How have brands successfully navigated similar challenges of price increases and cheaper private label competitors (especially in a downbeat consumer confidence landscape) to keep growing? and Is building/super-charging Trust through our marketing efforts effective in growing penetration? Let us know if there is something else we should be considering.

I have spent some time analysing the performance of our Innovation vs. key competitors and concluded that a critical, often neglected factor, is related to the structural market conditions. Market conditions includes things like # of players, # of available SKU, but also the trade structure (share of Hyper and Super markets vs. smaller surfaces) which impact shopper behaviour (stock up shops vs. smaller shopping mission). What I have observed is that some markets are ‘structurally’ less permeable to innovation while others are more. The same innovation launched across these 2 types of markets would generate different returns. I have therefore created a metric – called ‘permeability index’ – that highlights what type of markets we are dealing with when launching innovation.

I have reviewed some literature on NPD performance and haven’t seen anything that goes in the direction of explaining the Innovation success through the lenses of the market structure. All I have seen is related to consumer preference, level of support, etc..

Have you seen any studies that use the same lens I have taken?

We love that you’re doing some thinking into how market structure might have a role in new product success. Here is a a paper that might be of interest: How common is new product failure and when does it vary?

Overall, there is a general belief that small or stagnant markets, and markets that have many competitors (# brands or SKUs), are not the “best” candidates to launch in.

Higher sales categories typically have slightly higher new product failure rates (this is some of our work). This might be because the categories are more ‘competitive’ with more brands and products available in the market.

Some other studies look at market structure and new prod sales, and trial and repurchase. They show a ‘clutter effect’ where there is a negative impact in these metrics in categories with more competing brands and products.

It’s an interesting area and there’s a lot more that can be done to understand market structure as well as brand dynamics in success.

K.V.
21 March 2025

In this macroenvironment are you seeing any of the rules for elasticity impact change (i.e., is the +15% changing to +10%, as private label is improving quality?) and is this what your upcoming pricing experiments will look at? If so, when do you expect those to be published?

No, we don’t see price elasticity changing fundamentally. The body of work on brand price elasticity seems to have produced fairly consistent results over a long time (that has included periods of low/high inflation). One thing that does seem to impact price elasticity is the price gap, so the old idea, that if there were 3 brands priced at $2.00, $3.00, $4.00 then say, a 10% price change for the middle one still left an appreciable gap between it and the others ….. but if they are priced at $2.00, $2.50 and $3.00 then a price change by the middle one is much ‘bigger’ in the context of their relative prices. So as private labels get better and perhaps starts pricing closer to manufacturer brands then the cross price effect of private label/new brands becomes larger (in theory).

Yes, we’ve done data collection on our first pricing experiment, it turned out to be a lot more difficult to get it done than we anticipated but we’re looking into this. We mainly want to see if the overall elasticity level is about ‘as expected’ and then hopefully start to see some more details after that.  But I think it will unfortunately take some time to get it to a point where we could share/release.

J.D.
21 March 2025

1. How often should I visit my best prescribing doctors if not every month

   Your best prescribing doctors are unlikely to prescribe even more often than they already do but are also fairly unlikely to switch. A good approach might be to try an A/B experiment to see how much time could be devoted to signing up new doctors.

2. If I have to reach out to new prescribers which ones should I start with since I don’t have the budget to target broadly?

   Heavy category prescribers and specialists are a good bet not because they can be persuaded to become 100% loyal, but because they have wider repertoires. But your new customers will be light and heavy in the same way as your current customers are.

3. How can I stretch my media budget without targeting?

   Not all targeting is wrong – certain categories are gender, specialism or age specific obviously. Otherwise, explore the evidence in the advertising and media reports on the institute website, particularly on shorter TV ads, outdoor media and the number of channels to achieve cumulative reach and mental availability.

4. Surely I can go dark when advertising seasonal remedies?

   Why would you? Advertising works in memory – critical to maintain brand associations as broadly as possible. And perhaps less clutter out of season too.

P.S. & C.G.
21 March 2025

What research do you have available/would you recommend around metrics of reach and engagement?

We prescribe an emphasis on reach-based planning regardless of the media that you are using, because that is what the generalised evidence from decades of best-in-class Single Source studies tell us about how advertising and media impacts behaviour. But we can also appreciate some questions being raised amongst practitioners in recent years about whether all reach is equal, and what makes better quality reach. And we agree, not all reach is equal.

But what we disagree with such practitioners on is what makes better quality reach. In particular, we don’t agree that you should “throw away the baby with the bath water”, by taking on approaches and constructs that contradict established knowledge. And, unfortunately this often seems to be what happens in this space. Measures like engagement (attention is just engagement in new clothes) have been put forward for many decades as ways of determining whether reach quality is high. Alas, the actual evidence that your ads need to get more attention, to engage more or to result in conversion or click-through in order to be effective is sadly lacking.

Many of these arguments for such measures run counter long established evidence. For instance, Robert Heath nearly 20 years ago pointed out that audiences pay little attention to advertising, and this hasn’t changed (even beyond TV advertising with digital advertising), so do we really want to make this 70 years of the wrong advertising model?

