“Insights from Digital Data…Pictures You See with Your Brain and Feel with Your Heart, Part 1…Pictures You See with Your Brain and Feel with Your Heart, Part 2…“
Lesson 7: Pictures You See with Your Brain and Feel with Your Heart, Part 1
Lesson 7: Pictures You See with Your Brain and Feel with Your Heart, Part 2
Lesson 6: Insights from Digital Data
Three important rules of context ensure impact and analysis, another important point of this lesson and then the final one is that if there is no sixty-second stories there is no story at all.
There are five primary categories of marketing data analysis that we’ll use to drive those answers.
The first is descriptive and in this analysis, this is typically the first kind of analysis performed.
You’d have to do more work and that’s why the other categories of analysis are there.
The third type of analysis is exploratory and this is where you are seeking to discover connections and patterns in the data.
The fourth type of analysis is causal and in this analysis you are seeking to determine what happens when one variable moves a certain or changes and register the effect on other variables.
This is usually seen as average effects as it sort of smoothes out over time and over the large data set that you have and it’s typically the gold standard for data analysis.
One of the things that is important when predictive analysis is conducted is that it’s important to remember that x predicts y, right? When you’ve got one element of your model that you’ve moved it’s going to move the other.
Each of these categories of analysis provide different depth of analysis as well and a good way to see that is looking at the types of response we would get to one of the questions, the key questions that we ask.
Stepping it up to an inferential analysis of that question would provide us a description of the typical consumer who is interested in our brand, what they look like and how they behave.
An exploratory analysis of that same question may identify when interest actually started for the brand, when it ended, and other different interesting patterns in the data.
A causal analysis would explain what has been done in the past that successfully drove brand interest and other influential brand attributes.
A predictive analysis that would answer that question would offer us recommendations about actions that we could do that would successfully drive brand interests in the future, a very valuable answer to that question.
Now, the level of data that is going to be required is very different as well for each of these categories of analysis.
Descriptive takes pretty straight forward data, web traffic data reports, web server performance, web transaction, competitive analysis and click stream analysis, all pretty straight forward data that we would plug into our descriptive analysis to get pretty straight forward answers.
Exploratory would require outcomes analysis, other regression or correlation analyses to produce its findings where causal is going to require experimentation and testing, multi-variant and AB testing, site optimization, other campaign optimization such as heavy up media tests, all those things that are really looking to link some kind of causal relationship between attributes and the model.
Finally, predictive the most sophisticated of modeling and analysis that we would do would require just that, the most sophisticated modeling.
Media optimization modeling, attribution modeling, other kind of response modeling such as customer and channel response would be required to successfully complete that sort of analysis category.
Now, once those analyses are conducted there’s still a very important thing for us to consider and that is context and here are some simple rules on how you ensure context in your analysis.
Then including insights in words is a great way for you to ensure that the people, the audience that are reading your reports or receiving the analysis that you’ve done really understand what it is you think those insights mean.
It’s very clear what your intent is and the things that you’ve learned from the analysis that you’ve done.
So the last thing to think about in the analysis is how important it is to compress all the things that you’ve learned in to very tight, consumable pieces.
What did we talk about? What did we learn? We learned that key questions in your plan direct the analysis that you’ll conduct.
Lesson 7: Pictures You See with Your Brain and Feel with Your Heart, Part 1
One is that retentive attributes connect visuals to minds.
Third point is that using visual cues can lead an audience through your message.
So we have worked our way through our marketing analytic process.
So as analysts who have worked so hard to clean out insights, working through tons of data to get to those perfect nuggets of information, we want to make sure that our audience does remember those things.
So how do we do that? Well, first thing – let’s do a quick study in how visual perception actually works.
Our eyes are actually just part of our visual perception system.
You can leverage some tricks to get your message directly and more efficiently into someone’s brain through those eyes in order to ensure or better ensure that they will be memorized.
How many 9s do you see? Again, same number of 9s, even though the data is different.
It’s easier for you and your visual perception to find the number 9s here because of the way we have distinguished them.
You just saw hue working, but orientation, size, enclosure, width, intensity are all ways that we can use to get messages that we have in visuals that we are using through that visual perception system and into your brain – into our audience’s brains really quickly.
The first one is highlighting a message and eliminating distraction – making that message was clear and easy as for the audience to read as possible.
The other – the second one is that visual cues will help lead our audience through an insight.
All of these rules are going to leverage those retentive attributes of visual perception and will help make sure that the visuals we are creating are impact and are much more memorable.
We’ll start with highlighting a message and eliminating distraction.
In all of that, although the data is somewhat cheeky, it is very clear to get the meaning because the messages are highlighted and distractions are eliminated.
This is a very – a much unadorned analysis and a very – a great example of getting messages directly to an audience.
Second message of using visual cues to help lead an audience through an insight – here’s a great example of that.
Clearly, the visual cue of the dark vertical bar through the center says this is what is important and it draws your attention to the fact that when there was a football game going on, no one was out searching on search engines until half-time – right there in the middle.
What we’re comparing here is the number of ours that are spent by American adults watching TV each year, which totals about 200 billion hours, compared to the amount of time it took globally to create Wikipedia and gives you a sense of all the great value created in such a small amount of time for Wikipedia and how, if some of that time watching TV might be shaved off, what else can be produced.
It’s a great way of using size as a contrast tool in that visualization.
Lesson 7: Pictures You See with Your Brain and Feel with Your Heart, Part 2
Here’s a visualization that we’re going to look at this is widespread adoption of new technologies.
In the US in your record time and, and what you’re looking at here is a lot for sure a lot of data going on but you’ve got a number of different technologies and how fast it took them to get up to various household adaption rates in the US. So something in this, in this visualization is cell phones all the way over on the right.
There are a number of messages going on a lot of data I mean you’ve got, you’ve got over a hundred years worth of information here and one data point for each technology.
In a way that, that does prescribe, does fit our three different messages or three different rules to visualization.
Let’s walk through those, those rules of visualization and see how they’re represented in this graph.
We could even use another highlight if cell phone was really important to us we could make that, make that visualization check here as well to make cell phone jump right out.
Highlighting the message and eliminating distractions the message is clearly posted up there in the right and jumps out and says what the graph is all about and the in site that we have eliminating distractions we’re down to just three really important pieces of data we’ve got the name of the technology, we have the amount of time it took them to reach an 80% threshold and then we’ve got the date that technology was introduced as the sub point of consumption spreading faster today seems to be important.
Using visual quest to lead the audience through the in site well we’ve champed the different technologies based on how fast it took them to reach 80 and we’ll use different colors to distinguish those that did it within 20 years, 30 years, 60 and a hundred.
So how do you ensure that your visualization is as good and is as impact as it can be.
One is ask you this question, would eliminating this piece of my visualization change anything? If it wouldn’t get rid of it.
You’re much better taking it out of the visualization if it doesn’t really serve a purpose.
Second one is where is your eye drawn, and a way to do this is move the visualization away don’t look at it for a few seconds and then bring it back and where do you look immediately? That’s going to educate you on what is important, what is drawing your eye to your visualization.
Presenting them with the visualization, and seeing if they get out of it what you want them to.
Some supplemental reading things to go a little deeper into what we discussed here, some great readings from HBR on creating presentations your audience cares about that is that idea of connecting with passion, the glance test and how important that is in ensuring that your visuals can be instantly recognized by, by your audience.
