The Hair Salon Example: Latency
Note: If you are new to our group and want to know more about
the following ongoing discussion, the background is here.
Recall, you folks voted to continue the series on Latency.
for punishment, I tell ya.
Latency is a metric you can use to harness the power in these two
fundamental rules of High ROI Customer Marketing:
1. Don't spend until you have to
2. When you spend, spend at the point of
We finished off the first run at Latency with considerable
complexity; I'd like to step back now and provide some examples of how
all this works in the real world. I think this approach will
help cement some of the concepts and provide a platform for going
forward. It always does when I do speaking work, so I don't see
why it would not work here in the newsletter.
There are three main activities in a successful High ROI Customer
Marketing program: Measure, Manage, and Maximize. We'll tackle
each of these components one at a time in the Real World Examples I
present to you. First up: Tale of Two Hair Salons - Measure
Two hair salons operate in the same town, Salon A and Salon B.
Both are equally competent, one person operations and charge similar
prices for similar services and products. And both salons
There is a difference though - Salon A does not use customer data
to track and manage the CRM effort, but Salon B does. Salon B's
CRM toolset consists of a paper appointment book and a PC with a
spreadsheet program. Salon A has only a paper appointment book,
and can't really track anything.
One day the owner of Salon A is thinking:
Where has Mary Lou been? She's a high
value customer who comes in to get the whole job done - hair, nails,
massage, the works. Seems to me she hasn't been in the Salon for
a while. She's tardy in scheduling her session. I should
call her and find out when she is coming in.
The owner of Salon A is practicing CRM. High value customers
have been identified, and a change in the behavior of one of these customers has been detected. This situation
has been evaluated,
and an action to take has been decided on.
But the owner of Salon A is very busy that day, and forgets to call
Mary Lou. What's more, the owner has no system for classifying the
fact Mary Lou has not been in "for a while". How long
is a while?
Part of why the owner forgets to call Mary Lou is there is no real
urgency, she's just "tardy". But how tardy is tardy?
When should the
call be made? If there was a rule about "tardy",
perhaps there would
be more urgency to make the call. But there isn't, so it may
like a waste of time. The owner thinks later on:
She'll come in sometime soon. I'm too tired to make the call tonight.
As we sit here gazing into Salon A, some other thoughts probably come
to mind. How many Mary Lou customers are there? And how "tardy" will
they get before the owner calls them? When you are making money
cutting hair all day, it's probably hard to face calling Mary Lou
Time spent on the phone calling customers or sending them postcards
is time not spent cutting
hair, and the owner of Salon A can't afford to not cut hair. If
owner had only the time or energy to call just three Mary Lou
customers, which three would it be?
If the owner has to give up time cutting hair to make calls, these
calls better result in more business than was lost by not cutting hair
to make calls. This potentially negative outcome is called
"opportunity cost". If resources are allocated away
from an income
producing activity towards another activity, you better make sure
these resources create more value than they did before re-allocation.
If they do not, an opportunity cost has been created. The two
fundamental rules of High ROI Customer Marketing are designed to avoid
these opportunity costs:
1. Don't spend until you have to
2. When you spend, spend at the point of
Over at Salon B, the owner has been thinking along the same lines as
the owner of Salon A, about a High Value, tardy customer named Angela.
The owner thinks:
How many Angela customers do I have? If I
keep forgetting to
Angela customers, I may eventually lose them. But they always come
back. Or do they? I'm going to start Measuring Angela
customers. I'm going to start tracking "tardy" customers and find out
what this issue is about. If it's a real issue, I'll worry about
then. If it's not an issue, I can forget about it once and for
and spend my time cutting hair.
So the owner of Salon B sits down with the paper appointment book,
looks through the customer names, and enters all the "High Value"
customer names into the spreadsheet, one to a line. The owner
the choice to track high value customers in this way:
If there is anything to this "tardy Angela"
I get hurt
the most financially by losing High Value customers. If it's
going to be worth spending time on this instead of cutting hair, if I
am going to divert my resources away from cutting hair, then it will
be most worth it with high value customers. If it's not worth it
them, it won't be worth it for any customers and I can forget all
about the whole thing.
