Quality does not sell itself

Great to have you here! In this blogpost I want to share some background for the studies we did on Behavioral Economics as there was a lot of things going on that lead to the studies that the are now published in The British Food Journal (http://www.emeraldinsight.com/doi/pdfplus/10.1108/BFJ-03-2016-0127), Cafe Europa Magazine (September 2016) and Reco (https://youtu.be/3Jb03RWYrQ4).

The first study we did was done by Imane Bouzidi with myself and Thomas Zoëga Ramsøy from Copenhagen Business School at Decision Neuroscience Research Group (now Neurons inc (http://neuronsinc.com/)) as supervisors. This study is explained in details in SCAE’s members archive but here is a summary of the research design and the results.

A high quality and a low quality coffee were selected (a premium coffee from Kontra and a commodity coffee called Artnok which is Kontra’s commodity range (Kontra spelled backwards!)) and served for random customers in a shopping centre in Copenhagen. The coffee was served in cups with brand labels to influence the customer cognitively with the brand equity (https://en.wikipedia.org/wiki/Brand_equity) but in the cups was not coffee from any of those rands but just either the HQ or the LQ based on random selection as seen in the figure below.

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Before tasting the coffees the customers filled out a questionnaire about their expectations for each coffee based on the brand and then later they rated the coffee after having tasted them. After they had tasted the coffees the consumed amount of each cup was measured and then they were allowed to choose a coffee they could have as a small benefit for their time in participation. So in conclusion the effect measures in the study were

  1. Brand expectations (‘liking’ [conscious])
  2. Rating of coffee samples (‘liking’)
  3. Measure amount consumed (‘wanting’ [sub-conscious])
  4. Final choice of coffee brand (‘behaviour’)

A summary of the results

  1. High brand equity gave
    1. higher tasting scores
    2. lower difference between HQ and LQ scores
  2. Sensory Scores: LQ was preferred! (P< 0,001)
  3. Consumption: LQ was preferred! (P< 0,001)
  4. HQ was preferred without milk

So if the brand had high brand equity people scored it higher when tasting it (1a) but also distinguished less between HQ and LQ (1b) both of which might be expected. A slightly more surprising (and a bit disappointment as a specialty coffee professional) was how strongly the data proved that consumers preferred low quality (point 2 and 3 with a really strong significant result with a P<0,001) but the real surprise and source of wonder for me was that despite 2 and 3 consumers were clear in pointing to the HQ when asked which coffee they could enjoy without milk! Without support in the data this gave me a hint for a hypothesis that the consumers preferred the LQ out of habit but when asked which coffee they could drink without milk they were able to taste that there was ‘less unpleasant flavours’ that they wanted to remove in the HQ which is what milk does in my mind. I believe that there is a physiological response of aversion for the unpleasant flavours in coffee that we in the specialty coffee business do our best to remove by selecting defect-free green beans, slow roast to avoid burnt and bitter flavours and a less aggressive brew (20% extraction rather than 30% as is the norm in commodity). You can get used to these bad flavours to the degree that you develop af preference when offered a choice between HQ and LQ but you are still able to recognize that HQ is the most pleasant to drink if you are not adding milk.

This led us to another pilot study that is strictly a pilot in the sense that we did not have a big enough cohort of subjects but we just wanted to make a small test that we could do in a few hours to get ideas for future studies. At this point Thomas Ramsøy had left Copenhagen Business School to start his own consumer research company Neurons Inc. (neuronsinc.com) and then I was lucky enough to meet Toke Fosgaard (https://dk.linkedin.com/in/tokefosgaard) who is now my playmate when it comes to studies in behavioral economics. Toke Fosgaard, Ida Steen and I had a cohort of 11 of Toke’s students with age 22 to 28 and we selected a high quality coffee and a low quality coffee. For this study we knew, that we did not have enough consumers so in order to increase the probability that we got useful data we selected extreme HQ and extreme LQ. The HQ coffee was one of my favorites namely Coffee Collective’s (http://coffeecollective.dk/da/) Kenya and the coffee from the 20 liter batch brewer in the university canteen that is for sure the worst green, roasted in no time and extracted from here to hell which Ida and I could confirm was the case with this coffee. It is a strategic decision whether to choose a HQ and LQ within a very similar flavour range or you should choose a HQ that goes far beyond the LQ/commodity traditional flavour profile. LQ is traditionally rich in bitterness, chocolate, nutty and other non-fruity flavours where HQ chosen from the elite roasteries is rich in acidity and fruitiness that is considered strange for the average consumer. In Imane’s study the HQ was chocolaty and nutty and not acidic so the consumers could concentrate on the quality of the beans rather than being confused with low bitterness and high acidity and fruitiness. But in this study we wanted from extreme LQ to extreme HQ which lead us to the above decisions on samples.

