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Here’s the presentation I gave at the IAMAI Digital Commerce event this morning:
[Published in Pluggd.in]
I am speaking at IAMAI’s conference on Digital Commerce later this week in Mumbai. I thought I would put down a few thoughts here that I believe affect ecommerce in India as an industry.
There has been an increasing amount of debate recently around the sustainability of ecommerce companies in India. I believe that a key driver of sustainability is a sharp focus on long-term customer value and a deep understanding of customer metrics. Delivering strong value to customers results in high repeat purchase rates and low customer acquisition costs, while an analytical orientation enables companies to measure key metrics and take important business decisions based on real data.
Everyone understands intuitively that repeat customers are good for business. Yet very few e-commerce companies develop a deep understanding of customer behavior, measure and analyze key metrics and tailor their business strategy and internal focus accordingly.
Understanding the value of a customer – and how to measure it – is perhaps one of the most important questions for anyone running an ecommerce business. If you don’t know what a customer is worth, you run the risk of not knowing: (i) how much you should spend on marketing/customer acquisition, (ii) how much you should spend on customer support (customer service, fulfillment etc), and (iii) the levers that drive an increase in the value of your customers (and therefore the value of your business). Companies that don’t understand customer value may be able to grow rapidly, but this will be unsustainable over time, because the costs they incur to acquire, reacquire and support their customers may greatly outweigh what those customers are worth.
All e-commerce entrepreneurs I meet share a repeat customer metric (indicating an appreciation that repeat customers are valuable), but more often than not these metrics don’t reveal insight into customer behavior. Some examples include:
- “33% of the orders last month were from repeat customers”, OR
- “50% of the customers that bought last year bought again this year”.
OK, this sounds great, but what does it actually mean and how is this data actionable for the business?
Let’s take the first statement: “33% of the orders last month were from repeat customers”. Take a company that has been in business for a couple of years and during that time served over 200,000 customers. Assume that they currently serve 21,000 customers per month. So this statement means that 7,000 customers (or <4% of their cumulative customer base) in the month had bought at some point before. Is that good? Bad? What does it mean? What actions should be taken? It’s tough to tell because this statement is meaningless without more context.
Let’s take the second statement and apply it to the same company above: “50% of the customers that bought last year bought again this year”. This implies that 100,000 existing customers bought again this year. I think we would all agree that sounds pretty good. But is it actionable? What did they buy? How much did they spend? How often did they buy again?
To help answer some of these questions and drive specific, actionable insight for an online business, it helps to think of customers a little differently. On the internet, a customer is like a store in the physical world – you need to make upfront investments (acquisition cost on the internet; capex for a physical store) which will yield a margin stream in the future. Understanding the ratio of the upfront investment to the expected margin stream, as well as how you can reduce the investment and increase the margin stream, is critical. Just as the operator of a physical store thinks about time to break-even and pay-back, operators of online stores can do the same with their customers.
Once you accept this premise, you can use cohort analysis to estimate customer lifetime value. Cohort analysis tracks the behavior of a specific ‘batch’ of customers (for example the January 2010 cohort means customers acquired in January 2010) over time. You can do this for multiple cohorts as follows:
The table below illustrates the behavior of the January cohort, which is the customers that were originally acquired in January.
The next table indicates the order value generated by the January cohort.
What we see is that the January cohort of 1,000 buyers spent a total of Rs. 29L during the year. If this business has a contribution margin (i.e. shipped revenue less COGS and variable costs such as discounts, payment gateway, shipping & handling) of 15%, then the cohort of 1,000 customers generated Rs. 4.35L of contribution margin in the year or Rs. 435 per customer. Because the number of transacting customers as a % of the starting base has begun to asymptote in 12 months, you can project this forward and calculate lifetime value over 2 or 3 years (note, this is NOT year 1 value multiplied by 2 or 3). Lets assume the 3-year lifetime value of a customer based on this analysis is approx. Rs 800. This should become the maximum allowable acquisition cost you pay to acquire a customer in steady state.
Many of the most successful e-commerce companies in our portfolio achieve lifetime value to customer acquisition cost ratios (LTV/CAC) in excess of 3:1, which enables them to grow rapidly and profitably through aggressive marketing. Since acquisition costs can only be controlled to a certain extent given media costs, competitive dynamics etc, they do this primarily by focusing on customer economics, and specifically increasing: (i) frequency of purchase, (ii) margins, and (iii) order values (through effective retention marketing and initiatives).
Most companies that measure this data carefully allocate more of their resources to retaining customers and improving customer economics than companies that don’t. After all, it’s much easier to ramp up customer acquisition if you already have systems in place to maximize the value of those customers than it is to change the DNA of an organization that focuses only on customer acquisition.