Through the Looking Glass: Website Reporting Approaches

In this article I will describe some common approaches to reporting and analytics.

If you already have analytics system in place, it is time to think about reporting on your website. In this article I will describe some common approaches to reporting and analytics.

Ad-Hoc Reporting: This type of reports is done only once, usually to answer a research question or after a major update. For example, if you want to find out where the site visitors geographically come from but suspect this will not change significantly month on month. Regular reporting  can be done monthly, weekly or daily to track performance over time.

Reporting by segment summarizes information using the segments you have (see my post on segmentation). Detailed reporting examines the data at page level (this way you can find out how different pages perform and what key user journeys are). Holistic reporting  contains website data in general (for example, you want to display KPIs of the whole site). Most likely you will combine different levels of data granularity in your report.

The reports you produce will differ depending on the stakeholder group you create them for. For example, reports for senior management will demonstrate trends and bird’s eye view of data. Reports for Marketing department may contain numbers on campaign performance, click-through rates and page/product rankings.

Analytics reports can also produce different types of analysis from descriptive to casual. At the same time, the goal of reporting may include increasing conversion, analyzing user journey, leading users to certain pages, increasing time on site, decreasing bounce rates, optimizing campaign performance, understanding SEO or referral traffic, etc.

You will most likely need to decide how to benchmark or evaluate the data. One way is to compare the data to the previous reporting period (e.g. June data to May data). This, however, does not take into account seasonal or weekday fluctuations, which may have a significant impact on performance. Another way is to compare the reporting period to the same period in the past, e.g. June 2017 to June 2016. This will provision for fluctuations, however, will not consider how different the conditions could have been in the past or what overall traffic growth was realized. A third way to approach benchmarking is compare your performance to that of your industry segment or your main competitor (this data will seldom be available). And finally, if a business goal is set, the KPIs will be evaluated against this goal.

I hope this article has given you some ideas what can be included into your reporting. Apart from setting the framework, it is important to provide human and technical resources for successful reporting.

Picture source: Unsplash

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Divide and Rule: Types of Segmentation in Web Anaytics

In order to analyze traffic to your website you need to segment it. A lot of times, possibilities for segmentation will depend on the analytics system you use and the data you track. In this article, I will outline how you can create segments using the data commonly provided by analytics software. I will also evaluate, to what extent you can make use of the created segments.

First group of segments: user-based

Geo and tech characteristics of users belong to this group. If you have a way to find out the gender or the age of your users (registration, dialog windows), you can segment visitors even more granularly.

User-based segments: by device type.

Geo-segmentation includes segmenting by cities, regions, countries as well as browser languages.

Geo-data is usually collected based on user IP. Although this information seems to be interesting (often presented in form of maps), it is only relevant if you offer a location-based product/service (e.g. only available in a few cities) or if your website is localized for several countries and you want to make sure that international users are routed to the correct country site.

Tech segments are based, for example, on device type or browser name and version, as well on as the user domain. From my experience, these parameters are not going to be used extremely often either. If you get a significant number of visitors from a certain device or a certain browser,  you should better make sure that your site works on them. Other than that, it useful to group sessions where an HTTP or a JavaScript error occurred (if an error is recurring and you can reproduce it, a fix is needed).  Additionally, IP address or user domain name are used to exclude internal traffic (i.e. the traffic from your company).

Second group of segments: content-based

As can be guessed from the name, such segments relate to the content of the site. In other words, by applying this segmentation, you can see what happens on certain parts of your website or on groups of pages. If set correctly, this gives a structured and precise insight into the website performance.

Some ways to apply this segmentation are:

  • By product (if you have a few products, e.g. 1-5)
  • By brand or by product type (if your website showcases a lot of products)
  • By department/division (if this is how your site is organized)
  • By content for different customer groups (gender, age, type of customer: business or private…)
  • By site component (forms, product description, FAQ…)
  • By country site (if the data is not split in the database)

This is by no means an exhaustive list. By looking at your site, you may come up with a more suitable (for you) way to segment content. Then you can use the grouping, for instance, to compare how different products perform or to create a product ranking by popularity/engagement.

Third group of segments: traffic-based

This type of segments will contain general break-up of traffic, without precise description of particular users. The most common segments will include: by traffic source, by referring domain, by time of visit (day of the week, hour).

New vs repeat/returning visitors segment is based on the cookie set on a visitor’s device (however, as a growing number of visitors do not accept cookies or delete them regularly, the data from this segmentation should be treated as an approximation).

