(From chapters 17 and 18 of my second book ADVANCED CUSTOMER ANALYTICS, Kogan Page, 2017)

So, what is loyalty?  Should be easy to define, we all know what it is, right?  In the context of analytics, loyalty is when a consumer becomes a customer and likes the brand enough to come back again.  This customer likes the brand enough to continue coming back and even spread the word to their family and friends, even recommend it to their peers and network, even be an ambassador for the brand.  Note that at its base loyalty is about the customer, it is NOT about the firm or brand.  That is, loyalty analytics is (as it always should be) focused on the customer–what does the customer need, what does the customer like, what is the customer sensitive to, what will it take for the customer to become emotionally involved with the brand, what touchpoints are most important to a customer?  Often this means defining loyalty in terms of customer segments, especially how loyal a segment is, which needs or benefits does the brand satisfy for one segment over another, what is the range of loyalty–is it merely transactionally loyal or is a segment emotionally involved as an ambassador for the brand?

So the first issue is that loyalty should be designed as a win-win and viewed primarily from the customer’s POV, not the firm’s.  Note that most loyalty analytics, and even most loyalty books (even the pillar of loyalty books, Reichfeld’s The Loyalty Effect) is mostly about the firm.  That position tries to explain why loyalty helps a firm, how a firm should be interested in loyalty, what metrics should the firm track to gauge its customer’s loyalty, how understanding loyalty and increasing loyalty is a benefit to the firm.  This is short-sighted.  This approach will produce only a pareto effect achieved quickly and never increased.

While loyalty no doubt has an important value to the firm, the right framework is obsessing on the customer: their experience, their wants or needs, what is valuable to THEM.  This has everything to do with program design.  Why would a firm put a loyalty program in place?  If a firm is trying to collect members in order to send them emails about promotions and discounts, that is NOT a loyalty program, it is an email club.  That may have some value, especially if the firm’s products require a discount in order to buy, but that should not be called a loyalty program.  One thing to learn when understanding loyalty from a customer’s POV is that not all customers want the same thing, not all customers care about a discount.  (This is what elasticity modelling is all about.)  Some of them want something else!  Remember there are four Ps in tactical marketing and PRICE is only one of them.



There is a range of loyalty from none to transactional (rational) to brand (emotional).  The point of loyalty analytics is to understand where on this spectrum a customer or segment is and learn how to incentivize and change their behavior to move up the scale.  If done aright, this is not only for the customer’s or segment’s benefit, it is of benefit to the firm.  Some customers or segments will not move, or that it costs too much to get them to move on the spectrum, and that is a valuable insight!




Note that there is actually no such thing as a blatant entity called or quantified as “loyalty”.  It is a latent variable.  The idea is that it is like intelligence, which is also unquantifiable as itself; it can only be indirectly measured as something like a score on an IQ test, which in turn measures dimensions of intelligence: spatial ability, logic, mathematics, verbal skills, etc.  Same is true for loyalty.  It can be seen and surmised by other actions.

So let’s use our behavioral segmentation based on customer transactions and responses to marcomm.  We are interested in how loyal each segment is, which is not necessarily the same thing as how much they spend or how many transactions they have.  So we do primary marketing research and ask questions about opinions and attitudes around price, value, quality and satisfaction.  These metrics will show a range of loyalty.  We also ask about share of voice, competitive density and the convenience of our stores compared to our competitors.

See the below loyalty framework.  It posits that there has been a behavior segmentation finished.  Different segments score differently on loyalty metrics. One segment is emotionally (brand) loyal and the other is transactionally loyal.

Let’s say we have survey data on segment responders including the below attitudes and metrics: PRICE, QUALITY, VALUE, SATISFACTION, SHARE OF VOICE, COMPETITIVE DENSITY and CONVENIENCE.  Using SEM, these variables will score on the loyalty spectrum, from zero loyalty to transitional loyalty up to emotional loyalty.  Thus we can ascertain how loyalty and with what dimension each segment is.

The model above tries to put a framework together that says consumer behavior (transactions, responses, etc.) is caused by a spectrum of loyalty (from none to transactional to emotional) which are in turn caused by attitudes around price, value, satisfaction and quality as well as opinions / metrics of operational logistics like convenience, share of voice and competitive density.