Once the high value customers are entered into a spreadsheet (about
20% of the customers are considered high value), the owner of Salon B
then enters the all the appointment dates for each high value customer
into the columns of the spreadsheet, next to each name. To keep
project manageable, the owner decides to enter only appointments for
High Value customers for the past 6 months.
The owner also creates columns to subtract the dates from each other
for each customer and find the average number of days between visits
for each customer. The spreadsheet (nothing special, off the
software) is smart enough to know these entries are dates and is able
to easily subtract them and convert the result into days, so all these
calculations are easy and take less than an hour to create.
The owner of Salon B is then astonished to discover these facts
A significant number of high value customers have not had an
appointment in 6 months, about 20% of them.
The average number of days between appointments is very similar across
all the high value customers. It is, however, not the 30 days
owner expected, but 40 days.
The owner then assumes a high value, supposedly loyal customer who has
not been to the salon in over 6 months is a lost customer - at least
for the near future. The owner then calculates the value of the
business for the 6 month period by multiplying the number of customers
lost by the average sale of $150 per trip. Needless to say, the
resulting number is a very big one, representing many days of total
sales for Salon B. The owner of Salon B then thinks:
I must be crazy for not looking at this before.
I would make
money by not cutting hair for a couple of hours a week if I could
get back even one of these high value customers. I'm going to do
something about this right away - before I lose even more high value customers. Now that I have
Measured this effect and know how
money it is costing me to not address the tardy Angela customers, I
need to Manage the process somehow. How can I set up some
"system" that will help me figure out what to do with this
data I have
discovered? How can I turn the data into an action
Over at Salon A, the owner knows the names of best customers who
"have not been in for a while". But this owner has no
system, no way to measure what the dynamics of the situation
are. How long is "a while"? But at Salon B, the
owner knows the average time between best customer visits is 40 days,
and there are customers in this group who have not had an appointment
in over 6 months. How can the owner get this business
back? The owner thinks:
I'll just mail all these best customers who
have not had an appointment in over 6 months a postcard offering them
a discount. The postcards will say, "Since you are a best
customer, you are entitled to a 15% discount if you come in for a
visit within the next two weeks". They will come in and I
will start a new relationship with them, and find out why they have
not been in.
The owner of Salon B prepares the targeted postcards, mails them
out, and awaits appointments from these best customers.
The appointments never come.
A bunch of the postcards come back as "undeliverable",
and the owner gets several phone calls from customers saying "I
now go to Salon A, take me off your mailing list".
Undaunted, the owner of Salon B reasons:
Clearly there is something wrong with this
approach. Best customers who have not had an appointment for 6
months must already be "defected" customers. They
obviously do not want to come back to me, and feel the relationship is
broken already. They have moved on and established new
relationships with other providers.
I will try a new approach with the postcards,
and will use the same offer. But this time, I will mail the
postcards out as soon as the best customer has not been in for over 40
days. Since the average best customer comes in every 40 days, a
best customer who fails to do so is not acting like a best
customer. So each week I will use my spreadsheet to
identify best customers who have not been in for 40 days, mail the
discount postcard out to them, and track the results.
After a month of mailing the weekly 40 day postcards to best
customers, the owner of Salon B sat down to analyze the program.
Of all the best customers mailed to, 25% had made new appointments,
and 75% had not. So in the short term, the owner had cut the 20%
best customer defection rate to 15%, because 1/4 of the best customers
called to make appointments at $150 each - minus the discount.
But even with the discount, the additional profits from these
customers paid for the postcard mailing many times over.
Despite this success, two things bothered the owner of Salon
B. The first was what customers who responded said when making
their discounted appointments. The second was the 75% of best
customers who did not respond. The owner thinks:
Half the customers who responded said to me,
"I'm so glad you mailed me a discount, I was planning on making
an appointment in the next week and would have made one anyway, so it
was great to get the discount". So I gave up margin and
profits I did not need to give up.
And how is it possible that so many of my
best customers never responded to my offer?
I wonder if there is a way to address these
two issues? If I could reduce the number of "would have
come in anyway" customers who got a discount, and increase the
overall response rate, I would be really making a ton of money on my
best customer retention postcard program. I have Measured
my best customer defection, and am Managing it with this
program. I wonder if there is a way to Maximize, to make
it even more profitable?
Well, fellow Driller, have you got an idea? You know Customer
Retention is all about process: Action - Reaction - Feedback -
The owner of Salon B has taken an action, and there has been a Reaction.