So with the samples at hand we had a room where the students would come in one by one and we changed the setup to alternate between two different setups described below so that half of the students would experience one setup and the other half part of the students would experience the other setup.

Setup 1: Served with the full sales pitch

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In setup 1 we prepared two cupping setups, one for me and one for the student (consumer), and in the two cups were respectively the HQ and LQ. I presented myself as external lecturer at Food science with coffee as my full focus area, and I told them about my involvement in SCAE education and research and my many years as consultant world wide to choose high quality green coffee and how I design product ranges for clients so that it was very clear to the student that I was an international authority in coffee quality. After that introduction we did a cupping where I took my time to point specifically everything about the low quality coffee that I did not like and was a consequence of rotten beans, cheap and fast roast profile and an outrageously bad brew and I pointed to all the nice, elegant and juicy notes of the HQ with no attic/basement off flavours and no burtness nor bitterness and we went back and forth between the samples to make sure they really tasted themselves all the bad stuff about the LQ and all the good stuff about the HQ.

After the introduction pitch and the thorough tasting the students were told that they could choose to get one full cup of coffee to go of one of the two coffees as a small gift for their time, and this final choice was the ‘endpoint’ for this study since it was a study in Behavioral Economics where you measure behavior rather than asking for opinion. As a small little extra endpoint we did ask them what they liked about the coffee they chose.

Setup 2: Served with no comments

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In setup 2 the HQ and LQ coffees were poured into two cups and when the student entered we did not tell them anything about the coffee at all but we told them that we would like them to taste from both cups and choose which one they would prefer to have as a free gift they could have as a gift for the time they spent on this study. We also asked them what they liked about the coffee they chose.

So what were the results? (drum roll please..)

Which coffee would you like to walk away with?

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When the students had the sales pitch where I did EVERYTHING I COULD to heavily nudge them to prefer the HQ still 67% chose to WALK AWAY WITH THE LQ!!! They were nudged by my pitch about myself and the coffees to the degree that most of them excused themselves when choosing the LQ in front of me which was really interesting since even this embarrassment they felt for openly in front of an expert choosing the LQ did not shift the preference for the LQ to the less chosen cup!

Again only having 11 consumers in this study we can’t really calculate any valid statistics but I still think that it is surprising that 11 university students do not have a higher preference for HQ since I would expect this part of the population to be specialty coffee drinkers. Now that the statistics could not really be relied on, we found it interesting to hear the student’s comments when tasting and choosing between the LQ and HQ:

“I just really like a black coffee [the LQ]!”

“I like strong coffee [the LQ]”

“It [the HQ] does not taste like coffee”

“It [the HQ] tastes like tea”

“Is it [the HQ] a thin version of the canteen coffee?”

“This [the HQ] is not coffee this is something else”

These comments are really interesting I think. It points to the extreme HQ as being outside the category of coffee for these consumers which is often what I experience when people are new to the specialty coffee culture and one of the things that I have a keen eye on when I as a consultant help new roasteries design a product range (Online Lean Startup Process,https://coffee-mind.com/product/onlineleanstartup/) where I try to make my clients choose a product range where they can show their customers something new without pushing their customers off the cliff which takes careful preference mapping with surveys, focus groups and consumer studies since what is ‘too light roast’ in one area of the world or even city vs rural in one country is not the same from place to place.

