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On June 18, 2020, I sat down with Jeff Coyle and the MarketMuse community to answer questions after our webinar together on Natural Language Processing and its application to content marketing. Let’s see what’s on the minds of the content marketing community.

Tatiana asks, “I’ll kick things off with my question – do you have any articles or website recommendations to keep up with AI industry trends?”

Be reading the academic research published out there. Sites like KDNuggets.com, Towards Data Science, Kaggle, etc. all do a great job of covering the latest and greatest.

That and stay tuned to major research publication hubs at Facebook, Google, IBM, Microsoft, and Amazon. You’ll see tons of great material shared on those sites.

Jeff asks, I’m using a keyword density checker for all of my content. How far removed from being a reasonable strategy is this today for SEO?”

Keyword density is essentially term frequency counting. It has its place for understanding the very rough nature of the text, but it lacks any kind of semantic knowledge. If you don’t have access to NLP tools, at least look at stuff like “people also searched for” content in the SEO tool of your choice.

Jeroen asks, “You didn’t get very specific into how you operationalize this knowledge. Could you give some specific examples on how you generate content into… web pages? Posts? tweets? What could be an example of a strategy that you could implement to make use of this knowledge?”

The challenge is that these tools are exactly that – they’re tools. It’s like, how do you operationalize a spatula? It depends on what you’re cooking. You can use it to stir soup and also flip pancakes.

The way to get started with some of this knowledge depends on your level of technical skill. If you’re comfortable with Python and Jupyter notebooks, for example, you can literally import the transformers library, feed in your training text file, and begin generation immediately. I did that with a certain politician’s tweets and it started spitting out tweets that would start World War 3.

The strategy is always based on the goal. What goal are you trying to achieve? Are you attracting search traffic? Are you doing lead generation? Are you doing PR? NLP is a bunch of tools. It’s similar to – strategy is the menu. Are you serving breakfast, lunch, or dinner? What tools and recipes you use will be highly dependent on the menu you’re serving. A soup pot is going to be profoundly unhelpful if you’re making spanakopita.

Gina asks, “What is a good starting place for someone who wants to begin mining data for insights?”

Start with the scientific method.

  1. What question do you want to answer?
  2. What data, processes, and tools do you need to answer that question?
  3. Formulate a hypothesis, a single-condition, provably true or false statement you can test.
  4. Test.
  5. Analyze your test data.
  6. Refine or reject the hypothesis.

For the data itself, use our 6C data framework to judge the quality of the data.

Ahmed asks, “What are, in your opinion, the main search user intents that marketers should take into consideration?”

The steps along the customer journey. Map out the customer experience from beginning to end – awareness, consideration, engagement, purchase, ownership, loyalty, evangelism. Then map out what the intents are likely to be at each stage. For example, at ownership, the search intents are highly likely to be service-oriented. “How to fix airpods pro crackling noise” is an example. The challenge is collecting data at each of the stages of the journey and using that to train/tune.

Jeff Bezos famously said, focus on what doesn’t change. The general path to ownership doesn’t change much – someone unhappy with their pack of chewing gum will experience similar things as someone unhappy with the new nuclear aircraft carrier they commissioned. The details change, for sure, but understanding what types of data and intents is vital for knowing where someone is, emotionally, in a journey – and how they convey that in language.

Crivelli asks, “In your experience, how did BERT change Google Search?”

BERT’s primary contribution is context, especially with modifiers. BERT allows Google to see word order and have it interpret meaning. Prior to that, these two queries might be functionally equivalent in a bag of words style model:

  • where is the best coffee shop
  • where is the best place to shop for coffee

Those two queries, while very similar, could have drastically different outcomes. A coffee shop might not be a place you want to buy beans. A Walmart is DEFINITELY not a place you want to drink coffee.

Annabelle asks, “Do you think AI or ICT’s will ever develop consciousness/emotions/empathy like humans? How will we program them? How can we humanize AI?”

The answer to that depends on what happens with quantum computing. Quantum allows for variable fuzzy states and massively parallel computing that mimics what’s happening in our own brains. Your brain is a very slow, chemical-based massive parallel processor. It’s really good at doing a bunch of things at once, if not quickly. Quantum would allow computers to do the same thing, but much, much faster – and that opens the door to artificial general intelligence.

Here’s my concern, and this is a concern with AI today, already, in narrow usage: we train them based on us. Humanity has not done a great job of treating itself or the planet we live on well. We don’t want our computers to mimic that.

