5 Common Marketing Data Analytics Mistakes and How to Avoid Them

5 Common Marketing Data Analytics Mistakes

 Are you using the right metrics? Do you have the right tools? Find out why you may not be getting the best results from data analytics.

Data analytics is all the rage. Everywhere you turn, you see people talking about “data-driven decision-making” as the holy grail of marketing. 

However, not all data is created equal—many marketers are using data incorrectly and, as such, fail to reap the benefits of data analytics to maximize their marketing ROI. This is a shame because data-driven marketing decisions can deliver tremendous benefits:

  • Marketing campaigns that leverage data-driven personalization can yield a 5-8x ROI
  • Two-thirds of marketers say that data-based decisions are more effective than gut instincts. 
  • One-third of marketers identify marketing analytics and competitive insights to be the most important input for their marketing strategies.


After working with marketing data for over a dozen years, I have identified some common marketing data analytics mistakes that marketers need to avoid if they want to optimize their results:

Collecting and Analyzing the Wrong Metrics

Focusing on the wrong metrics can lead you down the wrong path and cause you to invest resources in chasing numbers that don’t matter to your bottom line. 

For example, many advertisers only look at cost-per-click (CPC) or the number of visits — some of them are doing so because they aren’t collecting the correct data. Meanwhile, other metrics, such as return on ad spend (ROAS), are more helpful but they still don’t show the full picture. 

If you’re not collecting and analyzing the correct data, you could be spending a lot of money on driving traffic yet not making any sales. Instead, consider which audience segment brings in the highest customer lifetime value (LTV). Then, optimize the customer acquisition cost (CAC) so you can allocate your budget effectively to maximize gross margin dollars.


Using the Wrong Tools

While most marketing platforms (e.g., Google, Facebook, email) provide measurement and analytics, you can only see the data collected from each individual channel. 

This siloed approach to data collection doesn’t give you a holistic picture, especially now that omnichannel marketing is the name of the game. As you’re reaching your audience via multiple platforms, you need to make sure that these customers aren’t counted multiple times in your analytics.

Instead, use analytics tools that allow you to consolidate data from all channels so you can see how each customer interacts with your brand and attribute every sale accurately to understand the effectiveness of your campaigns. In addition, incorporate metrics that show not only the effect of paid media but also how other traffic is affected by your media spend, such as organic traffic, direct traffic, and email click-throughs, etc.


Focusing Only on First-Click or Last-Click Attribution

Multi-touch attribution (MTA) models define how the credit for a conversion event is distributed among various marketing channels. Applying the wrong model could skew the understanding of the effectiveness of each touchpoint and misguide your subsequent investment.

Selecting the best model involves understanding how customers interact with your brand. For example, some channels (e.g., non-brand paid search) reach more customers at the top of the marketing funnel while others (e.g., coupon sites) are bottom-of-funnel touchpoints. These channels may get an unbalanced amount of credit if you only use first-click or last-click attribution.

Many marketers only consider last-click attribution because that’s the data made available by their tools which is one of the biggest marketing data analytics mistakes. Looking at a single type of attribution may cause you to shift marketing spend away from either top- or bottom-of-funnel activities. As such, your brand may not be present at crucial points to build brand awareness or facilitate prospects’ progression down the sales funnel.

The attribution model that you select will be unique to your company. There’s no one-size-fits-all answer and you need to spend the time to fine-tune your model.


Using the Same Strategy for Desktop and Mobile

Did you know that 40% of consumers only perform searches on their smartphones? Since they use desktop/laptop computers and smartphones/tablets differently, you need to design a device-specific user experience for optimizing conversion. It’s crucial to separate your data based on device types to measure results and inform improvements.

Your marketing content may perform very differently when viewed on desktop and mobile due to different screen sizes, behavioral patterns, and user intention. As such, you should adjust your message,  set KPIs, analyze data, and optimize your marketing strategy for each type of device individually. 

In particular, pay special attention to data that indicate the difference between mobile and desktop behaviors. Then, use the information to adjust media targeting, tailor website content, and address technical issues to deliver the best user experience for each device type.


Overlooking Statistical Significance

From messaging based on A/B testing to budgeting and ad bidding based on past performance, marketers make a lot of decisions using data.

However, many overlook the importance of statistical significance by making incorrect decisions based on data from a sample size that’s too small (e.g., too few visitors, conversions, or clicks) while others may run a testing cadence that doesn’t yield valid results due to shifting customer traffic or behaviors. 

For example, we often see these mistakes when marketers bid on long-tail keywords that are low in search volume. Doing so could add up to a considerable amount of traffic if done correctly because of more focused intent. However, with the inherent lower volume, it’s important to consider the statistical significance of your data before making a decision.


Final Thoughts

It’s no doubt that data-driven decisions will become even more important as consumers demand targeted experiences, while marketers are under increasing pressure to optimize their budget and results so it’s important to avoid these marketing data analytics mistakes.

To reap the benefits of data analytics, you need to collect the right data and know what to look for so you can derive actionable insights to make accurate decisions.

Here at Chameleon Collective, our team of expert marketers and data scientists helps brands analyze consumer behaviors and derive insights to drive success. We also help clients define meaningful KPIs and refine attribution modeling so they can focus their resources on what matters. 

Get in touch to see how we can help you tap into the power of marketing data.

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Vu Pham

With over 20 years of experience working with data, Vu has the unique ability to collect, analyze, and transform your data to solve your business problems.

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