References:

Sensory Science and Common Business Practises

Last Wednesday CoffeeMind held a presentation at Square Mile Coffee Roasters in London. The presentation focused on quality control and how to improve sensory skills.

It was presented by Ida Steen, MSc of Sensory Science from Department of Food Science in Copenhagen, and Morten Münchow, Lecturer at Food Science at the University of Copenhagen, this two-hour presentation inspired the participants to take a more scientific approach to their quality control program, new product development and possible approaches to judge their own sensory skills. By an introduction to statistics and a brief overview of different sensory methods, we showed the different biases and sources of random decision that you face as a cupper. We explained the principles behind our innovative sensory training program as well as some quick methods to develop a more evidence based approach to quality control and product development methodologies.

Over the summer Square Mile Coffee Roasters will host a series of courses with focus on sensory training in coffee and this event explained the principles behind the research. Already on the 11-12th of May we will run the first of these courses. The course will focus on your skills as a taster in a highly innovative way so that you will be trained directly based on your strength and weaknesses to speed up your personal skills.

Even though we live and breathe coffee, the focus on your skills as a taster made this course relevant and applicable for people in other areas of food and drink such as beer, wine, spirits, chocolate etc

See the presentations: Statistics and  Sensory methodology

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Sensory Methodology

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Ida Steen and Morten Münchow did a talk on Sensory Methodology during World of Coffee in Gothenburg and you can >find the presentation here<

The presentation compares sensory methodology from business practices and scientific methodology and also contains results from a sensory profiling Ida Steen did for Best Water Technology

 

Roast profile analysis

This blog post is sketching out the basics of a roast profile analysis and introduces the concepts and basic calculations that are part of the exam for the Roasting professional in the SCAE Coffee Diploma System.

A roast profile is a graphs that shows the temperature development during a roast cycle and preferably both bean and air temp is measured and logged. An idealized profile is illustrated here:

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During a roast you have different events, and the next illustration plots in these events:

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As you can see when the green coffee at room temp is added to a preheated roasters the temperature drops quickly but after some time you would get a turning point where the temperature starts rising rather than falling. After the turning point you have a period of the roast with maximum temperature increment speed and the speed of the roast is in the business often called Rate of Rise (RoR) and the maximum rate of rise is a good thing to log and will be explained in detail later in this post. After a while you get 1st crack and the last event is when the roast ends and the ‘development’ time is time from 1st crack to end of the roast.

The temperature difference between air and bean is an interesting measure since it gives in indication how much convection drives the roast at any part of the roast process. I recommend to calculate the temperature difference at the turning point, at 1st crack and at end as shown on this below illustration:

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Other interesting readings from the graph is the rate of rise as shown below:

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The rate of rise is the speed of the roast (degrees pr minute) at any given time at the roast. 3 relevant points at the roast is defined above namely the max RoR, RoR at 1st crack and RoR when the roast finishes.

If you understand everything so far, there is no need to read further, but at the exam for the Roasting Professional Certification calculations are important since it is part of the certification process.

Geometrically a rate is the slope of a tangent at a given point on a curve:

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A tangent only touch the curve in one point and it is exactly in this point that the tangent says something interesting about the slope namely how quickly the curve ‘changes’ in this point. So the tangent represents the speed of the curve in this particular point so we would like to calculate the inclination of the tangent because the inclination of the tangent equals the inclination of the curve in the particular point. So for a roast profile the tangent at any given point exposes the speed of the roast at this given point.

So measure the slope of a tangent you can just choose a random range of the tangent like in this example:

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On the above illustration we have °C on the vertical axis and time on the horizontal axis just like on a roast profile. Just as an example the slopes is calculated based on how much the speed of temperature rise is in 3 min, and as you can se the specific tangent will rise 42°C during 3 minutes which will give you 14 degrees per minute which is the Rate of Rise of the curve (the circle in this example) in exactly the point where the tangent touch the curve.

