How to analyze, interpret and report data trends in Google Analytics


Analyzing data trends is an age old tactic which is used to measure the performance of marketing campaigns over time and to predict future outcomes.

We do trend analysis to get answers to questions like:

1. What are my top selling products?
2. What are my top converting keywords?
3. Which keywords I should bid on?
4. Where I should invest my money and resources to get highest possible ROI?
5. Which is the most effective marketing channel in terms of conversions and revenue? 

In trend analysis we spot a pattern(s), interpret it and then make predictions on the basis of historical data.

How we analyze and interpret the ‘data trends’ plays a very important role in optimizing your marketing campaigns and making predictions about future outcomes. One wrong interpretation and you can end up losing hundred thousand pounds (depending upon the size of your business).

I am going to highlight few key rules which I follow while analyzing ‘data trends’ to get highest possible ROI from my campaigns:


Rule #1: Always question how the data is collected

Before you analyze and interpret any data, always make sure that the data has been collected accurately esp. for the time period you have chosen to analyze.

Often wrong goals, incorrect goal values, incorrect ROI calculations, improper installation of tracking codes etc muddy the analytics data.

Any decision made on the basis of muddy data could prove fatal for your marketing efforts and business. If you are not really sure how the data has been collected or if you can’t purge it then avoid taking business decisions on the basis of such data.

Collect fresh data and then wait for at least 3 or 4 months before you start analyzing data trends.


Rule #2: Understand that historical data is in fact “dated”

The insight that you get from analyzing historical data is often out of date and it does not always match with the present marketing conditions.

The older the data, the more unreliable it becomes.

This is because we live and operate in a constant change of marketing conditions, trends, buying behavior, pricing, competition and multi channel funnels. So comparing one year web analytics data to the last year could be like comparing apples to oranges because so much would have changed during that time from website size, traffic, products, competitors to your target market.

So rule of thumb is, don’t over rely on historical data esp. when it is more than one year old.

Related Post: Web Analytics Historical Data – what you should know about it


Rule #3: Select the right time period to analyze your data trends

Get a deep understanding of your business and its cycle of ups and downs. Understand your business “sales cycle”.  Use that understanding to select the right time period. As a rule of thumb:

“ 1 week doesn’t make a data trend.

1 month doesn’t make a data trend.

Even 2 months don’t make a data trend.

3 or more months make a data trend. “




Rule #4: Add comparison to your data trends

Comparison adds ‘context’ to data and make it more meaningful.

You get a better understanding of your marketing campaigns when you compare their performance using two different date ranges. Only through comparison you can find out whether you are making a progress or regress over time:


Rule #5: Never report standalone metric in your data trends

Stand alone metric doesn’t have any context associated with it. So when you report a standalone metrics it is hard to figure out whether things are going good or bad.

For example in the screenshots above the only metric we see is ‘visits’. So we have no idea whether the traffic quality is improving or declining over time. So we need to add at least one more metric to add context to our data trends:

From the screenshot above we can see that the ‘goal conversion rate’ has improved over the same time the ‘visits’ have improved. So now we have a better understanding of the quality of traffic coming to the website.

I didn’t select bounce rate because it doesn’t work as well as goal conversion rate in determining the quality of traffic at this macro level.

Related Post: Common Google Analytics Mistakes that kill your Reporting 


Rule #6: Segment your data before you analyze/report data trends

Ask any analyst who is worth his salt about which is the most important task in web analytics and he will tell you straightaway that it is “data segmentation”.

Segmentation not only add context to the data but also improves it measurement and make it more actionable.

For example:

Here we have no idea why the visits to the website are going down. Without segmenting the data trend we can’t take any action.

After segmenting the data trend, we can see that it is the visits through search which is the main reason of the decline in overall traffic. By segmenting this data trend further, we can figure out the role of organic and paid search in the decline of the overall search traffic.

More you can segment the data, better the actions you can take.


Rule #7: Report something business bottomline Impacting


Does it really matter that ‘visits with social referrals’ are going up?

Similarly does it really matter that facebook likes are increasing over time or twitter followers are increasing?

The answer is ‘no’. It doesn’t really matter, not unless you tie these metrics with conversions.

This is because social engagement can be for the all wrong reasons. May be you are engaging with random people who are not really your target audience. May be you are engaging with your competitors.

If this is not the case then your conversions must increase along with ‘visits with social referrals’ over time and you must be able to prove it.

Unless you won’t tie your metrics with conversions/transactions you will not be able to report something business bottomline impacting which can convince your client/boss to invest more money in your campaigns.


Rule #8: Make the insight obvious


What one can really understand through this data trend?

Yes the lines are going up and down. So what?

Unless you are creating reports for yourself, you need to add context here.

You can add context through: ‘comparison’, ‘use of two or more metrics’ and ‘data segmentation’.

You can also add context through the use of annotations, graphic elements (like arrows) and above all through written commentary.

By commentary I mean what the data trend is really telling you and others. Write at least 4 or 5 lines which describe what is going on, in easy to understand words. Show how the trend is impacting the business bottomline?

You need to explain the reason of big spikes and deep trough in your data trends before you present it to the senior management/client.


Rule #9: Use Sparklines


Sparkline is a new feature added in Microsoft Excel 2010 and beyond. It is a tiny chart embedded in a cell.

Through Sparklines you can easily spot patterns in the data presented in a tabular format.

You can enter text in a cell and at the same time use sparkline as its background. Any change in data of a cell immediately changes its sparkline. So sparkline is another way of adding context to the data.

Click here to learn more about Sparklines.


Rule #10: Don’t jump to conclusions

While doing trend analysis, it is very important to keep in mind that the data you are looking at is “dated”. You live in a constant change of marketing conditions, trends, buying behavior, pricing, competition and multi channel funnels.

History does not repeat itself in online marketing. 

It is highly unlikely that you can replicate your success rate by carrying out the exact same tasks you executed some 6 months ago with a particular campaign. A major Google update or arrival of a new and powerful competitor can easily screw the predictions you have made about your outcomes on the basis of trend analysis.

So you need to keep several factors in mind while drawing conclusions from data trends and not just the metrics you are analyzing in your trends.

Other Posts you may find usefulAdjusting Bounce Rate by Calculating Time spent on the Page


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