Eliminate 3 major blind spots in digital marketing

Management is all about decision-making. Management in a digital marketing team is all about data-driven decision-making. Nowadays, cookie, privacy, and MarTech restrictions make measurement harder and blind spots bigger. Once disregarded, it increases acquisition costs and makes it harder to prove marketing value.

In this article, we reveal the top blind spots you should be aware of in digital marketing in 2023. We also share ways to estimate their business impact and best practices to handle them.

Top blind spots in 2023:

• CRM data is not connected to digital analytics reports
• Significant proportion of traffic comes from unknown sources
• Non-consent data doesn’t have session traffic sources even for converted visitors
• Failing to utilize RFM analysis

Digital marketing campaigns are measured by online conversions, but C-levels are looking for completed orders or qualified leads to prove marketing value. If your CRM data is not connected to your digital analytics reports, you won’t know which acquisition campaigns are underestimated or overestimated from the perspective of real business value.

To build reports, data from different sources must be correctly grouped. For example, user actions need to be grouped into sessions in order to link conversions to advertising campaigns. Expenses from different sources should be converted into a single structure to compare the effectiveness of those sources. Data from website and advertising sources needs to be combined with data from your CRM system in order to evaluate the real effectiveness of marketing from a business point of view.

Analysts collect data from disparate services and systems. And naturally, the structure and format of data varies across sources. In addition, this data may contain errors, duplicates, and discrepancies. Dirty and fragmented data needs to be cleaned and normalized.

Cleaning, normalizing, and studying the compatibility of data from various sources takes a lot of time. In addition, regular data combing is required as source data is updated in order to flexibly manage the business logic in accordance with business requirements.

To prepare a reporting system, an analyst needs to create and maintain a whole cascade of interconnected SQL data transformations. In any project, over time this turns into a tangle of SQL queries and scripts, the debugging of which takes a lot of time and does not add new value.

With OWOX BI, you no longer need to look for KPI inconsistencies and switch between tools. You can reconcile all user action data with campaign and CRM data to prove marketing value. Whatever data you need, you can immediately see it on your dashboard.

Step 1. OWOX BI automatically collects data in a convenient format for analysis from all your marketing sources:

• Google Analytics 4, Firebase, and AppsFlyer
• Website (raw data with full GDPR compliance)
• Advertising platforms
• CRM systems
• Other marketing sources

Step 2. With OWOX BI, you don’t need to manually clean, structure, and process data. The service automatically normalizes raw data into analytics-ready format. You get precise structured information: unified tag formats, a single currency, data without doubles and anomalies, bot detection.

Step 3. OWOX BI Transformation automatically applies the basic transformations that everyone needs, such as sessionization, cost data blending, user type detection (new or returning), conversion modeling, custom channel grouping, and many others. Plus, you can easily make and apply your own (custom) transformations.

With this approach, you can:

• Increase the value and efficiency of analytics work by:
• Speeding up changes to report structures and metrics calculation logic
• Reducing the cost of supporting reports and dashboards
• Simplifying report discussions and approvals
• Increase data quality in reports by:
• Avoiding duplication when implementing parameter and metric calculation logic
• Having one data layer that is the source of accurate data

OWOX BI provides you with data that’s ready for analysis, saving hours on data preparation.

The required structure of source tables and logic for their formation will depend on the specifics of your business. With OWOX BI Transformation, you can get any data structures with any formation logic.

For example, in the native GA4 → Google BigQuery export, there is only one traffic source attribution logic — First User Source. That is, only the first source for a given user is taken into account, and all other sources that led to session are ignored. Therefore, some traffic sources will not be visible in your reports. This can be a problem for analysts, as many reports are built on top of sessions. Therefore, it is critical to define the source of each session.

In OWOX BI, you can set up any traffic source attribution logic, including Session Source, which is important for reports on marketing performance.

Let’s say you need a performance report with data about ad campaigns and online conversions. However, many website actions, including conversions, follow clicks on ads by individual users. That’s why you need to group online user activity by traffic source before you can study the relationship between campaigns and online conversions.

The OWOX BI algorithm automatically combines hits/events in sessions without resorting to the Google Analytics logic of session formation. You’ll get ready-to-use automatically updated session tables in a convenient structure without the need to write complex data transformations.

In addition, using OWOX BI, an analyst can group sessions using free transformation templates for Google Analytics Universal and Google Analytics 4. As a result, the analyst will get session data in a single structure that can be used for reports or expense attribution.

All templates are easy to modify — an analyst can change any part of a template to have a unique way of building sessions. Moreover, analysts can create their own unique algorithms using only standard SQL.

It is important for a marketer to understand differences between audiences. For example, to achieve growth goals, you need to track ad campaign performance separately for new and returning users. This is important because channels with a high proportion of returning users have the best KPIs, but they are almost impossible to scale.

