The Perfect Analytics Dashboard

Many companies still have a decisive overload when it comes to regularly creating analytics KPIs and making them the basis of strategic and operative decisions.

Data visualization offers a possibility for improving the interlinking between web analysis, online marketing controlling, as well as company management overall. In the meantime, with Tableau, Google Data Studio, and other visualization tools, there are powerful solutions for visualizing key figures and therefore data. But, there is also the matter of "one number smaller" when you use the dashboard function from Google Analytics for presentation and oversight. It matters, and this article will show you how to create dashboards in Google Analytics.

Data Visualization – the Fundamentals

An important feature of digitalization is that all processes that run via software generate data. With increasingly intense competition in digitalized industries, it is important to use these data for the analysis of results and the efficiency of processes. Analytics data can readily be integrated into a long-term, structured monthly reporting in companies. However, the potential of web analysis remains largely unused if you do not make it at least the weekly basis of your online marketing control.

It is still better to use web analysis for daily oversight, weekly control, and monthly evaluation. A factor not to be underestimated is the related and emergent practical knowledge in teams and companies, which results from the connection of external and internal experiences and the related development of Analytics Data. This practical experience is essential for the increasingly more effective interpretation of the data.

Goals and KPIs

The use of Analytics Data in some companies runs like a moderately comical film from the past few years as "People who stare at data." Impressive reports, artfully-structured dashboards, and complicated-soundings KPIs leave many awestruck without really having an understanding of the informational content of the data and, above all, of the outcome of the information. People often forget to connect Analytics Data with goals or global goals - such as sales or the cost-sales relation - created in the centre of the online marketing control without further dividing the logic behind the processes.

But, web analysis can do much more. Goals can be defined for all processes and phases of the online sales funnel and can be checked continually using Analytics. The best practice, which large players such as Amazon show, is defining and dismantling business processes as partial processes and expressing this success in mathematical relations. The key figures used in this way should be continually overseen in order to make the mid- and long-term achievement of goals possible.

Thus, the sequence of the analysis plays a decisive role. If you make yourself aware of the sales funnel, for many companies the control of their online marketing frequently begins on the upper side of the funnel, that is, they busy themselves with impressions and visits. Unfortunately, these figures are certainly important, but are only indirectly relevant for success.

To ensure true success - in general, the achievement of an economic profit - the goals in web analysis should be analyzed along the sales funnel from back to front. In E-commerce, return ratios, conversion rates, and shopping cart size decide the long-term success of an online shop. In the marketing affiliate, conversion on the partner site is decisive for success.

Target Groups for Dashboards

Once the significant KPIs are directed toward the most important goals, next, the question of who will engage with these KPIs must be clarified. Decisive for the content-related conception of dashboards are the target groups for certain control parameters in the context of the analysis. Thus, other key figures are relevant for the newsletter team than for an Adwords manager; the developers will engage with the technical site performance, while a shop manager will have in his or her focus the E-commerce performance data such as shopping cart and conversion rate. You can therefore display the organizational structure of the online marketing and the E-commerce team with dashboards very clearly.

If you work in smaller companies with few employees and your areas of responsibility is greater and more comprehensive, the dashboards should nevertheless be structured according to areas of responsibility. Thus, strengths and weaknesses in the online marketing process can be identified earlier and optimizations can be initiated. With this approach, the principle of "Property Ownership" can be implemented, through which a clear arrangement of responsibilities can take place via channels, processes, or parts of the website. This should absolutely be condensed in the Analytics Dashboard.

The target groups also determine the level of detail and complexity of the dashboard according to their analytical understanding. If the entire workforce is addressed, easily understandable and consistent key figures should be summarized in a dashboard.

With the target group, the choice of KPIs and presentation formats also arise. The dashboard should give those responsible a glimpse into the results of their work. Because the team knows that input is also a factor for output, they should establish which KPIs belong in the dashboard and which do not.

The Temporal Perspective – Strategic – Operative

In addition to the decisive questions about the target group and product owner of the dashboard, the question of temporal perspective also soon arises. The temporal levels of strategic, tactical, and operative, which can be translated with long- mid-, and short-term, arise from the controlling. While individual online marketing processes are subject to long-term changes - such as the change of device usage from desktop to mobile (and next, to voice search?) - key performance indicators in E-commerce are overseen daily to make fast access possible.

While long-term questions are best depicted through the display of data progress, values can be presented as individual values in the short-term oversight. If there are processes that can be seen as short-, middle- and long-term, the establishment of several dashboards is recommended because changed needs with regards to KPIs can arise from the change of temporal perspective.

Distribution Perspectives

The distribution of values on various geographical regions or channels or device types can be presented with the help of Analytics Dashboards. In addition, data can be displayed as a map presentation, as a bar, or pie chart. The distribution perspective is always important where there is a portfolio of factors. I can see the influence of external factors such as the weather on the geographic distribution of search inquiries.

The distribution of visit and interaction values has great importance for the central, but also the difficult topic of attribution. This has to do with the arrangement of online marketing success on individual channels. In order to achieve the right estimations in the long-term, naturally I first need sufficient experience with the various importance of channels, regions, etc. Analytics Dashboards can help with this.

