The role of leadership and strategy in digital transformation

We live in a rapidly changing world. Every part of our society is undergoing fundamental transformation driven by a fast-paced digital revolution. The revolution started more than a decade ago but is now accelerating and becoming more and more overwhelming.

We feel it especially in the business world. New business models are wreaking havoc across all industries. Google is transforming the advertising world, Amazon is transforming the publishing sector, Netflix is transforming the television industry, Airbnb is transforming the hotel business and Uber is transforming taxi services.

The list goes on and yet it is only the beginning. Emerging technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), machine learning, cloud computing, blockchain and 3D printing are giving rise to new amazing products: Self-driving cars, health-monitoring wearables, robotic surgery, precision agriculture, smart factories, etc.

With this tech wave coming at them, business leaders around the world find themselves at an inflection point. They are grappling with the implications of the digital revolution and feel an increasing pressure to act now – either to prevent disruption or to seize a competitive advantage.

Opportunities and threats

According to research, it is indeed important to act now. Companies that embrace new digital technologies combined with strong leadership tend to outperform their peers. They are 26% more profitable and generate 9% more revenue than their average industry competitor (Westerman et al. 2014).

If tackled right, digital transformation presents a tremendous opportunity for almost any type of company. A passive approach, on the other hand, poses the risk of disruption, leading to declining market shares or even bankruptcy.

A good example of the risks involved is the recent transformation of the video business. Netflix entered as a challenger to Blockbuster in the late 1990’s offering DVD subscriptions based on physical delivery. In the early 2000’s, however, the company switched to video on-demand, taking advantage of the improved internet bandwidth.

The growing popularity of streaming video coupled with lower costs turned Netflix into a deadly competitor. After years of declining income and many desparate attempts to catch up with Netflix, Blockbuster closed its remaining 300 stores in 2014 (Rogers 2016).

How to tackle the digital revolution

To take advantage of digital technologies, it is obviously necessary to invest in them. Investment, however, is not enough. Strong leadership is also needed. Without leadership, companies risk implementing trendy, but incompatible, platforms or services that make it harder to achieve true digital mastery.

Strong leadership means, first and foremost, commitment from top management. Chief executives must agree to make the digital journey a core part of the business. They must follow up by setting direction, building momentum and ensuring backing from the entire organization.

According to Ross et al. (2016), leaders should take their organizations through at least the following three steps:

  1. They should craft a compelling digital vision, which motivates employees, informs strategic planning and guides day-to-day decisions across the organization
  2. They should implement an operational backbone capable of serving as the single source of truth (i.e. integrating customer data, product data, transaction data, etc.)
  3. They should develop innovative digital services (websites, mobile apps, etc.) on the basis of the operational backbone

In doing the above, companies may use in-house developed platforms, purchased platforms or cloud-based platforms. The key to success, however, is integration of these platforms.

Crafting a digital vision

A digital vision is perhaps the most important part of the digital journey. It requires not only an understanding of what is technically possible, but also the ability to discover new business opportunities on the basis of this insight. It is not the technology itself that matters, but how it is used to create a strong value proposition in the context of a business plan.

Digital visions are best understood through examples. However, they come in many different forms and are difficult to categorize, let alone conceptualize. Some focus on improved customer relations, some on streamlining operations, some on transforming products and others on creating completely new business models.

The proliferation of digital visions is driven by the development of technology, which continues to create new opportunities. As an example, Artificial Intelligence (AI) is now giving rise to new digital visions in the retail sector. This is illustrated by the competition between Nordstrom and Stitch Fix.

Nordstrom: Bridging the on- and offline world

Nordstrom is a large fashion retailer in the US that uses technology in a clever way to improve customer service. At the core of its strategy is a reward program linked to a payment card that tracks spending and builds detailed customer profiles. Nordstrom can see how often each customer shops, what he or she buys, his or her favorite brands, etc. The data is collected, integrated, analyzed and used in different types of application.

For example, using a personal-book app integrated with Nordstrom’s Point of Sales (POS) system, shop assistants in physical stores can look up a customer’s unique profile when he or she asks for help. After the store visit, the assistant can even use the personal-book app to send emails to the customer about new arrivals or upcoming sales events.

PersonalBookNordstrom’s Personal Book (Sreekanth 2018)

Nordstrom keeps customers engaged beyond the physical store. Customers can log in to Nordstrom’s website or use its mobile app to access styling tips as well as tools that facilitate physical shopping. For example, using the mobile app they can look up availability of a product in a specific store, order the product for pick up or have a shop assistant reserve it for trial in a fitting room.

