Gone

Recall our Life Table for first-time buyers (click here).

What happens to the Monthly Rate after Month = 12?

It's pretty much over, isn't it?

One month after the customer was acquired, 8.12% of first-time buyers who haven't repurchased yet buy in Month = 01.

Fifteen months after the customer was acquired, 0.86% of first-time buyers who haven't repurchased yet buy in Month = 15.

This is why a Welcome Program is so darn important. If you don't capture the customer when the customer is INTERESTED in what you have to say, you don't get other opportunities.

Ultimately, marketing is divided into three core functions.

1. Awareness / Customer Acquisition.
2. Welcome Program.
3. Customer Loyalty.

We constantly read about (1) and (3), don't we??

We almost never think about (2), do we??

And yet, (2) is critically important.

12 Months After a First Purchase

Recall our Life Table for first-time buyers (click here).

Read down the MONTHLY RATE column. You see the high level of response in the first few months after a customer becomes a buyer. Then response quickly sinks. Now look at what happens at Month = 12.

Tell me what you think is happening there?

This outcome is common in my project work ... customers "reawaken" briefly at Month = 12. 

Say a customer purchased for the first time on Cyber Monday. That customer will "reawaken" twelve months later ... which ironically enough would be next year's Cyber Monday.

This timeframe (Month = 12) is the final timeframe where you (the marketer or analyst or Executive) make an effort with your Welcome Program. It's your last hurrah!

A Compressed Timeframe

There are two columns in our Life Table (click here to see the table - the table isn't emulating properly via email). 

The "Monthly Rate" is the probability of a customer who has yet to repurchase through "x" months repurchasing that month. Tell me what you observe as you look down that column?

The "Cumm Rebuy" column represents the cumulative probability of a cohort of new customers repurchasing over time. After twenty-four months, 39.1% of first-time buyers (in our example ... actual data) have purchased for a second time.

Read down that column (Cumm Rebuy). How many months does it take for half of the audience to repurchase?

• About two (2) months.

In other words, if you don't convert the customer (quickly), it quickly becomes unlikely that the customer will repurchase.

Look down the "Monthly Rate" column ... go to month = 17. If the customer has not purchased for a second time after sixteen (16) months, the customer has a 0.75% chance of buying again in month = 17. Yeah, in other words, the customer has close to no chance of repurchasing that month.

The customer is highly responsive in months 0/1/2/3, is somewhat responsive in months 4/5/6, then begins to lapse, and by month = 14 the customer is dormant.

As marketers, we think we can get the customer at month = 14 to "reactivate". Let's say we increased repurchase rates that month by a whopping 30% (which won't happen). The incremental repurchase rate changes ... from 0.97% to 0.97*1.3 = 1.26% ... still close to zero.

Meanwhile, imagine increase rebuy rates by just 10% for customers at month = 2 ... the rate that month goes from 4.6% to 5.1%.

Go after your new customers during the compressed timeframe when they are most likely to respond. Don't let your opportunity slip away, ok?

Why Does A Welcome Program Matter? Look At Months 1-2

Let's look at life table results from a recent project.

This table is for customers who just purchased for the first time. Each row represents months since first purchase. In the month of a first purchase (month = 0), 7.63% of customers buy for a second time ... that's a big number. A BIG number.

If the customer does not purchase for a second time in the remainder of the acquisition month, we move down one row. In the first full month on the file, the 1x buyer repurchases at a rate of 8.12%. That's also a BIG number! After a month-and-a-half, 15.1% of first-time buyers have already repurchased for a second time.

In the next post, we'll move down the table. Your homework assignment? Preview the rest of the table and be ready to construct a story.

Welcome Program

I always face a challenge from marketers when I talk about implementing a Welcome Program. When I tell marketers that a Welcome Program generates a ton of profit, I get the kind of blank stares that scientists get on their face when somebody tells them they won't be wearing a mask to fight a virus.

The reality is that marketers don't want to implement a Welcome Program ... because the program doesn't fit into a campaign-centric style of marketing.

The reality is that when I've observed Welcome Programs, they work.

At the very end of my tenure at Nordstrom, we experimented with a Welcome Program. If a customer bought for the first time, bought online, and lived within 10 miles of a store, my team sent the nearest store manager the phone number and/or email of the customer who just purchased. The store manager then reached out to contact the customer within 7 days of the first purchase, welcoming the customer into the brand, offering to help the customer in any way possible.

