Signalling quality on Instagram ads

I have mentioned multiple times here before that I love Instagram advertising. I love that whatever Instagram learns from my likes (and not likes) on the platform, and through the various pixels that Facebook leaves all over the interwebs, gets used in showing me highly relevant advertising.

Rather, ever since I started using Instagram, I loved the advertising for its visual quality (that made it hard to distinguish if it was an advertisement or native content), and as things have gotten more relevant over time, I’ve started clicking through. And as I’ve started clicking occasionally, the advertising has become more relevant.

I’m sure some silicon valley marketer has some imagery about flywheels. I’m reminded of that hamster spinning this wheel when I’d gone to this animal farm near Bangalore last year.

In any case, I read this article about “the hard thing about easy things“. The basic theory, if I understand it right, is that by commoditising all the tools of production when it comes to direct to consumer selling, the business of direct to consumer selling has gotten that much harder.

The article goes on to say that unless the brand has a competitive advantage in manufacturing (or sourcing by any other means), it is pretty much impossible to make money off direct to consumer products – you struggle to repel the attack of the clones, and you have to spend increasing amounts of money on online marketing (through Google and Facebook).

While this makes sense (or not?) from an investment and entrepreneurship perspective, it got me wondering – as a consumer, how can I distinguish the quality direct to consumer products from those that have somehow simply managed to get into my feed?

Some advertising is like a peacock’s tail – it doesn’t signal any direct value about the brand being advertised. However, it signals that if the brand can afford to spend such huge amounts of money on this form of advertising, it ought to be a brand with sufficient spare cash flow that it is a good brand.

For example, when Vivo got title sponsorship of the IPL, it not only created awareness (which possibly existed thanks to its retail stores and advertising on Amazon) but also signalled that it is a “good brand” since it had bought prime advertising real estate.

Similarly, when a brand advertises on the SuperBowl, the actual dollars per eyeball may not make sense. However, when you add in the signalling value of having been there on SuperBowl (“if a brand can afford to advertise on SuperbOwl, it ought to be a good brand”), it starts making sense.

This works with a lot of mass media advertising. Front page of Times of India is premium because of peacock’s tail. Advertising in the IPL for the same reason. Perhaps similar with hoardings on the way out of airports. And booking prime time slots on popular television shows.

The problem with online advertising is that it is so targeted (and algorithmic) that this signalling effect goes away. Your instagram feed is like the Times of India where every page is similar to every other page.

From that perspective, it is hard to determine whether an advertisement represents a quality product when it appears on your Instagram timeline.

I bought Vahdam tea after someone recommended it to me on Twitter. I bought Paul and Mike’s chocolates after a friend wrote her appreciation for it on Instagram. When I started buying Blue Tokai coffee, I needed good coffee powder and was in the mood for exploration, but was helped by multiple friends and acquaintances vouching for it .

Marketing solely using digital means runs into this problem of not having the signalling effect. And that means you need to invest in “social” also, however you can imagine that to be. Then again, people have started seeing through “influencers”, like how they started seeing through “endorsements” a generation ago.

Unbundling news and advertising

I’ve written earlier about how once news media became dependent on subscriptions, it started becoming partisan. Thinking about it, it is not particularly correct.

If we think of the traditional (physical) newspaper, it was seldom given away for free (when I lived in London I would pick up free copies of the Evening Standard on days when I needed to line my compost bin). Traditional newspapers relied (and still do) on a combination of subscription and advertising for their revenues.

In that sense, what the New York Times does now (read this nice interview with its outgoing CEO) is basically a digital transformation of what it has been doing for over a hundred years – make money off a combination of subscription and advertising.

So if the business model was the same, why did the online New York Times differ from its previous avatar and become politically partisan? Because the nature of advertising changed.

Nowadays I have this favourite theory that everything is a bundle (maybe I should write my next book about this?).

You can consider this post to belong to this meme.