Nordstrom’s digital strategy has required significant investments, but is not based on the most advanced technology available. Rather, it is guided by a clear vision of excellent customer service. The purpose is not to replace human work, but to help shop assistants become better at what they do in the physical stores. Every technological investment the company makes seems to be based on how well it fits into this vision.

Over the years, this approach has helped Nordstrom build strong loyalty and become a multi-billion dollar corporation.

Stitch Fix: Machine learning with a human touch

While Nordstrom continues to invest in technology and uses it in a sophisticated way, it faces a growing competition from new digitally savvy entrants. A case in point is Stitch Fix, a fast-growing fashion retailer in the US, which uses Artificial Intelligence (AI) to scale personalized customer service.

The vision of Stitch Fix is similar to Nordstrom: It wants to provide excellent customer service on the basis of a deep understanding of individual customer profiles. But there is a difference in the company’s business model and the way it leverages technology.

Stitch Fix operates purely online to reduce operating costs. Like Nordstrom, it uses humans (called “stylists”) to assist customers choosing the right clothes. As opposed to Nordstrom, however, Stitch Fix uses AI to help stylists keep track of customer profiles and to deliver personalized recommendations.

Customers start by filling out an online style profile, which provides basic information on preference, size, shape, budget and lifestyle. Next, both humans and machine handpick a selection of five clothing items that match each customer’s profile. Finally, customers receive the clothes by mail, try them on at home, buy what they like and return the rest.


The choice of customers – what they buy and don’t buy – is registered by Stitch Fix and fed back into the learning algorithms of the recommendation engine. This enables the engine to learn more and more about the taste of the individual customer.

However, the engine also learns from Stitch Fix’s entire customer base. It uses advanced algorithms to learn which types of clothing combinations customers tend to like. As the customer base grows, more and more successful combinations are added to the machine’s memory. By cross referencing this memory with the customer’s unique profile, the machine is often able to recommend new matching items that surprises, but also inspires, the stylist.

Stitch Fix is much smaller than Nordstrom, but its heavy reliance on data science has given it a strong foothold in a specific market segment: career-oriented millennials who have little time for shopping, but who still care about clothes and appearance.

The threat of disruptive innovation

For now Stitch Fix doesn’t seem to be much of a threat to Nordstrom, or to any other large fashion retailer for that matter. After all less than 3% of US consumers have ever tried Stitch Fix, whereas more than 33% have shopped at Nordstrom. Moreover, Stitch Fix operates at the fringe of the market and doesn’t seem to attract customers from traditional fashion retailers (Liverence 2018).

In a longer term perspective, however, Stitch Fix may well turn out to be what Christensen (2015) calls a “disruptive innovator”. Disruptive innovators start by targeting segments overlooked by incumbents. Their offerings typically provide unique benefits to their target groups, but do not appeal to mainstream customers. For this reason, incumbents tend to leave them alone. As their products or services mature, however, disruptive innovators begin to move upmarket, targeting mainstream customers while preserving the advantage that drove their early success. When they start winning over mainstream customers in volume, it is too late for incumbents to retaliate and disruption has occurred.

Although Stitch Fix isn’t yet targeting mainstream customers, it is quietly building a strong competitive advantage in an area of the market, which large fashion retailers don’t care much about. Using an advanced emerging technology mastered by few people in the word, it is not only accumulating data about fashion preferences, but also developing a method for turning this into successful recommendations with the help of human stylists.

It would be unwise for Nordstrom and others to ignore this. If Stitch Fix’s success continues, it may well decide to move upmarket and target mainstream customers by, for example, opening physical stores in addition to its online presence. The preference data it has accumulated, the learning algorithms it has developed, the network of stylists it has built could easily be leveraged in the context of physical stores.

This would unlock demand from mainstream customers and pave the way for a deadly attack on incumbent fashion retailers. Regardless of the response of these retailers, however, Stitch Fix is unlikely to enjoy an unobstructed path to success. A major threat to its business model is the continuous growth and diversification of Amazon – itself a recent entrant well positioned to disrupt the retail sector by means of innovative technology.


Christensen, C. M., Raynor, M. E. and McDonald, R. (2015) What Is Disruptive Innovation?

Liverence, B. (2018) Fashion retailers have nothing to fear (yet) from the rise of Stitch Fix.

