{"id":19912,"date":"2021-04-06T16:59:00","date_gmt":"2021-04-06T16:59:00","guid":{"rendered":"https:\/\/corvidae.ai\/?post_type=blt_blog&p=19912"},"modified":"2023-08-01T15:59:40","modified_gmt":"2023-08-01T15:59:40","slug":"how-to-fix-inaccurate-website-analytics","status":"publish","type":"post","link":"https:\/\/corvidae.ai\/blog\/how-to-fix-inaccurate-website-analytics\/","title":{"rendered":"How to Fix Inaccurate Website Analytics"},"content":{"rendered":"\n

It probably isn\u2019t surprising to any of us to learn that marketers leave money on the table. It\u2019s a concern that many in the industry have had for along time. <\/p>\n\n\n\n

But research by IDG<\/a> has highlighted that the fact that 9 out of 10 marketers <\/strong>indicate it is happening because they have not been able to harness the type of website analytics needed to properly assess the effectiveness of their marketing activity and pivot accordingly.<\/p>\n\n\n\n

However, for the lucky ones that do have access to effective analytics capabilities the rewards are significant \u2013 as they can expect to see an improvement in spending efficiency of between 20% and 40%<\/strong>.<\/p>\n\n\n\n

So, getting your analytics right really matters. In terms of impact on revenue and ROI.<\/p>\n\n\n\n

Existing approaches to analytics aren\u2019t cutting it<\/strong><\/h2>\n\n\n\n

Based on the findings above, the business and marketing rationale for better analytics is fairly clear.<\/p>\n\n\n\n

However, unfortunately, the evidence is that shortcomings in some of the most commonly used analytics packages on the market from players like Adobe and Google are creating real problems in terms of the nature and quality of the analysis they provide<\/a>.<\/p>\n\n\n\n

For example, when analytics platforms, including for example GA360, have a hard time tracking every source of traffic that lead to a conversion, it typically gets dumped into the \u201cDirect\u201d channel. This means that the campaigns you\u2019re running aren\u2019t getting the credit that they deserve. And neither are you!<\/p>\n\n\n\n

Add to that the fact that the default attribution model for GA is Last Click<\/a>, which is a complete oversimplification where 100% of the credit for conversion is given to the last marketing engagement<\/strong>, and you begin to understand the shortcomings in existing approaches at an outline level.<\/p>\n\n\n\n

But there are also deeper issues with the way data is collected and analysed including:<\/p>\n\n\n\n

1. The poor quality of data inputs<\/strong><\/h6>\n\n\n\n

One of the central issues for marketers who use Google Analytics, for example, is the poor underlying data that is generated for analysis.<\/p>\n\n\n\n

At the heart of their approach is a pixel-plus cookie-based approach which is essentially predicated on measuring a device accessing a specific web property and not on painting a picture of an individual who is highly likely to be on a complex, multi-channel and multi device journey.<\/p>\n\n\n\n

The result? This approach does a particularly poor job of attribution \u2013 in fact our own work with customers shows that it typically generates data that is an incredible 80% incorrect in attribution terms.<\/p>\n\n\n\n

2. Channel based data silos<\/strong><\/h6>\n\n\n\n

In reality many marketing teams, whether in-house or external agency \u2013 or a combination of both \u2013 are typically organized around individual channel lines. In fact research by Experian found that less than 30% of marketers work in fully integrated teams.<\/p>\n\n\n\n

What that means in practice is that no-one really has a \u201csingle source\u201d analytics view of the impact of all media channels and the wider customer journey. <\/p>\n\n\n\n

Which in turn means that individual channels like social, search, programmatic and outdoor are all cobbling together their own, inward focusing analytics and unfortunately can often lead to over-reporting of media effectiveness and overstating of contribution to revenue and ROI.<\/p>\n\n\n\n

3. Inability to integrate wider customer journey data<\/strong><\/h6>\n\n\n\n

Lastly, existing solutions analytics like GA are holding marketers back from a key strategic objective \u2013 to help them effectively \u201csee\u201d right across the customer journey. And effectively analyse ALL of the touchpoints that have an impact on conversion and revenue.<\/p>\n\n\n\n

From early stage, often hard to track initial research influenced on channels like social and organic search right through to lower funnel activity like paid.<\/p>\n\n\n\n

What does this looks like in practice?<\/strong><\/h2>\n\n\n\n

To better illustrate our point, let\u2019s take a look at a simplified example of a customer journey<\/a> and how an existing solution like Google Analytics evaluates things.<\/p>\n\n\n\n