Lifetime value (LTV) is the single most impactful metric for marketers to know since 20% of customers predictably contribute 80% of the total lifetime value. To understand this "Pareto Persona" you need to map data from every touchpoint in the customer journey, break down internal data silos, and adopt powerful frameworks like LTV for organizing & explaining data.
With lifetime value, you can optimize cost of acquisition decisions based on a persona that will have the highest satisfaction, longest lifecycle, and greatest likelihood to recommend you to their network. As a bonus, your product, sales, and customer experience teams will also benefit from knowing lifetime value, making you the hero of the day for delivering unparalleled ROI with data (possibly for the first time in your organization's history).
You can watch this presentation here: https://youtu.be/6x3Z7uRtvFc
Lifetime Value - The Only Metric That Matters (DMC September 2018)
1. Lifetime Value
The Only Metric That Matters
DMC September 2018
www.emperitas.com / 801.810.5869 / 6340 South 3000 East Suite 300 SLC, UT 84121
2. Hi. I’m Luciano Pesci, PhD…
Founder & CEO, EMPERITAS
● Team of economists & data scientists delivering Customer Lifetime Value business
intelligence so our clients can beat their competitors and build their market empire.
Founder & Director, Utah Community Research Group, Univ. of Utah
● Teach microeconomics, data science, applied research, & American economic history.
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3. Quick Lifetime Value Poll
● Raise your hand if you currently have
access to an LTV metric in your role.
● Keep them raised if you’re confident in
its accuracy (to ~5% of the true value).
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*Only 42% Know Their LTV: goo.gl/PwXreG
4. Where We’re Going Today
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*You can review this equation in detail here: goo.gl/WuY5UF
5. Today’s Presentation Outline
● Define lifetime value (LTV) & all of its components.
● Explain LTV, its use cases, & common pitfalls.
● Show a fully worked LTV example for DMC’s annual event.
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7. Differing Definitions of LTV
● “A way to understand a customer’s
revenue potential.” -Google at DMC 2017
● “A measure of the profit you can expect to
generate from a customer over the entire
time they do business with you.” -Forbes*
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*The Only Metric That Matters: goo.gl/MiE4h2
8. A.K.A. Customer Lifetime Value (CLV)
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*Google Trends (2004-Present): goo.gl/gkdjNh
● Searches for “Lifetime Value”
& “Customer Lifetime Value”
are highly correlated.
● Utah is a top 5 search region.
○ 80% of the searches focus on
how to calculate the value.
9. Formal Economic Definition
● The total value from a customer over the
lifetime of their relationship with your
brand (from initial awareness to death).
● Can be measured in revenue or profit.
○ Involves one of the most complicated things to
measure in econ: willingness to pay (over time).
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10. LTV’s 3 Key Components
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● “Total value” means capturing everything
in the customer journey that adds value.
● Can be broken down into 3 components:
○ Monetary value
○ Non-Monetary value
○ Future value
11. Monetary Component
● Historical monetary contribution to lifetime value
is usually modelled via the RFM approach.*
○ Only uses purchase data, which is a weakness.
● Seasonality affects this a lot in some industries.
○ Should control for change points like a new
product release, entry of a competitor, etc.
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*RFM Model Explained: goo.gl/1kpAMr
12. Non-Monetary Component
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● Can be a large contribution to LTV and is usually
modelled with Net Promoter Score (NPS).
○ Based on the idea that “word of mouth” is
still the most powerful marketing channel.*
● Overall satisfaction (OSAT) is another key
metric for understanding non-monetary value.
*One Number You Need To Know To Grow: goo.gl/vmFSft
13. Future Value Component
● Includes both monetary & non-monetary
components that will happen in the future.
○ Should also include a measure of consumer surplus,
the value you haven’t captured yet (but could).
● Impacted by churn & rebounding, requires a
present discounted value model approach.*
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*Present Value & Discounting: goo.gl/K7eyKG
14. *Exploit the Product Life Cycle: goo.gl/Q4eQom
Time Effects: Product Life Cycle*
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Product Dev
Introduction
Growth
Maturity
Death
Customer Discovery
Product-Market Fit Research
Competitive Intel
User Acceptance Testing
Lifetime Value & Persona Optimization
Project Pivot Research
REVENUE
PROFITS
15. Product Life Cycle Length?
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*Shake Weight Google Trends (2004-Present): goo.gl/Rp1CfU
16. LTV Data Origins*
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*Interpreting Data Like A Pro: youtu.be/SirK0SSBeZg
● Since a complete LTV model requires data
from every touchpoint in the journey, it’s
important to understand the data origin:
○ Observational Data
○ Survey Data
○ Experimental Data
● These are complements, not substitutes.
17. Origin - Observational Data
● Anything you can learn without directly
talking to humans. Often captured by
digital platforms (martech, apps, etc).
○ If you haven’t been using it, it’ll need some TLC.
● A strength is that it shows what happened,
but a weakness is it doesn’t tell you why.
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18. Origin - Survey Data
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● Anything you can learn from directly talking
to humans. Usually captured via qualitative
interviews & quantitative surveys.