Rogers, D. (2016) The Digital Transformation Playbook: Rethink Your Business for the Digital Age. Kindle. Harvard Business Review Press.

Ross, J. et al. (2016) Designing Digital Organizations, MIT Center for Information Systems Research.

Sreekanth, S. (2018) The Nordstrom Way to Successful Enterprise Digital Transformation.

Westerman, G., Bonnet, D. and McAfee, A. (2014) Leading Digital: Turning Technology into Business Transformation. Kindle. Harvard Business Review Press.

Tap into the power of segmentation with hit-level Google Analytics data!

Success in marketing is all about matching the right content with the right audience. You need segmentation for this.

Segmentation is what allows you to zoom in on your target group. It allows you to identify, then target, high-performing users whilst at the same time improving or removing low performers.

Measuring the total conversion rate is a start, but you should also break down your data to see if some users convert more than others.

For example, if you find that desktop users convert more than mobile users, you could adjust your campaign target-audience accordingly or — even better — make your website more mobile-friendly.

If you optimize systematically across many user groups (geographical location, content viewed, visit frequency, previous purchases, basket abandonment, etc.), imagine the effect on your overall campaign effectiveness and conversion!

Segmentation with hit-level data

While it is possible to segment users in Google Analytics, the data will often be sampled and, therefore, not necessarily accurate. In addition, there are a number of limitations to custom segments in GA such as a maximum date range of 90 days.

Fortunately, it is possible to avoid all of this!

Simply signup for and implement SCITYLANA (it’s free), pull out data from your GA account and transform it into a raw, hit-level form. Next, load the data into a self-service BI tool such as Power BI, Excel, Qlik, Tableau or Data Studio / BigQuery.

As it turns out, segmentation becomes easier, more powerful and more actionable once data is loaded into one of these tools.

In the rest of this post, I will walk you through how segmentation works in Power BI. You can use any of the tools, of course, but Power BI is free, easy to use and has great segmentation features.

Custom segments in Power BI

If you haven’t done so already, go ahead and download our free Power BI template for GA data extracted through SCITYLANA.

If you are not a SCITYLANA user, you can download a Power BI Desktop file with demo data here (requires Power BI Desktop).

Here is an overview of the main segmentation options once you have loaded your data into the template:

  • Date range selectors
  • Slicers
  • Multi-level filters
  • Calculated columns


Date range selectors

You can add as many slicers as you like directly to your dashboard (i.e. none of these limitations), and you can choose between different types of slicer, including date range selectors.

Simply select the Slicer icon, then drag & drop the relevant field onto the Slicer visual.


If you drag the Date field onto the visual, Power BI will automatically turn the slicer into a date range selector. You can switch between different types such as relative period, fixed period or lists with weeks, months, quaters and years.


Dimension slicers

Suppose you want to zoom in on visitors who entered your site through Paid Search, used a mobile or tablet device to browse your content, was situated in Copenhagen and had never visited your site before.

Would that be possible…? Absolutely!

Simply pull in the dimensions you need, turn them into slicers and select the relevant values.


Notice the logic behind these selections. Values selected across slicers are joined with AND logic (e.g. “Display” AND “Copenhagen”). Values selected within the same slicer are joined with OR logic (e.g. “mobile” OR “tablet”).

Multi-level filters

Multi-level filters work basically the same way as slicers. The difference is you can specify which report level they should affect. To do so, pull in the filter dimension to either the visual-level, page-level or report-level area under FILTERS:


To some extent, you can also define filtering levels for date range selectors and slicers. By default a slicer will affect the entire page – however,  you can overrule this setting by clicking Edit interactions of the Home ribbon (learn more here).

Calculated columns

Suppose you want to build a new variable, which doesn’t already exist in GA. Suppose you want to recode the deviceCategory dimension so that it only has two values instead of three:


You can do this by adding a conditional column in Power BI using DAX in much the same way as you would do in Excel.

Simply right-click on the Scitylana table under FIELDS and select New column:


Now the DAX formula bar will appear:


Enter the following text:

deviceCategory NEW =
IF ( Scitylana[deviceCategory] = "desktop", "desktop", "mobile/tablet" )

… which reads: If the deviceCategory variable equals “desktop”, then “desktop”, otherwise “mobile/tablet”.

And that’s it!

You have successfully created a new column which you can use as a slicer or as input in a chart:


[Download a demo Power BI file with all DAX examples in this post.]

