New Look vs Zara – They don’t compare. #smartretail #data

So I had a lot of fun talking loyalty, data and vouchers and generally dissing social media at Smart Retail last week. And while I enjoyed Adoreboard’s presentation I can stay silent no more, there’s one part I don’t agree with and it’s all to do with that slide on fashion retail.

The original blog post is here, it’s worth a read as it’s important to the context on what I’m about to say. Emotional metrics are fine, I’ve got nothing against that but they are not to be compared with others in the same space.

So what follows is merely my opinion but with some more numbers to back up my assumptions.

Why Does It All Matter?

I see a lot of these comparison reports. And like a data trail they are left on the internet for all to see. Now then, these findings are open to discussion but they do have impact in some quarters.

Take JP Morgan’s post on Bitcoin being fraudulent. The cynic in me sees it like this. JP Morgan are investigating blockchain technology for a long time, one which Bitcoin is built, so why diss it. Perhaps in the knowledge that if you do that then the price will drop. Markets are driven by emotions. So after the post the price of Bitcoin dives for a very short period of time, guess who had the highest purchase volume….. JP Morgan. I’ll let you derive your own conclusions from there, I have my own.

Same thing applies here, when you are talking about five fashion brands well valuation matters. And as markets are emotionally driven it can do as much harm as it can highlight a product. Don’t think posts have no ripple effect, they do.

Nothing gives you the fear of responsibility than a complete stranger walks up to you at an international conference saying, “Hi, I subscribe to your blog”.

One Dimension isn’t enough

Twitter data is dreadful, that’s the plain and simple truth of the matter. I’ve done enough of it over the last seven years to know. I personally can’t value it as a single data source. As well as that the quantity of data to get insight from, well the more the merrier. From the article “We analysed over 6,000 mentions of five of the leading online fashion retailers”, that’s not a lot of tweets and I wondered what day of the week, what time of the day etc etc?

As we don’t know the percentage mentions of those 6,000 tweets we don’t really know the true value of those scores. Were there 70% mentions of New Look and only 10% of Zara for example. These kinds of breakdowns need to be reported so we get the balanced view. Was the score weighted according to how many tweets were ranked against each retailer….. I ask a lot of questions.

The simple upshot it that you’ll get results from a small data sample but I’d like to see something over a million, twenty million or a hundred million tweets.  And don’t give me the cost and processing power, that’s utility stuff and right now it’s cheap. Many knock Hadoop now but it’s the first thing I’d go for in something like this. And it wouldn’t take long either. I’ve done sentiment studies with 8 million tweets and they were processed in just over 40 seconds.

So let’s introduce a second measure. There’s a few to choose from, I’ll go through each here. I need another metric to go against. In fact I’ve got three: the number of Twitter followers that brand has, the number of Facebook page likes and, finally, the number of physical stores.

Reverse Adoreboard against per 1000 Twitter Followers

Firstly, I know what you’re thinking, “what’s a reverse Adoreboard“, well the index score gives the positive emotion index. I want the complainy whiney index version of that…. it needs a nicer name so it’s a Reverse Adoreboard. I’m assuming here the score is based on 0-100 which is interesting in self as it means the top fashion retailer is still below 50% in customer satisfaction. I digress….. a reverse score is 100 minus the Adoreboard score.

The 6000 tweets is fine, what we don’t know is the number of followers each brand has. Well I made a cup of tea and found out. Once we have then then we can find out the RA per 1000 followers. My calculation was easy enough.

Reverse Adoreboard Score / (Followers / 1000)
Retailer RA Twitter Followers RA / T1000
Top Shop 58 1,330,000 0.0436090225
Zara 76 1,270,000 0.0598425196
Asos 64 1,040,000 0.0615384615
Boohoo 70 482,000 0.1452282158
New Look 55 362,000 0.1519337017

When you rank by the per thousand score from smallest to largest this changes the standings quite significantly, when you balance the negative score per thousand twitter followers for the brand then New Look actually come out bottom and Zara came out second.

Reverse Adoreboard against per 1000 Facebook Page Likes

Okay that was Twitter, let’s look at Facebook while we’ve got some numbers to work with. Using the same Reverse Adoreboard score how do the retailers stack up RA per 1000 Facebook page likes?

Retailer RA Facebook Page Likes RA / FB1000
Zara 76 25,907,851 0.00293347371
Top Shop 58 4,277,568 0.01355910648
Asos 64 5,035,399 0.01271001563
New Look 55 3,426,041 0.01605351483
Boohoo 70 2,534,629 0.02761745407

Fashion retailers get far more attention on Facebook than on Twitter, I think that’s important to point out. The other interesting fact here is that Zara’s presences is 1.69 times more than the other four combined. So when you run the RA score against the page likes then Zara just runs ahead of the competition.

I have to be careful here as the RA metric really applies to Twitter users and not Facebook ones. You’d have to run the study again on Facebook customer experience data to get a better idea but something tells me that Zara would still come out on top but that’s a gut feeling and not to be trusted. You need the data.

Reverse Adoreboard against per 1000 Physical Stores

Asos and Boohoo don’t get counted here as they don’t have physical stores but are purely online.

Retailer RA Physical Stores RA / PS1000
Zara 76 7,000 10.85
New Look 55 1,160 47.41
Top Shop 58 500 116.00

This is really as a guide, online and offline customer experiences are different beasts. A better gauge would be refunds from point of sale, there’s a good chance that complaints aren’t actually recorded but the reaction is merely dealt with.

In terms of Zara’s high RA score it comes out highest based on the number of stores that it has. I’d expect to see that. Even if there was a 10-15% tolerance in the scores Zara still comes out on top. As Zara’s core business is not online but in store then it should come as no surprise.


From the day I read the Adoreboard blog post I’ve never agreed with the results. What I have presented here, while not perfect, is an alternative view with extra data points. It’s only when you introduce a second metric that you can drill down into the results and get better insight.

Each brand performs well in their own way. You’d expect Asos and Boohoo to nail the online space as it’s their core business but they do a good job of staying middle of the road in terms of performance. For my money both Zara and Top Shop are doing a better job of New Look in terms of balanced ranking on Twitter and Facebook.

The Adoreboard index is fine, it ranks emotion but it’s only a single view in my opinion. Which is fine when one brand role up and want to see the emotional responses for their own brand. Once you bring in competitors then the results are very open to interpretation. As a blog post it is good. As a system it’s good, please don’t take this as me knocking Adoreboard because I’m not. I’m exploring the meanings of the original blog post that I disagreed with. As there’s context missing it’s always going to be an opinion whether things are right or wrong.

Best course of action: run the whole analysis again with a million tweets.


The original Adoreboard post:

Google Sheet of the raw data and calculations:

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