Such segments are extremely useful in order to analyze how the performance changes depending on the type traffic flowing to the site. What is more,  traffic type will most probably have a larger impact on performance rather than user-based characteristics,

Fourth group of segments: interaction based

This is a large group of segment that describes how visitors interact with the site.

You can create segments by number of page views per visit (page depth), by bounce rate, by time on site, etc. For the majority of these parameters, you will need to come up data ranges, e.g. number of page views per visit may be split into the following segments: 1 view, 2-3 views, 4-6 views, 7 or more views. Besides, you can include events – such as forms submission,

This type of segmentation will enable you to start the analysis “from the other end”, e.g. what are the traffic sources or pages in best performing segments.

As you can see, applying correct segmentation to site traffic will provide you not only with more data but also with more insights into what influences the amount of traffic and its performance over time. But what if you feel that besides segmenting you need to group and generalize traffic somehow? This would be a clear case for cohort analysis, which I may talk about in one of the next posts.

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360 Degrees: Types of Data Analysis

According to J. Leek, there are six data analysis questions we can ask. Let us look at these questions in detail. As an example, I will use a website of a marketing agency. The conversion goal of this website is the contact form that users fill out and send. We want to ask questions about the conversion.

Descriptive analysis: What is..?

Here the goal is to describe a set of data, without inferring anything from it or making a prediction. It can be performed by itself or be a starting point of a more in-depth analysis. An example in case of an agency would be the number of forms sent or the conversion rate (e.g. the number of submissions divided by the number of page visits).

Explorative analysis: Where is..?

This analysis looks at the data more deeply, discovering the connections between different variables. However, this cannot be used for prediction or does not necessarily imply causation. In the example above, exploratory analysis can be used to look for connection between form submissions and day of the week or where the form was placed on a page.

Inferential analysis: Who is..?

In this type, we infer about a larger group of users from a small group. E.g. we can set up a user survey or conduct focus group research with some users to find out how they interact with the site and the contact form in particular.

Predictive analysis: What will be..?

This analysis makes prediction about future occurrences based on some known variables. For example, we could predict the fluctuation in the number of form submissions according to the day of the week. Prediction should not be confused with causality, e.g. if there is a peak of form submissions on Monday, it does not mean that Monday causes form submissions.

Causal analysis: Why is…?

This is used to define one-to-one relationships between different variables. Causality will almost always look at the average cases, so some outliers or different behaviors should not be excluded. As an example, placing the form at the top of the page will usually increase the conversion, as opposed to placing it below-the-fold. Here, a better form placement actually causes more visibility and increases the chance of a submission.

Mechanistic analysis: How is..?

This a rare and difficult type of analysis that allows for understanding how exactly variables influence each other in individual objects. This would often bring the analysis of data down to the set of equations.

All in all,  you will need to perform different types of analysis of the raw data to get a clear picture and come up with a set of recommendations.

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Data at Your Fingertips: How to Design a Dashboard

Data dashboards are a way to represent large amounts of data in a condensed and visual manner. A dashboard typically consists of widgets or reportlets. Each of widgets includes one or several KPIs, supplemented by graphs, small data tables, etc.

If you are supposed to prepare a concept for a dashboard, the first task is to find out the stakeholders’ requirements. Who are you building the dashboard for? What data do they need to see, what insights do they want to gain and how detailed should the information be? E.g.  the dashboard for the marketing department will substantially differ from the dashboard you are building for the CEO of the company.

Secondly, prepare a rough draft of the dashboard layout and the widgets it will have. Try not to include more than 10-12 widgets, otherwise your dashboard may become too cumbersome to view and understand. This is also where you define what KPIs the dashboard will show.

Rough draft of a dashboard.
Rough draft of a dashboard.

Thirdly, decide what time period will be included. The majority of dashboards will show only today’s or this week’s data. However, if the website has significant monthly or daily fluctuations in performance, you may want to include a more extended period of time. If stakeholders want to use the dashboard for performance monitoring, the data naturally has to be real-time or with a minimum time lag.

In the fourth place, consider how to represent the data you will gather. A widget may contain a number, a graph, a data table, or a combination of these. The graphical representations of numbers is the most visual way that allows to grasp the meaning of data within seconds. Choose the type of graphics wisely: a line graph will show the development of KPIs in time, the pie chart will show what percentage each segment or product contributed to the total and a bar chart is a good way to visualize and compare several dimensions. The picture below shows how different data may be visualized. In addition, you can use scattergrams, process visualizations, bubble diagrams, etc. In any case, make sure that your graphs are not cluttered and convey a meaningful story.

graphics on a dashboard
Examples of graphics.

numberIf you choose to use a data table in your widget, select only top 5 entries and do not include more than three  columns. In addition, do not be afraid to put a single number in your widget, in case this number is important.

