Tag Archives: big data

Is Digital Targeting Just a Hoax?

Before I went to holiday, there was lots of chatter about the ”failed” Facebook targeting experiment of P&G. This naturally gave fuel to the fire to those denouncing digital advertising (namely Ad Contrarian). Essentially P&G run targeted Facebook for Febreze (pet owners and large families for example), but they got better results when they were just targeting broader audience of just over 18 year olds.

If you have been doing marketing at professional level for a while the results were not surprising at all. However, you should not use this as a proof point that targeting does not work, because of the following reasons:

1. FMCG is a different kind of beast, you can just blast your audience with bazooka

“The bigger your brand, the more you need broad reach and less targeted media,”

– Brian Weiser, Pivotal Research Analyst

Majority of P&G brands (including Febreze) are unique brands because they are truly for everyone. Majority of FMCG is mass reach, so it is not surprising that when you have broad targeting you have better results than when just focusing on few sub-segments. Actually in most of the markets you should not even bother with Facebook. If you have money running TV ads, they would still probably be more effective than doing anything on Facebook. And that is essentially what P&G has done. They have increased their TV spending. FMCG is first-and-foremost about top-of-mind and visibility on shelf. To achieve that you opt for the channel getting you maximum awareness.

Pretty much all the rest of the brands cannot work with such a broad sweep. Not all of the products live and die through the mass awareness. If you need to get 1000 quality leads, targeting the whole population is not most likely be more cost-effective than smart targeting. The main benefits of digital advertising come when you are selling in eCommerce, because you can then truly track your results and optimize. Then shooting with bazooka is not the right tactic.

2. Targeting without personalization is not targeting

Apparently they run the same creative to all the different segments. This is akin to running nighttime ad at 11AM. It is like narrowing the list of girls you want to go out to date with, but addressing them all with the same name. If content is king, context is truly the king kong. As you have narrowed your audience, you should also narrow your message to be as relevant as possible to your target audience.

3. Targeting based on intuition is not targeting

In the articles it was not said how the different target groups (pet owners and large families) were selected, but I would assume that they were based on human intuition. The beauty of digital advertising is that you let machines to try out different target groups, different messages and let them automatically favor what truly works. Humans are incapable of handling that many tasks and they are more biased than smart algorithm.

So the failure of Febreze seems obvious in hindsight. You started narrowing although your audience is as broad as it gets. You did not narrow your message to your narrow audience. Lastly you based your targeting on human intuition instead of testing potential audiences with machine learning.

The more we let algorithms handle our marketing, the more effective it will become. P&G experiment shows more human fault than failure of highly-targeted, highly automated algorithm-driven approach.

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Why Spotify Discover Weekly Is The Best Music Curation Tool?

Apple Music arrived with big bang. Its approach to music streaming is surprisingly old school. It relies a lot on human curation and its programming resembles old radio (some of the shows are definitely worth listening though). It´s biggest rival Spotify is relying more on big data. At the moment it seems that latter approach seems to be the winning formula. Eventually recommendation engines will become a core differentiator (as the libraries will become quite identical) for streaming services, so the headstart Spotify has is not insignificant.

Human curation was the way taste making happened back in the day. I used to rely almost totally to Dj Anonymous on my music recommendations. Best dj´s in the world have much more refined taste than any machine yet. The challenge with human curation is that it does not scale.

The recommendation engines were not really been yet up to task because the algorithms have not been advanced enough to recommend right songs. Music is nuanced thing and linear recommendation is not usually providing satisfying listening. Previous Spotify recommendations have been borderline ridiculous:

Prince Spotify

Previously there has not also been enough data available. For recommendation engines to work, you need to have massive amounts of data and something that is relevant. The key for Discover Weekly to work so well is that Spotify realized that the data they should be mining are the playlists people are making.

“For all the special sauce and the algorithmic work, the fact that we’ve kept it simple and that it’s just a playlist has really helped it resonate with people”
Matthew Ogle (Discover Weekly Product Owner)

The more people are making playlists in Spotify more “human curation big data” they are gathering. Currently there are over 2 billion playlists in Spotify. Spotify has been able to strike the right balance on learning about your listening habits and combining that with the big data:

“On one side, we’ve built a model of all the music we know about, that is powered by all the curatorial actions of people on Spotify adding to playlists. On the other side, we have our impression of what your music taste is. Every Monday morning, we take these two things, do a little magic filtering, and try to find things that other users have been playlisting around the music you’ve been jamming on, but that we think are either brand new to you or relatively new.”
-Matthew Ogle (Discover Weekly Product Owner)

In the beginning I wasn´t that impressed with Spotify´s weekly recommendations. Majority of the songs I knew already (20+ years of record collecting has its handicaps). After couple of weeks I started to appreciate the brilliance of it. Spotify Discover Weekly has become my “comfort playlist”. It plays stuff I know, but drops every week couple of nice gems I had not heard or had totally forgotten. During working week I listen to lots of weird stuff outside my usual taste profile, Spotify´s weekly recommendations don´t seem to pick on those anomalies and the quality is constant:

Like mentioned earlier, eventually data will trump human experience. In many fields, we are already there.

