Reporting Issues: Why You Can (or Cannot) Trust Your Reports

90% of business leaders think that data literacy is essential to the success of their companies (Harvard Business Review). But 84% of marketers say it’s hard to get data and insights. Reports and dashboards are not always accurate and should not be taken at face value.

You cannot trust your marketing reports for several reasons, the most prominent being poor data quality and mistakes. It is challenging to produce comprehensive reports that are easily customized to effectively track key performance indicators when you do not have access to all your marketing data. “The average financial impact of poor data quality on the organization is estimated to be $15 million per year,” per a Gartner survey.

Management decisions won’t be founded on factual data if you don’t trust your dashboards and the data that supports them. And that’s awful for businesses.

Note: This article was originally published in November 2021 and has been updated in October 2024 to incorporate recent changes.

Marketing reporting is the process of collecting, analyzing, and presenting data to measure the performance of marketing efforts. A well-structured digital marketing report is essential for tracking marketing metrics, KPIs, and business impact.

It involves tracking key performance indicators (KPIs) such as website traffic, social media engagement, lead generation, and conversion rates to understand the effectiveness of marketing campaigns.

By systematically gathering and evaluating this data, businesses can gain valuable insights into their marketing strategies and make informed decisions to optimize their marketing efforts.

Marketing reporting is essential for marketing teams as it helps them:

• Track the performance of marketing campaigns and channels
• Identify areas of improvement and optimize marketing strategies
• Measure the return on investment (ROI) of marketing efforts
• Make data-driven decisions to drive business growth
• Communicate the value of marketing efforts to stakeholders

Marketing reporting tools provide actionable insights and easy-to-read snapshots of campaign performance. When considering what is a marketing report, it’s important to note that the content will vary based on specific objectives, but a comprehensive report should typically cover key areas to address reporting issues and improve reporting marketing.

Output metrics track the daily or weekly activities performed. These are direct measurements of the work being done, such as the number of sales calls made or blog posts published. Including output metrics helps improve reporting in online marketing by tracking ongoing efforts.

Outcome metrics focus on results and are often shared with stakeholders like managers, clients, or investors. These include important metrics like website traffic, lead generation, and sales. Tracking outcome metrics can highlight common issues in marketing reporting, such as gaps in performance visibility.

Quality metrics measure the effectiveness of your efforts, such as conversion rates from visits to signups. They provide insight into the overall quality of your marketing outcomes. Addressing marketing report challenges requires focusing on these metrics to ensure that performance is not just about quantity but quality.

Every report should show your progress toward your goals. Clear visualizations of whether you are on or off track help guide future strategy and adjustments. Effective reporting marketing includes clear indicators of progress to avoid common reporting issues.

Comparing performance over different periods—such as month-over-month or year-over-year—helps assess growth, track trends, and measure ongoing success. This type of analysis is crucial in reporting online marketing, as it highlights patterns and helps resolve marketing report challenges through data-driven insights.

Marketing reports can be categorized into different types based on their frequency, scope, and purpose. Here are some common types of marketing reports:

Daily, Weekly, and Monthly Reports offer varying levels of insight into marketing performance, helping marketers make informed decisions from immediate adjustments to long-term strategies.

These reports provide real-time insights into marketing performance, allowing marketers to quickly identify and respond to changes in the market. Daily reports are essential for monitoring immediate trends and making swift adjustments to ongoing campaigns.

These reports provide a summary of marketing performance over a week, helping marketers to identify trends and patterns. Weekly reports are useful for evaluating short-term strategies and making tactical decisions.

These reports provide a comprehensive overview of marketing performance over a month, allowing marketers to evaluate the effectiveness of their strategies and make adjustments. Monthly reports offer a broader perspective on marketing efforts, helping in strategic planning and long-term decision-making.

Quarterly and annual reports are vital components of marketing reporting, offering a comprehensive overview of marketing performance over extended periods. The quarterly reports, covering a three-month span, and annual reports, encompassing a full year, help marketers identify long-term trends, patterns, and areas for improvement.

These reports are instrumental in evaluating the success of marketing strategies, setting future goals, and making necessary adjustments to optimize performance. By providing a broader perspective on marketing efforts, quarterly and annual reports enable businesses to make informed decisions that drive sustained growth and success.

Specialized reports focus on specific marketing channels or campaigns, offering in-depth analysis and insights that are crucial for optimizing performance. For instance, social media reports track engagement metrics, follower growth, and content performance, providing a clear picture of how social media efforts are resonating with the audience.

Similarly, email marketing reports focus on open rates, click-through rates, and conversion rates, helping marketers understand the effectiveness of their email campaigns. By honing in on channel-specific metrics, these specialized reports enable marketers to make data-driven decisions that enhance the performance of their marketing channels and overall strategy.