Business Logic and the Display of Dashboards

Composing a logical statement is important for the creation of a dashboard. What is the primary target size, what are the influencing factors, and how are these related? Are benchmarks, thus comparative figures, sensible, and how can the comparison best be shown? These questions immediately arise in the display on the dashboard. Thus, elements of a dashboard should be pictured along with their sequence according to business logic.

Example:

• Primary objective: Sales

• Influencing factors: ds. shopping cart, conversions

• Sequence of the KPI in the dashboard: Sales [=] shopping cart [x] conversions

In this sequence, the elements in the dashboard can thus be arranged either from left to right or from top to bottom. If a partial process - such as the rise in conversions - is displayed, a line or column or even a new dashboard is displayed.

An example dashboard shows the key figures as primary target figures in content marketing for all sides and with AMP sides. The sides per session are shown as secondary KPIs. The user's development and the distribution of devices relevant for the AMP sites are factors. The data are also presented for 2 comparable periods of time.

Creation of an Analytics Dashboard – This Is How It Works

If target groups, KPIs, and temporal perspectives, distribution and logic for a dashboard are established, it makes sense to set up the dashboard. Analytics is thus very user-oriented and offers a multitude of possibilities for creating a dashboard. Under the Analytics menu point "Personalize," you can access existing dashboards, create new ones, and divide the Analytics accounts with other users.

Figure 1: Create a new dashboard in Google Analytics

An Analytics administrator in general has access to all dashboards, the above-described property owner in general only to the dashboards with the KPIs for which they are responsible.

If a new dashboard is created, Analytics offers several possibilities. You can create an individual dashboard or import a completed dashboard from the gallery from the Analytics "gurus." Analytics also offers templates that you can use.

Figure 2: Create dashboards using templates

If you have decided on your own, unformatted dashboard, you should also provide this with an informative name. Ideally, you should incorporate the process (such as "SEO" or "Social Media Marketing") and the most important key performance indicators into the name of the dashboard. A possible name would also be such as "SEO Usage Data - Time on Site Bounce Rate." But, you can also use general names such as "Social Media Dashboard."

In the next step, you come to the widget "Assembly kit," the core of the dashboard function. Here, you can create to the fullest and use individual values, data series, maps, and other visualization formats for each measurement value and thus each KPI. The whole functions for historical figures as well as for real-time data. In the choice of widget format, the temporal perspective plays a decisive role. If longer timeframes are considered, data series are very good, but when it comes to average values or daily values, individual values can also be displayed.

Figure 3: Choose various widgets

You must proceed carefully in the choice of measurement values or metrics. Analytics uses a structure other than in the perspective of standard usage. It is best if you know the metrics and then search for "attach measurement value" in the selection box. Standard measurement values are, for example, the E-commerce conversion rate or the ROAS in paid advertising. A widget can also compare measurement values. But, the comprehensive filter function, which allows smaller partial amounts to be filtered out of a large number of data sets, may be still more important.

Figure 4: Insert dimensions

With an informative directory structure of the entire website, in this example there are many possibilities to limit partial areas of pages.

After the choice and creation of the first widget, you should also establish the layout. Analytics offers several variants with different distribution on the dashboard area. The dashboard is then structured according to the choice of KPI.

Figure 5: Structuring of the dashboard

While the number of columns is limited to a maximum, the number of rows is variable. However, the character of the dashboard as an overview of the most important control parameters is not adhered to if the dashboard contains more than is visible with one glance at the screen.

Finally, the last significant function for the quality of the dashboard is the use of user segments. The comparison of mobile and desktop users is by now indispensable. In order to use the segment function, you can take hold of standard segments, import segments from the Analytics gallery, or create your own segments in the management overview of Analytics property.

Figure 6: Use of the user segments

Finally, the possibility of dividing the dashboard is very important for being able to implement web analysis in your company. This can be done, on the one hand, through the creation of joint usage, as well as through export as a PDF and sending as an email.

"Make or buy" – the Dashboard Gallery

As described briefly above, you do not have to create each dashboard yourself. The dashboard gallery from Google Analytics is a really helpful collection of dashboards. Authors of these dashboards are such well-known authors as Avinash Kaushik, who regularly makes himself known with particularly original dashboards. You can also find segment templates in the gallery. This is designed to be comfortable and user-friendly.

But it can also lead to you quickly downloading too many dashboards. You should never forget: When it comes to Analytics Dashboards – less is more! You should use only those dashboards that clearly fit your processes. Moreover, you should use only one dashboard for each process – and of course the best.

Conclusion: Data visualization with Tableau or with Google Data Studio?

If you have successfully entered into the contest of the creation of Analytics Dashboards, you wil quickly be faced with further and more complex questions which you cannot readily answer with Analytics alone. For such advanced questions, the powerful visualization tools from Google Data Studio and Tableau are available. Both are therefore interesting, because they make it possible to merge a large amount of data sources.

Thus, the tools fulfill the important data warehouse function at least in the front lines. For presentations and reports in a visualization, the same rules apply as for Analytics Dashboards. Additional complexity arises from the question of whether the linked data are consistent – and therefore comparable. Advantages are the greater bandwidth of the presentation and the possibility of jointly working on dashboards.

Avinash Kaushik shows that the visualization of data has no limits and that visualization can also be a creative process. In his post “Create High-Impact Data Visualizations: Nine Effective Strategies,” you will find data visualization for the advanced. It will pay off to take a look at it.