● A strength is that it shows why something
happened, but a weakness is that it’s
attitudinal & people lie or make mistakes.
19. Origin - Experimental Data
● Anything you can learn from talking to or
observing humans in a predetermined
design for understanding causal patterns.
● Provides the strongest predictive power,
but can be difficult to design effectively.
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20. Picking Your LTV Data
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● The type of data you use for LTV will be
constrained by what’s actually available.
○ Or what you can get in the future.
● LTV has the highest ROI of any metric &
should inform every decision you make.
22. Doing Better By LTV
● You can be doing well at the expense of
doing amazing, and never even know it…
● If you’re relying on luck (fortuna) it’s just a
matter of time until a competitor learns
your real LTV & easily out maneuvers you.
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23. The Pareto Persona
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*Lazy Ant Study: eoht.info/page/Lazy+ant+study
● Within LTV there’s a “Pareto Persona.”
○ Related to the 80/20 rule in marketing.
○ It’s been repeatedly validated in nature.*
● Means you have to move beyond the
average LTV value to understand personas.
○ The ultimate goal is knowing individual LTV.
24. Marketing Use Case For LTV
● LTV should guide customer acquisition cost
decisions because it includes everything
from the journey, not just the next stage.
● Using LTV, Overstock has been able to
more than double their return on ad spend.
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25. Sales Use Case For LTV
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● Sales leads should be scored based on
their LTV, since it includes the probability of
a successful high value conversion.
● Using LTV, IBM saw a 10x revenue increase
without changing their marketing budget.
26. Product Use Case For LTV
● Product development is expensive, so using
LTV to prioritize feature requests of highest
value users is key to efficiency gains.
● Using LTV, Chatbooks has streamlined their
dev efforts to focus on increasing profit.
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27. CX Use Case For LTV
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● It can be 25x more expensive to replace an
existing customer, LTV allows for the kind
of personalization that reduces churn.
● Using LTV, Netflix cut off their mail-based
service to those with a net negative value.
28. What Can Go Wrong?
● There are 4 common pitfalls that derail
efforts to effectively use lifetime value:
○ Failing to overcome a cold start
○ Failing to get to an individual level value
○ Getting stuck with revenue (instead of profit)
○ Making incorrect (simplifying) assumptions
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29. Problem - Cold Start
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*Cold Start Problem: goo.gl/rN3bSs
● When first attempting to use LTV, you may
not have the right data or enough data.
● Even if it’s available, often the quality is
terrible & you have to start recollecting.
30. Problem - No Individual Value
● Average values for LTV are a natural
first step, but you can’t stop there.
● Personas are the next best alternative, but
eventually you need individual-level values.
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31. Problem - Stuck On Revenue
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● You may be forced to use revenue when
you initially create your LTV model, but you
have to move beyond that to profit.
● This requires really solid cost accounting
(at product & individual customer levels).
32. Problem - Making Bad Assumptions
● Making simplifying assumptions for your LTV
model a necessary art (not a science).
● These need constant validation because
underlying drivers (patterns) can change.
○ New products, new competitors, & changing
preferences can affect assumptions instantly.
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33. Test. Validate. Repeat.
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*Agile Analytics: goo.gl/ZpxwKV
● The only way to avoid these 4 common
pitfalls is to use the scientific method &
demonstrate reproducibility via testing.
● You should approach this like a Bayesian:
○ Update what you already know given new
information, don’t just throw out the old info.
35. Guessing For A Gladius
● Time for another gladius game!
○ Can you guess the lifetime value of
DMC’s “Pareto Persona” to within $10?
● Tweet your answer to @EmperitasSG
○ We’ll notify winners (via Twitter) & you
can leave tonight with a new sword.
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39. Process Of Thinking Broadly
1. Create SMART goals & do a data audit
2. Roadmap the entire project (visually)
3. Do secondary research & competitive intel
4. Conduct qualitative interviews
5. Field a quantitative survey
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40. SMART Goals & Data Audit*
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*DMC SMART Goals: goo.gl/kMNzYN
● “If you don't know where you're going,
you'll probably end up somewhere else.”
● It’s the most important step in the project.
○ Define goals, measures of success, & due dates
○ Assign directly responsible individuals (DRIs)
○ Identify data & key performance indicators (KPIs)
■ For DMC it was Eventbrite, Hubspot, & Qualtrics
42. Secondary Research
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*DMC Secondary Research: goo.gl/Lhqpvf
● There’s a wealth of existing info about any
research problem, including the LTV of
digital marketing conference attendees.*
● Learning from this information created
better qual & quant instruments for DMC.
43. Qualitative Interviews (IDIs)
● The next step in any LTV project is
having qualitative conversations.
○ Structured but not rigid in form.*
● We interviewed actual customers
who’d attended previous DMCs.
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*DMC IDI Script: goo.gl/HVkGxB
44. Quantitative Survey
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*DMC Survey Preview: goo.gl/nuxeoD
● Building on the secondary
research & qual interviews, a
survey provides LTV precision.*
● By far, the best tool for this job
is Qualtrics. It’s easy & powerful.
