Developing analytics for web domination involves developing insights related to your online performance then using those insights to optimize the performance of your website to grow and excel in reaching your goals. Unfortunately, most businesses fail to get insights from their web analytics that improve their business decisions. A whopping 98% of businesses in a recent survey said they’re not getting what they’d hoped from the marketing analytics, including web analytics.
Any number of reasons account for this failure, the most common reason businesses fail to use analytics for web domination involves deriving meaningful insights from raw data. Fixing this problem requires analysts and business owners to determine:
- What data is important for developing insights that guide decision-making
- The terminology used by Google Analytics and other tools so you can draw accurate conclusions related to the metrics
- Where to find those metrics
- Putting metrics into context, ie. target market segmentation
- How to develop insights from raw data
- Avoid misinterpretation
- Believing data imply truth; ignoring data quality issues
Decisions based on flawed data or inappropriate interpretations of that data offer the potential (likelihood?) for catastrophic consequences for the organization. Hence, today, we’ll go through each of these factors to provide information to guide you toward better insights.
1. What data is important for insights
A quick trip over to Google Analytics shows the vast amount of data available on the platform. Connecting the platform to Google Ads and the Google Search Console (formerly Google Webmaster Tools) further expands the amount and complexity of data shown on the platform.
Much of the data shown on the platform doesn’t directly correlate with business success. Some metrics, often termed vanity metrics, only loosely correlate with business success. Sure, they deserve some attention but building your digital marketing strategy around these metrics doesn’t deliver the biggest bang for your buck and may result in declining profitability versus increasing your ROI. Unfortunately, far too many businesses focus on these metrics; ignoring more substantive metrics that have a big impact on performance, we call these actionable metrics
Among the metrics experts define as vanity metrics, page visits and page views are the most commonly cited. Of course, web analytics isn’t alone in mistakenly driving attention to vanity metrics, so metrics such as followers, subscribers, and customers fit that definition, as well.
Obviously, the most important metrics to include in analytics for web are those leading to conversion, either directly or leading to conversion, see the conversion funnel below for some idea of metrics that lead to conversion.
This funnel shows the typical process for an e-commerce site where a purchaser goes through several steps prior to consummating the sale, with a sizable dropoff at each step in the process. A high priority involves determining WHY visitors drop off at each stage in the process is critical for success.
In answering this question, you use the data but you must go beyond by conducting experiments to determine the effect of changes on performance.
Here are among the first things to consider as you create these experiments based on findings from other businesses:
- Removing clicks in the process. Amazon dramatically increased its conversion rate by going to the 1-click process using stored information, such as credit card numbers and addresses, so customers only required a single click to complete a sale.
- Eliminate requirements for registration by offering guest purchases
- Get rid of unnecessary data collected during the checkout or registration process. The criterion should be which data are essential and which data are nice to have. Collect “nice to have” data after consummating the sale via an email sequence, phone follow-up, or some other means.
- Institute cart abandonment strategies, such as exit intention forms to encourage visitors to finish the buying process when potential abandonment is detected. Some websites routinely offer exit intention coupons in a popup to encourage customers to make the purchase, others use a fallback offer (something many salespeople recognize as a fruitful strategy) to collect email addresses and later follow up with an offer.
Every business lists conversion as one of its highest goals. In addition, businesses likely have other goals as well as KPIs (key performance indicators) associated with those goals. Goals may relate to sales increases for specific products or product lines, improving the performance of one or more channels, or other goals specific to the business that you measure through your web analytics.
2. Terminology used by Google Analytics
Google has a unique terminology used to identify metrics across its various platforms. For instance, sessions refer to visits to the website. If you want a great resource defining terminology used by Google Analytics, check out this post or this more comprehensive post billed as the ultimate glossary of terms.
3. Where to find key metrics in Google Analytics
Analytics for the web come mainly through Google Analytics, supplemented by Google Search Console and, maybe Google Tab Manager. The best tool for finding metrics related to your KPIs is a dashboard and Google Analytics comes pre-loaded with multiple options for dashboards or you can create your own. Dashboards are flexible, offering options for custom data ranges and allow for comparisons across different time periods.
In fact, the home screen (shown below) is a dashboard containing metrics Google finds valuable to most users [click the image to expand].
By choosing the customization option, just below the home link in the left-hand menu, you can import, choose, or create your own dashboards. Currently, 6 dashboards are available including those assessing SEO, e-commerce, and audience snapshot. A number of analytics experts across the web offer optional dashboards you easily add to Google Analytics. This post provides some options for dashboards and shows you how to import the dashboard into Google Analytics with just a few clicks.
In addition, a host of reports, a custom reporting option, and Google Data Studio offer a wealth of options for businesses to collect and visualize metrics in a manner that addresses their individual corporate needs.
