Answering critics

Our critics have been few, and rather kind (nothing of substance has been raised).

Now and then a marketing guru issues a thinly disguised advertisement for their consulting services that tries to have a go at the laws and strategy conclusions in How Brands Grow.  They usually say something like:

“Our data confirms that larger market share brands have much higher market penetration BUT our whizz-bang proprietary metric also correlates with market share, and this proves that it drives sales growth, profits, share price, and whether or not you will be promoted to CMO”.

Often some obscure statistical analysis is vaguely mentioned, along with colourful charts, and buzzwords like:
algorithm
machine learning
emotional resonance
neuroscience

And sexy sounding (but meaningless) metrics along the lines of:
brand love
growth keys
brand velocity
true commitment
loyalty intensity

All of this should raise warning bells amongst all but the most gullible.

Let me explain the common mistakes….

Ehrenberg-Bass say brands grow only by acquiring new customers.
These critics somehow missed the word “double” in Double Jeopardy.  Larger brands have higher penetration, and all their loyalty metrics are a bit higher too, including any attitudinal metrics like satisfaction, trust, bonding… you name it.

Brands with more sales in any time period, are bought by more people in that time period.  So if you want to grow you must increase this penetration level.  In subscription markets (like home loans, insurance, some medicines) where each buyer has a repertoire of around 1, then penetration growth comes entirely from recruiting new customers to the brand.  In repertoire markets penetration growth comes from recruitment and increasing the buying frequency of the many extremely light customers who don’t buy you every period.

The “double” in Double Jeopardy tells us that some of the sales growth also comes from existing customers becoming a little more frequent, a little more brand loyal.  Also their attitudes towards the brand will improve a bit, as attitudes follow behaviour.

Improved mental and physical availability across the whole market are the main real world causes of the changes in these metrics.  The brand has become easier to buy for many of the buyers in the market, it is more regularly in their eyesight to be chosen, and more regularly present in their subconscious, ready to be recalled at the moment of choice.

Why does it matter anyway? Can’t we just build loyalty AND penetration?
Yes, that’s what Double Jeopardy says will happen if you grow.

Loyalty and penetration metrics are intrinsically linked.  They reflect the buying propensities of people in the market – propensities that follow the NBD-Dirichlet distribution and Ehrenberg’s law of buying frequencies.  Growth comes from nudging everyone’s propensity up just a little bit.  Because the vast majority of buyers in the market are very light buyers of your brand this nudge in propensities is seen largely among this group – a lot go from buying you zero times in the period to buying you once, so your penetration metric moves upwards (as do all other metrics, including attitudes).

For a typical brand hitting even modest sales/share targets requires doubling or tripling quarterly penetration, while only lifting average purchase rate by a fraction of one purchase occasion.  That tells us that we need to seriously reach out beyond ‘loyalists’, indeed beyond current customers, if we are to grow.

When budgets are limited (i.e. always) it’s tempting to think small and go for low reach, but this isn’t a recipe for growth, or even maintenance.

A focus on penetration ignores emotional decision making.
This is odd logic.  A focus on mental and physical availability explicitly realises that consumers are quick emotional decision makers, who make fast largely unthinking decisions to buy, but who if asked will then rationalise their decision afterwards.

Ehrenberg-Bass say there is no loyalty.
Really?!  On page 92 of “How Brands Grow” we write:
“Brand loyalty – a natural part of buying behaviour.  Brand loyalty is part of every market”.

On page 38 of our textbook  “Marketing: theory, evidence, practice” we write:
“Loyalty is everywhere.  We observe loyal behaviour in all categories” followed by extensive discussion of this natural behaviour.

In FMCG categories, buyers are regularly and measurably loyal – but to a repertoire of brands, not to a single brand.  And they are more loyal to the brands they see a bit more regularly, and buy a bit more regularly.

All brands enjoy loyalty, bigger brands enjoy a little bit more.

Ehrenberg-Bass analysis was only cross-sectional.
Actually, we published our first longitudinal analysis way back in 2003 (McDonald & Ehrenberg) titled “What happens when brands lose or gain share?”.  This showed, unsurprisingly, that brands that grew or lost share mainly experienced large change in their penetration.  This report also analysed which rival brands these customers were lost to or gained from.

In 2012 Charles Graham undertook probably the largest longitudinal analysis ever of buying behaviour, examining more than six years of changes in individual-level buying that accompanied brand growth and decline.  This highlighted the sales importance of extremely light buyers.