The next decision is what technology you will use. The majority of modern analytics systems include a dashboard feature. If the feature is not sufficient for your requirements or is missing altogether, you may consider using specialized data visualization software (in fact, even Excel offers dashboard building). The best solutions will be those that allow for automatic export and processing of data, without much manual work. Ideally, you should be able to pull data from several sources – e.g. from the website itself, from the order processing system and from social media.

Also think how you will share the dashboard with stakeholders and if the system provides you with this option. Some will prefer viewing the real-time data, others will be satisfied with a weekly/monthly report in PDF format.

And finally, create a visual mock-up of your dashboard for the stakeholders before starting to implement your concept.

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10 Best User Features of Adobe Analytics

Adobe Analytics (former: Omniture) is a powerful Web analytics tool that provides a user with information of different levels of complexity and for different purposes and contains several useful features.

Adobe Analytics (former: Omniture) is a powerful Web analytics tool that provides a user with information of different levels of complexity and for different purposes. After having been working with this solution for three months, I can point out its ten best user features.

Feature one: flexibility in adding metrics. All reports already contain data, but Adobe Analytics allows you to select additional metrics for most reports. Those include: bounce rate, visits, visitors, exits, entries, etc. In addition, you can create calculated metrics from existing ones, using a variety of mathematical functions.

Feature two: segment creation. Segments basically filter data by different parameters, e. visit number or device type. To create a segment, select the parameter and one of the following: value equals/does not equal, value exists/does not exist, value is larger than…, smaller than..., then enter the value. You can segment your audience on hit, visit, or visitor basis. Examples of segments: all page URLs that contain “product”, all visitors with more than one visit, all visits from mobile devices, etc. You can also compare segments— display the metrical data from two segments side by side and see the difference in percentage. E.g. you can compare users of smartphones to users of tablets.

Adobe Analytics Adjust Chart Type
Visualization in Reports

Feature three: visual representation. Every report includes a graph that can be either hidden or adjusted: the chart type can be changed, metrics can be selected or deselected or even split to be displayed on two different graphs. Data visualization helps you to perceive the trends or shares at first glance.

Feature four: Favorites and Bookmarks. Favorites function lets you save the created reports in your personal account and display their names on the right-hand side when you log in. Bookmarks is a similar function, however, you can share the bookmarks with other users of Adobe Analytics reports. One more way of sharing is a report is generating a short link to it and sending it per email.

Feature five: Dashboards. You can include any existing report into a custom dashboard. You can arrange repotlets in the dashboard the way you need and select if only the graph, the data table or both should be displayed. The dashboard allows you to set rolling or fixed time range for all the reports it includes. It is also possible to print, share and download dashboards. Dashboards are a convenient way to to summarize the performance of your website and monitor how business goals are being met.

Download Options

Feature six: download options. Each report can be downloaded and saved in different formats, e.g. XLS, CSV, PDF, etc. You can set up automatic download and forwarding of a certain report to a stakeholder.

Feature seven: pathing reports. There are several reports in Adobe Analytics that enable you to record and analyze the user journey on the site to improve its usability. The most flexible reports are Path Finder and All Paths. In Path Finder, you can select user journeys that start with, end with or contain any page you are interested in. All Paths report shows user journeys on the site for a selected period, and you can additionally filter the report by path length.

Feature eight: Activity Map. This browser extension is easily installed and visualizes user activity on a given page. It records what links on a page were clicked within a reporting period and presents the data as bubbles with numbers or as a heat map. This is especially useful if you want to investigate where to place the most important information on your page or how the links placed “below the fold” perform.

Feature nine: Workspace. Workspace is a tool that allows you as a user to create customized reports and visualizations as well as print and share them. For example, you can select any segments and present them on a Venn diagram or a doughnut chart. Other diagram types include: bar charts, scattergrams, cohort tables, etc. Workspace gives you immense flexibility in working with data and presenting the results to stakeholders.

Feature ten: Targets and Alerts. Target empowers a company to access its current performance against a business goal (e.g. a desired number of visitors or page views). Adobe Analytics will generate a  report that shows how the target is being met. Alerts, once you set them up, is a basic way to oversee the stability of the system. Should  the main indicators fall below or rise above a secure span, the system will send out an email alert to the specified users.

Adding an Alert

As you see, Adobe Analytics offers a number of possibilities for deep-dive into the data. However, due to its complexity and cost, this software is only useful for large companies with significant amount of traffic on their website.

All Images: Adobe Analytics (screenshots and featured images).
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