This is the first entry in my blog that it is basically 99% opinions and almost no facts. It does not intend to be anything else than a point of view. I am also aware that I am not the first one to express similar opinions.

To my experience a lot of data in marketing is collected and used with the purpose of increasing retention and profitability of existing customers rather than improving processes around acquisition.

There aren’t universally accepted definitions, but it is commonly accepted that the discipline of using data in the best commercially viable way to increase retention is LOYALTY MARKETING.

It is also true that some marketers and entrepreneurs find this exercise a waste of money and the ROI of loyalty initiatives is quite evasive.

In general I believe there are two types of individuals:

• Those who believe experience tells us about the future
• Those who don’t

This distinction is non-trivial and there is a lot of people that think, in their hearts, that each fact of life is always so new that past experience are practically useless, better go with intuition with gut feelings. Nevertheless it is my experience (no irony here) that those individuals always approach different problems in the same way. If you have a marketer or someone at board level with this inclination, loyalty marketing and anything data driven will be difficult to implement.

The second category loves to use previous data and information to see the future in an almost magical fashion.

The data lovers will then resort to an arsenal of techniques and, going back to the main theme, will shape loyalty marketing quite like the famous behavioural psychologist B.S. Skinner conditioned the subjects of his experiments:

Reward a certain behaviour and that behaviour will, in the long run, become hard coded in the subject.

Loyalty marketers are slightly more sophisticated and permissive and seem to work like:

Find what the customer need from the brand and push the lever all the way up rewarding any interaction that satisfies the customer need.

This usually involves some research to find out which customers are actually in need of the service and product the brand provide over a long period of time and what rewards they would really appreciate. After that though, it’s Skinner all the way. The relationship dies there and an “exchange of this for that” keeps happening (buy 10 coffees and I give you one free).

This is boring, and while I am not saying at all that the work of Skinner is boring (he made a duck play foot ball!), this static way of doing marketing is definitely boring.

Unfortunately there are at least two human reasons why it is diffused:

• The data lovers love to find the needs and rewards and apply this mechanical method. It seem to make sense.
• The “gut feeling” tribe see this as pretty unsatisfactory, but also something they can understand and they might think the masses will buy into it, in other words this is a trick for the morons, the “gut feeling” individual is superior to this. But, let’s do this while the next spell of genius comes (after all everyone is doing it).

And then we have Led Zeppelin. I don’t hide the fact that I am a huge Led Zeppelin fan, but this is beside the point.The point is that Led Zeppelin have incredibly loyal fans and yet they have not given them more of the same throughout their career and that’s, to my opinion, why most Led Zeppelin fans love Bonzo, Jones, Page and Plant as much as the music they created.

I am also fond of data analytics, yet I don’t find experience (that song sold so much, let’s do another one similar) as the uniquely defining aspect of analysis; a lot of it depends on the overall strategy that precedes the analysis itself.

I believe that transactional data doesn’t need to become a prison and we are far from a world where creativity can be automated.

In loyalty marketing we miss a bit of Zeppelin attitude. I just dispose of the “gut feeling” people as a bunch of lucky individuals who are at a loss when trying to grasp complexity but arrogant enough to think they don’t need to.

To all the data lovers though, I would say: take more risks and allow for an element of surprise. Reward the right behaviour but also show your customers that this is not the end, that there is an evolving relationship.

Might be true that that Hotel is the best for me and I will go back there since I get discounts anytime I go but, my desire for novity will ultimately prevail unless, like with the Zep, the element of surprise is part of the deal.

How to use data to surprise customers?

This would be another long chain of thoughts but, it might be the case that the whole process should be a function of the brand identity. The core of the identity.

There are various tools the data scientist need to master:

1. Generalized linear models
2. Hypothesis testing
3. Principal component and factor analysis
4. Market basket analysis
5. Choice modelling
6. Optimisation (finding extremes and particular points in curves)
7. Time series analysis

and

SEGMENTATION

Now, in terms of segmentations there are more ways to do a segmentations than to combine coffee and milk. A few very popular methods are:

1. K-means with the various distances
2. Hierarchical clustering
3. Bi-clustering
4. Plain crosstabs
5. Bayesian classifiers
6. Two steps K-means
7. Latent class analysis

and more…

In particular I believe there is a way to find segments that it is underestimated: Latent class regression.

In particular this methodology can find clusters on the basis of, for example, a particular customer spend over time.

You cold, in principle, find a cluster of people that increase spend over, say, a hundred days steadily, another group that increases very steeply and a group that seems to behave the same over the time observed etc etc…

Below there’s some code in R to explain what I am talking about

#Playing around with flexmix

library(flexmix)

## Loading required package: lattice

#Simulating the data

interval <- 1:100 #Transactions over a 100 weeks
group_a <- rep(50 + rnorm(100,0,2),100) #spending the same
group_b <- rep(100 + 0.05*interval + rnorm(100,0,5),100) #spending more
group_c <- rep(150 -0.05*interval+ rnorm(100,0,5),100) #spending less
id <- list()
for(n in 1:300) {id[[n]] <- rep(n,100)}
id <- unlist(id)
data.df <- data.frame(date =rep(interval,100), amount_spent = c(group_a, group_b, group_c),id = id)

#Flexmix working its magic

model_1 <- flexmix(amount_spent ~ date | id, data = data.df, k=3)
model_1

## Call:
## flexmix(formula = amount_spent ~ date | id, data = data.df, 
##     k = 3)
## 
## Cluster sizes:
##     1     2     3 
## 10000 10000 10000 
## 
## convergence after 4 iterations

#The algorithm rightly identified three transactional trajectories
parameters(model_1, component =1)

##                        Comp.1
## coef.(Intercept) 100.99432992
## coef.date          0.03633786
## sigma              4.63147132

parameters(model_1, component =2)

##                        Comp.2
## coef.(Intercept) 149.86774002
## coef.date         -0.03905822
## sigma              4.91297606

parameters(model_1, component =3)

##                        Comp.3
## coef.(Intercept) 49.804688685
## coef.date         0.005226355
## sigma             1.916632596

#The parameters also are rightly estimated

# flexmix did the job apparently but let's check the groups compositions
# Component 1 should give us IDs between 100 and 200 etc...
data.df$cluster <- model_1@cluster
unique(data.df$id[data.df$cluster == 1])

##   [1] 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117
##  [18] 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134
##  [35] 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151
##  [52] 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168
##  [69] 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185
##  [86] 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200

The example above it’s just an illustration but, this methodology can be used in various way, e.g. in insurance to distinguish between claimants populations and detect fraudsters.

Like most analytics methodologies the applications depend on the user imagination.

I believe with segmentation technique there is a real danger of data scientists avoiding the use of various methods for the sake of simplicity.

On the other hand, to really take commercial advantage of segmentation techniques, there aren’t many shortcuts. Hard work and creativity are the only way to gain an advantage from competitors.

In short my advice is: be adventurous anytime but test how robust the segmentation is, quite to the detail.