If we choose to calculate the RoR at another point of the curve I could be a little later in the process like this:

r7

Here you can see, that the slope of the curve is different and the inclination of the tangent in the point is less steep. Here the time period is chosen to be 4 minutes and during the 4 minutes the temperature rises with 22°C giving a Rate of Rise of 22°C / 4 minutes = 5,5° pr minute. I prefer to always look at the inclination of the tangent in a 4 minutes period because I find it easy to divide the corresponding temperature range by 4 in my head.

But let us look at an example that is relevant to the RoR of a roast profile:

r8

As with the other tangents we would like to know how many °C the temperature changes during the 4 minutes of the tangent that represents the inclination of the curve in the one point where the tangent touch the curve. The challenge here is that the tangent is completely horizontal so there is not change in temperature which means that the temperature difference is = 0! So if we divide 0 with 4 we still have 0. So the RoR of the curve in the point where the tangent touch the curve is 0. In other words: the temperature is not going up anymore so the speed of the roast has stalled which is announced by the RoR by taking on the value of 0.

There is one last state of the RoR worth a mention and that is when the slope of the tangent is negative:

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When the curve starts to go down after having stalled the RoR becomes negative because when you consider a change you always calculates the change by subtracting the initial state from the resulting state and if the process has gone in the reverse direction you subtract a bigger number from a smaller number and the result is negative (see the blogpost about change for a deeper explanation) so here it is -22°C/4min = -5,5°C/min. A negative RoR is something you don’t want in your roast profile in general yet for the last few seconds in the roast some coffees would work with a very low RoR, a stalling RoR (=0) and only few profiles would you ever find with a negative RoR in the end of the roast!

So to conclude this post the following is an illustration of all the main points of a roast profile:

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Percentage difference

This blogpost explains how to understand and calculate percentage change as this calculation is part of the SCAE certification system on the roast log template you can download: here

So let us get right go business:
If a process changes from x to y the percentage of change is referring to how big the change is seen in relationship to where the process ‘came from’ namely x.

So the general formula for a percentage change is

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If you are not used to do these kinds of calculations, I would like to explain you this formula in more detail as follows:

In the figure below you see a process that goes from value x to y (could be an increasing temperature during roasting) and you can see how you can calculate the difference between the starting point of the process to the endpoint of the process by subtracting x from y.

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Let us take an example. You started you coffee roastery 12 months ago and you currently you have 15 customers. After 9 months you had 10 customers. How many more customers do you have now compared to when you business was 9 months old? In other words: what is the difference between the number of customers you have now compared to when the company was 9 months old:

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So in absolute numbers of customers this is 5 more than after 9 months.

But how to calculate this value as a percentage?

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As you can see from the above calculation, you relate the change (5 customers difference) to the starting point of comparison (10 customers after 9 months) by deviding the change with the starting point of comparison. And as you can also see from the above figure the value is +50% which is a positive number since the process increased in the period.
So the following figure shows you the general formula for the above calculation:

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But what happens if you monitor a process that is decreasing? Namely where y is smaller than x because the process is decreasing. This is illustrated graphically here:

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The value of y – x becomes negative because x is bigger then y so a decreasing process would give you a negative change and if you have a negative change that you divide with the starting point of comparison you also get a negative percentage.

In the following example we look at roast loss which is a process where you compare the result of the roast (y) with the initial amount of coffee you added (x) and find a negative value for the percentage of change. Let us assume that we put in 1kg (1000g) into a roaster and perform a light roast and measure the weight of the roasted coffee (when calculating roast lost please remember to NEVER remove any beans with the sample spoon during the roast!) and find out that 850g of roasted coffee came out of 1000g of green. The calculation looks like this:

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So the percentage of change is -15% which roast masters would refer to as 15% roast loss since the word ‘loss’ implies a negative change.

Customize the fat percentage in your milk

First a warning:

This is geeky, slightly – if at all – useful. It might be just me, but I think its fun..