More advanced segments

The IF() function is great if you only have a few items in your conditional expression. If you have many, however, you can take advantage of the SWITCH() function. With this function you avoid writing complex nested IF() expressions.

Suppose you want to divide all the US states into US regions: Northeast, Midwest, South and West. You can do so with the following SWITCH() expression:

US Region =
    "Connecticut", "Northeast",
    "Maine", "Northeast",
    "Massachusetts", "Northeast",
    "New Hampshire", "Northeast",
    "Rhode Island", "Northeast",
    "Vermont", "Northeast",
    "New Jersey", "Northeast",
    "New York", "Northeast",
    "Pennsylvania", "Northeast",
    "Illinois", "Midwest",
    "Indiana", "Midwest",
    "Michigan", "Midwest",
    "Ohio", "Midwest",
    "Iowa", "Midwest",
    "Kansas", "Midwest",
    "Minnesota", "Midwest",
    "Missouri", "Midwest",
    "Nebraska", "Midwest",
    "North Dakota", "Midwest",
    "South Dakota", "Midwest",
    "Wisconsin", "Midwest",
    "Delaware", "South",
    "Florida", "South",
    "Georgia", "South",
    "Maryland", "South",
    "North Carolina", "South",
    "South Carolina", "South",
    "Virginia", "South",
    "Washington D.C.", "South",
    "West Virginia", "South",
    "Alabama", "South",
    "Kentucky", "South",
    "Mississippi", "South",
    "Tennessee", "South",
    "Arkansas", "South",
    "Louisiana", "South",
    "Oklahoma", "South",
    "Texas", "South",
    "District of Columbia", "South",
    "Arizona", "West",
    "Colorado", "West",
    "Idaho", "West",
    "Montana", "West",
    "Nevada", "West",
    "New Mexico", "West",
    "Utah", "West",
    "Wyoming", "West",
    "Alaska", "West",
    "California", "West",
    "Hawaii", "West",
    "Oregon", "West",
    "Washington", "West",
    BLANK ()

The above expression reads: If the variable region equals Connecticut, then Northeast, else if region equals Maine, then Northeast [….], otherwise Blank.

[Download a demo Power BI file with all DAX examples in this post.]

This new calculated column can now be used as a slicer in Power BI, enabling the end-user to zoom in on the region he or she is interested in. For example, in the setup below, selecting Northeast will make the map dynamically zoom in on this region:


Now, suppose you want to build an even more complex segment. Suppose you want to create a variable which shows a specific geographical market division.

For example, you might want to create an overview of the US as your main market and Europe, Asia, Oceania and the rest of Americas as secondary markets.

The challenge here, however, is that whereas Europe, Asia and Oceania are continents, the US is a country belonging to the continent Americas.

To get an overview of your markets, you’ll have to create a new calculated column which draws on both Continent and Country.

You can do so by using SWITCH() in combination with TRUE():

Market =
    TRUE (),
    AND ( Scitylana[Continent] = "Americas", Scitylana[Country] = "United States" )"The US",
    AND ( Scitylana[Continent] = "Americas", Scitylana[Country] <> "United States" )"Rest of Americas",
Scitylana[Continent] = "Africa""Africa",
Scitylana[Continent] = "Asia""Asia",
Scitylana[Continent] = "Europe""Europe",
Scitylana[Continent] = "Oceania""Oceania",
    BLANK ()

Notice how the TRUE() function allows for logical expressions in your conditions, including not only the equal operator, but also the AND operator.

[Download a demo Power BI file with all DAX examples in this post.]

Again, the new variable can be used as a slicer or as input in a chart:


Beware of filtering scope

Now that you know how to use segments in both basic and advanced ways, you should know that variables in Google Analytics (dimensions and metrics) have different scope:

  • User scope
  • Session scope
  • Hit scope
  • Product scope

When building segments, user-scope variables filter users, session-scope variables filter sessions, hit-scope variables filter hits and so on.

For example, since the dimension Continent is session scope, selecting Europe will give you all sessions from Europe. Even if a user has previously visited your site from the US, you will still only get the sessions he or she made from Europe.

You can get an overview of GA variables and their scopes available in Scitylana here.

Scope is important because sometimes you want to filter a unit which is different from the variable’s scope.

Suppose you want to  build a segment with converted users, i.e. users who have completed one or more goals. The challenge here is that goals are hit scope, so a simple filter expression such as goal1Completion > 0, will not give you converted users, but only the specific hits that count as goal 1.