Marketing reporting tools and software are indispensable for collecting, analyzing, and presenting marketing data. Some of the most popular tools include:

• Google Analytics: A comprehensive web analytics service that tracks website traffic, user behavior, and conversion rates, providing valuable insights into digital marketing performance.
• Looker Data Studio: A powerful data visualization tool that allows marketers to create custom dashboards and reports, making it easier to interpret and share data.
• Databox: A versatile marketing reporting tool that offers data visualization and automation features, streamlining the reporting process.
• AgencyAnalytics: Designed specifically for agencies, this tool provides robust data visualization and automation capabilities, making it easier to manage multiple client accounts.
• Whatagraph: A user-friendly marketing reporting tool that simplifies data visualization and automation, helping marketers create visually appealing and informative reports.

These tools help marketers streamline their reporting processes, gain actionable insights into their marketing performance, and make data-driven decisions to optimize their strategies. By leveraging these advanced tools, businesses can enhance their digital marketing reporting and achieve better results.

Providing business users with superior data insights is essential to your company's success. With all the information at their disposal, marketers can quickly and effectively explain why a choice was made, what factors contributed to an advertising campaign's success or failure, what can be improved, etc.

With all available information at hand, marketers can easily explain why a decision worked, what affected the success or failure of advertising campaigns, what can be done better, etc.

Customers are also accessing an increasing number of media channels due to the quickly expanding digital media ecosystem, and they anticipate a personalized experience supported by analytics to provide accurate suggestions.

With nearly 80% of marketing budgets dedicated to digital platforms, the potential of marketing analytics is undeniable - yet it's often underused. It's like having a strong Ferrari in your garage and being scared to take it out!

It is difficult to implement this sensible and simple strategy, though, because executives and marketing experts must have confidence in the collected data before they can use it. The source of all issues is found there.

What steps may be taken to raise the caliber of data gathered from all marketing channels marketers use and boost the trustworthiness of data conclusions? Avoid errors from occurring.

There are three stages in which data errors usually emerge:

1. Measurement planning
2. Data collection and preparation (normalization)
3. Report creation

Note: At each of these stages, data can be mishandled due to human factors.

Let's examine some preventative measures for error prevention. After all, it's simpler to avoid mistakes than to search the entire reporting system for faults and data inconsistencies.

Marketing reporting often faces several challenges that can hinder data-driven decision-making. Common issues in marketing reporting, such as data discrepancies and inefficient tracking, can impact the accuracy of online marketing efforts.

Understanding what a marketing report entails and addressing these marketing report challenges is crucial for improving the effectiveness of reporting in online marketing strategies.

Organizing the gathering of all the necessary marketing data appears like a simple initial step when working with data. However, data collection is often planned only for a specific task, so you may not have enough data for new tasks you undertake or new projects you start. Therefore, skipping the planning step is unreasonable.

The OWOX team always does an express analysis before starting a project. Why? To identify possible bottlenecks in data collection. Sometimes, the team even develops entire systems of metrics and enumerates all possible parameters.

The difficulty lies in gathering all the fragmented data from all data sources – the various advertising networks and services you use – and utilizing it to your advantage.

When this planning phase is skipped or an unstructured method is used, the transfer of identifiers must be reconfigured in the absence of previous data. Your decisions and actions will be based on inaccurate or partial information if you don't have all the facts.

To prevent this, you must consider every point of contact with customers by gathering:

• User behavior data from your website and/or application
• Cost data from advertising platforms
• Call tracking, chatbot, and email data
• Actual sales data from your CRM/ERP systems, etc.

A well-structured data collection plan is crucial for a stable marketing strategy incorporating successful tactics and metrics derived from marketing reporting and campaign performance evaluation.

Data Collection and Preparation involves gathering and organizing information from various sources to ensure accuracy and consistency. This step is crucial for generating reliable insights, as poor data preparation can lead to inaccurate reports and flawed decision-making.

The main errors that can appear at this stage are the result of:

• Getting aggregated, sampled data. Sampled data is generalized data that occurs when only part of the data is analyzed and used for reporting. Let's imagine you have a kilo of apples, and your task is to decide whether these apples are good or rotten. If you use sampled data (two or three apples from the whole bunch), you can decide that this kilo is of excellent quality when, in fact, all the other apples you didn't sample are rotten.

Why is this happening? Since processing massive data arrays in the shortest possible time is a complicated and resource-intensive task, systems process data quickly by using data sampling, aggregation, and filtering to generate reports as fast as possible.

What impact does it have on report and data quality? Clearly, not well. It is impossible to make wise conclusions with a limited sample of data. For instance, skewed facts in a report can make you overlook and even turn off a productive advertising campaign.

How do you recognize this issue? This report is based on N% of sessions, according to the message at the top of the report in the Google Analytics interface.

To obtain reliable reports, raw, unsampled data must be obtained. Therefore, once all data sources have been determined, you should ideally employ connections that automatically gather raw data and periodically verify that the data is complete.

• Getting incomplete and incorrect data from an ad service's API. Advertising services collect a lot of data about users and their behavior; however, errors such as data doubles, data loss, or discrepancies during a retrospective update may appear while transmitting data. These errors are transferred into reports, and, as a result, it's simply impossible to make the right decisions due to inaccurate reporting.