Baristas play with all sorts of parameters. Why not play with the fat percentage of the milk? I have developed a method and a accompanying formula you can use.

In the example below I have used a Danish semi skimmed milk that contain 1,5% fat and a Danish cream with 38% fat (1 in the figure).

blog4The procedure is as follows: Take a liter of the semi skimmed milk and pour out exactly 150 mL (figure 2) to make room for addition of cream. Decide the fat percentage you would like in your milk. In the formula below you can find the amount of cream that you need to add to the 850 mL milk left in the milk carton in order to reach the desired fat percentage (figure 3). The formula that will give you the right answer:

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which with the above products (skimmed milk with 1,5% fat and cream with 38% fat) gives:

v2

So now you can just insert the fat percentage you would like in formula above where it says Cmixture

Say you happen to live in a country where a semi skimmed milk has 1,2 % fat and a cream has 42% fat, and for some reason you would like to adjust the milk to 3,2 % fat. In that case you  can insert the numbers in the formula as follows:

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So using the formula we have found out that we need to ad 44 mL of cream to the semi skimmed milk in the container in order to end up with a milk with 3,2 % fat.

Geeky and slightly useful. I know.

If you would like to go a step deeper, here is how the formula is developed:

Obvious parameters in the problem:

Cmixture: concentration of fat in the mixture of milk and cream
Cmilk: concentration of fat in the milk
Ccream: concentration of fat in the cream
Vmilk: volume of milk
Vcream: volume of cream

So how do we put them into relation, so that we can get Vcream isolated in the formula so that it can be calculated as a function of the other parameters?

A concentration is the amount of something per volume and in the case of milk and cream it is the amount of fat i grams per liter. Expressing ‘F’ as the amount of fat we could put up these that will come in handy in a while:

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and this could be rewritten into

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and the same relation goes for cream:

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So what do we know? We know, that the total amount of fat in the mixture is the sum of the fat in milk and cream:

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And it should be obvious that the total volume of the mixture is the volume of milk and cream put together:

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And the concentration of the mixture is the total amount of fat (as indicated above) divided by the total volume of the mix (also indicated above) and mathematically this claim looks like this:

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We don’t know Fmilk and Fcream directly but notice how I above calculated F for both milk and cream by multiplying concentration and volume so if we put that into the equation we get this:

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So now we have all the important parameters put into a mathematical relationship and therefore solved the problem conceptually and the solution is now implicit. We can make it explicit by isolating Vcream on the left side by doing some classical school math:

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v12

v13

v14

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Which solves the problem

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Research questions answered?

One of the main things on my mind is the lack of scientific knowledge on high quality aspects of coffee.

Many initiatives are going (WCR, ASIC – please inform me of more initiatives!) on and it seems promising and I strongly wish to participate in this movement. In Denmark we have excellent opportunities to contribute in this area. I’m so fortunate to have a hang-out status at Rolighedsvej 30 where Department of Food Science is situated. At this department we have research groups of Food Chemistry, Food Microbiology, Dairy Technology, Sensory Science, Quality & Technology (the latter is number crunchers doing multivariate statistics). One great thing about this is, that many of the researchers are already internationally respected researchers when it comes to food science. Another trivial but important point is, that they are in the same small building. Everybody who has dealt with multidisciplinary project know, that physical distance between individuals in the project is a barrier. Here you can make an event and nobody will have more then a 5 minute walk!

I have been used as secondary supervisor on the few projects dealing with coffee but now I would like to intensify the number of projects dealing with coffee.

I will search for funding to make events where researchers, students and the general coffee industry is invited to focus on research questions form a high quality perspective. An important step in the success of this vision is that we as specialty coffee people carve out the questions that we find most interesting and most promising to get answered.

I have created a forum where we can brain storm questions and together we can vote amongst the questions so we also can indicate towards the scientist which questions the specialty coffee business are most interested in.

The forum can be found here: questions.coffee-mind.com

If you think the term ‘research question’ sounds strange, please consult wikipedias article