4. Putting metrics into context
Often, point metrics don’t capture the full impact of the data on insights but require context to drive insights. One of the major aspects where this is true comes through analyzing who does what, which involves adding segments that parse data into meaningful groups to answer questions such as:
- How does my conversion rate for millennials compare with the conversion rate for users over 55?
- Do consumers in one geographic location prefer one type of product over another?
- Which channels bring in visitors who convert at the highest rate?
- Determine which devices account for the highest bounce rates (a measure implying mobile performance).
- Which posts or ads performed best based on using Tag Manager to create custom links?
- And, many more such questions.
Answering questions such as these provide indications of problems and potential. For instance, if performance excels among millennials, crafting messages targeting this group offers the highest return while a high bounce rate among mobile users compared to desktop users indicates a need to improve your website’s look, feel, and performance on mobile devices (something you can test using the Google Search Console).
5. Developing insights from raw data
Raw data doesn’t do much value to an organization regardless of how it’s analyzed, visualized, or presented. Insights come through a combination of raw data, assumptions, and an understanding of the business and its customers. Developing insights is where the art of data analytics comes into play more so than the technical mechanics of data analysis.
Consider this quote from Google:
In 1910, Scottish writer and poet Andrew Lang said, “He uses statistics as a drunken man uses lampposts—for support rather than illumination.” Decades later, many modern businesses still do just that, using data to support rather than drive their decisions.
Deriving insights requires analysts to tell a good story with their data, not merely present the data as pretty visualizations, although that helps develop the story. The first step in developing great stories about your data involves filtering out the noise to focus on meaningful metrics, such as described in our section on dashboarding above.
Next, the analyst must sort the data based on its impact on performance metrics so the most critical elements for success are grouped together. The next step involves segmenting your data, as mentioned earlier in this post. Finally, visualizations help build an interesting story drawing parallels between your data and the implications for performance. The right visualizations make developing these stories easier while the wrong visualizations obscure insights.
Of course, it doesn’t nothing to support business performance if you don’t employ insights in making decisions. The goal isn’t to measure, visualize, and explain your data, it’s to improve performance.
6. Avoid misinterpreting data
This is a classic rookie mistake, attempting to develop insights unsupported by the entirety of the data (not just some isolated metrics) or not understanding the meaning of the data. For instance, bounce rate refers to visitors who leave after viewing a single page but I’ve encountered many businesses who thought the metric referred to visitors who left after a certain amount of time on the site — which is time on site.
In fact, time on site might be a great indication you’re providing value to visitors while a high bounce rate suggests the opposite. Alternatively, you may want to drive visitors to the next page quickly if the page is part of the conversion process, thus resulting in a short time on site. A short time on page means your instructions and requests for input were effective in driving visitors to the next step in the conversion process. The fix for a high bounce rate may mean creating more value, but likely it means creating better internal linking to offer visitors additional pages to satisfy various aspects of their interest. The fix for time-on-site depends on its impact on conversion as people need to spend as little time making a purchase as possible in many cases.
For example, you might improve a high bounce rate by offering additional product pages for products similar to the one driving the initial visit.
7. Data doesn’t imply truth
First, as mentioned several times in this post, data doesn’t tell you anything. Data are just numbers. Truth comes from understanding the numbers: where did they come from, what do they mean, how do they impact your goals.
A more insidious problem involves the veracity of the data and not all data reflect the truth. For instance, I commonly find there’s a slight discrepancy between the number of clicks recorded in Google Ads, and thus my cost, and those reflected in Google Analytics as coming through Google Ads — I’m commonly charged for more clicks than I detect in Google Analytics (putting your Google Analytics tracking code in the <head> section of each page helps reduce this difference as the head loads first). This seems to happen most of the time and makes me nervous as I like my data definitive. I asked Google for an explanation and they never gave me one that makes sense. To the extent that the missing clicks showing up in Google Analytics are random, the difference doesn’t matter much as analytics for web and other channels involve primarily comparing performance, ie one day versus the next; one channel versus the others. However, if the differences aren’t random but systematic, then the difference potentially impacts any truth derived from the analysis.
In a perfect world, data quality wouldn’t be an issue and all data would be perfectly accurate. Unfortunately, we don’t live in a perfect world and even analytics for the web suffer some data quality issues. At this point, we must simply accept the data quality problem and assume that quality issues affect all metrics to the same degree, thus making comparisons valid even if point data are off a little.
I hope you find these tips for creating analytics for web domination helps as you try to use your Google Analytics data to create more substantive insights designed to improve performance. If you have questions or comments, feel free to add them to the comments and I’ll answer them to the extent of my knowledge. Also, use comments if you have suggestions for future posts and be sure to follow us on social media.
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