“In the next generation of software, machine learning won’t just be an add-on that improves performance a few percentage points; it will really replace traditional approaches.

Today, you’re much better off building a smart system that can learn from the real world – what actual listeners are most likely to like next – and help you predict who and where the next Adele might be.”
Eric Schmidt, Alphabet executive chairman

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Obey Your Data

For various reasons I have been reading quite a bit about big data lately. Based on everything I read and the experience, I have come to three conclusions:

  1. It does not matter anymore why something happens. It matters that it happens.
  2. In life there is no such thing as certainty; therefore you should just aim for high probability.
  3. Correlation trumps causality.

“In many cases, the deeper search for causality will take place after big data has done its work, when we specifically want to investigate the why, not just appreciate that”
Viktor Mayer-Schönberger, Kenneth Cukier (Big Data)

I would say that in our industry knowing why is more of a philosophical question. Examples show that when you just work on what you have (data) and act on that, the results are better. If data shows that it works, you don´t really need to know why it works. It might be interesting from academic point-of-view, but should not matter when doing marketing for the people.

There is still aversion against data in our industry. Maybe it is because logically thinking people generally don´t gravitate towards advertising (and why our business models are so antiquated). For lack of better data, we have tolerated egomaniacs (disguised as gurus) in our industry for too long.

Advertising is not a rocket science; it is just a data science. Just like everything else in this world.

We have self-inflated view of how difficult our work is, but eventually we will be replaced by algorithm. Gut feeling is not necessarily bad if it is based on experience. I.e. if you have done thousand display campaigns you know anecdotally that call-to-action button should be red. Too often, someone just wants it green because it is his favorite color. And person with no experience could say the right thing just based on the right data.

Experience will trump stupidity.

Data will trump experience.

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Do You Still Want to Hear About Social Media Trends 2013?

Although everyone and their mother has already given their point of view about current social media trends, here are couple of more predictions.

Good folks at Kurio (Finnish Digital Marketing Think Tank) asked me and 17 other Finnish “social media experts”(stated by the report, I prefer to just be “the planner guy with funny glasses”) about social media trends in 2013 and conducted interesting study about the results. They identified nine emerging trends in social media for the year 2013. The report is in Finnish, but the main trends can be read below. All the finnish-speakers should definitely download the report below.

Big Social Media Themes for 2013  (my comments in italics):

1. 2013 is the year of Mobile (this has been my favourite prediction for at least five years. The main difference is that this year I am actually believing it)
2. Multichannel story-telling is the way to create modern phenomena (Or put it this way: If your marketing activity is not multi-channel/channel-agnostic/holistic/360/add your favourite buzzword by nature, it will be doomed to fail)
3. Lack of human resources is the main constraint in social media (The problem is two-folded, there is definitely lack of people actually working with social media. But there is actually even bigger talent problem: there are lots of “social media experts”, “community managers” but not enough actual strategic thinking about what we should do with and in social media. Social media without strategic thinking behind it is irrelevant at best and purely dangerous at worst.)
4. Big Data (Like the report also points out: we have lots of data & information, but do we have talent, resources and capabilities to turn that information to action?)
5. Content: Interesting, current and value-adding (Which starts the discussion about what is the good content? I still believe that good content is either 1)useful 2)funny 3)on some rare instances both)
6. Picture tells more than 2013 words (Despite the revolt against Instagram, people will rather share more pictures online than less in 2013)
7. Social cannot be just a digital or marketing function, but should be thought holistically (Definitely true, but also the most difficult to achieve. On some instances to make the change requires totally new management, who understands the possibilities of digital and social media. On other instances it requires adequate mix of sticks and carrots to ensure the competitive advantage in digital age)
8. Social media channels are starting to resemble more bought media (The pressure to monetize is already showing with different social networks, especially with Facebook)
9. Users are more and more critical towards marketing activities in social media (You might fool a customer once, but not twice. The opportunity to fool once has already passed)

Download the report (in Finnish).

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