Understanding Google Analytics Data for Greater Customer Insights

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We’re all familiar with some of the data available in Google Analytics but are we using it as the rich asset it is, to better understand the customers visiting a business website?

Of course, huge quantities of data can be overwhelming and there’s a tendency to just skim the surface by monitoring and reporting on just a few Key Performance Indicators (KPIs). But that limits opportunities to improve products, services and customer experience; and, more importantly, limits what we can learn about our customers and website visitors. This rich analytical data is available for every website so why would any business ignore such a rich source of growth?

The Present is Already Data-Driven

Many well-known international organisations, including the World Economic Forum (WEF) and Accenture, believe a 4th industrial revolution is already upon us with Artificial Intelligence (AI), disruptive businesses and technological complexity already part of our daily lives. Companies could take better advantage of this revolution if they were armed with more information about their existing and potential customers. We’re not talking personal information here but the basic, anonymous information available from Google such as customer location, demographic, average engagement time and behaviour on a web page.

Understanding data
Source: freerangestock.com

Why Understanding Analytics Data is Crucial

Take an example of a company that provides translations, voice-overs and subtitling for businesses in the UK. Like many companies they were monitoring essential KPIs such as Bounce Rate and knew it was far too high on several of their pages but didn’t know why.

The pages in question had been optimised for conversions with compelling calls-to-action (CTA) as a variety of large buttons in prominent colours and with clear messages encouraging the desired actions. A/B testing had been carried out over a long period of time but results were inconclusive, as were the results of heatmap analysis. There seemed to be no obvious explanation for the high bounce rate and consequent low conversion rate.

However, an in-depth review of web visitors to these pages quickly revealed the issue:

High Indian and USA Visits
Data source: matinee.co.uk

Target customers for this business were exclusively in the UK but 37% of web visits were coming from India and the USA and only 12% of visitors from the UK – see chart above. Since the company did not provide their services in India or the USA this was at least one explanation for the high bounce rate. Bounce rate hovered around 90% for the Indian and American audiences as opposed to 77% for UK audiences. 77% is still high so there is certainly more work to do to bring that down but work on the landing pages in question to divert non-target audiences to an informational page is reducing the average bounce rate in the meantime. And helping the company better understand the issues they were having.

All Data is a Valuable Asset

Organisations rightly view customers’ personal data as something to be protected and securely stored but there is also the anonymous customer data that can, and should, be used to improve customer engagement and the overall customer experience on a website.

The example given above resulted in at least 37% of web visitors having a poor online experience when some proper data analysis could have easily identified the problem and an obvious solution. It highlights the dangers of making assumptions (in this case that web visitors were predominantly UK-based) and why all data should be treated as an asset to be mined for valuable information.

Of course, the amount of data available to businesses can be overwhelming but there are plenty of tools available (some of them even free) to make it quicker and easier to perform in-depth data analysis, which could result in better customer engagement, higher conversion rates and increased revenue.

1 COMMENT

  1. What are some of the “tools available (some of them even free) to make it quicker and easier to perform in-depth data analysis”?

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