OWOX BI provides the ability to define the type of user (new or returning) so that you can build reports on different user cohorts. For this, various types of data can be used, collected by OWOX or other systems from the website, CRM system, etc.:

• Cookies and their duration on websites (like GA, but with more accuracy and flexibility)
• User transactions on the website
• Information about users’ registration on the website
• Information about users’ registration in the CRM system
• Information about sales from the CRM system
• Any other information that the company has about its users

OWOX BI has algorithms and tools out of the box to combine all this data and determine whether a user is new or returning in the most accurate way possible without losing any information about the user.

Analysts can apply such algorithms from ready-made templates written in standard SQL. You can also quickly modify a template or write your own user type detection algorithm.

With OWOX BI, you can build analytics around the user, not browsers, devices, and campaigns. All data about user behavior from your site as well as various devices and applications will be merged into a single profile. You’ll get a complete picture of each user’s behavior to analyze the quality of advertising campaigns.

OWOX BI combines dozens of user IDs on the Web and App platforms into a single user profile (ProfileID):

ProfileID allows you to combine a chain of sources on the way to conversion. This increases attribution accuracy and allows you to answer questions about the effectiveness of cross-device advertising campaigns.

Google Analytics and other analytical tools have predefined data transformation algorithms for sessionization, attribution, ad cost blending, and so on. They allow you to do everything necessary for marketing analysis transformations in a general way. They have some opportunities for customization, but it isn’t enough if you want to apply your own algorithms with business-specific approaches.

OWOX provides a well-developed and effective set of transformations to transform marketing-specific data. Each is designed to prepare and transform data in a way that works best for the majority of websites. We provide them as a set of transformations’ templates.

If you need unique data transformation according to your specific requirements, OWOX can provide custom transformations. They can be developed in three ways:

• from scratch by your analysts
• from scratch by OWOX professionals according to your requirements
• by modifying an existing data transformation from an OWOX template (the fastest way)

Custom transformations use SQL queries and have an unlimited number of queries in a chain. You are able to create data transformation rules without any limits. Custom transformations can be used together with transformations from templates to create a smooth data flow. You can save a custom transformation as a project template and reuse it in another data flow within the same project.

In order to make the right decisions about things such as distribution of the advertising budget, you must be sure that data in reports is updated in a timely manner.

OWOX BI provides an easy way to schedule data updates without any coding or third-party tools. A scheduler is a part of any process that can be created within the OWOX BI platform.

There are two types of schedulers for better tuning of data updates: by time and by event. The first type of scheduler provides the exact timing when data will be ready; the second type makes this setup easy with a dependency flow (when processes run one after another). The combination of these two scheduler types gives clients the possibility to update data for Performance reports quickly, easily, and flexibly.

You might find that​​ a significant proportion of sessions and conversions come from direct/none. Thus, it is almost impossible to understand from which sources these sessions and conversions truly come. The most common cause is a limited cookie lifespan, which leads to the situation where each subsequent session by a particular visitor is defined as a new session, and the connection with the very first session is lost.

First-party cookies in the Safari browser have a limited lifetime of seven days. The bottom line is that the clientId identifier is used to identify a specific user in Google Analytics. Thus, it is used by analytics tools as a key by which you can understand a user’s actions over a long period, where the user originally came from, what pages they visited, and so on.

The clientId identifier is written to the ga_ cookie and stored on the user’s device. This is considered a first-party cookie, but since it is written in JavaScript script, it is subject to ITP restrictions. So it lives no more than seven days.

This means if a user visits your website today from a Facebook ad and places an order eight days later, then for your analytics tool this will be a new user and the order will not be attributed to Facebook advertising in any way. The marketer becomes blind to this part of the traffic and, not understanding the real source of the order, may disable ineffective advertising on Facebook. This could lead to a drop in orders and business profits. Hence, an increase in the proportion of new users in analytics leads to a loss of income.

The ITP Impact Calculator for Google Analytics may be useful for understanding how cookie changes will affect you.

With OWOX BI, you can increase the accuracy of your ad campaign estimates by 70% and identify the true sources/mediums/campaigns that generate income. OWOX BI server-side tracking monitors any user activity on your website, extends cookie lifespans, and is not affected by ad blockers, allowing you to see the whole path to conversion.

With OWOX BI, you can set up first-party data collection to solve ITP problems. To do this, at the integration stage, we create a separate subdomain on your website on which the collection will take place.

With each hit/event, OWOX BI creates a cookie ouid and renews it for 364 days. This cookie will have a longer-lived user ID, owox.user_id. Based on it, we may build analytics reports without a large share of fake new users and build a user path for a longer period. This makes it possible to correctly evaluate the effectiveness of advertising campaigns and track the entire user journey.

Ad services provide cost data in different formats and data structures. But no ad services provide cost data in UTM format. In order to provide such data, they would have to store URL records with UTM parameters for every click, and this would be too expensive.

Importing cost data to any data warehouse leads to the need for keys for merging ad cost data with other data. To get a full picture of ad effectiveness, merging such data is a must. UTM parameters are those keys.

When importing advertising costs into Google BigQuery, OWOX BI automatically recognizes UTM parameters. We know where to look for a URL and how to fetch it. A range of dynamic parameters is available for some of the most popular data sources: Facebook Ads, Bing Ads, etc.