In 2014 we published a landmark article in the Journal of Business Research showing that sales and profit growth/decline was largely due to over or under performance in customer acquisition, not performance in retaining customers.  Far earlier we had explained that US car manufacturers did not experience a collapse in their customer retention when Japanese brands arrived, they each suffered a collapse in their customer acquisition rates.

But if we can change attitudes then surely that will unlock growth?

It’s rare that it’s a perceptual problem holding a brand back.  Few buyers reject any particular brand (and even most of these can be converted without changing their minds first).  The big impediment to growth is usually that most buyers seldom notice or think of our brand, and that the brand’s physical presence is less than ideal.

For more on “Marketing’s Attitude Problem” see chapter 2 of “Marketing: theory, evidence, practice” (Oxford University Press, 2013.

Attitudes can predict (some) behaviour change.  Light buyers with strong brand attitude were more likely to increase their buying next year.  And heavy buyers with weak brand attitude were more likely to decrease their buying next year.

The real discovery here is that a snapshot of buying behaviour (even a year) misclassifies quite a few people.  Some of the lights are normally heavier but were light that particular year.  Some of the heavies were just heavy that year (kids party, friends visited, someone dropped a bottle) and next year revert closer to their normal behaviour.  Note: for many product categories just a couple of purchases is needed to move someone into, or out of, the heavy buyer group.

Attitudes tend to reflect any buyer’s longer-term norm.  So someone who is oddly heavy in buying this year will tend to be less attitudinally loyal to the brand than ‘regular’ heavies.  Someone who is oddly light this year will tend to be more attitudinally loyal to the brand.  Next year, odds are, their buying moves closer to their norm and their expressed attitude.

This statistical ‘regression to the mean’ is not real longer-term change in behaviour of the kind marketers try to create.  Nor does this show that attitudes cause behaviour – their real influence is very weak, while the effect of behaviour on attitudes is much stronger.

Ehrenberg-Bass analysis is very linear reductionist, whereas we take a quadratic holistic approach.
Really not sure what these critics are talking about, nor perhaps do they.  This is pseudo-science.

I have a super large, super special data set.
Please put the data in the public domain, or at least show the world some easy-to-understand tables of data.  If you want us to consider your claims seriously then please don’t hide behind obscure statistics and jargon.

I have data that shows Ehrenberg-Bass are wrong, but can’t show it.
MRDA.

A short critique of “a critique of Double Jeopardy”

Bongers, M. & Hofmeyr, J. 2010. ‘Why modeling averages is not good enough – a critique of Double Jeopardy.’ Journal of Advertising research, 50:3, 323-33.

A longer explanation of the mistakes made in the above article can be read in:
Sharp, B., Wright, M., Dawes, J., Driesener, C., Meyer-Waarden, L., Stocchi, L. & Stern, P. 2012. ‘It’s a Dirichlet World: Modeling individuals’ Loyalties reveals How Brands Compete, Grow, and Decline.’ Journal of Advertising Research, 52:2, 203-13.

Here is a short critique:

The title is misleading, this is not a critique of the empirical phenomenon ‘double jeopardy’ but of the theoretical model ‘the Dirichlet’ – a stochastic model of purchase incidence and brand choice, which predicts double jeopardy and several other empirical laws in choice behaviour (see Ehrenberg et al 1994). The critique is naive, and the “test” of the Dirichlet is wrong.

To explain:

The Dirichlet is used daily within the marketing science units of corporations to benchmark their brand metrics against the model’s predictions for patterns of buying in a stationary and non-partitioned market. It is useful because the market conditions it models are well understood. It is also interesting to marketing theory because these conditions have been shown to be so prevalent (contrary to the world portrayed in most marketing textbooks).

Bongers & Hofmeyr use the purchasing of non-stationary brands to test an assumption of a stationary model. Even if they had found something real all they’d be saying was that non-stationary behaviour doesn’t look stationary.  Unsurprising.

It is, however, good and appropriate to question the underlying assumptions of models – even ones that work very well. In this case there has been more than 30 years of serious investigation of the NBD and Dirichet’s underlying assumptions (e.g. Kahn and Morrison 1989), a literature that the authors of this paper should have read. There is also new work seeking to expand such models to non-stationary conditions and to add co-variates (causal variables).

