StopPress Article | March 2017

StopPress Article | March 2017

This Article was originally published on StopPress NZ

The two key nascent technologies being showcased at SXSW 17 in Austin Texas, are undoubtedly AR/VR and AI cognitive learning, the latter of which essentially involves talking to computers through conversational search, chatbots and natural language processing.

AR and VR are, of course, fascinating, though still feel a little gimmicky, but AI has the possibility to not only fully disrupt the marketing world over the next few years, but to fundamentally shift the way we interact not just with machines but with people.

Over the last few days, I have seen technologies and services from IBM, Google, Amazon, Facebook, Dell, and heard from companies either just starting their journey or well into AI, chatbots, personal assistants, and machine learning that either make you very concerned or very excited about the future, depending on your perspective. We heard about a fashion retailer who last year allowed an AI to design a number of skirts and tops based on current trends and styles, and a recipe website that has built an AI to take lots of recipes for the same thing (say chocolate chip cookies) and try and develop a super recipe based on all the attributes of the recipes on site.

Take a very simple task for a human: “I’d like to get a bottle of wine that goes with pasta, on the way to Charles’ house”. For your human brain it’s easy to process. You can instantly picture the route to your friend’s house, recall where the bottle shop is, allocate a suitable budget, and decide which wine (red or white at least) goes with the pasta dish you are taking. But if you were to ask an AI the same question then the data sets the computer would have to know are staggering. At the very least it would need to know:

  • Your address
  • Charles’ address
  • The route to Charles’ house, and the closest bottle shop to that route
  • Your normal budget for a bottle of wine
  • What wines go with pasta, and in fact what type of pasta dish you are making
  • The inventory of the store closest to your route

That is a lot of information – address books, bank data, maps, wine database, food database, wine matching API, store inventory, etc. It’s especially complex because unlike search as it stands today that gives you a number of options based on keywords that you search for (in this case something like ‘bottle shops near me’),  in audio search you only want one response. The right answer. But this is exactly what companies like Google, Apple and Amazon are trying to achieve, through natural language processing and cognitive computing.

At the moment, the solution for AI to be able to understand what you need is through conversational search. Basically, the computer asks you questions that makes answering your main question easier, because—and this is really important—you only want one answer; not a list of choices. So, in this case, your AI Personal Assistant, may respond with, “what is Charles’ address? Which pasta dish have you made? What is your budget? Are you happy with Glengarry on New North Road?” And like any AI worth its salt, it will remember every answer you give, so that over time it will make it smarter and smarter.

I went to a talk that included one of the leads on “conversational search” from Bing and his advice was, plan now, as it will be the single biggest shift in consumer marketing behaviour, since search itself. This is because the web as it stands today has been indexed and is searched through keywords and phrases, sifting through search results until you get what you are looking for. With AI, keywords become redundant in many respect and through conversational search the focus shifts from keywords to intent. Search engines, like Google and Bing, are monetised through brands buying keywords and then serving an ad to a consumer along with other topically relevant ads.  So, what happens if rather than having multiple results, you only want one; how would that be decided, what is the role of SEO, versus SEM? In the example above, in which we’re looking for a bottle of wine you wouldn’t want a wine brand to be able to bid on your question and get their brand of wine recommended over others simply because they paid for it.

So what’s the advice from SXSW? Ignorance is not a strategy; start thinking now about what tasks an AI, be it a chatbot or something more elaborate. Could take over and start building a case? Happy to point you in the direction of some of the vendors I’ve met here. And remember that the sooner you get started the sooner your AI will start learning.

In the world of search, now really is the time to start thinking about voice search and conversational search. Discuss with your SEO team a 12-month roadmap to be ready. Alexa, Google Home, Cortana, and Siri are here, and they are only going to get smarter. You don’t want to be left behind.