The traditional newspaper, if you think about it, was a collection of news and advertisements all bundled together. While you could choose what part of the paper you wanted to consume, when you went to a page you would inevitably scan all the headlines. And whether you liked them or not, you would actually eyeball all the advertisements.

The important thing to note is that the paper was a physical product and what advertisement the reader was shown did not depend on that person at all. Whether you were a raving communist or a slaveholder, you would be shown the same set of advertisements.

This meant that physical newspaper advertisements were (and still are) dominated by mass products that were aimed at everyone. And since these advertisements were usually paid for based on an estimate (sometimes highly inaccurate) of how many people saw them, the newspapers wanted to maximise the eyeballs. This meant not taking any extreme political stances, and keeping all parts of the political spectrum onside.

What changed with the move to digital was that this bundle containing the news and the advertisements broke down.

With advertising being sold through data-driven ad exchanges, it was now possible to show different advertisements to different people. And with advertisements now dependent on your search and browsing history (apart from your political preferences), it was effectively personalised. The New York Times did not need to directly sell advertising any more. All they needed to do was to sign a contract with Google or Facebook or both. Job done.

Digital advertising doesn’t make sense for mass brands. Rather, it is highly likely that the availability of data will mean that they will frequently get outbid by highly targeted brands. So whether mass brands wanted to advertise in the New York Times became a less important decision. The paper had no compulsion to be politically neutral any more.

And once their early set of subscribers showed a marked preference for one kind of politics, it made sense to them to go after the subscription dollars of this audience rather than the already uncertain dollars of potential subscribers that preferred another kind of politics. And then there as a self-reinforcement cycle.

Media can crib as much as they want about the likes of Google and Facebook taking away their money. They can lobby, like they have done in Australia, to “levy a google tax“. People can crib about media having become biased.

However, we need to remember that all this mess started with the unmaking of a bundle – once news and advertising had been separated, there was no turning back.

Amazon and brand-building

Sometimes shopping on Amazon feels like shopping in Burma Bazaar or National Market or any of those (literally) underground “shopping malls” where you get cheap imported stuff of uncertain quality. This is especially true when shopping for things like children’s toys and some electronics, where you don’t have too many established brands.

The only times I feel completely comfortable shopping on Amazon is when I’m buying known brands – like last month when I bought a LG monitor or Logitech keyboard and mouse. LG and Logitech have built their brands sufficiently outside of the Amazon ecosystem that I trust their quality even while buying on Amazon.

This is not the case when it comes to other categories, though. One day I was browsing for toys on Amazon and was simply unable to decide what to buy – it all looked so “cheap”. Finally, my wife noticed one brand of which we already had a toy (that we liked), and we ended up buying that (that was a sound decision). Once again, we had used our knowledge of brands that had build their brands outside of Amazon to make our decision.

The thing with Amazon is that it is an “everything store” – one store to serve all markets. That’s not how offline markets work. In offline markets, stores fairly easily differentiate themselves based on the markets that they serve – by their locations, by their price points, by the overall “look and feel” and so on. That way, when you go to a store that you know serves your segment, you can be confident that what the store sells you is what you’re looking for.

This is not the case with Amazon. Since one store serves all, it is very difficult to know upon seeing a product whether it is “made for you”. Well, Amazon has information about your previous purchases on the platform, which should give them a good idea of the “segment” you belong to, but I guess making money from advertisers on the platform trumps making your choice easier?

From this perspective, if you are a hitherto unknown brand trying to sell on Amazon, it makes sense for you to build your brand elsewhere. Here, we run into the “double cost problem” (that I had used to describe long ago why Grofers is not a sustainable business). Essentially, building a brand is expensive and once you’ve spend your dollars on (let’s say) the Facebook ecosystem to build your brand, does it make sense to also pay Amazon to push up your product when it comes to search?

