by Byron Sharp
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1 1 This is real data from a Chicago panel reported in Spaeth and Hess (1989).
3 Yet the world’s largest selling economics textbook by Paul Samuelson (the first American economist to win the Nobel prize) even as late as its 13th edition said “The Soviet economy is proof to the contrary to what many skeptics had earlier believed, a Socialist command economy can function and even thrive”. This in 1989 the year that the Berlin wall fell! It seems university textbooks can be hopelessly out-of-date, and stubbonly cling to theory in spite of the facts. The facts in this case being the tens of millions who starved as the historic command economies of Russia and China failed to produce and distribute enough of even basic necessities.
4 Study led by Kate Newstead & Jenni Romaniuk.
5 Near identical results are reported 15 years ago in du Plessis (1994). A figure around 16% is also not uncommon in advertising recall (i.e. where the brand name is the cue for recalling the ad).
6 The statement said that in conducting advertising experiments, test cities should be isolated so that promotions in one city don't influence sales in another.
7 Aspirin was not only one of the very first drugs to actually have proven affect, it was also the first to benefit from mass marketing. Every doctor in the UK was mailed information on the new drug which dramatically sped its adoption, much to the relief of early patients.
8 Actually it is a myth that lemmings commit mass suicide, but it is a useful analogy.
9 It's a common mistake, made even by senior academics, to think that statistical significance tests tell us something about generalisability, i.e. if or where the result will hold again. They don't, and this isn't their purpose. Rather, they merely tell us something about the possibility that our result was merely an artefact of random sampling variation, that is, because we examined a small sample not a population census. Statistical significance tests don't tell us which population our result might represent, or anything about the conditions where the result would vary. A highly (statistically) significant result may apply only in the particular conditions that the study was done, e.g. in one place and time.
10 All the tables in this book try to conform to Andrew Ehrenberg's principles of data reduction; see Ehrenberg (1998, 1999, 2000).
11 Much of this research was conducted by Andrew Ehrenberg and Gerald Goodhardt and associated colleagues from the 1960s onwards. It has also been verified by commercial analysts within large marketing firms such as Kraft, PG and Unilever and the large research houses such as TNS and Nielsen. The Double Jeopardy law has also been recently independently rediscovered in an analysis of 10,000 brands in the US by researchers with no prior knowledge of the law (Hall & Stamp 2004), and in an analysis of growing brands by Research International (presented by Jim Findlay in 2003 Advertising Research Foundation (ARF) 'Week of Workshops').
12 An interesting question is which brand you would rather manage or own? The brand with the small but apparently very loyal customer base, or the brand with the larger customer base who buy it less often? The current fashion is for the former, with the argument being that it would be cheaper to service this smaller client base. But an equally compelling case can be made for the larger customer base being of greater strategic and financial value. Don't worry though, as all of this turns out to be academic. Reality (the double jeopardy law) renders this question pointless.
13 Penetration is a metric that records how many people bought the brand, at least once, in a particular time period. So while any brand's penetration increases with time it doesn't double if you double the length of the time period, because a lot of sales come from the same customers coming back and repeat buying. In many categories a brand's penetration fails to get anywhere near 100% even in a very long time period (e.g. years) because many buyers simply don't have the brand in their repertoire.
14 Consumer panels measure the buying of individuals or households over time, providing individual level longitudinal data. In many cases respondents are given a small version of a supermarket barcode scanner and they simply re-scan their purchases when they bring them home. Other major panels track things like TV viewing (using set top boxes to do the measurement).
15 A cynic would say the term was invented to replace the word 'small' because most brand managers are in charge of small brands. If you tell someone you meet at a party, “I'm brand manager for X”, and they say, “Oh, I've never heard of X”, what would you rather reply: “That's because it's a small brand, it isn't bought by many people, and even these people don't buy it very often”, or “That's because it's a niche brand, with a very particular (discerning) client base”?
16 Theoretically a niche brand can be any size: small market share would be more probable, but a large market share niche brand would be theoretically possible. In reality niche brands are rare, but do come in all sizes, just mostly very small.
17 This is not hidden in the Harvard Business Review article; while it is fair to accuse of Reichheld and Sasser of misleading language, they do clearly show how they reached their (nonsense) conclusion. Scary how many business-minded CEOs got excited about this – did they really read the article?
18 The pharmaceutical panel data was kindly provided by ISIS Research (now part of Synovate), covering ten years of anti-depressant prescribing by UK doctors. It is an extraordinary data set because it covers such a long time period and so many 'purchases' – perfect for studying sales growth and decline. See Stern, P. & Ehrenberg, A. S. C. 1995. 'The Marketing Performance of Pharmaceutical Brands.' Marketing and Research Today, 23:4, 285-90.
19 Adelaide Bank is, and has been for a long while, a profitable well-performing bank. Contrary to Reichheld and Sasser's implication, its defection rate has not decimated its profits.
20 Reichheld is a consultant selling a loyalty consulting service; he does not disguise this. But academics have no excuse for citing his work without critical examination (the obvious faults in his work are there for anyone to read). Unfortunately, mainstream textbooks have slavishly hopped onto the loyalty bandwagon. For example, “Many companies are realising the importance of customer retention and that the key to retaining and building market share is building relationships with loyal customers” (Belch &
Belch 2008).
21 Category purchase rates also show a skewed distribution. While the average buyer might purchase from the category 10 times, the typical category buyer purchases from the category just a few times a year.
22 Soft drinks that contain caffeine may show ever so slightly higher repeat-purchase rates (i.e. higher loyalty).
23 This was first publicly documented by the marketing research director of Oscar Mayer & co, Madison, Wisconsin (see Twedt 1964) from the Chicago Tribue panel data.
24 At the end of the book there is a list of the laws introduced here in “How Brands Grow”.
25 This doesn't mean that every single buyer changes his or her buying frequency. It means that every group of buyers shows some changes. Hence, the idea of targeting a particular group (and just affecting this particular group) runs counter to what occurs in the real world.
26 The profile of a monopoly brand is identical to the category profile.
27 For an example of Yorkie’s television advertising, see www.youtube.com/ watch?v=ctpOxfA2gDY
28 Professor Gerald Goodhardt remembers that Yorkie was launched, by the company Rowntree (later bought by Nestle), at a time when chocolate had become very expensive and major players like Cadbury reacted by making their bars thinner. The shape of a piece of chocolate affects its taste, or certainly the eating experience. Yorkie was released as a small bar that actually had traditional chunks of chocolate.
29 See www.youtube.com/watch?v=olI5xzshtFQ
30 Many research companies offer proprietary products that measure attitudinal constructs that they claim cause, and can predict, behaviour change. To (attempt to) prove it they report correlations with behavioural measures like loyalty or market share. These causal predictive claims are largely nonsense: the correlations simply reflect that people have attitudes and more knowledge about brands they buy (i.e. behaviour is a powerful driver of attitudes).