The Dirichlet belongs is a class of stochastic models, which is a technical way of saying that it assumes a particular distribution of purchase probabilities (concerning the probability to buy from the category, and the probability to buy particular brands within the category). These purchase probabilities (loyalties) for each buyer in the population are fixed in the model, that’s why we say it’s stationary – and therefore the brands obviously don’t change share because if people aren’t changing their loyalties then brand shares stay stable (which in reality they often do, at least over normal planning periods).  These stationary benchmarks are useful to compare change, when it happens, against.

Bongers & Hofmeyr tackle the Dirichlet model’s assumption that consumers have steady-purchase probabilities (steady loyalties); their paper attempts to refute this by showing a selection of purchases of individual panellists of non-stationary brands. They see what looks like lots of variation, natural wobble in purchase runs, and mistakenly interpret this as changes in loyalties.  The Dirichlet correctly incorporates a degree of random variation in purchasing even for stable loyalties.  Now if I gambled regularly I’d have a steady on-going propensity to lose money at the casino – but some nights I would actually make money yet that doesn’t mean the casino investors assumptions are wrong.  All B&H show is that runs of purchases exhibit variation (as do gamblers), rather than nice neat identical purchase weights in each quarter period. Similarly, if we had a panel of coin tossers we would see that only a few panel members made nice neat runs of tosses HTHTHTHT. If we looked at small runs of tosses we would see very many where ‘Heads” was far from 50% of the tosses. However, it would be foolish to send off a paper to a statistical journal critiquing the long-standing assumption of coins being weighted 50:50.

Now the Dirichlet models something much more complex than coin tossing. We have the probability to buy from the category mixed with the probability to buy particular brands.

The Dirichlet assumes steady-state probabilities with substantial stochastic wobble (around each individual’s steady-state mean). The multinomial assumption of choices amongst available brands means that a buyer with a 10% probability of buying the brand will buy it in the long run on 10% of their category purchase occasions but this buying will be in an as-if random fashion independently of the brand they bought on last occasion. Put simply when you look at any individual’s brand purchasing for short periods you see a lot of stochastic variation (which the model accounts for – hence its very accurate predictions).

It’s quite reasonable that even though I didn’t buy chocolate last quarter that I still have a on-going probability of buying it 4 times a year on average – that doesn’t mean I buy it exactly 4 times every year, some years I only once or twice, some years 8 times (and how I distribute my purchases amongst brands adds further (predictable stochastic) variation).

The authors don’t understand this stochastic variation and it has tripped them up. If it is any consolation it’s not uncommon for analysts to misunderstand stochastic variation in purchase data. Common mistakes include taking a group of heavy buyers (e.g. the top 20%) then noticing that in a subsequent period their purchasing is lighter and assuming that real changes in propensity have occurred (rather than regression to the mean). Or simply noticing that a person who bought in one period did not in a subsequent period and inferring that they have defected from the brand. I recommend reading Schmittlein, Cooper and Morrison (1993) for their discussion on true underlying propensities.

References:

Ehrenberg, Andrew S C, Mark D Uncles, and Gerald G Goodhardt (2004), “Understanding brand performance measures: using Dirichlet benchmarks,” Journal of Business Research, 57 (12), 1307-25.

Kahn, Barbara E. and Donald G. Morrison (1989), “A Note on ‘Random’ Purchasing: Additional Insights from Dunn, Reader and Wrigley,” Applied Statistician, 38 (1), 111-14.

Schmittlein, David C., Lee G. Cooper, and Donald G. Morrison (1993), “Truth in Concentration in the Land of (80/20) Laws,” Marketing Science, 12 (2), 167-83.

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Three Conceptualisations of Loyalty

This article hasn’t been available on line for ages.  Well here it is back again.

Click here to download.

ABSTRACT

The objective of this paper is to throw some light on the issues of conceptualisation in brand loyalty research. Distinctions are made between three brand loyalty conceptualisations: attitudinal loyalty, repeat-purchase loyalty, and differentiation loyalty. The latter conceptualisation having received far less attention in the marketing (cf economic) literature on brand loyalty. This paper then details some recommendations for future research concerning the operationalisation of these concepts and exploring the relationships between each concept.

Key words: brand loyalty, customer loyalty, consumer loyalty

Emotional Branding Pays Off illusion

Behavioural loyalty is strongly correlated with propensity to agree to ‘brand love’ survey questions but…… most lovers still buy other brands, and most of a brand’s buyers don’t love it.

John Rossiter & Steve Bellman (2012) “Emotional Branding Pays Off – how brands meet share of requirements through bonding, commitment and love”, Journal of Advertising Research, Vol.52, No.3, pages 291-296.