Shash asks, “In your opinion, how do you see content marketers integrating/adopting Natural Language Generation into their daily workflow/processes?”

Marketers should already be integrating some form of it, even if it’s just answering questions like we demoed in MarketMuse’s product. Answering questions that you know the audience cares about is a fast, easy way to create meaningful content.

My friend Marcus Sheridan wrote a great book, “They Ask, You Answer” which ironically you don’t actually need to read in order to grasp the core customer strategy: answer people’s questions. If you don’t have questions submitted by real people yet, use NLG to make them.

Victor asks, “Where do you see AI and NLP advancing in the next 2 years?”

If I knew that, I would not be here, because I would be at the mountaintop fortress I purchased with my earnings.

But in all seriousness, the major pivot we’ve seen in the last 2 years that shows no sign of change is the progression from “roll your own” models to “download pre-trained and fine-tune”. I think we’re due for some exciting times in video and audio as machines get better at synthesis (for example, MelNet). Music generation in particular is RIPE for automation; right now machines generate thoroughly mediocre music at best and ear-sores at worst.

That is changing rapidly.

I see more examples like blending transformers and autoencoders together like BART did as major next steps in model progression and state of the art results.

Crivelli asks, “Where do you see Google research heading with regard to informational retrieval?”

The challenge Google continues to face, and you see it in many of their research papers, is scale. They’re especially challenged with stuff like YouTube; the fact that they still rely heavily on bigrams isn’t a knock on their sophistication, it’s an acknowledgement that anything more than that has an insane computational cost.

Any major breakthroughs from them aren’t going to be at the model level so much as at the scale level to deal with the deluge of new, rich content being poured onto the internet every day.

Remy asks, “What are some of the most interesting applications of AI you’ve come across?”

Autonomous everything is an area I watch closely. So are deep fakes. They are examples of just how perilous the road ahead is, if we’re not careful.

In NLP specifically, generation is making rapid strides and is the area to watch.

Natalia asks, “Where have you seen SEOs use NLP in ways that don’t work or won’t work?”

I’ve lost count. A lot of the time, it’s people using a tool in a way that it wasn’t intended and getting subpar results. Like we mentioned on the webinar, there are scorecards for the different state of the art tests for models, and people who use a tool in an area it’s not strong don’t typically enjoy the results.

That said… most SEO practitioners aren’t using any kind of NLP aside from what vendors provide them, and many vendors are still stuck in 2015. It’s all keyword lists, all the time.

Ulrich asks, “Where do you see video (Youtube) and Image search at Google? Do you think the technologies deployed by Google used for all types of searches are very similar or different from each other?”

Google’s technologies are all built on top of their infrastructure and use their tech. So much is built on TensorFlow and for good reason – it’s super robust and scalable.

Where things vary is in how Google uses the different tools. TensorFlow for image recognition inherently has very different inputs and layers than TensorFlow for pairwise comparison and language processing. But if you know how to use TensorFlow and the various models out there, you can achieve some pretty cool stuff on your own.

Annabelle asks, “In what ways can we adapt/keep up with advancements in AI and NLP? Thanks!”

Keep on reading, researching, and testing. There’s no substitute for getting your hands dirty, at least a little. Sign up for a free Google Colab account and try things out. Teach yourself a little Python. Copy and paste code examples from Stack Overflow.

You don’t need to know every inner working of an internal combustion engine to drive a car, but when something goes wrong, a little knowledge goes a long way. The same is true in AI and NLP – even just being able to call BS on a vendor is a valuable skill. It’s one of the reasons I enjoy working with the MarketMuse folks. They actually know what they’re doing and their AI work isn’t BS.

Camden asks, “What would you say to people who are worried about AI taking their jobs? For example, writers who see technology like NLG and worry they’ll be out of work if the AI can be”good enough” for an editor to just clean the text up a bit.”

“AI will replace tasks, not jobs” – the Brookings Institute

And it’s absolutely true. But there will be net jobs lost, because here’s what will happen. Suppose your job is composed of 50 tasks. AI does 30 of them. Great, you now have 20 tasks. If you’re the only person that does that, then you’re in nirvana because you have 30 more units of time to do more interesting, more fun work. That’s what the AI optimists promise.

Reality check: if there are 5 people doing those 50 units, and AI does 30 of then, then AI is now doing 150 / 250 units of work. That means that there are 100 units of work left for people to do, and corporations being what they are, they will immediately cut 3 positions because the 100 units of work can be done by 2 people.

