CLV2.0 - Generating demand, not just estimating demand
CLV is a sound quantitative metric provided the data fed to it has a sound basis in customer’s fundamental approach to making choices. CLV2.0 is a revised metric inspired by Jobs theory, that addresses CLV’s limitations in addition to making growth and margin the lingua-franca of the whole organization.
Recap of the problem
CLV does not help you design or build a decent product. It's great for the nerds to crunch numbers and for C-suite to develop strategy. JTBD thinking is great to figure out customer needs, but it does not tell me which customer is valuable to the business.
We did the research
We went deep and researched the foundations of JTBD. We studied the behavioral psychology assumptions embedded in the theory. We studied the foundational ideas embedded in demand influencing theories and other alphabet soup theories surrounding the simple (but elegant idea) of Jobs.
We went meta. We looked at the philosophy of CLV, the different research streams into CLV including marketing science, finance, management science and what management strategy has to say on the topic. We realized that the foundations of customer lifetime value have long been forgotten in the frenzy that has followed big data/data science and computational prowess of contemporary Machine Learning (ML) methods.
CLV is a fantastic metric to gauge the customer population, only if the data fed into the CLV engine meets some common sense criteria, namely
The data you have on customers is indeed complete i.e. you did not leave potential customers out of your data collection process
The data should reveal customer preferences / needs, not just take customer opinion about solutions/products as reflective of their underlying needs. In nerd speak, conditional variance in data would lead you to bias your conclusions on CLV.
These two observations are not just marketing / data fixes. It has huge ramifications to how CLV centric organizations function. When the data fed to the CLV engine is biased, you end up doing 4 things inadvertently
You are orienting your whole organization around an assumption of who your customer is instead of having a fact-base on your customers. That's a lot of manpower working to serve a subset of customers when you can be serving more customers (at a lower cost).
Your product, UX/UI and operations are working their tail off and not seeing the results in the numbers. Why? Simple, customers’ actual behavior is not what they claimed it would be in the surveys. Remember, your data collection process? How can UX/UI be blamed when survey data design was flawed to begin with.
The organization has a cognitive dissonance around growth and margin discussions. Why? The organization’s structure makes acquisition, retention, churn, recurring revenues as the responsibilities of teams, none of which actually is accountable for customer seeing value in his purchase.
Your organization only reacts to demand and is never really creating demand. Why? CLV is a number, even in its predictive avatar, it does not tell you what product to build, who gains value and what their willingness to pay is.
JTBD theory addresses demand generation itself fundamentally
Firstly, Jobs theory helps you understand DEMAND GENERATION fundamentally (among other fundamental aspects of the customer), not just estimate demand and its derivatives like CLV. It helps orient your whole organization, leadership down to the copy writer and his assistant to be working on DEMAND GENERATION. The loop below tells your how. But if you call us, we can tell you more interesting stories.
CLV2.0 = CLV x JTBD
We put together a framework - CLV2.0 which will help CLV centric organizations do three things
Lower your acquisition cost
Create demand, not just calculate it.
Also you can keep your existing CLV apparatus and calc LTVs.
Who will this help?
We have assembled a team of great #MarketingScience and JTBD experts to bring the best of both worlds to the table.
CLV centric organizations - we can help audit your demand generation and estimation approach including working with your teams on short sprints.
JTBD adopters - If you have a scientific temper and would like to bring in Marketing Science mindset into the company’s thought process, we would love to speak with you.
How to reach us?
We are researchers and trusted client advisors, not your average Joe consultant looking to make a dime on your buck.
Dr. Jayanth Krishnan has worked with several organizations around the world in bringing together Management Science, Marketing Science and Organization Science concepts to life across a variety of engagements.
Alan Klement has done pioneering research in JTBD and consumer psychology and has worked with clients in software development, consumer goods, high tech, security, to name a few.
Eric White is a serious JTBD practitioner and has worked extensively with clients in telecom, healthcare, financials and business services.
You can reach us by dropping an email here. firstname.lastname@example.org