For example, how powerful would it be for a consumer to ask Alexa on a Saturday morning “what’s the best deal on Samsung TVs in New Zealand at the moment?” and immediately the response comes back: “Noel Leeming has 50 percent off all Samsung TVs this weekend. The St Luke’s store, which is closest to you opens at 9am. Would you like me to send directions to your phone…”

Ad Viewabilty – The 21st Century Kettle

Ad Viewabilty – The 21st Century Kettle

Back in the day, the kettle was often cited as the reason not everyone could watch your ad on TV (as in off making a cup of tea). Today, “viewability” is the villain being blamed for ads not being seen on the internet.

The other week a colleague send me an article about the fact that 100% viewability was not necessarily a good thing. As I was reading this article I started thinking about this, and all I could think of was how bonkers our industry is sometimes. I mean that in a good way, but seriously when you start to explain the question of viewability to a person outside of our industry you can come of sounding like a crazy person.

Let’s just recap the argument, and I suggest you read this a couple of times, and see how irrational you sound. Even say it aloud for extra crazy credits. Here we go: when you buy digital media placements from banners to videos we do so in the complete knowledge that on average only 48% of your ads will be seen by a human ( comScore study). This is exacerbated for video where the average viewability drops to 41%. That means that our tracking tools tell us that an impression has been served, but the reality is that that impression was triggered by a robot or some other kind of internet trickery. Which therefore means that 52% of your expensive ad campaign was seen by robots (well really just lines of code).

Even the actually definition of a viewable ad is somewhat ridiculous; as far as the IAB are concerned, a display ad is viewable if 50% or more of its pixels appear on-screen for at least one continuous second. A video ad is deemed viewable if 50% of its pixels appear on-screen for at least 2 consecutive seconds.”

So what this means is that of that 41% of video views this also includes people that saw half of the physical ad for less than 2 seconds. Nuts right? Just try watching 50% of a video and tell me how much you were able to recall…

And it gets even more like a sub-plot of Catch-22. It has been proven numerous times that actually knowingly buying ads with low viewability rates actually performs better, from the perspective of outcomes that one with high viewability. Huh? So in plain English that means that by targeting more robots and less humans you will sell more of what you want to sell. According to separate pieces of research conducted by digital marketing agency IMM and programmatic media planning and buying company the Goodway Group, the economics of delivering 100% viewable campaigns doesn’t make financial sense, at least not yet. In the words of Jay Friedman, COO of the Goodway Group “I maintain to this day that 50% in-view at $4CPM beats 100% viewability at $10CPM all day long”.

Which really is the crux of the argument. If you focus on on-site outcomes such as sales, engagement, ROI or other clear conversion metrics then viewability shouldn’t really enter the equation, as what is really key is driving relevant traffic, and then focus energies on conversion optimisation. However the argument becomes a little more blurred when your objectives are about reach and frequency of say a video campaign where onsite objective are less important.

Research supports the fact that completed views are more valuable to advertisers than click-through rates, with brand recall being significantly higher if the entire ad was watched. So again the focus for advertisers should be around what metrics are important to them, and develop creative and targeting that works for those objectives. If you need reach then focus on viewability and completed views with shorter videos with multiple versions. If you need engagement then build creative that entices engagement, and focus the buy on Cost per Engagement. And if your goal is on-site engagement then optimise your buy on what drives whatever conversion you need.

So, I hear you ask, what should I be doing, or more precisely what should I be asking my agency to do about viewability? Should I be demanding 100% viewability? Should I invest in technology that monitors viewability? Is this a storm in a tea cup? And those of course are great questions. And I wish I had the answer, but like the client ten years ago who no doubt asked their media agency what they can do to stop people making a cup of tea during an ad break, I don’t have a perfect answer, but what I can tell you is that ensuring that 100% of your audience actually saw 100% of your ad is impossible. And anyone that tells you differently is a liar, or trying to sell you something!

However the clearest way to ensure your ads are being viewed for the vast majority of your buys is to focus on publishers who are actively trying to improve ad visibility on their site – improving the location and size of ad placements, this ensuring maximum impact. Look at premium publishers, ensure your programmatic buys have a human-vetted whitelist, and if you are using networks ask how they define viewability and ensure that it lines up with your objectives.