In my next post I will explain how to overcome this problem, how to give your goals and other hit-scope variables a broader scope.

Retroactive goals with your raw Google Analytics data

Goals in Google Analytics are not retroactive. They only start showing data once you have configured them.

So, if you forget to set them up, this will happen:

But don’t despair!

If you have downloaded your raw GA data with Scitylana, you can use tools like Power BI, Excel, Tableau, Qlik or similar to show goals retroactively.

Once the data are outside of GA, you can define or re-define your goals on the fly and see the effect immediately on the entire time range.

How to set up retroactive goals in Power BI

You can use Scitylana with any BI tool. However, here we will show you how to set up goals in Power BI simply because we know this tool better. It’s easy, even if you don’t know DAX.

  1. Open the Power BI file with your raw GA data
  2. Right-click on the Scitylana table under Fields and select New measure
  3. In the formula bar above the reporting canvas, enter this DAX expression:
Conversions = CALCULATE ( [Users], Scitylana[Page] = "/thank-you" )

But remember to replace “/thank-you” with the URL of your own conversion page!

And that’s it!

You have a new measure called Conversions which you can drag and drop into a chart, break down on time, traffic sources, geography and more – and it works instantly on all of your historical data.

Switch your base metric instantly

In the example above, we calculated the goal in terms of users, not sessions. We wanted to know how many users have seen the thank-you page.

If you want to calculate by sessions instead, simply replace [Users]  with [Sessions]:

Conversions = CALCULATE ( [Sessions], Scitylana[Page] = "/thank-you" )

Turn your goal into a percentage

You can also express your new measure as a percentage and turn it into a conversion rate. To do so, simply create a new measure called % Conversions and define it as the number of converted users (i.e. your original measure) divided by the total number of users:

% Conversion = [Conversions] / [Users]

Notice how we use the original measure within the new measure, which is a nice, powerful feature of DAX.

Conversions divided by users gives you a decimal number, but you can easily change format to percentage from the top Modeling ribbon.

Select the new % Conversion measure, then click the % icon:

Add as many filters as you like

Suppose you want to define your goal as the number of users who have seen the thank-you page with a mobile device while staying in London.

No problem!

You simply add more filters to your CALCULATE () function:

Conversions =
    Scitylana[Page] = "/thank-you",
    Scitylana[DeviceCategory] = "mobile",
    Scitylana[City] = "London"

… and you can add as many as you like!

Troubleshooting: Beware of your regional settings!

Please notice that the regional setting of your computer affects the required format of DAX (your goal expression).

In the above we have used US / UK regional setting, which requires you to use comma ( , ) as the list separator and period ( . ) as the decimal point.

If your computer has a different regional setting, you must use semicolon ( ; ) as the list separator and comma ( , ) as the decimal point. In this case, your goal expression would look like this:

Conversions = CALCULATE ( [Users]; Scitylana[Page] = "/thank-you" )

Next step

Now that you have created your own conversion measure and conversion rate, it’s time to add it to the Power BI template.

You can create a blank page dedicated to goals. Here you can insert a KPI chart that shows the status of your goal relative to a target.

You can also insert a combined line and bar chart that shows how your total number of users and conversion rate develop over time. The visualization possibilities are almost endless.

We hope you have enjoyed reading about how GA goals can be defined retroactively within Power BI. As always, we welcome any comments or questions you might have.

Visualize your raw GA data instantly with this Power BI template

Okay, so you’ve implemented SCITYLANA and pulled out loads of nice hit-level data from your Google Analytics account.

Now you want to put all that data to impressive use! But what to focus on and how to get started?

Use this free Power BI template

Although SCITYLANA works with all sorts of tools (BigQuery, Data Studio, Tableau, Targit, Qlik, etc.), we suggest you start with this template for Power BI.

We’ve chosen Power BI as our starter tool, because it’s free, easy to use and extremely versatile when it comes to analyzing, reporting and integrating data.

In this post we’ll give you a quick rundown on the contents of the template. At the end, we’ll also show you how to populate it with your own data.


The first report tab in the Power BI template gives an high-level view of your traffic.

You can use each visual as a filter. For example, clicking on Organic search in the pie chart will immediately filter all other visuals.

You can also define a custom date range using the slicer in the upper right corner.


Traffic sources

The next tab shows your traffic sources. The bar chart shows all traffic sources, but you can easily filter these by channel grouping using the slicer to the right.