○ You can embed observational data.
46. Process Of Mining Deeply
1. Manually inspect data using a data map
2. Perform extract, transform & load (ETL)
3. Perform exploratory data analysis (EDA)
4. Run descriptive analysis (like clustering)
5. Build predictive models
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47. Manual Inspection
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*DMC Survey Data Map: goo.gl/azNejR
● Whatever you do, don’t skip this step.
○ Seriously...don’t do it.
● You need to make sure you understand
what the rows & columns represent.
○ Compare the data to a data map.*
○ Ensure you understanding value codings.
48. ETL - Melting & Casting*
● Often, the data you need to use for
LTV isn’t in the correct form for analysis.
○ 50% of your time may be spent cleaning.**
● There are 2 procedures you’ll have to do:
○ Turn multiple columns into rows (melting)
○ Turn multiple rows into columns (casting)
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*Melting & Casting Data In R: goo.gl/rdF1EA
**Emperitas Survey Data Cleaning Checklist: goo.gl/nKT8P8
49. Exploratory Data Analysis (EDA)
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● You can’t just jump into predictive
modeling. Once your LTV data is cleaned
you need to fully explore all variables.
● Understand the shape, center, & spread.
○ Can be done with 5-number summaries & plots.
○ For LTV, this included ~20 variables in total.
50. Never Ending EDA
● EDA isn’t a one-and-done process.
○ Plan for multiple waves of exploratory analysis.
● For the DMC survey data, we did
5 waves of exploratory analysis on
the variables used to calculate LTV.
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51. Descriptive Analysis (Clustering)
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● After exploring the data, the
next step is descriptive analysis.
● For DMC, this included clustering
(to find personas) & change
point analysis (for ticket sales).
*DMC Persona Clustering R Script Example: goo.gl/BdPvdz
54. Reproducibility (Via R)
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*DMC LTV R Script Example: goo.gl/h31Wbc
● All of your LTV results should be
reproducible at a moment’s notice.
● It also allows you to begin to
automate analytical processes.
○ Use R or macros in Excel.
56. Process Of Explaining Simply
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1. Create dashboards where needed
2. Map the entire customer journey
3. Make the pareto persona sheet
4. Build a LTV 360 report & recording
5. Compile all deliverables in a final folder
58. Customer Journey Map
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*Preview DMC Customer Journey Map Here: goo.gl/FzpuXd
● Visualizations are the key to
expressing complex LTV ideas.
● The journey map is perfect for
organizing multiple variables in
an easy-to-understand way.*
59. Pareto Persona
● It’s important to give a face to the
customer described in your LTV model.
○ Many personas are possible, Pareto is best.
● Persona sheets are a perfect way to
map many different data points into
an easy-to-understand format.
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*Preview DMC Pareto Persona Here: goo.gl/2eEtfp
60. LTV 360 Report & Recording
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*Sign Up To Get The Report: emperitas.com/DMC
● The ultimate deliverable is a
deep-dive LTV report answering
each of the project SMART goals.
● Should be heavy with visuals &
built to be presented in person.
○ We always provide a recording that
walks through the report findings.
61. Benchmark Organization
● You’ll have multiple pieces from the project:
○ SMART Goals & Secondary Research Sheets
○ Qual/Quant Instruments, Data, & Analytics Code
○ Journey Map, Personas, & LTV 360 Report/Recording
● If you can’t find something within your final
deliverables in 30-seconds, it isn’t organized.
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62. Truck Factor Proofing
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● A gauge of your project effectiveness is
whether it could survive your demise:
○ Would someone else be able to pick up where you
left off if you were (fortune forbid) hit by a truck?
● If the answer is no, you need to make
adjustments to prepare for the future...
63. Looking Ahead To 2019
● The emphasis on reproducibility &
organization has another benefit:
○ Continual improvement of the LTV model.
● For DMC, this means improving their
customer experience & profitably
growing it into a national event.
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65. What We Covered Today...
● Defined lifetime value (LTV) & all of its components.
● Explained LTV, its use cases, & common pitfalls.
● Showed a fully worked LTV example for DMC.
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67. You Should Read - [CLV - The Only Metric That Matters]*
Authored by H.O. Maycotte, CEO of Pilosa.
Cliff Notes: As a measure of the amount of profit you can
expect to generate from a customer over the entire time
they do business with you, LTV tells you a lot. Yet it’s hard for
organizations to calculate because of disconnected data silos.
Unifying data and operations is essential for succeeding with CLV.
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*Read The Forbes Article Here: goo.gl/MiE4h2
68. You Should Watch - [Calculating Your Customer Lifetime Value]*
Authored by Luciano Pesci, CEO of Emperitas.
Cliff Notes: Not all customers are created equal. Finding the
average customer lifetime value is a quick win for your
organization, and you have the data necessary to do it.
But to beat your competition for the best customers you need
individual-level insights about the value of each customer.
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*Watch The Video Lecture Here: youtu.be/iCX-afWhmZ4