Should you be worried about AI taking jobs? It depends on the job. If the work you do is incredibly repetitive, absolutely be worried. At my old agency, there was a poor sod whose job it was to copy and paste search results into a spreadsheet for clients (I worked at a PR firm, not the most technologically advanced place) 8 hours a day. That job is in immediate danger, and frankly should have been for years. Repetition = automation = AI = task loss. The less repetitive your work, the safer you are.

Madison asks, “How can I pair Google Analytics data with NLP Research?”

GA indicates direction, then NLP indicates creation. What’s popular? I just did this for a client a little while ago. They have thousands of web pages and chat sessions. We used GA to analyze which categories were growing fastest on their site and then used NLP to process those chat logs to show them what’s trending and what they needed to create content about.

Google Analytics is great for telling us WHAT happened. NLP can start to tease out a little bit of the WHY, and then we complete that with market research.

Brian asks, “I’ve seen you use Talkwalker as a data source in many of your studies. What other sources and use cases should I consider for analysis?”

So, so many. Data.gov. Talkwalker. MarketMuse. Otter.ai for transcribing your audio. Kaggle kernels. Google Data Search – which by the way is GOLD – and if you don’t use it, you absolutely should be. Google News and GDELT. There are so many great sources out there.

Elizabeth asks, “What does an ideal collaboration between marketing and data analytics team look like to you?”

Drinking on Fridays.

Not joking; one of the biggest mistakes Katie Robbert and I see all the time at clients are organizational silos. The left hand has no idea what the right hand is doing, and it’s a hot mess everywhere.

Getting people together, sharing ideas, sharing to do lists, having common standups, teaching each other – functionally being “one team, one dream” is the ideal collaboration, to the point where you don’t need to use the word collaboration any more. People just work together and bring all their skills to the table.

Jeff asks, “Can you review the MVP report that you frequently preview in your presentations and how it works?”

The MVP report stands for most valuable pages. The way it works is by extracting path data from Google Analytics, sequencing it, and then putting it through a Markov chain model to ascertain which pages are most likely to assist conversions.

Here’s an example:

MVP Report

And if you want the longer explanation, visit this page.

Stephen asks, “Can you give some more insight into data bias? What are some considerations when building NLP or NLG models?”

Oh yes. There’s so much to say here. First, we need to establish what bias is, because there’s two fundamental kinds.

Human bias is generally accepted to be defined as ““Prejudice in favor of or against something compared to another, usually in a way considered to be unfair”.

Then there’s mathematical bias, generally accepted to be defined as ““A statistic is biased if it is calculated in such a way that it is systematically different from the population parameter being estimated.””.

They are different but related. Mathematical bias isn’t necessarily bad; for example, you absolutely want to be biased in favor of your most loyal customers if you have any business sense whatsoever.

Human bias is implicitly bad in the sense of unfairness, especially against anything that’s considered to be a protected class: age, gender, sexual orientation, gender identity, race/ethnicity, veteran status, disability, etc. These are classes that you MUST NOT discriminate against.

Human bias begets data bias, typically in 6 places: people, strategy, data, algorithms, models, and actions. We hire biased people – just look at the executive suite or board of directors of a company to determine what its bias is. I saw a PR agency the other day touting its commitment to diversity and one click to their executive team and they’re a single ethnicity, all 15 of them.

I could go on for QUITE some time about this but I’ll suggest that you take a course I developed on this topic, over at the Marketing AI Institute for more on the bias components.

In terms of NLG and NLP models, we have to do a few things.

First, we have to validate our data. Is there a bias in it, and if so, is it discriminatory against a protected class? Second, if it is discriminatory, is it possible to mitigate against it, or do we have to throw the data out?

A common tactic is to flip metadata to de-bias. If you have, for example, a dataset that is 60% male and 40% female, you recode 10% of the males to female to balance it for model training. That’s imperfect and has some issues, but it’s better than letting the bias ride.

Ideally, we built interpretability in our models that allow us to run checks during the process, and then we also validate the results (explainability) post hoc. Both are necessary if you want to be able to pass an audit certifying you’re not building biases into your models. Woe is the company that only has post hoc explanations.

And finally, you absolutely need human oversight of a diverse and inclusive team to verify the outcomes. Ideally you use a third party, but a trusted internal party is okay. Does the model and its outcomes present a skewed result than you would get from the population itself?

For example, if you were creating content for 16-22 year olds and you didn’t once see terms like deadass, dank, low-key, etc. in the generated text, you’ve failed to capture any data on the input side that would train the model to use their language accurately.


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