We’ve included a bubble chart showing visit engagement by channel grouping. The y-axis shows the average number of pages viewed by a session, while the x-axis shows the average time spent on each page.

Notice the Organic Search bubble in the upper right corner? These sessions view many pages and spend a long time on the site.

Content usage

What’s special about this report? Well, it shows Hostname (domains) and Page combined with Sessions.

In GA you actually can’t do this. You can’t combine Hostname or Page with Sessions. This is because of the way GA has organized its data storage.

What is great about the report tab below is that you can easily filter your top pages by hostname. Simply click on one of the domains in the bar chart to the left, and you will see the other one changes immediately.


Visiting time

Here you can see traffic during working hours, weekends and even seasons such as summer or winter. Again we’ve gone a bit beyond what you can do in GA. We’ve added a calendar and a time of day dimension with more interesting attributes.


Now comes our favorite part: Clickstreams! This is kind of the “proof” that we deliver hit-level GA data. Each row is a hit (a pageview or en event).

You can see exactly what individual visitors do!



Geography: Isn’t that more or less the same as in the GA interface? Not quite! The treemap in the upper left corner divides North Americans into Northeast, West, South, and Midwest. Yet this dimension doesn’t exist in GA!



Waiting for another surprise? Well, this one is not that special, but nonetheless useful. It shows statistics on devices, browsers and operating systems.


How to use the template with your own data

  1. Sign up for and implement SCITYLANA (it’s free!)
  2. Start a new data extraction so that your GA data is downloaded to your hard drive
  3. Download and install Power BI Desktop (it’s free!)
  4. Download and open our Power BI template
  5. Enter the path of the data folder (the destination you chose during step 2)



Now it’s your turn 🙂 Do you have ideas on how to visualize GA data in new interesting ways? Please let us know and we’ll add them to the template.

The Cost of Sampling in Google Analytics

Recently, Jonathan Weber at LunaMetrics wrote a great post regarding the accuracy of Google Analytics sampling, revealing how users shouldn’t just take it at face value but also check for potential inaccuracies.

Weber shows that while sampling works well for overall trends on your website, it can become very inaccurate. One good example of this is when you’re looking at smaller subsets of data, such as the conversion rate of a specific campaign.

To help calculate the accuracy of your data, Weber even included an online tool, which you can use to determine the “margin of error” for your campaign conversion rate.

In this post, I want to take his argument a bit further. I wanted to ask a slightly different question and see what the sampling inaccuracy could actually COST you, particularly if it led to taking the wrong decision in a campaign.

First, calculate margin of error

Imagine that you have a website with some 10M sessions in a particular time period, and that sampling kicks in. Now, suppose you’re comparing five different online campaigns, each of which is reported to send 4K sessions to your website with conversions of 2%.

Using Jonathan’s tool, linked above, we can now calculate the margin of error for these reported conversion rates. Here is the result:


As you can see, even though Google Analytics reports a conversion rate of 2%, the real rate for each of the five campaigns is actually between 0.55% and 5.04%. A significant difference, whichever way you look at it.

Next, calculate sampling cost

Let’s assume you have invested $6K in each of the five campaigns (for a total cost of $30K). Let’s then say that each converted session is worth around $100, making your total revenue from the campaigns $40K. This gives you a total profit of $10K:


Now assume that Campaign A is doing much better that the rest. Assume that its conversion rate is 5.04%, while the other campaigns only convert to 1.24%:


In this case, you would still see a profit of $10K across all your online campaigns, but you would only profit from Campaign A.

Now imagine that you have an additional $30K to invest in the five campaigns. If you allocated this money based on GA’s sampled report, you would still end up with $10K profit.

But, if you had had access to the unsampled data, you would have known that Campaign A is the only profitable one, and that investing the entirety of your $30K into this campaign was the smart move.

Indeed, this simple decision would net you nothing less than $70.8K in profit:


As you can see, sampling in this hypothetical case would have cost you $70.8K – $10K = $60.8K.

That’s not small change by any means. Think about that next time you run into this seemingly innocuous message:


Unsample your data with Scitylana

The potential cost of sampling in Google Analytics for business is one reason we created  Scitylana. This tool lets you extract data from your free Google Analytics account, transforming them into a raw, hit-level form.

The data is free from sampling, giving you more freedom to analyze, visualize, and integrate GA and CRM data.

The best thing? Signing up is absolutely free, and you can do it right now in just a couple of seconds: