Saturday Night Data Party: Advanced Content Marketing with Data

Alright, this is the Saturday Night Data Party Advanced Content Marketing Edition. So what we're doing tonight is a project that I've been meaning to get to for about three years now. Just never got around to it. And what we're doing is we're going to do some advanced content marketing. So one of the things about content marketing, it always puzzles me when I hear people say this like, oh we don't have any content.

Back in the my PR days, we'd say, we don't have any news. Well, make some news. It's not that hard. You have to have a story, something worth talking about. And you need to create something that is interesting.

So what we're gonna do is we're going to take uh nutrition database, a publicly available one, and convert it into some content marketing. So let's look at what this might look like. First, what is the database? If you've never heard of it before, Open Food Facts is a pretty cool uh free open project to the world. Uh it's open source, and it contains uh 1.3 million food products of every kind generics, brand names, pre-packaged foods, you name it.

And users take photos with their smartphone of like nutrition labels and upload it to this database. Uh so this is a gold mine of stuff about all kinds of food from countries all over the world, 1570. You can get the actual data that makes the database itself. Uh, you can get it in a variety of formats. Uh, I've just downloaded the CSV format.

The CSV format is actually a tab separated file, it's not comma separated. And I'm gonna import this into R. Let's go ahead and get our libraries running here. Now in the database, we have one here, we have 1.1 million foods. First thing we're going to do, we need to do a bit of cleaning.

The goal of this, by the way, is to come up with something that you could talk about. It's going to be a cool story. So let's do some cleaning. We're gonna take that data frame, we're gonna fix the names because we look at the fields, the names are kind of a mess. We're gonna make sure we don't have any duplicates.

We're gonna make sure that different values are typed correctly, meaning it's like numbers, letters, etc. And then for anything that's a number, we're going to that is not available, meaning it's blank. I'm gonna convert it to a zero. This is a huge uh database. 1.1 million lines of data, two gigabytes of text.

Two gigabytes, just for comparison sake, is about the size of like uh a 1080p movie. So if you download like a movie or you're watching something on Netflix, like a 90-minute you know, two-hour movie, that's two gigabytes of data. So that's how much data is in the thread. We used to have a client back in my agency days. I mean, I guess trust us, it's still technically an agency, but uh back in my PR days.

We used to have a nutrition client, and this was one of those projects I always wanted to work on with them, but we could never get the funding or the approvals to actually do something like this. But wouldn't it be cool to take the idea of this database and build it into something that you know custom, something that interactive? Alright, so what do we got here? We should have a database that's still still running, so it's going through still cleaning. Great.

Same number. Nope, we lost 50 records because they were duplicates. Now one of the things I want to do is in here, I want to restrict this. Uh we're gonna work with the United States. So let's do let's take this and turn this into and countries.

I just use country codes if they don't have them. Alright. Countries English. So here we're now going to filter this table down to just foods available in the United States. Again, if this is your country.

Yeah, that's null out. So I'm gonna go ahead now and null out. Remove data frame and remove clean BF. Because these are two very, very large objects that occupy a lot of memory. You don't have to do that.

Um if it's not seeming gigantic. So okay, we've got our clean USA data frame. Let's look at what's in this thing. Let's do 181 fields. Just gonna go ahead and copy these to a text file.

And let's make this a little more orderly. There we go. Okay, so here's everything that we've got. We've got you'll notice there's a labels called 100 grams. Basically, it's it's this ingredient and however much of it is in 100 grams of that ingredient.

So we've got carbohydrates, the different categories of foods, cities, does it contain cocoa, energy, fat, fiber, uh, images, the product name. There's a lot of good stuff in here. If we wanted to create a nutrition database that was uh that really focused on what was good to eat, how would you do that? Well, you'd have to decide which of all these different components uh you liked, you cared about, and build essentially um like a an index, if you will. So let's start taking some of these.

I see a lot of good vitamins in here. That's really cool, right? Um let's see. We'll say vitamins are a good thing. So the more vitamins something has, the more we'd want to score it.

Alright, so let's start by making something called vitamin score equals. And I want to take all those vitamins, because they're all numbers. Add them up. Alright, so there's our vitamin score. That's a pretty sensible formula.