How should the owner go about Analyzing the Feedback?
The owner of Salon B then has an idea:
What about this group of customers who said "they would have scheduled anyway without the postcard".
Are they similar in any way?
If there is a common reaction to the postcard among these customers, perhaps there is a commonality in the behavior or backgrounds of the customers.
If I can find the key linking these customers together, perhaps I can understand why this is
happening with these customers.
The owner of salon B goes back to the CRM software (a paper appointment book and the customer spreadsheet).
The owner has entered "response date" in a spreadsheet column for each customer who responded to the postcard and any comments.
The owner sorts the customers by the responders and looks at those customers who said "would have scheduled anyway without a postcard".
For each customer who responded and said this, the owner looks the customer up in the appointment book to find
"Long hair cuts!!!!", the owner exclaims. "They all have long hair
cuts!", which the owner immediately realizes is the problem
with the discount postcard mailing program.
The owner thinks:
Best customers with long hair styles can come in
much less often than every 40 days, even through the average of all best customers is a cut every 40 days.
So customers with long hair cuts are getting the postcard too early - they're not really "defected", and schedule a planned appointment with a discount I did not have to offer.
They should get a postcard possibly at 60 days, or even 90 days or
longer after their last appointment.
Since the owner has a lot of customers with long cuts, most are getting the postcard too early for the cut.
This explains the low overall response rate.
Best customers with short cuts however, are probably getting the postcard too late.
By the time I get them in the mail and they reach the customers with short cuts, it could be too late, they may have already gone elsewhere for their
short hair cut.
The owner of Salon B resolves to recalculate the average days between appointments separately for best customers with long cuts and best customers with short cuts.
The owner divides the customer base in two - by length of cut, and finds the average time between trips of long cut customers is actually 75 days, and for short cut customers is actually 20 days.
Rethinking the retention campaign, the owner resolves to track each group individually, and
to do two types of mailings each week - one to long cut customers over 75 days since last visit, and one to short cut customers over 20 days since
the last visit.
Using the advanced CRM system (a spreadsheet program with one customer per row), the owner creates a
column for acceptable number of days since last visit - 75 days for long cut customers and 20 days for or short cut customers.
Using the date of last appointment, the owner creates a simple equation that uses today's date and last appointment date to calculate days since last visit, and to subtract this number from the number in the "acceptable" column. The salon owner thinks:
When the number in this column approaches zero or goes negative for a customer, it is time to mail the discount "where have you been" postcard.
Since each customer has an acceptable number of days since last visit based on hair cut length, the timing of the mailings should more closely reflect
whether or not the customer has actually defected.
The salon owner tests the new campaign - and it works. Not only does the owner get many fewer customers saying "thanks for the discount, would have been in anyway", the response rate among targeted best customers increases by 30%.
The program now is maximized for this level of detail - it makes even more money than it did before, and retains more customers while decreasing the cost of discounts given away.
A beautiful thing, the owner thinks. But then another Eureka moment
comes to the owner of Salon B:
If I use this system there is another benefit - I should be able to actually
forecast what my volume should be months in advance based on customers likely to schedule an appointment.
If I see a week coming up where visit volume looks to be low, I can promote to some customers and fill up empty slots, maybe give them a discount for scheduling on a specific day when my traffic is light.
That way the customer is happy because they get a special one-time discount, and I am happy because I am maximizing my revenue per day by reducing light traffic days!
Just then, the owner of Salon B hears someone walk in the door. A voice calls out, "Can we schedule appointments?"
The owner recognizes the voice - it belongs to lost best customer
Angela, the one who started this whole project by being tardy in scheduling an appointment.
Angela is the reason the owner of Salon B first asked the question, "How many tardy best customers do I have?"
But what does she mean "we"?
As the owner of Salon B comes around the corner, Angela smiles and says, "This is my friend
Mary Lou. She was going to Salon A, but is dissatisfied with the results she is getting.
She would like to try Salon B. And I need a cut too! I tried growing my hair out long, but
I decided I like it better short".
The owner of Salon B thinks: I can't predict everything, but my new
system is sure better than not predicting anything at all!
The Drilling Down book teaches you how to Measure, Manage, and Maximize
Customer Retention with proven High ROI methods.
What would you like to do
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