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All right.
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So it's time for part two of our conversation about the power by key influencers visual.
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Remember that part one we analyzed the impact to a categorical outcome.
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So what influence is a Kickstarter project to be successful.
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Now we're going to run a very similar analysis except we're going to use a continuous or numerical value
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for outcome instead.
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So in our power by a visual file I'm going to add a new page here.
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I'm going to call it key influencers continuous now I'll just drop in a new instance of that key influencers
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visual and we've got a nice blank slate to work with.
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So just like before we're going to start by dragging a field into analyze.
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But instead of using a categorical field like Project outcome this time I'm going to use a numerical
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or continuous field like amount pledged.
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So things look pretty similar here.
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The only difference is that now we're saying what influences amount pledged to either increase or decrease.
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Those are the standard options that we get with a continuous variable.
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Now if you go into the Format tab and click into analysis you'll see that power b I detected this as
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a continuous analysis type.
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You can override that if you want.
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I could say no no.
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This is categorical.
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And what we'll do is try to bucket every unique value in that field into its own category which is utter
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nonsense.
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So in this case you've got it right.
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This is continuous and we're left with that increase decrease option.
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So the question that we're posing here is what influences the amount pledged to increase to go up.
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So let's start thinking about potentially influential factors here.
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We know that the number of backers is highly correlated so we can pull that into the explained by well
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here and check it out.
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We get this nice scatter plot with dots representing each project in the table and we see this nice
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clear linear relationship between the number of backers on the x axis and the amount pledged on the
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Y which makes sense we have more backers more backers raise more money and we get that nice upward sloping
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line.
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This is a linear regression model and the slope of this line is what allows us to determine these factors
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and these influencers.
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So what we're saying is that when the number of backers goes up by nine hundred forty six the average
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amount pledged increases by about one hundred nine thousand dollars.
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Now here's where things get a little bit interesting which is that by default when you pull a continuous
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variable into the analyze field you're going to see don't summarize.
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And what that means is that power buy is going to run this analysis at the table level of granularity.
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And what I mean by that is that the level of granularity in our source table is at the project level.
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Each row each record represents one project.
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That's why we see each dot in the scatter plot represent one single project.
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But what if this is just too granular for me to even make sense of or generate insights that I can use
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to optimize my business.
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What if I actually want to see the relationship between backers and amount pledged at a higher level.
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Like buy category or country or subcategory.
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Well I can use the expand by option to do that.
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What we can do is aggregate these values for both amount pledged and the number of backers that could
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choose.
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Average here instead.
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Or it could take a measure.
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In this case we've defined average amount pledged as the average pledged.
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Makes sense.
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We could use a measure here instead.
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Same exact thing.
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And we can choose an aggregation for our explained by fields as well.
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So let's choose average for both.
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And now what we're gonna do is determine the level of granularity that we want by pulling a field into
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this expand by well.
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So if we want to see the relationship between average backers an average amount pledged by category
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we just grabbed category pull it in here and look at this.
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We get that same scatter plot but now each dot no longer represents a project.
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Each dot represents a category.
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So the way to interpret this tooltip here is that on average projects in the games category have three
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hundred and forty three backers and have raised about twenty five thousand five hundred dollars.
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Same goes here with design projects with technology projects and so on.
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So now on average for an average project when you get eighty nine more backers you can expect to raise
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one point eight five thousand more dollars.
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That's just a way to run a very similar analysis but explore things at a different level of granularity.
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And when we pull a field into expand by what we're doing is determining that granularity without treating
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it as an influencer which is what we'd be doing if we pulled that in here instead.
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So there you have it.
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That's how you can use power because key influencer visual to explore or predict a continuous outcome.
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