It seems like brands are now choosing one way or the other. Mass market brands (it appears) are sticking to the Amazon ecosystem. Some premium brands are using Instagram to acquire customers, and then using the Shopify-Razorpay-Delhivery ecosystem to deliver. Some other premium brands are using a combination of Instagram and Amazon, but only using the latter as a fulfilment mechanism – not spending money to advertise there.

In any case, it seems to me that building brands on Amazon is not a viable business. Now I’m reminded of my other old post where I talk about how platforms are useful only if they aggregate unreliable supply. And this is a path that Amazon seems to have firmly taken.

And the moment you focus on branding, you are trying to send out the message that you are not “unreliable supply”. And this means that getting mixed up with other unreliable suppliers is not good for your business. Which is why you find that the direct to consumer brands that advertise on Instagram (have I told you I love instagram ads?) usually stay away from Amazon.

(you might think I’m going round and round in circles in this post. This is because it’s been about a month since I thought of writing this but only got down to it today. It’s also funny that I’m writing  this less than an hour after talking to someone who builds her brand on Instagram and then sells through Amazon (and offline shops) ).

PS: I got reminded of when I initially thought of this post. I bought a yoga mat from Amazon a couple of months ago. Quality turned out to be pathetic. And there was no way for me to know that when I was buying.

Advertising

When I first joined Instagram in 2013 or 2014, the first thing that fascinated me about the platform was the quality of advertisements. At that point in time, all advertisements there looked really good, like the pictures that the platform was famous for helping sharing.

It wasn’t like the clunky ads I would see elsewhere on the internet, or even on Facebook – which mostly stuck out like a sore thumb in the middle of whatever content I was consuming at that point in time. Instagram advertisements looked so good that I actually paid them considerable attention (though I hardly clicked on them back then).

Over the years, as Facebook has gotten to know me better (I hardly use Facebook itself nowadays. But I use a lot of Instagram. For now I’ll believe Facebook’s claim that my WhatsApp information is all encrypted and Facebook doesn’t learn much about me through that), and the advertisements have gotten better and more relevant.

Over the last one year or so (mostly after I returned to India) I’ve even started clicking on some of the ads (yes they’ve become that relevant), giving Facebook even more information about myself, and setting off a positive feedback loop that makes the advertisements more relevant to me.

Over the years I’ve attended talks by privacy experts about the privacy challenges of this or that platform. “They’ll get all this information about you”, they say, “and then they can use that to send you targeted advertisements. How bad is that?”. If I think about all the problems with telling too much about myself to anonymous platforms or companies, receiving better targeted advertisements is the least of my worries.

As a consumer, better targeted ads means better information to me. Go back to the fundamentals of advertising – which is to communicate to the customer about the merits of a particular product. We think advertising can be annoying, but advertising is annoying only when the advertisements are not relevant to the target customer. 

When advertisements are well targeted, the customer gets valuable information about products that enables them to make better decisions, and spend their money in a better fashion. The more the information that the advertiser has about the end customer, the better the quality (defined in terms of relevance) the advertisements that can be shown.

This is the “flywheel” (can’t imagine I would actually use this word in a non-ironic sense) that Facebook and affiliated companies operate on – every interaction with Facebook or Instagram gives the company more information about you, and this information can be used to show you better targeted advertisements, which have a higher probability of clicking. Because you are more likely to click on the advertisements, the advertiser can be charged more for showing you the advertisement.

Some advertisers have told me that they elect to not use “too much information” about the customer while targeting their advertisements on Facebook, because this results in a much higher cost per click. However, if they look at it in terms of “cost per relevant click” or “cost per relevant impression”, I’m not sure they would think about it the same way.

Any advertisement shown to someone who is not part of the intended target audience represents wastage. This is true of all forms of advertising – TV, outdoor, print, digital, everything. It is no surprise that Facebook, by helping an advertiser advertise with better (along several axes) information about the customer, and Google, by showing advertisements after a customer’s intent has been established, have pretty much monopolised the online advertising industry in the last few years.

