Hello Readers of Thinking Category,
I hope you got vaccinated, are healthy and Life is coming back to normalcy for you.
Last few months were intense so I took a pause. To reflect. But for now I am back with another post.
If you just landed to this newsletter, I would encourage you to explore some of the early posts:
1. Story of category from 0 to 1:
https://thinkingcategory.substack.com/p/taking-a-new-consumer-category-from
2. What is category management
https://thinkingcategory.substack.com/p/week-0-introduction-to-category-management
And many more. In case, you like it, do subscribe:
So off to the topic of today,
Few months back, I was working on this grocery data project when I observed that the company (a marketplace for grocery wholesalers ) was not collecting customers data.
The dataset had these information:
Order data - order ID linked to products, order quantity, Order value
Product data - Category, subcategory, SKU, manufacturer / brand, MRP
So I raised a question why customer data (pertaining to order IDs) is not being collected. The project I was working on was for a startup which is building branded Kirana stores. It might be the case that the Infrastructure required to collect the customer data might not be placed yet.
My assumption here is that the customers of these Kirana Stores might be their regular and frequent customers because that’s the whole point of visiting Kirana Stores - Familiarity, experience, touch and feel, or small purchases. So customer data is important to analyze what is it that drove them back to the store or push them off the cliff.
This is not the only case I have seen where customer data takes a backseat. It’s a hit and miss for even established legacy companies.
It’s a missed opportunity because with customer data, you can understand who are your customers and how frequently they visit the shop, what do they generally, what is the average basket size for them, and who belongs in the long tail.
We talk about #customer-centricity, but my experience tells many uses this in the conversation but rarely it is followed up with the action (such as Primary research with users to understand their pain-points)
CLV (Customer Lifetime value) metric helps to bring customer centricity, and long-term perspective in your decision-making with respect to acquisition cost, marketing spend.
WHAT IS CLV?
Let’s imagine when you walk into a store, a dollar / INR value is hovering around your head that implies profit that store will earn because of you in your lifetime, that’s CLV. This metric is especially helpful in subscription based business.
CLV = Total profit that is expected to be generated from one client / customer over her lifetime
OR Net present value (NPV) of all future stream of profits from a customer over the time she stays with the business
CLV CALCULATIONS
CLV = Average no. of purchase * average purchase value * average customer lifespan * average gross margin
CLV > CAC (Customer acquisition cost) to build a sustainable business in the long-term
Challenges in CLV calculations
- Data silos, disparate technology system, top leadership buy-ins not there
CLV can help you to identify your 20% of your customers that brings 80% of your business. This doesn’t mean that you ignore rest of your customers, it just means that you pay special attention to the needs of those 20% of your customers.
Now lets build an excel model to perform CLV analysis.
Imagine you are a furniture e-commerce player and you want to evaluate which set of customers are more important to you:
Enterprise business who buys office furniture from you
Homeowners
By CLV analysis, You would get to know if your customer lifetime value is higher than your customer acquisition cost. This will help you to evaluate sustainability of your business as well as Marketing budget for the year.
Assumptions:
I am assuming that my discount rate is 10%, WACC. I have seen from the past experience that my customer retention rate is 80%. That means if I have acquired 100 customers in a year, 80 would stay with me after the year ends.
Based on these information, and assumptions, I am calculating NPV for this particular set of customer. (NPV = Net Present value)
I am multiplying profit with my retention rate so that I can factor the no. of customers that stay with me every year. Every year my profit is going down because of retention rate.
So overall CLV = NPV - acquisition cost at the beginning of the year.
Lets assume for homeowner CLV is 7000 Rs.
So then clearly, my CLV is higher for homeowners. Even if my margins are not good, but say retention rate is 95%, this will make all the difference to my CLV.
I can shift my focus accordingly. That’s the essence of this entire analysis. On the face value, it seems that margins are better for Enterprise business, but future seems bright for homeowners.
You can do the same analysis for Individual customers (In case, it is B2B business). There are other ways to calculate CLV - we can keep adding complexities in it, Data scientists work with advanced model on CLV, CAC) In upcoming post, I will write about that. Because only Bite size :)
Meanwhile, ciao.
Have a great weekend.
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