Having cost data along with UTM parameters allows you to merge data from different ad services with user behavior data and therefore get a real picture of how a specific campaign relates to the traffic source and precisely calculate ROAS. Merging data by up to five UTM parameters gives a more detailed picture compared to merging by Campaign Name only.

Some businesses use short links to their website in their ads. URL shorteners are used to improve the visual appearance of a URL and for additional tracking. However, it becomes tricky to build ad efficiency reports (ROAS) based on ad spend containing short URLs. Why? Because it’s impossible to retrieve a final URL with UTM parameters, while it’s critical to have a final URL for joining data in a report. UTM parameters are the key to joining data for reporting.

OWOX BI automatically follows short links in order to get UTM parameters from the full (original) link. This allows for merging ad cost data with users’ data in order to get a full picture of ad effectiveness.

In OWOX BI, you can connect any standard attribution model to your reporting, set up a data-driven model based on OWOX conversion forecasting, or build a custom model based on your own rules and sales funnel. All this can be easily done in the product interface without the help of data engineers.

There are some well-known models including Last click, First Click, and Rule Based. In addition, there are specific and advanced algorithms for building models to meet unique demands. All of them use standard SQL queries for transforming data. These queries need to be constantly reviewed, adjusted, and applied.

OWOX BI provides a simple UX to manage the execution of and monitor those transformations. The combination of ready models with easy-to-manage tools gives the analyst the opportunity to confidently manage data and take responsibility for the result. On top of this, OWOX BI functionality provides the possibility to have more than one attribution model simultaneously and test models to find the most appropriate for the marketer.

The GDPR and CCPA require users’ consent before collecting analytics data. Thus, even for users that convert within a single session (Non-Consent Data), traffic sources might be unknown. This also leads to a discrepancy between CRM data and digital analytics data.

Businesses that work with EU residents must comply with GDPR requirements regarding user privacy. Large fines may be imposed for noncompliance.

If you have website traffic from Europe and you use Google Analytics, GDPR requirements are definitely being violated, as a cookie _ga with a client_id contains personally identifiable information, and this data is sent to Google Analytics and processed on servers as determined by Google Analytics itself. You cannot be confident that your users’ data will stay in the EU and be processed only within the EU. At the same time, your marketing team needs to measure the efficiency of ad campaigns and to have an impact on users' journey without blind spots.

Google Analytics 4 provides clear and detailed options to manage imported data. However, there is no way to store data only in the location of your choice. You can request the processing of personally identifiable information in the EU or export data, but you cannot store all collected data in the selected location only.

With OWOX BI, you have full control of consent and non-consent data. You can collect and use personally identifiable information (PII) in line with the strictest privacy and security requirements. In addition, OWOX BI allows you to choose data residency (EU, US, or global), remove PII for non-consent users, and encrypt all user data with custom keys.

OWOX BI collects data in the location of your choice, including 10 locations in the EU. Additionally, OWOX BI stores collected data in the location of your choice. On top of that, OWOX provides all details regarding the data flow for your legal and security teams.

• Unlike GA4, OWOX BI has a transparent data flow.
• Data can be stored in any location supported by Google BigQuery.
• You have complete control over all your stored data and can decide who to share it with.
• OWOX BI automatically cuts PII data if the user has not given consent to tracking. Thus, you cannot accidentally shoot yourself in the foot by violating the GDPR.
• There is an option to encrypt all user data in Google BigQuery with your own keys.

To comply with GDPR requirements, the site owner must refuse to identify users who do not want to share their cookies and do not click the magic “Accept Cookies” button. As a result, Consent mode reduces the number of conversions for which a traffic source can be determined by 30%.

Conversion modeling helps solve this problem. First, machine learning systems process available data and historical statistics. Then, knowing what percentage of users allowed cookies to be set and how those users converted, an ML system determines the most likely attribution path for those who did not consent. This allows you to more accurately match campaign results to campaign costs — and at the same time comply with user decisions regarding cookies.

OWOX BI has a unique solution that addresses the non-consent gap in the Performance report. This solution allows analysts to study, tune and even change the algorithm for distributing non-consent data among existing campaigns. This allows analysts to confidently manage data and take responsibility for the result.

The modeled conversion algorithm is developed on standard SQL, so it is transparent and can be understood by any analyst who knows SQL. The OWOX BI specialists can help analysts set up or make changes to the algorithm if necessary.

OWOX BI functionality provides the possibility to work with more than one conversion model simultaneously, to test different conversion models to find the most appropriate for a marketer. Such algorithms can be run manually or automatically, with the possibility to monitor them and be sure they provide the right data at the right time.

Recently, restrictions on cookies, data privacy, and marketing technologies have complicated the work of analysts and marketers. In digital marketing, there are more and more blind spots that prevent data-based decision-making.

The good news is that with the help of OWOX BI, you can eliminate these blind spots:

• Close the loop between marketing and revenue
• Handle direct/none traffic in your reports
• Collect and use personally identifiable information (PII) in line with the strictest privacy and security requirements

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