Finally, I was thinking about advertising in the time of adblockers. Thanks to extensive use of ad-blockers (Safari is my primary browser across devices, so ad blocking is effective), most of the digital advertisements I actually see is what I see on Instagram.

Today, some publication tried to block me from reading their article because I had my ad-blocker on. They made a sort of moral pitch that advertising is what supports them, and it’s not fair if I use an ad-blocker.

I think they should turn to banner ads. Yes. You read that right.

To the best of my knowledge, ad blockers work by filtering out links that come from the most popular ad exchanges. Banner ads, which are static and don’t go through any exchange, are impossible to block by ad-blockers. The problem, however, is that they are less targeted and so can have higher wastage.

But that is precisely how advertising in the offline versions of these newspapers works!

Something is better than nothing.

Alchemy

Over the last 4-5 days I kinda immersed myself in finishing Rory Sutherland’s excellent book Alchemy.

It all started with a podcast, with Sutherland being the guest on Russ Roberts’ EconTalk last week. I’d barely listened to half the podcast when I knew that I wanted more of Sutherland, and so immediately bought the book on Kindle. The same evening, I finished my previous book and started reading this.

Sometimes I get a bit concerned that I’m agreeing with an author too much. What made this book “interesting” is that Sutherland is an ad-man and a marketer, and keeps talking down on data and economics, and plays up intuition and “feeling”. In other words, at least as far as professional career and leanings go, he is possibly as far from me as it gets. Yet, I found myself silently nodding in agreement as I went through the book.

If I have to summarise the book in one line I would say, “most decisions are made intuitively or based on feeling. Data and logic are mainly used to rationalise decisions rather than making them”.

And if you think about it, it’s mostly true. For example, you don’t use physics to calculate how much to press down on your car accelerator while driving – you do it essentially by trial and error and using your intuition to gauge the feedback. Similarly, a ball player doesn’t need to know any kinematics or projectile motion to know how to throw or hit or catch a ball.

The other thing that Sutherland repeatedly alludes to is that we tend to try and optimise things that are easy to measure or optimise. Financials are a good example of that. This decade, with the “big data revolution” being followed by the rise of “data science”, the amount of data available to make decisions has been endless, meaning that more and more decisions are being made using data.

The trouble, of course, is availability bias, or what I call as the “keys-under-lamppost bias”. We tend to optimise and make decisions on things that are easily measurable (this set of course is now much larger than it was a decade ago), and now that we know we are making use of more objective stuff, we have irrational confidence in our decisions.

Sutherland talks about barbell strategies, ergodicity, why big data leads to bullshit, why it is important to look for solutions beyond the scope of the immediate domain and the Dunning-Kruger effect. He makes statements such as “I would rather run a business with no mathematicians than with second-rate mathematicians“, which exactly mirrors my opinion of the “data science industry”.

There is absolutely no doubt why I liked the book.

Thinking again, while I said that professionally Sutherland seems as far from me as possible, it’s possibly not so true. While I do use a fair bit of data and economic analysis as part of my consulting work, I find that I make most of my decisions finally on intuition. Data is there to guide me, but the decision-making is always an intuitive process.

In late 2017, when I briefly worked in an ill-fated job in “data science”, I’d made a document about the benefits of combining data analysis with human insight. And if I think about my work, my least favourite work is where I’ve done work with data to help clients make “logical decision” (as Sutherland puts it).

The work I’ve enjoyed the most has been where I’ve used the data and presented it in ways in which my clients and I have noticed patterns, rationalised them and then taken a (intuitive) leap of faith into what the right course of action may be.

And this also means that over time I’ve been moving away from work that involves building models (the output is too “precise” to interest me), and take on more “strategic” stuff where there is a fair amount of intuition riding on top of the data.

Back to the book, I’m so impressed with it that in case I was still living in London, I would have pestered Sutherland to meet me, and then tried to convince him to let me work for him. Even if at the top level it seems like his work and mine are diametrically opposite..

