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. Gluttons 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 maximum impact
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 practice CRM.
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 seem 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 customers, right?
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 the 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 maximum impact
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 call my 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 exactly what this issue is about. If it’s a real issue, I’ll worry about it then. If it’s not an issue, I can forget about it once and for all, 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 reasons the choice to track high value customers in this way:
If there is anything to this “tardy Angela” customer thing, I get hurt the most financially by losing High Value customers. If it’s ever 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 for 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 this 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 shelf 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 about customers:
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 the 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 lost 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 more 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 much 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 kind of “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 plan?
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:
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.
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 – Repeat.
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.
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 more details.
“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.
Download the first 9 chapters of the Drilling Down book: PDF