In short, advertising needs to work by creating and refreshing memory structures, which will in turn nudge propensities to buy, rather than bring about seismic shifts to buying or to convert or change behaviour. And this is the case regardless of the type of media you are using (digital or otherwise). Many of the digital platforms for instance, offer awareness, traffic or conversion campaigns, and optimise for impressions or click through according to the advertisers’ objective. Our recommendation would be to worry less about the engagement you get and more simply about the scale of the audience for all of your communication (digital; or otherwise). In this example, a digital awareness campaign may not be the ultimate reach-based campaign, but they will get you heading in the right direction (in comparison to aiming for maximum conversion, which mainly delivers heavy users who were going to buy regardless of any advertising exposure).The following answer to another sponsor’s question from our website, may be of further interest: What is the Ehrenberg-Bass guideline for an effective digital marketing strategy?

E.R.
21 March 2025

Does the Ehrenberg-Bass Institute have any reports or research focused on India, and are there any differences in patterns or the application of the Laws of Growth local market?

S.D.
21 March 2025

I would love to get the Ehrenberg-Bass scientists point of view on the scope of Social Listening tools to effectively track and measure progress made on Brand Consideration, Mental Availability and understanding Brands’ Associative networks.

• We’re considering adopting a social media monitoring tool to help us understand:

• What people are saying about our brands

• Which of our memory assets contribute to distinctiveness and whether these are the aspects consumers are discussing

• Our category entry points and our ownership of them in terms of share of mentions

• How to measure mental availability

Given the principles of Mental Availability and the Laws outlined in “How Brands Grow” I’d like to know what key metrics we should monitor to assess if our brand is growing in awareness, consideration, and love, and what key questions the tool should answer to align with our strategic goals.

Firstly, I’ll preface by saying that social listening tools are great for some things, but have their limitations. They can be a great source of knowledge for identifying the new ways people are using your product/category, which can aid innovation and portfolio development. They can also be useful to track sentiment, and in particular, benchmark negative sentiment to identify jumps and shed light into the reasons behind these jumps. These uses can help growth efforts by giving you a finger on the pulse to issues that might hinder mental or physical availability (e.g. issues with distribution, barriers to purchase, new uses of the product) etc.

However, social listening data comes from a biased and incomplete sample of typically heavy category users, and we see extremes of positive and negative sentiments that don’t reflect the ‘norm’.

This makes it difficult to use social listening tools to understand Category Entry Points (CEPs), Mental Availability and distinctiveness. For example, for CEPs, you’ll likely pick up the unusual and shareable, rather than the typical, everyday occasions/contexts that bring the most buyers into the category. These CEPs will likely be less valuable. Moreover, social listening tools don’t allow you to look at buyers and non-buyers separately. We know that to grow, brands need to increase penetration especially of non-buyers. Tracking non-buyers on these metrics is important, and the heavy category users that talk online likely mean you don’t have a representative viewpoint. It’s also hard to know what you are measuring –  for example, with brand awareness, you don’t know the cue that stimulated the brand mention so you can’t be sure whether you’re measuring unaided or aided brand awareness (e.g. did the respondent type in a google search or are they responding to a post/comment that mentions the brand). As I mention below, one is more valuable than the other. With distinctiveness, while social listening tools may provide some insight into fame, another key aspect is uniqueness and social listening tools don’t allow you to be sure that people don’t link the asset to other brands as supposed to just not mentioning it online.

Therefore, we recommend measuring Mental Availability, CEPs and distinctiveness with consumer surveys from a representative sample of category buyers. We also don’t recommend measuring these metrics as frequently as social listening tools would allow. Mental Availability can be measured at an approximate, every year, (depending on the category and stability of the category) and attitude much less. Social listening tools can encourage too-frequent tracking of these metrics, that is unnecessary and risks the business reacting to results that might be transient. We offer both measurements for Mental Availability, CEPs and Distinctiveness at the institute, as well as help to integrate these metrics into existing brand health trackers. For more information on our Research Services click here.

With regard to key metrics to monitor Better Brand Health goes into much more detail but hopefully the below gives you a good starting point.

Brand awareness:
There are two main reasons for measuring brand awareness: category identification (e.g. to check if category buyers are aware the brand is a member of a specific category) and ease of retrieval (e.g. to assess how easily category buyers retrieve the brand from memory). As discussed in Better Brand Health, using brand awareness to measure the latter has limitations, as it doesn’t effectively capture how memory works; Mental Availability is a better capture of brand retrieval. Hence, I’ll focus on measuring brand awareness for category identification purposes.

There are three different measures of brand awareness: Top of Mind (TOM), spontaneous awareness (unprompted/unaided), and prompted/aided awareness. As I mentioned above, we know that non-buyers are a key part of a brand’s growth. Unfortunately, these three metrics are not equal when it comes to effectively capturing responses from non-buyers. TOM measures, especially, are difficult (they require the brand to be the first to be recalled given the category cue) and thus fewer responses come from non-buyers. Our recommendation for tracking brand awareness is to measure prompted brand awareness of the brand’s non-buyers. It’s important to note that prompted brand awareness does not easily erode, so collecting a benchmark for brand buyers and then ongoing tracking of non-buyers is sufficient.