An online newbie had a 30% chance of buying again in the next year. 

An online newbie contacted by the store manager had a 50% chance of buying again next year, with an approximate 30% chance of buying again in the next month (based on what I remember and the relationship we fit to the data).

It's not everyday that you increase annual rebuy rates from 30% to 50% for a segment of customers, now is it?

Shortly after I began my consulting career I worked with a B2B brand that executed a Welcome Program ... a different email cadence, a different print cadence, and outbound calls / emails to first-time buyers. The results were staggeringly positive.

In B2B marketing, it is common for somebody to reach out to the customer, and it is not unusual to see different print/email campaigns for first-time buyers ... in B2C? It is rare to observe a credible Welcome Program.

So we're going to spend some time talking about "why" you should have a Welcome Program ... we'll analyze data that supports the case for having a credible program. More on the topic tomorrow, ok?

Even Loyal Customers Quickly Equalize

Look down the column where "Freq = 15", a column that represents customers who purchased 11-15 times in their history.

At recency = 14 the customer has a 44% chance of buying again. This customer, remember, has 11-15 life-to-date orders.

Now look at recency = 01 / Freq = 02:

• Annual Rebuy Rate = 44%.

These two customers are equal.

• Frequency = 11-15, Recency = 15 months.
• Frequency = 2, Recency = 1 month.

Even your loyal buyers quickly equalize themselves, constantly trending in a negative direction unless they purchase again.

This is one of the main reasons why customer acquisition is so darn important. We think we are dealing with a loyal customer base. We simply aren't. We deal with customers who achieve loyal status and then begin to immediately decay. We constantly need new customers, don't we?

Make Sure You Control For Key Attributes

When you control for recency, frequency, monetary value, channel, merchandise preference, and price ... you'll learn that certain attributes that appear important at an "indicator level" are not important at all, or are important for a very short period of time ... or are still very important. Anything is possible. But you have to control for key factors first.

Let me tell you a story about a client ... this client wanted me to prove that their loyalty program was "working". It wasn't working ... loyal customers in the loyalty program were spending 10% less per year and loyal customers not in the loyalty program were spending 10% less per year and just about every customer segment was off 10% year-over-year. The business just wasn't resonating with customers, and the loyalty program wasn't helping.

So that's what I presented.

Guess what?

The loyalty vendor didn't like what I shared. Go figure!!

So the Loyalty vendor puts together a slide that has a horribly biased bar chart ... the slide shows that customers with a Loyalty Indicator = Yes were 160% (or whatever the percentage was, I don't recall the exact number) more valuable than everybody else and therefore the Loyalty program was generating enormous value. The slide used words like "PROOF" and "VALUE" in all caps, so of course you had no choice but to believe the vendor ... a vendor with a vested interest in protecting the program defended by the "indicator variable".

I ran about 50 separate queries. 

I ran a query segmenting Mens customers from all other customers. Guess what? Mens buyers were more valuable.

I ran a query segmenting Online visitors from all other customers. Guess what? Online visitors were more valuable.

I ran a query segmenting discount/promo buyers from everybody else. Guess what? These buyers were more valuable.

I ran a query segmenting liquidations buyers from everybody else. Guess what? These buyers were more valuable.

In fact ... in all cases (for that client) the chosen attribute suggested customers were more valuable if they aligned with the chosen attribute.

Why did this happen? Because of the highly biased nature of the queries that some vendors and some marketers construct to prove their point. 

• The attribute isn't what matters.
• The fact that good customers do many things means that you can select nearly any attribute and many / most good customers will possess that attribute and therefore the attribute will show that customers are better.

Don't believe me?

Run the queries for yourself on your own data.

Seriously. Go. Now!!

Meanwhile, there are times when indicator variables are truly important. In a recent analysis I saw that an email subscriber variable (1 = yes, 0 = no) showed a 45% lift in a segmentation analysis ... but get this ... it showed a 52% lift in a logistic regression after controlling for recency and frequency.

In other words, I'm asking you to do some work ... be a bit more sophisticated than normal. Your indicator variable might well be meaningful and important. Prove it by controlling for other factors, ok?