Google Universal Analytics Data Trend Analysis – Complete Guide

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


data trend analysis

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 goal conversions and revenue? 


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

How you analyse 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 analysing ‘data trends’ to get highest possible ROI from my campaigns:

Rule #1: Always question how the data is collected

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

Often wrong goals, incorrect goal values, incorrect ROI calculations, incorrect installation of tracking codes etc can corrupt the data.

Any decision made on the basis of corrupted 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 major business decisions on the basis of such data.

Collect fresh data and then wait for at least 3 months before you start analysing 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The insight that you get from analyzing historical data is often out of date and it does not always match with the present marketing conditions.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: What you should know about Historical Data in Web Analytics


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

Get a deep understanding of your business and its cycle of ups and downs. Understand your business “sales cycle”.

Your business just like any other business tends to have natural ups and downs over the course of a year. We call this ups and downs as seasonality.

We can never really understand this seasonality if we don’t compare last year data with the present year data. This is one of the few situations where more than 1 year old sales data becomes so important.

Use this understanding of seasonality 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. “

not a trend

this is 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 a marketing campaign when you compare its performance with the past performance using two different date ranges.

Only through comparison you can find out whether you are making a progress or regress over time.

For example look at the following one week traffic report:

useless report

What insight do you get from this report? Can you determine whether the traffic has increased or decreased in comparison to last week? …. No you can’t.

For that you need to compare this data with last week data:

comparison of data

Now after looking at the report above, we can conclude that traffic has dropped a bit in comparison to last week. You can’t get such type of insight without data comparison.


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 why things are going good or bad.

For example in the report below the only metric we see is ‘Revenue’. So we have no idea why the revenue declined so drastically in the middle of the week:

stand alone metric

So we need to add at least one more metric to add context to our data trends:

two metrics

Now from the report above we can conclude that one of the reason of drastic drop in revenue is the drastic drop in average order value. You won’t get such type of insight if you report only a single metric in your data trend.

Related Post: Common Google Universal Analytics Mistakes that kill your Analysis & Conversions


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 the measurement and make the data more actionable.

For example, in the report below we have no idea why the overall website traffic is going down:

website traffic

You need to segment this data trend to get a better insight:

segmenting data trend

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

sessions with social referral

Does it really matter that ‘sessions 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 ‘sessions with social referrals’ over time and you must be able to prove it.

Unless you don’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: Spell out the insight

make the insight obvious

What one can really understand through this data trend?

For an average person, the lines are going up and down. So what?

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

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 story the data trend is really telling you.

Write at least 4 or 5 lines which describe what is going on, in plain english. Show how the trend is impacting the business bottomline in monetary terms.

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

Related Post: How to become Champion in Data Reporting


Rule #9: Use Sparklines


Sparkline is a 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.

Related Post: How to select best Excel Charts for Data Analysis & Reporting


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 in Google & Universal Analytics

Learn about the Google Analytics Usage Trends Tool

The Google Analytics usage trend is a new tool which is used to visualise trends in your Google Analytics data and to perform trend analysis.

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Himanshu Sharma

Certified web analyst and founder of

My name is Himanshu Sharma and I help businesses find and fix their Google Analytics and conversion issues. If you have any questions or comments please contact me.

  • Over eleven years' experience in SEO, PPC and web analytics
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  • Bachelors degree in Internet Science
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