I leave you with my highlights and notes from the book, and this tweet.

Here’s my book, in case you are interested.

 

Instagram targeting

Instagram is really good at what I call “one dimensional psychographic targeting”.

Essentially, based on the photos and videos (more likely hashtags) that you see, spend time on, like and comment, the platform figures out some of your interests and targets at you advertisements of products that serve these interests. And instagram manages to combine this with demographic information (where you live, etc.) to target advertisements better at you.

For example, of late I’ve been looking at a lot of weightlifting stuff on Instagram – I follow most of the coaches at my gym, and a few other handles that post fitness stuff. I’ve even posted a video of myself deadlifting.

As a result, Instagram has been following me with advertisements related to fitness, and the combination with demographics means I’m being served stuff I can get in Bangalore. For example, last two days I’ve been seeing ads of my own gym (!!). There are ads for whey proteins and healthy foods of all kinds as well.

This targeting is not perfect – for the last few months, ever since I returned to India, I’ve been bombarded on Instagram with advertisements asking me to emigrate to Canada (I don’t know what makes it think I want to move abroad again given I’ve just moved back home). The seemingly un-targeted mattress advertisements are everywhere. The shirt advertisements as well (though recently I uploaded a picture of my wardrobe on Instagram).

Nevertheless, this is a massive step up from what marketers were able to do a generation ago, where they could at best target based on a demographic. Marketers might have created elaborate psychographic or behavioural profiles of their target audiences, but when it came to advertising, the media available (newspaper, television and outdoors) meant that they had to collapse it into a demographic profile.

Instagram is not perfect, though. To the best of my knowledge, it can only target me on one “psychographic dimension” (“interested in weight lifting”, “interested in coloured chinos”, “likes Bangalore”) along with a multitude of demographic dimensions (I’m sure it’s figured out my gender, age group and maybe even caste, even if it exists in some vector somewhere and no human knows these classifications).

However, when you have created elaborate psychographic profiles, collapsing them into one dimension is still a simplification process. And so you get a reasonable degree of error in targeting. So I’m wondering what can be done that can enable advertisers to target me with more specific products that I might be interested in.

Finally, really how much are the likes of Charles Tyrwhitt, and some mattress brand whose name I don’t recall, willing to pay for their campaigns, given that their untargeted campaigns have beaten the highly targeted campaigns of the fitness guys and coffee companies to reach my eyeballs?

Mass marketing and objective journalism

This is a fascinating essay by Antonio García Martinez on the history and future of journalism (possibly paywalled). The money paragraph is this:

The bigger switch happened as a national market for consumer goods opened after the Civil War, when purveyors like department stores wanted to reach large urban audiences. Newspapers responded by increasing the number of ads relative to content, and switched to models that went light on the political partisanship in the interest of expanding circulation. This move was driven not exclusively by lofty ideals but also by mercenary greed. And it worked. Newspapers used to make lots of money. Mountains of money.

Basically, the move to objective journalism came in the late 1800s when advertisers such as Macy’s wanted to take out full page ads, and wanted to do so in newspapers that served the largest sections of the market. And when a newspaper had to reach a large section of the market, it inevitably had to tone down the partisanship, and become more objective.

Over the last decade, we have been witnessing (across the world) the decline of objective media. All media is “#paidmedia” based on which side of the political spectrum you stand on. There aren’t that many truly objective papers around, and social media is bombarded left and right by extremely politicised reporting that goes as “news”.

It is perhaps no coincidence that this period has coincided with a time when print circulation has been dropping steadily (in the developed world at least), and where online advertising can be highly targeted.

In theory, mass marketing is inefficient. When you pay to put up a hoarding somewhere, you’re possibly paying a small amount for each person who sees the hoarding, but not all of them might find it interesting. Consequently, this reflects in a depressed per-person price of the hoarding implying the owner of that real estate can’t make as much as she could if the hoarding were to be more “targeted”.

When you can target your advertisements more precisely, everybody wins. You as the marketer know that your advertisement is only being shown to your intended audience. The owner of the real estate where you put your advertisement can thus charge you more for your advertisement. Even the customer will be less pained by the advertisement if it is highly relevant to her.

Another way of seeing it is – an advertisement shown to a customer who doesn’t want to see it is wasted. The monetary cost of this waste are borne by the owner of the real estate and the advertiser, and the non-monetary cost is borne by the customer (being forced to see something she didn’t want to see). And so one of the biggest technological problems of today is on how we can target advertisements better so that we can minimise such costs – and in the last decade and half, we’ve made significant progress on that front.

The problem with greater efficiency, however, is that it comes with the side-effect of biased media. When Nike knows that it can precisely target an advertisement at American leftwingers, it makes an ad with Colin Kaepernick and shows them to American leftwingers to sell them more shoes.

This doesn’t however, mean that Nike only sells to left-wingers. The same company can make another advertisement targeted precisely at right-wingers and use it to sell shoes to them!

So now that you can make left-wing and right-wing ads, and you have the ability to target them, you want to cut the waste and place the ads so that you can target as best as possible. In other words, you want to place your left-wing ads in places that only left-wingers want to see, and right-wing ads only in places that right-wingers will see. And so you prefer to advertise in CNN and Fox rather than in a hypothetical “broad market” media outlet.

And the reason you created the politically charged ads in the first place was because there were some outlets (Facebook, for example) where you could precisely target people based on their political orientation. And so you see the vicious cycle – that you can target in some places means you want other places where you can target and that creates demand for more polarised media.

It was the opposite cycle that took effect in the late 1800s and early 1900s. There was no way brands could target (also, when you make physical advertisements, with 1900s technology, each advertisement is costly and you don’t want to make one per segment) too effectively, and so they went mass market in their communication.

And this meant advertising in the outlets that could get them the maximum number of eyeballs. When you can’t discriminate between a “right” and a “wrong” eyeball, you pay based on the number of eyeballs. And the way for media organisations to grow then was to cater to everyone. Which meant less less bias and more objectivity and more “features”.

Sadly that cycle is now behind us.

The finiteness of the global advertising market

In this excellent post on social media companies, Aswath Damodaran articulates something I’ve long wondered – about the finiteness of the global advertising market. Given the number of companies that come up with new mechanisms to match advertisers with consumers, one can be forgiven for believing that the market for advertising is infinite. That the more avenues you create for serving advertisements to people, the more the advertising that will flow, and there won’t be a let up anywhere.

This picture here is from Damodaran’s blog (which I recommend you subscribe to, since every single post is worth reading). Based on the numbers that Damodaran presents here, the overall growth of the worldwide advertising market seems rather low.

Source: Aswath Damodaran (http://aswathdamodaran.blogspot.in/2014/11/twitters-bar-mitzvah-is-social-media.html). All numbers in billions of dollars

The number to take away for me from this calculation is the shrinking pie of non-digital advertising. Based on these numbers, the total non-digital advertising market in 2008 was $468 billion. In 2014, going by the same numbers this is down to $400 billion. This de-growth is significant and holds important lessons for other sectors that are dependent on advertising.

So far, the flow of advertising capital has been taken for granted and the number of business plans made (in both old and new economies) with an assumption on advertising growth is endless. If you want your local bus utility to make more money, you rent out advertising space on buses. If a low-cost airline wants to make more money, they put advertisements on the back of seats (a very good idea since it gets undivided attention for the duration of the flight). It is a surprise that insides of toilet stall doors (which again get undivided attention) haven’t fallen prey to advertisements yet.

The point here is that while it is all well and good to plan businesses based on advertising income, what we need to keep in mind is that the advertising pie in the long term grows at the same rate as the global economy. Sooner or later the waters will recede to the natural level, and then we will know who is swimming naked!