The metrics that Google Analytics and Google AdWords can't measure alone

Have you ever heard the quote "if you don't measure, you cannot improve"? This is something that our team here at Internetrix swears by. After all, measurement is critical to success.

The majority of online businesses take advantage of tools such as Google Analytics and Google AdWords to monitor their performance and set their strategies. Why? because these tools enable businesses to obtain vital information about the status of their websites by measuring several factors. These factors include:

  • audience demography
  • audience engagement
  • traffic sources
  • site contents
  • search behaviour
  • events
  • funnel visualization
  • products' performance and so on.

However, whilst the default metrics in Google Analytics and Google AdWords are undoubtedly valuable, our understanding of our business is incomplete unless we consider some additional metrics. There are some insights that these analytics platforms are currently unable to measure. These additional metrics help us not only put the Google Analytics and Google Adwords metrics into context but also help us make a connection between them to make more informed decisions about our digital presence.

What metrics are missing in Google Analytics?

In this article, firstly, a comprehensive framework that demonstrates the essential aspects of a business is introduced, and secondly, some of the key metrics in each aspect is summarized. This framework will consider 7 factors:

  1. How many users are available in the market for your products and services? (Market)
  2. Who are the users come across the website brand? (Impression)
  3. What proportion of people visited the website? (Traffic)
  4. How well are the users getting engaged with the brand? (Engagement)
  5. Which customers interactions are leading to a conversion? (Conversion)
  6. How well are we retaining customers and where can we improve? (Retention)
  7. How do customers perceive our brand? (Advocacy)

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Before going to the details of each of these factors, four important points should be considered:

  • Some of the metrics might not be related to a particular business.
  • We focus on the metrics that do not exist explicitly in Google AdWords and Google Analytics and often ignored by the businesses.
  • We only provide some simple formulas that can be used by marketers without statistics and mathematics background.
  • It is assumed that you are already aware of Google Analytics and Google AdWords metrics and you are are looking for an additional level of complexity to have a more comprehensive understanding of your website.

Market

Firstly, we are interested to know, how many users are available for our product and services. In order to answer this question, we measure the following metrics:

  • The number of related search in Google Trend and its monthly growth.
  • If the number of searches in Google Trend is correlated with the number of the audience?
  • Real Advertising spending (RAS) : \(Net\ Media\ Investment + Agency\ Fee\)

Impression

How many people have come across our brand? In order to answer this question, we need the measure the following metrics:

  • Average Impressions from Google Adwords and monthly growth of this impression
  • Average Impression from Search Console and monthly growth of this impression
  • Average Impressions from Facebook and monthly growth of this impression
  • Average position in Google search during month and monthly growth of this position
  • Average Youtube Views and monthly growth of this view
  • Average Facebook Video Views and monthly growth of this view

If a guest blog is used, the following metrics also should be measured:

  • Total Number of Monthly readers
  • Unique Number of Monthly readers
  • Email list size

Traffic

In order to know how people visited the website, following metrics should be measured:

  • What is the degree of matching of keywords and search query? Is it improving or not.
  • Avg. Cost per Session: \(Real\ Advertising\ Spending\ (RAS) / Sessions\)
  • The transition probability of a channel to another channel: This can be measured using different approaches. By using Markov chain approach the probability of going from state \(i\) to state \(j\) in \(n\) time step is

$$p_ =Pr (X_ j | X_0 = i)$$

Engagement

What portion of visitors who visited our website, engaged with both our brand and our website? The Google Analytics metrics focus on the engagement with the website. In order to have a better understanding of the engagement of our users with our brand measuring the following metrics can be valuable:

Engagement with Brand

All the Google Analytics metrics focus on the engagement with the website. In order to have a better understanding of the engagement of our users with our brand measuring the following metrics can be valuable:

  • Email CTR : \((Total\ Clicks / Total\ Email\ Delivered ) * 100\)
  • Average Likes per post (Facebook)
  • Average Likes per Post (Instagram)
  • Average Favorites per post (Twitter)
  • Average Likes Per Video (Youtube)
  • Average shares per post on Facebook: \(Total / Posts\)
  • Average Retweets per post on Twitter
  • Average Comments per posts on Facebook
  • Average Comments Per post on Instagram
  • Average Replies per post on Twitter
  • Average Comments per video on Youtube
  • Average Click Per post on Facebook
  • Average Click per post on Instagram
  • Average Click Per post on Twitter
  • Average Click per post on Youtube
  • Email List Growth Rate: \(Total\ New - Unsubscriber\)
  • Email Bounce Rate

Engagement with Website

In order to know what our customers are doing on the website, the following metrics can provide insight:

  • Events Per User : \(Total\ Events / Users\)
  • Javascript Error per visits
  • 404 Errors per visit
  • Average load time for each browser
  • Percentage of users who spend more than 2 minutes on the website
  • Percentage of users who spent more than 2 minutes
  • Percentage of users who visit more than 4 pages
  • Total users who visited more than 4 pages
  • The percentage of pages with more than 50 % exit rare
  • Pages with more than 50% exit rate

It should be noted that the numbers presented here ( such as 2 minutes, 4 pages and 50%) are just examples. These numbers should be calculated for each business specifically by using some statistical techniques.