From a media agency perspective we know that the industry is still trying to work out a globally defined standard, and this is getting better but we still have a way to go. We also know that there are significant flaws in the technology that measures viewability – each vendor measures it differently, so the best advice that I can offer is that if when you are buying digital media viewability is a primary objective then make this very clear to your agency, and ensure you definition of viewability lines up with theirs and the technology they use to measure your ad visibility.

And if you have objectives beyond viewability then right now focus on that metric first, and who knows the robots might really enjoy your creative!

Why are we afraid of Big Data?

Why are we afraid of Big Data?

“Data driven digital marketing” is undoubtedly the current trend du jour in the advertising world, and clients in New Zealand are clambering to get on board, but what does it really mean?

The need for data led insights stems from the reality that there is no longer a “target market” but “target markets” – in some cases, up to thirty or forty different audience segments (age, sex, location, education, income, marital status, and more), each targeted using different messages, and different channels (paid, owned and earned), but ultimately selling the same product. Yes, it’s often complex, but you need to get it right. And, to do this you need data points.

Personally, I prefer the term “discovery driven marketing”. Semantics, the cynical among you might say, but data in itself is quite uninteresting: just siloed records of purchases, browser behavior, and personal information.

But what you discover when you analyse this data is really what will drive your future marketing strategies; fuse weather patterns and store footfall to provide insight into what environmental factors influence purchase behavior; discover the relationship between Facebook and paid search, or billboards and social media; discover who actually purchases your products and what factors drove those purchases. Discovery leads to knowledge and, as we know, knowledge leads to power.

To put this all into context, permit me to rewind the clock twenty years; I recall back at school in the UK, we had the privilege of spending a week working with the top Tesco executives. These executives spoke of their passion for data, and how they were able to leverage their loyalty programs to segment their audiences to drive insight into buyer patterns – in the first instance helping them to give their customers more relevant mailers. And I’ve watched over the years as Tesco, using this data, came to dominate the supermarket category and then was able to diversify into finance, insurance, electronics, apparel, and more, because they truly understood their audiences and what drove them.

Today, we can discover the same insights, segment data and develop look-a-like audiences for a fraction of the cost, and a fraction of the time that it took Tesco to evolve and develop these strategies, thanks mostly to cheap cloud storage and off-the-shelf systems such as Google Analytics that allow us to collect and leverage data in ways that up until now only multi-nationals could afford to do.

This isn’t just a case of “because we can”, but because the smart use of data in the future will be the only way companies will be able to stay relevant. In the fragmented, converged world we live in, it’s only by leveraging the information our consumers happily (for now) pass on to us, can we begin to understand how our customers and potential customers alike interact with our brands, our stores, and our call centers.

Just twenty years ago, an average consumer could choose between a handful of TV channels and radio stations, a national and a regional newspaper, and a few magazines. There was no Internet shopping, there was no Google, Pandora, Lightbox, Facebook, or Snapchat.

Consumers today have hundreds of ways to learn and research your brand. And not surprisingly, the path to purchase has become more complicated, more fragmented and just plain difficult to keep track of. As such the consumers themselves are more fragmented, less attentive, and more confused!

The biggest barrier to making sense of the data your organisation has, is ironically often your organisation itself. When developing a data strategy for clients, it not unusual to find that all the data that needs to be analysed sits in different systems: CRM on one system, eDM databases on another, sales data on yet another, and web data elsewhere. In this instance, the first thing that needs to be done is select a data management platform that plugs into all these disparate data sets and over time allows you to pull insights to drive marketing and media decisions, as well as actionable audience segments to talk to across email, web, mail, and the like.

Choosing a data management platform is a big decision. If you are unsure, seek advice from specialists who will assess your data requirements and suggest the right management platform.

Then, in real-time, you will be able to add to the data-sets, share your data with partners, add in third party data to a point where you will truly be able to say that every communication you make is targeted, or decision you make is backed up by statistics.

(This article first appeared in the National Business Review)