So the general analytic idea is that there are no such metrics or quantities as emotional or transactional loyalty.  These are latent variables.  But adding these variables helps explain the behavior of customers purchasing and customers responding.  This latent variable is discovered by a factor analysis-type technique used in SEM.  That is, the manifest variables indirectly show the influence of the latent variable and that latent variable is “teased out” and labeled.

(A quick note about the difference between transactional and emotional loyalty should clarify this important point.  It is possible for a customer to appear very loyal in terms of buying a lot of products, having a short time between purchases, responding to marcomm, etc., but not be in fact actually very loyal.  These are heavy purchasers because there might not be any competitors around, or our stores are very convenient or our share of voice is comparatively large.  Thus it’s important to know how “loyal” customers are, independent of other dimensions.  That is, a transactionally loyal customers may jump ship if competitors move in near their location, or change their share of voice.)

The results below are from applying the loyalty model to two different segments, say X and Y.  The segments were defined by (transactions and marcomm response) behavior.  The question is how loyal (what kind of loyalty) they are and what can be done about it.  Let’s say that each segment has generally the same metrics on transactions and responses.  Segment X scores as a transactionally loyal customer.  Note the parameter estimates of convenience and competitive density are very high and significant while share of voice is strong and negative.  These are traditional indications of the transactionally loyal segment.  Note also high and positive impacts of attitudes around price and quality.  And recognize that most of the variables on the emotional path are insignificant.



Path variable parm est st error t value
price 5.65 3.23 1.75
quality 6.21 1.65 3.75
value 3.03 2.07 1.47
satisfaction 1.35 0.66 2.05
convenience 5.22 0.75 6.96
competition 2.66 0.99 2.68
share of voice -1.55 1.03 -1.51
Path variable parm est st error t value
price 0.03 2.66 0.01
quality 0.56 1.07 0.53
value 1.04 2.36 0.44
satisfaction 1.66 1.03 1.62
convenience 1.99 1.66 1.2
competition 0.66 2.04 0.32
share of voice 2.55 1.69 1.51



Now, a segment that scores as a strong transactionally loyal only segment is something of a red flag.  This is especially true if they LOOK like they are loyal based on their number and amount of purchases.

How can we use the above model to move the segment from mere transactionally loyal to emotionally loyal?  The answer is in the emotional loyal path.  The single largest impact is share of voice and that is a metric we can (somewhat) control.  There is a business case around what is the cost to spend and increase our relative share of voice applied against the added security (and perhaps increased purchasing) of a segment that evolves into emotionally loyal.  See that share of voice is negative in the transactional path?  As SOV increases this segment is less transactionally and more emotionally loyal.

Now let’s look at the opposite kind of loyalty, the brand or emotional kind.  These are customers that love our brand, no matter what.  View the output below for segment Y, which scores mostly as an emotionally loyal group.  Note on the emotional path convenience and competitive density are negative.  This segment is so connected to the brand that even if it is inconvenient to go to our store they go anyway and even if more competition moves in these customers come to our store anyway.  This is emotional loyalty.  You see also that on the emotional path, while price is positive it’s insignificant and quality is very small.  It should be no surprise that both value and satisfaction are high.  On the transactional path none of those metrics are significant.



Path variable parm est st error t value
price -1.27 5.65 -0.22
quality 2.07 6.24 0.33
value 2.07 1.65 1.25
satisfaction 0.03 5.07 0.01
convenience 0.23 0.2 1.17
competition 0.04 0.02 1.8
share of voice -2.65 1.54 -1.72
Path variable parm est st error t value
price 3.25 3.04 1.07
quality 0.24 0.12 2.06
value 1.26 0.76 1.67
satisfaction 3.23 1.23 2.63
convenience -3.65 1.26 -2.91
competition -2.07 0.56 -3.66
share of voice 1.27 0.87 1.45



This is the power of SEM, hypothesizing and testing a latent variable.  This latent variable accounts for movement in the customer transactions and customer responses.  If only a blatant or manifest model was used the fit would not have been so well and the insights (differentiating between the two kinds of loyalty) would not be realized.  So is that cool, or what?

Structural Equation Modelling (SEM) is a powerful systems method especially in dealing with latent variables.  This has great importance into subjects like satisfaction in terms of loyalty and quantifying various degrees of loyalty.


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