Q: What is the difference between measurement and analytics?
A: Measurement is a way of showing data, or visualizing it - but analytics is where you calculate the relationships in the data and show how the relationships affect each other.
Here is a simple analogy: you can measure the number of car accidents each year, but unless you can analyze all of the related causes and effects, those numbers won’t tell you very much. If you want to know what’s affecting those numbers and how to change them in the future, you have to know a lot more information about the cars, the drivers, the roads, etc.
You may see an increase in the number of visitors to your website, but if those visitors aren’t potential buyers – that is not a number that will ever show up on your balance sheet. Analytics helps you figure out the relationship between your measurement data and your results.
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Q: Will analytics hurt my creativity?
A:With Proof you can quickly see which efforts are failing. As Sochiro Honda, the founder of Honda, once said, "Success is 99% failure." If the freedom to fail truly leads to more innovation - because it generates more ideas - then Proof is an indispensable tool for marketers.
By reducing the cost of failure, it frees you to think bigger than you might otherwise. Instead of being paralyzed by fear, it enables you to quickly eliminate the losing strategies – and focus your creativity on the categories where you are getting the most traction.
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Q: Why Proof instead of a visualization tool?
A: CMOs frequently turn to visualization tools for answers. However, while these are great tools for processing a handful of variables, they cannot handle a significant amount of data.
They cannot discern how much value came from each marketing category and activity, for instance. Typically, they cannot tell you much about digital assets with a long tail, or worse: they give you the wrong “insights” because they cannot see that far back. Nor can they track anything offline – like outdoor and other advertising, events, or physical mail. Your analysis will therefore inevitably be skewed, and you will end up overspending on marketing that has short-term payback.
Most importantly, visualization does not represent a relationship between variables. For this you need an algorithm that incorporates regression capabilities, time lag and market forces – and self-selects the right model for your data set. And while there are a handful of other cause and effect tools on the market, they are built for mathematicians. Proof is built for business.