The day you sign a client is the day you start losing them, says the (legendary) fictional ad man Don Draper. Makes sense? Let’s say I replace the word “Client” in the above quote with the word “Customer” or “partner” or “employee” or even personal relationships, the quote will probably still hold. So is loyalty an output of a program designed to apply brakes to slow-down the inevitable outcome of (more or less all types of) commercial relationships? Or is #loyalty a Human Trait? This article is about REPURCHASE, third of the 3 R s of automotive #Revisit, #Refer & #Repurchase.
The ideal loyalty journey is that base #satisfaction leads to revisits, referrals & finally to repurchase. At the same time, it is possible to be Loyal + Dissatisfied & vice versa but for now let’s look at end results to see how loyalty is generally understood (in a vehicle sense. I talk vehicles because that’s where I have spent more or less all my professional life till date) So say Loyalty % = repeat customers / total customers . Or is it ?
Lets look at this hypothetical vehicle business (table below). The table shows only new customer acquisition. At the start of operations Year 1, two customers C1 & C2 are acquired , three customers in 2nd Year (C3/C4/C5), four customers in 3rd year , five in 4th year and six customers in 5th year bringing total customer population to 20 by end of 5th year. Here each customer is buying 1 car in a given year.
Now let’s assume that every 3 years people in general buy another vehicle to either replace the one they have or add to their existing fleet (i.e 1st year customer returns in 4th year , 2nd year customer in 5th year & so on) and, that they use the same name/address/contact details to buy the vehicle again. Now let’s look at the table below which gives the combined Visualisation of New Vehicle sales & Repeat sales with some customers coming back to the brand in subsequent years.
Now lets look at Year 4 with the formula #Repeat Loyalty % = Repeat Customers / Total Customers which is as follows
- In 4th year repeat customers are C1 , C4 & C7 and my total population before the start of 4th year is 9. Because 3 of those 9 give me business again so my repeat rate is 3/9 = 33%. Can it be looked at any differently ? lets see
- A possible counter to the above analysis is that C4 Buys 2 vehs within 2 years (Year 2 and Year 4) and C7 buys 2 vehs within one year itself (Year 3 and Year 4) so they have repurchased too soon w.r.t 3 year logic. And they belong to wrong vintage (being 2nd & 3rd year customers) so i consider them as outliers & remove them from my analysis. So i will now look only at what my 1st year customers C1 & C2 did in the 4th year. Well, One of them did repurchase so my loyalty is = 1/2 =50% ? Can it still be looked at differently ? let’s see again
- Now we Look at only the 4th year (row only), & we see that total 8 vehs are sold in 4th year and 3 of them came from already existing customers and 5 from new customers so loyalty = 3/8 = 38% ? This logic begins to take away the loyalty from human behaviour to VIN numbers
So we see 3 different results (33%, 38% and 50%) in an example of 20 customers. Are all three correct or is it torturing the data till it confesses ? Try the same exercise with 5th year repurchases in the above data and you will see what i mean.
I feel it is too easy in it’s current form & should be measured in a continuum. Allow me to explain. Say on any given day (cars are bought every day and not in yearly batches hence continuum), what is the ideal maximum number of customers who should have re-purchased our brand by that day (based on their Vintage, Kms / hours run , satisfaction levels, service retention etc) w.r.t how many actually did.
Of course, there will be customers who would have driven those required kms / hours, turned up for all regular services, had no complaints (sorry “concerns” !) yet did not repurchase our brand, for the simple reason that they did not repurchase any brand at all, trying to get the maximum possible usage from their existing vehicle . Does that make them not-loyal? i don’t think so. Should we exclude them from the denominator? Maybe we should. But, how do we know who is who AND who is doing what? The answer to that, my friends, is the Holy Grail of the business in which i have grown up (so far at least 🙂 ).
In my own small way, as an act of paying obeisance to the business where i learnt all that i know, I made an honest attempt to create a data continuum on the above lines (for the purpose of targeting). The results were indeed surprising. For now, let me sign off with what the vehicle buyer keeps saying (without getting heard at times)- Fool me once, shame on you. Fool me twice, shame on me.
Comments are welcome. The author Amit Chand is an Expert on Topics of CRM , Loyalty , Digital Marketing, Customer Experience & Customer Journey mapping. Can be contacted at firstname.lastname@example.org. You can follow him on @amitchand
The author has rich experience in the area of CRM/CX/CSAT & can be contacted for related topics
Guest Post BY Amit Chand
CX Lifecycle Empath|WCGW Intuit Sales|Marketing|Operations|Service|CRM|Maverick Data|Analytics|Digital|Media|Mass|Luxury