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All marketers are short on time and find it hard to prioritise tasks. Spending 15 minutes on analytics can help a lot.

Here's a simple how-to guide to help you get started...

Marketing is a tough job these days. It feels like we are never done with our work. There’s always something that we could be working on, improving. And it often feels like we’re jumping from one thing to another all day long.

We’ve all been there: fixing the layout of a web page, writing headlines, changing button colours. Things which should be a quick fix suddenly end up taking a good part of your day.

All of this can lead to 'marketing anxiety' and you become very reluctant to get involved in something in case it becomes a total time suck.

And that’s not good, because we all want to be helpful. But we want to be helpful in a way that matters to the business. How can we make sure we’re doing this?

Enter analytics

Well, analytics can help get you out of the weeds so you understand how your work affects the business. But with all of the marketing reporting systems available now, getting started using analytics for your everyday job can be hard.

You can, however, avoid 'analytics anxiety' by coming up with your own analytics. It doesn’t take much time and can be done using any old spreadsheet.

And not only will your home-baked analytics help you see what is going on, but developing them can also help you write a marketing plan which you can share with your boss or your customers.

Plus, your analytics don't need to be complicated. In fact, simple analytics can provide better results than complicated systems.

Analytics demystified

Now, many books on the topic are quick to dive into detail and equations, but what is most important with analytics is just how to do it step-by-step.

That way, you can come with an approach that works for you - and use it consistently.

Luckily, there is a fairly simple methodology for doing basic analytics:

  1. Start with an objective. What do you want to know?
  2. Come up with a data model. What data do you need? What calculations will you use? What is the result?
  3. Get the data, do the calculations, review the results.
  4. Decide what you can do to improve the results.
  5. Get more data and see if you've improved.

That's it. Data analytics really just involves setting an objective, finding what you need to achieve it, and then using data to measure your improvement.

Getting started

So how can you start? Well, below is a short guide to building a simple report which I use every day to help me prioritize my marketing.

And this approach can help you, too. Just spend 15 minutes or so going through the following four steps and you will be on your way to having analytics which tell you what you need to work on next and why.

1. The objective

The most important thing with analytics is to start from the end. What would you, or your stakeholders, really like to know about your marketing?

That is, don’t just do things which are easy to measure or stuff your boss won’t care about, like 'average conversion time'. Instead, think of something meaty which, if you could change it, would really matter to the business.

At my company, we need to know how much it costs us in advertising to get a new customer. Pretty fundamental stuff. To find that out, I look at the daily cost of two ad campaigns - one of which is on Facebook, the other on LinkedIn - and then get the daily conversion figures to do the calculation.

But this information also helps me in other ways. By comparing data between the two platforms, I can decide whether to work on creative, change the audience targeting, or even how to balance our spending between the two platforms.

So, you can see, even a simple question can offer really useful info which helps you prioritize your work. This should only take two minutes.

2. The data model

'Data model' sounds like something scary that should involve statistical calculations and Greek letters.

Well, no need to be afraid. For us, a model is just columns in a spreadsheet and calculations which give you your results.

To start, I create a spreadsheet with these figures:

  • Campaign
  • Daily spend on LinkedIn
  • LinkedIn Conversions
  • Daily spend on Facebook
  • Facebook Conversions
  • Total Spend
  • Total Conversions
  • LinkedIn Cost Per Acquisition
  • Facebook Cost Per Acquisition
  • Total Cost Per Acquisition

And then with some dummy data in there, do these calculations:

  • LI Conversions / LI Spend = CPA LI
  • FB Conversions / FB Spend = CPA FB
  • Total Spend = LI Spend + FB Spend
  • Total Conversions = LI Conv + FB Conv

Let's give ourselves five minutes to make sure we have it right. So we're now up to seven minutes in total.

3. Enter data and review

Now it’s time to try the spreadsheet, or model rather, with real data. Don’t worry about loading it in automatically just yet, it’s fine to just cut and paste it from another system.

For my data, I just go to the ad tools on the platforms, copy over the daily numbers. For conversions, luckily I captured the origin in the campaign - but if you don’t do that, then - aha - you know what your most important task is now!

Once the data is in, I can see that Facebook typically converts at a higher rate than LinkedIn. But I also notice that as Facebook spending goes up, so does the conversion costs.

It seems, then, that there may a limit of how many people are going to convert from Facebook on any given day, so to maximise numbers it’s worthwhile to stay on both platforms.

And, of course, there are many more things this data tell us – but we only have 15 minutes, so time to move on!

Another three minutes here takes us up to 10 minutes.

4. Decide what to improve

To avoid analysis paralysis, you should make an effort to find one thing that matters the most. Then, prioritize your work around improving that one thing. Doing so is a very important part of 'right-sizing' your analytics.

For me, it was to reduce the cost of my LinkedIn conversions. Through changing the numbers on the spreadsheet, I could see that reducing the cost of LinkedIn was probably the most likely way to get my total conversion costs down.  

So my goal, then, was to reduce the cost of LinkedIn conversions so that my overall conversion costs are under $2.

Spend your remaining five minutes on this, and there you are - only 15 minutes and you have a data model and a good idea of what you need to work on next.

5. Repeat the same analytics and measure your results

One of the best things about digital marketing is that we can see the results of our changes almost as they happen. It’s tempting to make changes immediately, but it’s important to stick to our original plan and see whether we moved the needle.

For me, I wanted to get total conversions below $2 by spending more time on my LinkedIn campaigns.

I was close, but did not quite hit my goal of getting a conversion, on average, for less than $2 across both platforms.

What I did learn, though, is that there were some underperforming ads in the campaign and some bad targeting which led to significantly lower click through rates. Those are now fixed, so although I didn’t hit my target, I did improve the campaign.

So...

So, from my brief example, hopefully you can see that spending a bit of time on your own analytics – no matter how basic - can deliver some very useful information which gives purpose to your work and lets you know whether your marketing is improving.

Without analytics, you can get bogged down with work which is going nowhere, gives you no pleasure, and may turn you into an anxiety-ridden and unhelpful marketer.

No one wants that. So spend 15 minutes as soon as you can, and go and really fix something!

Econsultancy's Measurement and Analytics Report 2014 provides both a survey-based snapshot of the digital analytics sector and commentary which outlines the challenges and opportunities we are all facing. 

Jeff Rajeck

Published 27 June, 2014 by Jeff Rajeck

Jeff Rajeck is the APAC Research Analyst for Econsultancy . You can follow him on Twitter or connect via LinkedIn.  

85 more posts from this author

Comments (3)

Dipesh Shah

Dipesh Shah, Digital Consultant at Data Translators

Explained very well

almost 2 years ago

Avatar-blank-50x50

DJ

Great idea, but I'm confused by your calculations:

You've detailed the CPA calculation the wrong way around.

LI Conversions / LI Spend = CPA LI

should be

LI Spend / LI Conversions = CPA LI

almost 2 years ago

Avatar-blank-50x50

Suzanne

I agree with DJ: If with cost 1 the campaign got 2 LI conversions, then the CPA is 0.5, not 2.

almost 2 years ago

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