Vincent asks, “What’s the one insider secret for marketing you’d never put in a tweet or a YouTube video, the kind of secret sauce that you withhold for everyone but your best clients?”
If the work I do with Trust Insights were cooking, I give away the utensils and the ingredients. Customers pay for the final dishes. I withhold the recipes. That’s where the secret sauce literally is, and something to think about in your own work. In this video, I go into detail about one of my favorite machine learning libraries for text analysis and look at how your secret sauce is impacted by trends like digital transformation. Enjoy!
Can’t see anything? Watch it on YouTube here.
Listen to the audio here:
- Got a question for You Ask, I’ll Answer? Submit it here!
- Subscribe to my weekly newsletter for more useful marketing tips.
- Find older episodes of You Ask, I Answer on my YouTube channel.
- Need help with your company’s data and analytics? Let me know!
- Join my free Slack group for marketers interested in analytics!
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today’s episode, Vincent asked the question What is the one or two secrets that you would never give away in a tweet or YouTube video that insider trading stuff but from marketing, the marketing stuff that all that works really well for the only use for yourself for your best clients
is kind of ISIS. This is a YouTube video but I feel comfortable saying that a lot of the stuff that that we reserve that I reserved for trust insights, best clients and things is all on the machine learning front because for people who are customers of trust insights they are or have demonstrated a willingness or readiness and ability to engage a lot of the machine learning technology that other companies sometimes aren’t ready for. For
A variety of reasons.
And the secret sauce isn’t necessarily the technologies themselves, it’s how you use them. So
the closest analogy that I give to this is, is cooking implements, right? You have frying pans. You have spatula, is you have ingredients and things. And it’s not that you can just use anything because you can’t, you have to use good ingredients, you have to use a minimum quality of pots and pans. A minimum quantity spatula, you know, you can’t have a spatula made of, you know, cheap plastic melts, the moment it touches a patent, you have to have some level of quality, but once you get past that minimum level of quality, it’s all about the recipe and it’s all about the talent of the cook.
The same is true for marketing technology. You do have to have a minimum level of quality for your vendors for the tools that you’re working with. But honestly, most tools these days are pretty good.
You know there’s some crappy ones out there but generally speaking once you get beyond a certain minimum level of quality once you get beyond a certain minimum budget level right
you get what you pay for still very much applies to marketing technology tools then it becomes about you your team your knowledge, your skills, your process and how you do things that becomes your secret sauce so when Vincent asks what’s the the insider secrets the insider secrets are in a lot of cases the code that I write on top of
machine learning tools and software so there’s a really fantastic library for example that Facebook released called fast text fast text is a vector ization library, which means that it looks at that not just how many words are in a piece of text, but then what are the relationships of one word to another? How, how close are they I’m working on a piece right now for me.
Looking at the words that people use interviews their companies
and older approaches, like they’re called bag of words. approaches are okay for at least understand like what’s in the box, but they don’t do as good a job of understanding how words relate to each other. So
there is a difference between a there’s a difference linguistically between
somebody saying, you know, was the example for the other day, I’m trying to remember who said it
between saying something to shit and saying something is the shit right, mild profanity,
and those are very different expressions. And so being able to use sentiment libraries and things
you may not necessarily pick that up. But if you’re using an algorithm that, like vector ization, you can detect those phrases or those clusters of words and phrases together now fast text is a library it
Is the pan if you will, or the spatula, it, how you use it and how you integrate it with the other things in the recipe is where the secret is, if you will vector ization is a very powerful tool but by itself it’s not super helpful you need to change it to other things vector ization gives you a
word and phrase level insights
now what do you do with that then you would use that to use it to help you code text faster so for example, if I have 6000 reviews to go through if I can use vector ization to to
analyze and say, Okay, these are the words and phrases that are key to these specific topics like pay and salary or benefits or culture war management, then
I can use that to tag and an annotate each week.
Review and say like, Hey, this is a review that mentions pay that mentions, benefits and in all the incarnations so
just take a look here at some of the tags music so for benefits terms like health care, health plan maternity, paternity, vacation, charitable retirement, tuition, pension, holidays, voluntary time off, all these are are things that are connected and benefits. And you wouldn’t necessarily
know that just by looking at an older version of a bag of words. But by seeing these are the terms that are most closely related to benefits. You can contact these reviews and and get a much greater look at the text then you could have otherwise so that’s the secret sauce to the extent that there is an is that’s the recipes it’s the recipe book it’s the it’s the cookbook that
is the secret
One of the reasons why machine learning I think, is so powerful and so,
so difficult for people to get their heads around, because part of the machine learning process is when you are building models, the model itself then becomes a tool. So imagine there’s no good analogy for this. Because when you’re cooking, you never manufactured like you’re frying pan does it automatically manufacturer spatula, but that’s essentially what’s happening at machine learning. The frying pan is isn’t is being fed enough data and is intelligent enough that it can spawn new tools that help you do stuff even better and even faster. And those models themselves are also part of the secret sauce for companies that are using machine learning in a more advanced way.
There’s a set of laws that bill from also formerly of VMware and Dell. And now I believe he’s over at Hitachi, because the laws of digital transformation and as much of a buzzword is that is there is still value to
Law, I think it’s law number two, which says that
part of digital transformation is the development of these byproducts as you do machine learning, or AI, or whatever. These byproducts become products in and of itself. So as I build this employment model,
that model itself is now a data product of digital product, something that we can go to other customers and reuse the model. We don’t use the customers data. each customer’s data is kept quarantined, but the model it’s built on can be reused over and over again. It’s the same idea as if you built a machine learning model to predict what we’re
what makes coffee trees grow. Well, you could repurpose the model with a new inputs for corn or soybeans or things like that. So that’s the answer. It’s a it’s a
Not an easy answer. And certainly in a YouTube video, not really giving anything away, but encouraging you to figure out in your own data in your own analysis in the software that you build. If you do build software,
what is the process? What are the recipes? What, who are the cooks? And are you leveraging those things with the tools and the data that you already have to build your own secret sauce to create that recipe that only you know how to do and bring it to market, bring it to market, bring those data products to market to say, Yep, this is what we’ve got. That is a differentiator from what everybody else in the market has when you have that it’s a competitive advantage. So great question visit. Interesting question. And it’s one question that if you are not a data driven company, if you are not analytics driven or insights driven or whatever you want to use the term for
it will be harder to compete. He’ll be harder.
compete against your competitors. Because those competitors that do have this capability are very, very difficult to beat because they’ve got an advantage. And so one of the things we urge people in in our machine learning materials is to say get started collecting your data now and building models now because data piles up every day that you wait to start doing stuff is a day that you lose that the ability to get that data whereas every day that you are at least collecting good quality data, you are building assets that you can then reuse later. So as always, please subscribe to the YouTube channel and the newsletter I’ll talk to you soon want help solving your company’s data analytics and digital marketing problems. This is trust insights.ai today and let us know how we can help you
You might also enjoy:
- The Basic Truth of Mental Health
- How I Think About NFTs
- The Biggest Mistake in Marketing Data
- Understand the Meaning of Metrics
- Transformer les personnes, les processus et la technologie - Christopher S. Penn - Conférencier principal sur la science des données marketing
Want to read more like this from Christopher Penn? Get updates here:
Get your copy of AI For Marketers