Conversion

Which Customers Interactions with our brand is leading to a sale? In order to answer this question, the following metrics should be measured:

  • Conversion per store
  • Total Sale per store
  • Revenue After Refunds: \(Revenue - Refund\ Amount\)
  • Transactions Per User: \(Transactions / Users\)
  • Effect of PPC on Organic Search Traffic
  • Effect of Organic Search on PPC traffic
  • Effect of Organic Search on Direct Traffic
  • Effect of Display on Organic Search Traffic
  • Effect of Social Media on Organic Search
  • Effect of PPC on Direct Traffic
  • Effect of Social Media on Direct Traffic
  • Effect of Email on Direct Traffic
  • The total conversion of each channel (Attribution modelling)
  • The value of each channel (Attribution modelling)
  • The total conversion of each source/medium (Attribution modelling)
  • The value of each source/medium (Attribution modelling)
  • The total conversion of each campaign (Attribution modelling)
  • The value of each campaign (Attribution modelling)
  • The incremental probability of adding a channel to a customer journey on conversion (Attribution modelling)
  • The incremental probability of adding a channel to a customer journey on conversion for different segments (Attribution modelling)
  • Recommendation system based on the correlation between products. Which products should be suggested to a customer?

Attribution modelling that is written for some of these metrics is a general topic and beyond the scope of this article. It should be noted that data-driven attribution modelling in Google Analytics like anything else has both advantages and disadvantages and one should be careful to avoid misinterpretation of the data. As we explained here, digital advertisers use the last click attribution model to evaluate the success of campaigns. In other words, during the customers’ journey and among different touch points or ads which impressed the customers, only the very last event is considered as significant while the others are ignored. This is simple and easily implemented but, in most of the business cases, it doesn’t help in making smart marketing decisions. Alternatively, we should use a data-driven model that considers all the paths in a customer journey and uncover the real value of our marketing campaigns and channels.

Retention

How well are we retaining customers and where can we improve?

  • Customer churn rate (CCR) is the percentage of customers who stopped making purchase that can be calculated as follows

$$CCR= \frac {(Number\ of\ Customers\ Begining\ of\ Month) - (Number\ of\ Customers\ End\ of\ Month)}{ Number\ of\ Customers\ Begining\ of\ Month}$$

  • Repeat purchase probability (RPP) is the likelihood of a customer making another purchase. Instead of X in the following formula, we can have different numbers. For instance, x=2 tells us the percentage of customers who had two repeated purchase during the last year.

$$RPP=\frac{ Number\ of\ Customers\ who\ Purchased\ x\ Times}{(Total\ Number\ of\ Customers} $$

  • Profitability per order (PPO) shows you how much profit we are making on each purchase and it can be calculated as follows:

$$PPO= \frac {(Total\ Revenue) * (Average\ Profit\ Margine)}{ Number\ of\ Orders}$$

  • Purchase frequency (PF)It refers to how often the average shopper makes a purchase.

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  • The time between purchases (TBP) shows you how many times a year a customer completes a purchase:

$$TPB= \frac { 365\ days} { Purchase\ Frequency}$$

  • Customer value (CV) is the value of a customer in a particular date range and can be calculated as follows:

$$CV= Average\ Order\ Value\ *\ Purchase\ Frequency$$

  • Customer Lifetime Value (CLV) is a projection of how much revenue the average customer will bring you over the course of their relationship with your brand.

$$CLV = Customer\ Value\ * Probability\ of\ Purchase$$

  • Redemption Rate (RR) refers to the percentage of loyalty rewards that are being redeemed.

$$RR= \frac { Number\ of\ points\ Redeemed}{ Number\ of\ Points\ Issued} $$

  • Customer Retention Rate (CRR) is the percentage of customers staying with you over time.

Alternatively, we can use this formula:

$$Retention Rate= \frac { Number\ of\ Customers\ who\ Shopped\ 6-12\ Months\ ago\ and\ Shopped\ within\ Last\ 6\ months} { Number\ of\ Customers\ who\ shopped\ 6-12\ Months\ ago}$$

  • Likelihood of Retention for each customer segmentation: Tell us which customers are more likely to return to the website.
  • Predictive analysis about which customers should be retargeted

Advocacy

How do customers perceive our brand? It is important to ask the opinion of users to truly gain an understanding their expectations, perceptions, satisfaction and areas for improvement. There are two potential metrics that could provide insight into advocacy.

  • Net Promoter Score is the opinion of our customers that are gathered by surveys $$The\ Percentage\ of\ Promoters - The\ Percentage\ of\ Detractors$$
  • Comments on Social Network Website that are gathered by some text mining approaches $$The\ Percentage\ of\ Promoters - The\ Percentage\ of\ Detractors$$

Key Takeaway

If there is one thing to take away from this article it is that Google metrics are undoubtedly powerful, however to have a complete understanding of your website, you need to add one more layer of detail to the metric these platforms have to offer in order to truly understand how your users are connecting with your brand and their customer experience journeys. Whilst Google Analytics and Google Adwords, currently aren't able to provide you with the whole story, the metrics as discussed above should be able to help you start 'filling the gaps'. This is not to say, that you should abandon your Google products, but more so to say that the data collected from these platforms can be leveraged and enhanced with the use of these metrics.

At internetrix, we believe that it is necessary to go beyond straight numbers. Our User Experience and Optimisation services take a holistic approach and look to measure the trust and emotional connections of your customers with your brand.

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