Rossiter and Bellman (2012) purport to show how consumers’ attachment of “strong usage relevant emotions” to a brand affects behavioural loyalty. All they actually show is that if you buy a brand more then you are more likely to agree (on a market research survey) to positive statements about that brand. We’ve known for 50 or so years that people do this – that stated attitudes reflect past behaviour. Or more succinctly: attitudes reflect loyalty.

Specifically Rossiter & Bellman showed that people who ticked “I regard it as ‘my’ brand” tended to report that this brand made up more of their category buying (than for buyers who didn’t (regard it as their brand)). What an amazing discovery!

“I regard it as ‘my’ brand” was, by far, the most common of the ’emotional attachments’ they measured – with about 20% of the buyer bases of particular brands of beer, instant coffee, gasoline, and laundry detergent ticking this box. It was also most associated with higher share of requirements (behavioural loyalty). I’m not surprised because it is most like a direct measure of behavioural loyalty. If I mostly buy this brand of coffee then I’m much more likely to tick “I regard it as ‘my’ brand”. If I buy another brand(s) more then I’m hardly likely to tick that I regard this one as my special brand.

So reasonably we’d call this question (“I regard it as ‘my’ brand”) a measure of reported behavioural loyalty, and so it would have to be highly associated with any other measure of reported behavioural loyalty. But Rossiter & Bellman in classic sleight-of-hand call this question a measure of “bonding”, which they say is a measure of an emotion (not a self-report of behaviour)! Naughty naughty.

On safer ground their measure of “brand love” was if brand buyers agreed “I would say that I feel deep affection for this brand, like ‘love’, and would be really upset if I couldn’t have it”. Interestingly, hardly any of any brand’s buyers ticked this box. Just 4% of the average beer brand’s (male) buyers, just 4% of the average laundry detergent’s (female) buyers, 8% of the average instant coffee brand’s (female) buyers, and a mere 0.5% of the average gasoline brand’s (male) buyers. Restricting the samples to the specific gender that represents the main weight of buyers reduced the proportion of light and lower involvement category buyers. This would have increased the incidence of brand love yet it was still about as low as is possible. Rossiter & Bellman wrote that these results “reveal the difficulty of attaining strong attachment-like emotions”. Hmmm, well yes and these results also reveal how successful brands largely do without brand love.

With so very few of any brand’s buyers agreeing that they feel deep affection for the brand we would expect the few that did would be quite different from the average. We’d expect that they would be the heaviest, most loyal in the buyer base. And these lovers did report higher behavioural loyalty though it was far from absolute (100% share of category buying). In fact, ‘lovers’ only reported buying the brand about half the time (50% SoR). Behavioural loyalty is strongly correlated with propensity to agree to ‘brand love’ questions but…… most lovers still buy other brands, and most of a brand’s buyers don’t love it.

Rossiter & Bellman interpret their results differently. Their article title says emotional branding pays off, even if the article does nothing to investigate marketing practices. They act as if they are unaware of the research going back decades that shows, over and over, that usage affects propensity to react to attitudinal type survey questions (see Romaniuk & Sharp 2000). Instead, this single cross-sectional survey data is supposed to show that if marketers (somehow) run advertising that presents attachment emotions, then consumers will link these to the brand, and then change their behaviour to buy that brand more often than they buy rival brands. Rossiter and Bellman’s results show nothing of the sort, their clearly written article turns out to be highly misleading. Yet I fear that this will not stop many unscholarly academics citing the article, and many believers in this discredited theory citing it as evidence to support their blind faith. Beware of such nonsense.

Share of wallet isn’t enough

In a recent Harvard Business Review article TIm Keiningham et al (Oct 2011) argue that managers should pay attention to “share of wallet”. To grow brands should aim to improve their share of wallet rank.

To do this you obviously have to get customers who currently give you a very small share of their purchasing to give you a greater share – it’s logically impossible to get much more share out of customers who already give you near 100%.

So Tim Keiningham et al have discovered the importance of light customers. Great.

Unfortunately, in their article they then make an unsupported assertion that the way to improve a brand’s share of wallet metric (and hence market share) is to survey customers on their motivations for buying each brand and then whatever it is that they like about a competitor should be improved in your brand. This ignores the very weak link between claimed motivations and behaviour. But is an unsurprising recommendation from someone who works for a market research agency.

Like Reichheld and Sasser (see retention profit myth) they also imply that improving loyalty metrics is easy – just ask people what they are looking for, provide it, and then your share of wallet metric will jump.