Let's go ahead and run that. And now if I look at this database, and let's arrange it by vitamin score. Which foods have the most vitamins per 100 grams of the food. The ideal shake vanilla superfood blend, applesauce, fettuccine, lots of vitamins, right? Lots of vitamins.

But now here's the thing. So it's a carbohydrate. A lot of something that people care about a lot is uh is a food like calorie dense, uh or how much fat does a food have? So we want to create uh something along those lines as well. But what we're gonna run into is that vitamins occur in like very small trace amounts, right?

Micrograms, milligrams. Things like fats and uh carbohydrates occurs at like the grams, even technically the kilograms, right? You know, you eat a pile of bacon and uh eating like 70% of it's gonna be fat. So we're gonna want to do um it's called scaling here. And scaling it means taking that wide range of numbers and making them apples to apples so we can make that index together.

So let's do this. Vitamin score. Yeah, let's do another mutate. And let's do let's do uh scale. We're gonna rescale scale to vitamin score equals rescale vitamin score, and we're gonna do it two, let's do a one to one hundred.

So this is gonna take that entire table and essentially instead of having the the individual amounts of just a one to one hundred uh uh score. Now let's do fat. Actually, let's do calories, right? Because calories, we we're gonna say that calories and maybe calories, waiting, calories from fat, calories. We wanna what do we want to think about here.

Got salt, saturated fats, what are the things we know are bad for you? Uh things we know what other things we know are good for you. So fat's not great for you, it's not good for your health. Too much of it, I should say. Uh so it's energy in kilocalories per 100 grams, energy from fat.

So let's do that one. Because fats are, yeah, let's do energy from kilograms. So add a mutate scaled calories. So again, this is going to create another 0 to 100. But what we might want to do is if we think vitamins are good, that's a 1 to 100 positive, right?

If we think fat is bad, then we should take this and make it negative. So if a food has yes, a vitamin score 70, but it has uh uh a calorie score of 80, and we make that 80 negative, then it's 70 plus minus 80, so it's a minus 10 for you, right? So you can see how we're gonna start manipulating uh this data. So let's do that. Take that rescale times minus one.

Did I do that right? I think I'm missing a parentheses. One, two, there's my rescale. That one right there. Okay, the end of the mutate.

Good. Let's clean that up a little bit, make the use of C. And now let's add our call healthy score. Equals scaled vitamin score plus scale calories. Right, so we've got our negative thing there.

And now instead of vitamin score, we're gonna do healthy score. Let's go back into our table. So we've still got lots of let's take a look. Actually, let's go to the end of the table here. So there's our scaled vitamins, there's a scaled vitamin score, scaled calorie score, our healthy score.

In descending order. Something does not look right about the calories, though. Let's look at the actual database itself. See if you want to do like a socially responsible database code. Let's see, food groups, composites.

Energy take out one hundred grams. Let's do energy one hundred grams instead. See if that comes up any better. No, also for sanity's sake. Let's knock this down to just the fields that we're working on so we can see what it is we're doing.

This score through 182 fields every time. It's not a product. So we want product name. Actually, let's separate our processing from our presentation. Was it best practice?

Our scale value score, scale calories, healthy score. And look at what data in there. So scale button score. Scale calories still does not look right. Let's take out this the minus one.

Just see what that looks like. What the heck? A hundred grams of potato chips. That's interesting. I wonder why some of these foods are coming up.

Sorry. Strawberry layer cake. Hundred grams of Sergio and Baker Pelopit is E seven hundred. Energy one hundred G less than five thousand. All right.

Sorry. That makes sense. These are fairly what sort of peach mango. Hardwood smoked bacon. Why does cash seem ketchup does not seem right, but all these other things?

Oils, yeah, oil, any oil. Okay, I feel like we've gotten rid of some of the spurious data in that database. Okay, so with that in mind, now I feel like comfortable putting in our multiplier. So that's times minus one. Okay, so we should have our small database here.

And our healthy score. Still pretty hefty. So now the question is, is that good enough? Well, there are plenty of other things that we might want to add in here. So let's go back to our list.