Brand consideration:
Our recommendation for measuring brand consideration is to remember that buyers do not have one single consideration set that they always use to make decisions, but rather the set of brands they consider varies across time and contexts depending on the cue that is available. For this reason, we prefer the measure of Mental Availability, which captures the likelihood for a brand to come to mind (so it can be considered) across relevant purchase occasions. Mental Availability is a multi-cue measure of retrieval and helps to address some of the above concerns with unprompted awareness measures.

If you want to use a consideration measure, then capturing consideration ‘post decision’ may be more effective than a ‘pre-decision’. For example, it may be more insightful to ask consumers which brand(s) they purchased (or which retailers they visited) and which other ones they considered, rather than ‘which brands/retailers would you consider’. This shifts the purpose of the measure away from retrieval.

Brand love:
When measuring brand love, I presume the assumption is that this is more desirable and important to strive for than someone just ‘knowing of’ the brand – eg brand lovers will have a higher propensity to buy the brand. In Better Brand Health, we talk about a few challenges with this viewpoint, e.g., by showing that few people hold these extreme attitudes; that often attitudes reflect behaviour rather than drive it; and that buyers often do not need to hold a positive attitude to start/continue buying a brand. Reflecting these findings, when we plan brand attitude questions (brand love being an attitude), we recommend having a question that measures buyers and non-buyers’ full spectrum of attitudes towards the brand – e.g. positive, negative and neutral. In particular, we recommend benchmarking the distribution of responses for buyers and non-buyers, and checking it’s normal for a brand your size and within your category (NB brand attitude scores follow a Double Jeopardy pattern, where big brands have a slightly higher attitude score than smaller brands a slightly smaller score). In addition, check brand rejection scores for both buyers and non-buyers are low and inline with competitors, and identify any reasons for rejection. This will be much more useful for diagnosing issues than striving for brand love.

R.F.
20 March 2025

I’d be interested to know the Ehrenberg-Bass Institute’s point of view on big brands versus the total number of brands purchased. I suspect that big brands have significantly more light and occasional buyers, which in turn lowers the average number of brands purchased – due to big brands having greater mental and physical availability.

Yes indeed, this is a manifestation of the Natural Monopoly (NM) effect.
Way back in the 1960’s, it was discovered that “the most popular alternatives in a category were strongest among people who knew the least” (about the category) – in other words, light buyers.

Big brands get to be big because they’ve done better at reaching the mass population; if a light buyers knows anything about that category it will be the biggest brand, whereas the people who know (or buy) smaller brands also know more brands (so, lower loyalty toward the small brand).
So yes, market leading brands tend to unduly attract lighter category buyers, (and they tend to dominate their requirements, ie high Share of Category  Requirements) which means buyers of the big brand tend to buy fewer other brands.

I wrote a paper on this, which can be found here: “The Natural Monopoly effect in brand purchasing: do big brands really appeal to lighter category buyers?”

J.D.
20 March 2025

We are looking to build a story for the broader sales team throughout the organisation, explaining why distribution is so critically important for brand growth. We were hoping that through all the Institute’s empirical analysis that there might be some quantifiable data you could share or point us to that helps communicate how physical availability is a major lever for brand growth.

The best thing to do would be to replicate the physical availability vs market share curve for your brands. Basically, big brands (or SKUs) need to have greater than 80% ACV. It’s a necessary but not sufficient condition for being a big brand.

It’s incredibly difficult to get a lot of market share when you aren’t in all the places that buying happens! Some brands are in all those places (i.e. have a lot of distribution), but don’t have the Mental Availability to convert that Physical Availability into sales, and so they might not get the same share. They could also be lacking in the other forms of Physical Availability, i.e. Prominence and Portfolio.

The best strategy is always going to be covering off the places where most buying happens to begin with, before going for smaller and smaller places. And, in order to reach those last points of ACV, you also have to get into lots of places where you might be the only option, or one of only a few options. Which means you have a better chance of being bought on those occasions too!

If you can push a brand along the curve towards the 80% inflection point, then you have the best chance of it becoming big – you’re no longer restraining it, and can make the most of the investments in Mental Availability that are happening outside the store.

The below report and thesis may be helpful:

Physical Availability – What makes a brand easy to find & buy?

An empirical analysis of stock-keeping units deviating from the Reibstein-Farris distribution and market share model: The role of product- and distribution-related characteristics

B.P.
21 February 2025

As budget setting is inherently a forecasting task, we follow the latest recommendations from the forecasting literature, which suggest that estimates should be conservative, and should involve the triangulation of several different evidence-based approaches, rather than using just one defined “best” option.

When we undertake a budget determination for clients we use the current 3 techniques covering an Internal, External and Task/Objective perspective. These being, Advertising Intensiveness Benchmarks, Profit Algorithm and Task Objective.