They provide (only) a hypothetical example of a supermarket. So let’s look at real data on supermarket loyalty. This is Kantar Worldpanel data (2006) on UK supermarkets (a very vibrant and competitive grocery market), market share is in the left column, penetration next, and share of purchases in the right:

 

 

Like all loyalty metrics, share of purchases rises with penetration and market share, in accordance with the Double Jeopardy law. As expected, there is much greater variation in penetration than in the loyalty metric.

In the HBR article’s fictional example the supermarket achieves a 7 percentage point gain in share of wallet (at some unknown cost), the implication is that this is an easy task. But this would be equivalent of Sainsbury’s doubling its market share – that’s a Herculean task!  And, very importantly, Double Jeopardy shows us that Sainsbury can’t do this without also increasing its penetration from an annual 64% to something nearer 80% – in other words it has to gain more customers.

That means the supermarket has to increase its reach (in space or time), e.g. more stores, longer hours.  This vital message is missing from the HBR article.

Professor Byron Sharp

Oct 2011

US brands are not losing their loyal customers – even more misleading metrics

Oops they did it again. Catalina Marketing have announced that packaged goods brands in the US have lost about half of their loyalty customers – AGAIN. Oh no how horrific. It’s a wonder they have any customers left. It’s a wonder that major brands aren’t tumbling out of the market. Sell your shares in Kraft, P&G, Unilever, Coke….

Of course it is complete nonsense. There is nothing wrong with the data, just faulty analysis.

This is the 2nd time Catalina Marketing have made this mistake. They are misinterpreting the natural wobble in people’s purchasing histories as real change in their loyalties.

Marketing scientists have know about this wobble for decades, it can actually be quantified using the NBD-Dirichlet. But Catalina in ignorance instead report that brands have lost nearly half their loyal customers. Oh no the sky is falling!!! Nice headline but it is wrong, plain wrong.

A few years ago when they reported this they said it was an unusual event, due to the GFC. Now having noticed it happens each year they say it is just a terrible indictment on marketing.

All of this is wrong, because this would still happen even in perfectly stationary conditions where no brands are growing or declining, and no consumers are changing their propensity to buy the category nor their loyalties to the particular brands in the category.

Let me explain. Each of us has a tendency to buy from the category, some of us are heavy category buyers and most of us are light. On top of this we each have our own particular loyalties so we buy some brands more often than others. These two mixed distributions mean that there is a lot of diversity between consumers of any product category. Diversity which is modelled extremely well by the Dirichlet.

On top of this we don’t buy like robots. I might have a tendency to buy chocolate bars 5 times a year, and have loyalties so that I buy Snickers 30% of the time, so that’s once or twice a year. But some years I’ll buy chocolate bars more than 5 times a year, and some years less – for thousands of random potential reasons. Plus some years I’ll give Snickers more than 30% of my purchasing and some years less.

So even if I don’t make any changes to my tastes, habits and loyalties I could buy zero Snickers in a year or 5+.

If we classify people into “loyals” or “heavies”, or whatever, based on what they do in one year then a lot of people are going to be misclassified. They aren’t really super-loyals it’s just that in that year they were – perhaps they had a party, some friends visited…a thousand potential reasons. Next year they are likely to revert to closer to their normal purchasing. It looks like they have changed when they haven’t. This is behind the phenomenon statisticians call regression to the mean. Catalina Marketing don’t seem to have learnt their basic statistics.

Catalina categorised someone as loyal if they gave 70% of their purchasing to the brand. If they didn’t in the next year they said they were lost. This means it is largely an analysis of lighter category buyers, as heavier buyers are less likely to give one brand such weight. So it’s people who bought the brand once out of one category purchase, 2 out of 2, 3 out of 3, or 3 out of 4, or 4 out of 5. So buying the brand just once less, or buying the category just once more means you get classed as lost, defected, no longer loyal. That’s why they get such a high figures as 50% being ‘lost’.

So It’s all an illusion. I explained this back in 2009. Sad that I have to say it again. I’ll repeat what I said then: Catalina Marketing sell targeted marketing services based on using this loyalty program data – which is a bit odd because this fluctuation seriously undermines the capacity to target consumers based on their past buying.

Byron

Professor of Marketing Science

Director, Ehrenberg-Bass Institute

University of South Australia

See the official website for the book “How Brands Grow”
http://marketinglawsofgrowth.com/