Fiber per 100 grams. If you like fiber, fiber's good for you. Fiber is also one of those things that nutritionally is filling, right? Which is a good thing to have. So let's add in scale fiber.

Just gonna copy this whole here. For sanity's sake. Fiber 100 grams. Fiberscore. Now what's you're gonna notice is that we're gonna start tilting the scales around.

So now you have to start getting to more complicated math. Do you want to do an average of the positive attributes and an average of the negative attributes? It's a valid question. Scale fiber and fiber 100 grams. Let's see what happens.

That sounds tasty. Sugar and spice cinnamon six. And one of the things that's interesting here is that a lot of these things are like seasonings and stuff. So if we go back to our original database, there's some categories in here. So you have the product name, brand, brand categories, meal replacements, cooking helpers, snacks, plant-based food and beverages.

So if there's a specific thing you wanted to go after, like a category, you could use the categories to divide these up and specify whether something's organic or not. Let's see, ingredients. Traces, what traces of items, serving sizes, additives. Let's see what's in some of these English categories here. Fat-free milk, meat, breakfasts, meals.

Okay. Let's do this next. What if we want to choose? Let's go back to our field list. What are some other things that we could we might want to do with this?

What are some other facts or things? Main categories. Let's put it in. Ah, there you are. Meal replacement, sugars.

So I like that. I like the fact that you have these main categories. Let's do this. Let's do a repeat. Main category.

And in each category, what would the top foods? So top and let's do a top ten by the and weight it by our healthy score. Let's see what this looks like. Orange by head. Advent calendars, alcoholic beverages, alfredo sauces, almond butters, aloe holy, it's a lot of categories.

Appetizers, popcorn cheddar, apple pies. So what we've got is these huge main categories, but you could start to put together like menus of food. So you get the idea of what we're doing here. We're building a database that's a subset of another database, adding our own special sauce of data, and our choices that we're going to make for these algorithms to say, like, yeah, I care a lot about fiber. I want filling foods, or I want high fiber foods.

You could say I want lower calorie foods and deduct calories for them. You could actually if you really don't like high calorie foods, you could take that multiplier and put your thumb on the scale pretty heavily, make that a minus uh multiply times a negative 10, right? You're gonna clobber a lot of the food in here. So let's take a look at that small data table now. So you've got your healthy score.

So there's uh these many things, tea bags, etc. Uh, because you've said I want no calories at all in anything that does. So you've got fiber, which is positive, but you've got almost no energy. This is how you make content, right? Because now you take these outputs, you turn them into infographics, you turn them into meal plans, you turn them into whatever you want.

You build your own algorithm. If you have a point of view that you want to espouse, you know, paleo or keto or whatever, you can do that. You could say how if you have like carbohydrates is a bad thing, say, you know, I want minus five on carbohydrates, and all that goes into the score. And then you have the ability to turn that into any form of content. Top 20 foods, you know, start making a list of top 20 foods that are high fiber and low uh low glycemic index for diabetics.

Uh there are scores in here for processing. So there's what's called a Nova score, is it a processed food? Top low processed or not non-processed foods if you want to eat what is it, farm to fork, farm to table. Um so it's up to you. But this is what you would do to build custom content that nobody else has.

We all start with the same data source, right? We all start with the open food facts database. But it's everything that you engineer on top of that that makes it special and makes your point of view what it is, and you're manipulating these fields in a good way, not like uh an evil one, I guess it could be easy about it, but you're you're deciding what does and doesn't go into the database. Now, I will be the first to say I am in no way qualified to be a nutritionist or any of anything medical related. So please do not take any of this as medical advice or health advice before you do anything, contact a qualified healthcare practitioner, not some guy on the internet, uh, and figure out how to use this information in a positive way.

But from a marketing perspective, take a data set and transform it into advanced content that nobody else could have, that nobody else would have your point of view on it. So, thanks for watching and following along. Hope that uh things are going well for you. If you have follow-up questions, join our analytics for marketers Slack group. Go to trustinsights.ai slash analytics for marketers and uh enjoy uh spending time there asking fun questions about analytics.

I'll talk to you all soon. Take care. Want help solving your company's data analytics and digital marketing problems? Visit TrustInsights.ai today and let us know how we can help you.