend ("alter")
3. Retention call to both ego and alter
4. No retention call (control)
During each call, rep first assessed satisfaction; then offered dissatisfied customers a menu of 3 discounts for proactive retention
- Suppose you tutor a high school student for $250/month. There is a 10% chance the student will move and find a new tutor in any given month. There are six months remaining in the school year, at which point the tutoring arrangement will definitely end.
- What missing information do you need to calculate the current value of the tutoring arrangement?
An ML based approach to proactive advertiser churn prevention (Pinterest 2023)
Innovation Diffusion and New Product Growth Models (Peres et al. 2009)
Customer-Base Valuation in a Contractual Setting with Heterogeneity (Fader & Hardie 2010)
Exploring the Distribution of Customer Lifetime Value (Fader & Hardie 2017)