The trust score is an average of responses to these two questions on a 1-7 scale:

Brand X is a brand I trust; Brand X delivers on what it promises.

The PL or Store Brand market shares are from

https://www.nielsen.com/wp-content/uploads/sites/3/2019/04/state-of-private-label-around-the-world-nov-2014.pdf

Some minor points of interest are that the UK and Spain have somewhat higher trust scores given their level of store brand market share. And that Russia has a comparatively lower trust score given its tiny private label share. Perhaps this could be because the top three brands have on average lower market share in Russia than in other countries.

The study also analysed what it called ‘Advertising Intensiveness’ and concluded that higher levels of advertising is correlated with higher brand trust. But the Advertising measure was asked of survey respondents (‘Brand X advertises a lot’), who then give the trust scores – so there may be a response bias issue with that conclusion (i.e. I said I see it advertised a lot, which implies I should trust it; or vice versa).

The study did identify that there are differences across the spectrum of brands in perceptions of menu variety and perceived value. But all this really says is, there is a certain group of brands that simply do have limited menu variety; and there are some brands that emphasise “value” offerings. And within each sub-market or brand group, multiple brands possess that feature.

The overall findings suggest – since some of these restaurant brands are much more successful than others, yet are not markedly differentiated – that success doesn’t seem to necessitate creating a highly differentiated offering, as has been the mantra in marketing textbooks for 50 years.

A series of experiments and a field-study finds that Facebook ‘liking’ generally reflects existing favourable attitudes; and that the act of ‘liking’ does not alter consumer behaviour toward a brand afterwards.

The study recognised a major issue to control for was that consumers who ‘like’ a brand on Facebook are very likely to already have existing favourable attitudes, and/or past brand usage. They endeavoured to control for this selection effect in a series of cleverly designed experiments. (Otherwise, the pre-existing positive attitudes would link to more buying etc. in the future compared to non-‘likers’, but this difference would not be due to actually deciding to ‘like’ the brand, it would simply be due to the pre-existing attitude or behaviour)

The study also did a field experiment in which consumers were offered a coupon for a brand of face cream under three different conditions:
1 A friend had ‘liked’ the brand on Facebook, and so a redeemable electronic coupon for a sample of the cream was sent to the respondent;
2 A friend actually did like the brand (not on FB) and had arranged for the coupon – this was called the ‘meaningful endorsement’ condition;
3 A friend had simply arranged for the coupon with no FB ‘like’ or indication of personal endorsement as a control condition.

The redemption rate for the coupon was 5.9% for the meaningful endorsement, 5.2% in the control, and lowest, 3.7%, in the FB ‘like’ condition. The authors concluded from this result that if consumers notice that friends ‘like’ a brand on Facebook, they see it as a far less meaningful endorsement than knowing the friend is actually (i.e. not just a ‘like’ click) fond of the brand.

Dozens of studies have been done on the sales effects of advertising, using aggregate time-series sales data for brands, coupled with ad spending. Meta-analyses of these studies has arrived at average advertising elasticity figures of around 0.1 to 0.2. That means, a 10% increase in advertising spend results, on average, in a 1 to 2% sales increase, in the short term. Short term here means from the date the ad airs, to about 4-6 weeks later.

But a new study finds a much lower ad elasticity figure, only around 0.03 – not 0.1 to 0.2. The authors use a massive dataset from Nielsen of over 200 US brands including weekly sales and advertising spend. The authors Shapiro, Hitsch and Tuchman suggest their lower figure is more accurate than what comes from meta-analyses because of the ‘file drawer problem’ – only published work gets used in meta-analyses, and there is a bias against publishing studies that show null or counter-intuitive results such as sales going down during advertised periods.

The implication of this piece of work is that on average, the short-term effect of advertising on sales is extremely low, almost zero. For established packaged goods brands, at least. That’s not to say it doesn’t work – we know it does. But it doesn’t work to noticeably boost sales in the short term. It works via reminding consumers and refreshing memory. More detail about how advertising does work is in the books How Brands Grow and How Brands Grow 2 as well as Building Distinctive Brand Assets.

The managerial implication is, brand managers should not expect their advertising campaigns to increase sales in the short-term. But they also need to realise that a large part of the the reason some brands are big is their investment, over years, in brand-building via advertising. And they won’t stay that way unless they keep doing it.

Note, the study makes a distinction between short term and long term effects, but in reality it only measures the effect of advertising over a fairly short time-span. To measure a long-term effect they set a decay parameter for brand advertising whereby an ad in one week has a ‘carryover’ effect the week after, and the week after (this is known as the ‘adstock’ model). For example they used a decay parameter of up to 0.9, so one week after the ad is aired, its sales effect is assumed to be 0.9 of the first week’s effect, then (0.9×0.9 = .81) 0.8 in the second week, then (0.9×0.9×0.9) = 0.73 et cetera.

Note, this study relates to established consumer goods brands. It did not consider advertising contexts such as price-oriented retailer advertising.

Research has also examined if price elasticity differs across products that are more ‘functional’ versus products that are more ‘hedonic’. Examples of functional products are petrol or groceries compared to more ‘hedonic’ categories like sporting events or movies. Findings have been that indeed price elasticity is lower for categories with hedonic qualities. And indeed within grocery, some products are more functional such as paper towels or laundry detergent, and others are a bit more hedonic (consumption gives pleasure) like certain food & drinks. Again, price elasticity analysis finds lower elasticity for products with hedonic qualities. The study cited here examined if the situation for which a product is bought for influences the price paid. While it was for one specific product, pizza, the findings seem consistent with related work. Indeed, our own research on high-priced wine found lower elasticity when the wine was bought for a special occasion.

The luxury brand JW Marriott gets a satisfaction score of 84/100 in the latest American Customer Satisfaction Index report.
Next is a group of what are called Upper-Upscale hotel brands that get 79/100, as do Upscale brands. Then Upper Midscale get 78, the Midscale brands get 74, and down the bottom, the economy chains like Motel 6 get 66/100 on average.

So what? Well, it’s interesting that even though it costs less to stay in the cheaper hotels, they still get lower satisfaction scores. Which suggests when customers give a satisfaction rating they’re not necessarily thinking “how good was it, for the money I paid”. They just think “how good was it”.

A: Buyers of new products tend to be heavy buyers of the product category. There is ample evidence about this, but here are some new findings.