Conversion is the process of converting data into information.
How are you doing? Does it make sense? Have I explained what a conversion is in data analytics? If yes, good on you! If not, please read on...
You see, this is why I am explaining what conversion means in Data Analytics. As much as I would like to think that my communication skills are awesome (and they are), most people still couldn't understand my explanation. That's because most explanations of what conversions mean in data analytics don't really explain it well enough for laymen to get it. So, if your only understanding of the concept comes from my awful explanation above, here is how Wikipedia explains it:
"A conversion is the successful fulfilment of an action by a customer, such as signing up for a newsletter or completing a purchase."
That's better, right? You can now understand that conversion is basically an action that a customer has taken on your website, making this person qualified to buy from you. What they do after is important but it's not part of the conversion definition. That's why the success of conversion lies in creating trust and earning trust requires time, which is why you need to read these posts about generating leads online.
1. It puts your product into perspective - it provides a way to "compare apples with apples" so to speak, for decision-makers who are comparing different products/solutions.
2. For publishers - it's an additional revenue stream that can help subsidize the content they are creating
3. To improve the customer experience by identifying where people are dropping out or getting stuck in your solution
4. To identify opportunities for optimizing your website sign-up flow and landing pages
5. To provide insights into the language, wording and design of your company's products, which can be used to inform marketing efforts across multiple channels (SEM, Email Marketing etc.)
6. Most importantly - because conversion is what you're after.
I'm often asked "where do I start with conversion rate optimization?" and if I had a nickel for every time that question was asked, I'd be retired by now :) What's more common is that we're all very good at identifying the problem areas and pain points associated with our business and company, but not so good at translating those into actionable steps to improve them.
So here are some ideas on how to get started:
1) Use Google Analytics - you know where your screens & pages are already. Use the behavior flow report as a starting point (outlined below). You don't need a copy of Visual Website Optimizer or Optimizely to run tests. This isn't about testing technology; it's about generating insights.
2) Prioritize your ideas - unless you have a bottomless pot of cash to pay for testing, prioritize the ideas that will have the biggest impact. If you already know where your problems are (areas of high drop-off or low engagement), those should be top of the list as running tests on those areas will give you immediate results and provide insight into other opportunities for optimization.
3) Outline success criteria - before you dive in too deep, ensure everyone's aligned on what success looks like and how it'll be measured. Without specific targets, it's hard to accurately measure progress and ROI once a test has been completed. It also ensures folks don't get about a result that doesn't meet their expectations.
4) Identify traffic for each test - in Google Analytics you can configure where your tests are run from in the testing options section. You can then use that as a starting point to identify how much traffic that area of your site receives, before & after running a test. If you have enough traffic, you should be able to get meaningful results within the first few days so you can start learning quickly and iterating on based on those insights.