In this episode of The Undiscovered Metric, we sat down with Johannes Höller, Head of Data Consulting and Solutions at diva-e, to discuss how clients can find the single source of truth in their data, which key metrics can improve marketing performance, and some of the common pitfalls marketers face when trying to improve marketing analytics reporting.
Find out how to avoid common marketing reporting traps.
Hi Johannes, could you give us a quick intro into your role and company, and how you got into working with data?
Hi JJ! First of all, thank you for having me on your podcast today. I’m Johannes and at diva-e I’m responsible for the business unit data consulting and solutions. What we do at diva-e.com is help our customers to create high-end digital experiences to improve customer interaction and business success.
After studying business informatics, I went straight into the world of online marketing. Through classic paid advertising, I immediately came into contact with lots of data and the challenge of managing and using it. Meanwhile, I've been working with data and tools for over 15 years and still try to make my daily life and that of our customers easier.
What are some common traps that people fall into when coming to you to help build marketing analytics reports?
Sure. Let me point out some of the most common challenges we experience again and again within our projects at diva-e:
Sometimes simpler visuals can be more impactful than complex ones. My advice is to keep fancy charts and graphs to a minimum and always have the purpose of the report in mind. The same applies to the number of digital marketing reporting dashboards — more is not always better!
Not understanding requirements
Digital marketing reporting dashboards should be tailored to the specific needs and interests of the intended users. Without understanding the audience's perspective, it can be difficult to create a useful marketing analytics report. A marketing manager has different requirements than a BI specialist. I can build anything, of course, but if I'm not aware of what's really required, I will fail.
Lack of consistency
Marketing analytics reports should have a consistent look and feel, with clear formatting and a logical structure. Inconsistencies can be distracting and make it more difficult for the audience to understand the information.
Bad data quality
Digital marketing reporting dashboards are only as good as the data they are based on. It's important to ensure that the data is accurate, complete, and relevant. Bad data quality can lead to inaccurate conclusions and decisions.
Simply presenting data without context or explanation can be overwhelming and confusing for the audience. It's important to use data to tell a story and provide insights that are relevant and actionable. Quite often we see random visuals or graphs with no structure, and this makes it very hard for users, especially the ones who are not so experienced, to understand those reports.
Dashboards must use data to tell a story
What are the common metrics that clients should be focusing on when approaching marketing analytics reporting?
The most relevant metrics will always depend on the specific goals and objectives of a client's marketing campaign. Clients should work closely with their marketing team to identify the metrics that are most important for measuring the success of their campaign. A few common ones are:
- Traffic Sources:
Understanding where website traffic is coming from can provide insight into which marketing channels are performing well, and which aren’t.
- Click-Through Rate:
This metric is a good indicator of how well an ad or link or even a channel is performing.
- Cost per Acquisition:
You need to know the cost of acquiring a customer through a specific marketing channel to allocate an optimized budget.
- Conversion Rate:
This metric is crucial to understanding the effectiveness of a marketing campaign.
- Engagement Metrics:
Metrics, such as time spent on site and bounce rate, can help clients understand how visitors are interacting with their website.
- Social Media Metrics:
For clients using social media for marketing, metrics such as likes, shares, and comments can provide insight into how their content is performing.
- Return on Investment:
Finally, you must measure the profit generated from a marketing campaign compared to the cost of running the campaign.
How can marketers use a single source of truth to improve their performance?
Marketers and analysts need to view each optimization not just as a one-off action, but as an ongoing process. With our clients at diva-e, we usually discuss the following steps:
Use the data to identify areas where performance is lacking or could be improved. Look for patterns and trends in the data to determine which areas require attention.
Set goals and targets
Personally, I think this is the most important one! Once you've identified areas for improvement, set specific goals and targets for your actions. This will help focus efforts and measure progress over time.
You need to regularly monitor progress when making changes against goals and targets. This will ensure that the right actions are being taken to drive improvement.
Make data-driven decisions
Use the data to make informed decisions about what actions to take to improve performance. This will help ensure that resources are used effectively and efficiently.
Share the data
Share the data with relevant stakeholders to ensure everyone is on the same page and working towards the same goals.
Compare your data
Clients should use metrics to benchmark their performance against competitors. This can provide valuable insights into areas where they may be lagging behind and where they need to improve.
Are there any metrics that you feel clients aren’t looking at enough?
Every customer case is different. But there are definitely things that we stumble over again and again. Let me bring up two of them:
Scroll Depth is an often underestimated but also wrongly used KPI. If the user scrolls down once briefly and then up again, this triggers 100% scroll depth, even though there’s no way they’ve seen the page properly. I’d recommend always including Time on Page as a second KPI for context if you’re looking at scroll depth.
It’s also worth considering that scroll depth doesn’t make much sense for short pages, as 75% or more is often triggered automatically when loading, depending on the average screen size. That's one way this metric is often misused. KPIs like this need to be evaluated in tandem with other KPIs and metrics.
Referral Exclusion List
Another example is the Referral Exclusion List, which is often forgotten. If, for example, PayPal or another payment service provider is linked during checkout and the user then returns to the actual website, the source / medium is overwritten by "referral", if the referral exclusion list isn’t set up correctly.
As a result, valuable information about the source is lost, and mislabeled, and any traffic analysis will have incorrect results. It's a small and easy setting to change, but it's one of those low-hanging fruits we see in every second audit we do for clients.
If you could give one tip for anyone building out and trying to understand their metrics and data better what would it be?
Focus! Absolute focus! I know, it sounds so simple but reality often looks very different. People are quickly tempted to build marketing analytics reports for anything. In the end, many of them are rarely used or have no added value.
It’s important to tell a story with your data — but it should be a short and easy-to-understand story. It shouldn’t be a novel. So my advice is this: only build what will be used, less is more!
Johannes Höller, Head of Data Consulting and Solutions at diva-e
Looking forward, what are the top three things set to catch fire in marketing data over the next 18 months?
I think at the moment there are a lot of topics that are coming even more into focus. In general, changes in the area of data protection and privacy will continue to have a major influence. Besides that, I personally see the following topics as enormously important:
The impact of artificial intelligence is already a big part of the current landscape, and will continue to grow. It's not just ChatGPT, there are way more things you can do when it comes to AI. For example, AI can analyze huge amounts of data within seconds to identify patterns and insights, to improve targeting and personalization.
Companies are becoming increasingly aware of the importance of providing a positive customer experience, and are investing in technologies that can help them do so. This may include chatbots, virtual assistants, data platforms, and other tools that can help customers find the information they need quickly and easily.
As customers interact with more and more channels, having multiple touchpoints and generating tons of data all this needs to be harmonized, managed, and analyzed.
Finally, what’s the one thing about data and analytics you wish you’d known earlier in your career?
I think I've spent far too many hours doing manual work and trial and error. So my advice: Use more automated tool support.
Don't get me wrong, Excel is a great tool. I love it, but it has its limits at some point. It requires a lot of time, and I think I've spent far too many hours doing manual work and writing formulas, only to end up with no results because it was wrong.
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