IBM’s Watson: World’s First Artificial Marketer

Watson is being infused into all of IBM’s marketing, commerce and supply chain products. The company Apple once smashed as the place that turns humans into machines is now in the business of turning machines into something like humans, and they’re getting pretty good at it.

That headline may exaggerate a bit, but the message from IBM Amplify a couple weeks ago in Las Vegas was all Watson all the time.

Watson is being infused into all of IBM’s marketing, commerce and supply chain products. The company Apple once smashed as the place that turns humans into machines is now in the business of turning machines into something like humans, and they’re getting pretty good at it.

I’m writing this a bit removed from the show, so I have the time to see what stuck with me … And yeah, Wayne Brady’s freestyle marketing rap is high on that list:


But beyond that, what stands out to me now is that you can have Watson act as your personal assistant, and you can talk to it. And if Watson doesn’t understand the word you use, it — he? let’s go with he — he will stop you, say the word he doesn’t recognize, and ask you to define it.

So if Watson isn’t going to be your next CMO, he really might be your next marketing assistant.

Yes, Tony Stark did have something that sounded a lot like Watson.
Yes, Tony Stark did have something that sounded a lot like this.

(I’m also curious how much you can train Watson to curse like a Biker’s parrot, but IBM failed release his profanity coefficients.)

The emphasis on Watson makes sense because this is something IBM has that its competitors really don’t. Last year, Salesforce rolled out Einstein. Last week, I wrote about how Adobe rolled out Sensei. But my understanding is that those are both collections of recommendation engines that learn, not quite the same as true artificial intelligence.

Watson, on the other hand, has been out-thinking humans since 2011 when it won Jeopardy. And IBM feels it can help you out-think your competition too.

“Watson is on an incredible roll,” said Harriet Green, IBM general manager, Watson Internet of Things, commerce and education. “It has now been adopted by nearly every industry and every professional discipline. This year alone, at least 1 billion people will be touched in some way by Watson.”

Talking about the recently announced partnership with Salesforce, Green also said, “You know you’re doing something right when even your competitors are turning to you for your technology.”

It is fair to say that wherever machine-learning is going, Watson looks closest to that today and everyone else looks like they’re trying to catch up. But does that translate into better marketing for IBM users? That’s the big question.

Watson promises to enable what IBM is calling “The Cognitive Era.” This is IBM’s vision for an era of marketing where thinking machines help marketers create unique customer experiences based on what those customers are doing, thinking and feeling in real-time and at the largest scale. The system uses the Watson AI to “understand, reason and learn.”

For example, Watson will identify problems and anomalies in your audience segmentation. And he will do that automatically and suggest fixes, without the marketer even having to initiate the process.

Mindy Grossman, CEO of HSN (formerly Home Shopping Network), said they are counting on Watson to use data and help identify the right new customers, opportunities, at scale.

So, I’ve seen the vision …

No, not that Vision.
No, not that Vision.

Now the only question is: Can IBM bring this home to marketers in a way you can use?

If they can, we’ll be well on our way to augmenting our teams with, essentially, the world’s first artificial marketers.

Machine Learning and AI — What’s ‘Real,’ What’s Required

Big data has gone full-cycle. Quite a while ago, big data had its beginning within the realm of academic research. Recognizing its usefulness, niche businesses then began implementing big data. Massive companies, such as Google, began commercializing big data — these large companies then spawned smaller companies. Those companies are now getting big. This all makes for a lot of noise in the marketplace.

Data graphicBig data has gone full-cycle.

Quite a while ago, big data had its beginning within the realm of academic research. Niche businesses then began implementing big data after recognizing its usefulness. Next, massive companies (like Google) started commercializing big data — these large companies then spawned smaller companies. Those companies are now getting big.

This all makes for a lot of noise in the marketplace.

Today, we hear folks without applied mathematics or computer science backgrounds talking big data, algorithms and artificial intelligence (AI) at cocktail parties. The fluency has grown rather quickly: A CMO I’ve known for years used to wince when we talked analytics, but now she enthusiastically discusses her firm’s AI initiatives. She’s not running marketing at Google or IBM Watson, either — she sells clothing online.

While we’re likely in one of the most amazing periods in history to be in business, it does not come without its challenges. These days, you have to sift through all of the clutter when it comes to innovations in the marketing space.

Let’s see if we can simplify what the data pundits are tweeting and discern where the value really is.

Machine Learning

Machine learning (ML) occurs through networks of algorithms.

First, the good news: ML really works.

As we’ve discussed in “Marketing Machines — Possible or Pipedream?” ML is used to ingest large amounts of data and identify patterns in that data. The machine “learns” by ingesting, transforming and then conditioning a learning algorithm with your dataset.

ML will find the statistical relationships (models) between your various data points to articulate how efficiently your business is running. By calculating the best potential models, it can also show you what improvements you can make. ML can deduce your most profitable business targets. It can tell you who is likely to buy shoes priced over $800, or which production line is most likely to break down in the wintertime.

But ML Isn’t Foolproof

Machine Learning can surely help us find structure and patterns in data through statistics and the power of cloud computing. Amazon’s ML cloud computing capability, for example, isn’t specific to any domain and arguably works with any inputs. It will consistently output a result or target. Yet that very flexibility is where ML can prove risky:

“If you can dump anything into an ML process, and have it come up with an answer, you’d be wise to be wary of that answer.”

ML techniques all require you provide it with a “universe”. This universe consists of all the likely permutations representative of your purpose. If your conditioning data is skewed heavily to sneakers under $75, it will prove very hard to predict what customers are likely to buy $800 shoes.

This may sound like an unfair example, but consider the marketers who are out to break into the higher-end sales but only have data from their pre-existing customers. If skewed interpretations were applied to new-customer marketing (and they can be), your returns could be even worse than without any ML interference. The fact is, there are far more experiments where ML doesn’t produce a valuable outcome than those that do. But as technology and big data are refined over time, better results will be achieved across the board.

Analytics and model-building are highly iterative processes. If an ML process is focused on only a particular niche, the likelihood of getting better results sooner is higher — but still iterative. Despite its current limits, AI offers a deeper and more layered method of applying iterative math to break down large data questions than raw manpower.

Google’s AlphaGo AI beat champion Lee Sedol in a tournament of Go by exponentiating component questions, covering as many bases as it could. While AlphaGo works similarly in many ways to the human mind in this way, it did also have the advantage of iteratively playing against itself thousands of times.

Humans can’t do that.

The Bottom Line: Good Data In, Good Comes Out

Whether Google’s AlphaGo, Amazon’s ML tools or your home-grown mashup, the quality of the data that goes into ML is the largest factor you can control in creating value with systems-driven optimization.

In an age where many organizations have siloed data or cumbersome messes, along with marketing organizations that don’t even have a reliable marketing operations database, this is no small challenge. Getting your data centralized, organized and accessible is a requisite first step. Get that right, and there may be opportunities ahead to drive value up.