How can this issue be resolved? Interacting with services can be challenging since dealing with APIs makes it impossible to directly manipulate code. Using data import tools that support API updates is highly recommended. These tools can display data gaps that now exist and download data later on if data is unavailable.

It will be extremely expensive for you to keep every bit of data on your own servers. Cloud solutions are advised as they will conserve your resources and offer worldwide data access. Google BigQuery is, without a doubt, the greatest solution that considers marketers' demands. This cloud service can store raw data from websites, CRM systems, advertising platforms, etc.

Generally, the best way to prevent errors in data collecting is to never, ever gather data by hand. Many marketing software programs available today, like OWOX BI, automatically gather data from many services and websites and store it in a data warehouse, also known as a data lake.

Marketing relied on third-party data for a long time. However, major, powerful corporations like Apple and Google are now altering how they use personal information. Everyone will soon abandon third-party cookies.

First, you must focus on collecting first-party and second-party data.

Second, since only your data (and not that of advertising platforms) is owned and controlled in data lakes, you should be ready to use them. We advise utilizing Google BigQuery due to recent changes made to Google products (Google Analytics 4, the Privacy Sandbox effort), as the latter offers complete data exports and native integration with GBQ.

For years, marketing strategies relied heavily on third-party data. However, with major corporations like Apple and Google reshaping their data privacy policies, third-party cookies are in decline. Google recently shelved its plan to eliminate third-party cookies but introduced more accessible opt-out options, signaling the steady phase-out of third-party tracking.

To prepare for the cookieless future, it's crucial to manage your data in privacy-focused environments like Google BigQuery. Given Google’s recent product updates, such as Google Analytics 4 and Privacy Sandbox, BigQuery provides full data exports and native integration, making it an essential tool for controlling and utilizing your data.

Additionally, consider diversifying with zero-party data - where you ask consumers directly about their preferences - and ensuring your loyalty programs deliver real value to foster deeper engagement. Experimentation and rapid testing of marketing campaigns are now more critical than ever.

After gathering all the data, the next step is to use it. But you can't do it immediately. Working with data is not possible unless it is prepared (normalized). Why is this the case? The formats, currencies, and structural characteristics of data vary across various advertising platforms and services. Your data must be full, updated, and structured in order for you to create reports.

While trying to normalize data, you can encounter challenges related to:

• Different data formats, structures, and levels of detail. Why is this the case? Different services use different schemes for uploading data. For example, one advertising service may have a column named Product name, whereas another has a column named Product category.

What impact does this have on data and report quality? Building reports with data in multiple structures is just not possible.

How can this issue be resolved? Data must be transformed into a single format before the analysis; otherwise, your study won't yield any useful results. For instance, to determine which advertising campaigns generate the most money for you, combine user session data with advertising cost data to assess the influence of individual traffic sources and marketing channels.

• Different currencies. Different advertising services use different currencies, and to get the correct numbers in your reports, you should always check which currency is used and convert all currencies into a single base currency.
• Insertion, updating, and deletion dependencies. While eliminating unstructured data to be perfectly uniform across all records and fields, various undesirable side effects may appear.

What impact do these dependencies have on the caliber of reports and data? The most frequent outcome of these mistakes is that the system discards the data and doesn't consider it when generating reports, which causes the reports to contain inaccuracies.

Let's take an example where we have two objects: sessions and advertising. We have data for days 10 through 20 in sessions, and data from days 10 through 15 in adverts (for some reason, days 16 through 20 do not have any cost data). As a result, either data from sessions will only be available for days 10 to 15, or we will lose data from adverts for days 16 to 20.

How can this issue be resolved? Error risk is particularly high if the user is unaware of the quirks involved in combining data and doesn't double-check the data they work with. Thus, the best course of action is to verify your facts before utilizing it.

Using automated systems to gather, clean, normalize, and monitor the quality of your data and make it suitable for commercial use in the creation of precise reports for digital marketing is the ideal answer. An even better option would be if the all-in-one OWOX BI platform or your preferred data connector could handle everything for you. You may simply overcome every problem that faces analysts and marketers with OWOX BI's assistance and obtain reliable, business-ready data.

With OWOX BI, mistakes related to data collection and normalization won't ever bother you. This service manages and frees up your precious time by handling:

• Data collection. Get all the data you need from Google Analytics, advertising services, your website, your offline store, call tracking services, and CRM systems into Google BigQuery. OWOX BI allows you to obtain reports without sampling on any available parameters. The service gathers raw data and warns you in case of mistakes in an API’s data transfer. All you have to do is provide access to data sources and choose what data you want to gather.
• Data monitoring. You can always see where you have data discrepancies, during what period, and how critical they are with the help of OWOX BI. It compares the amount of data in your BigQuery project daily by hits, sessions, users, and transactions with Google Analytics and signals significant discrepancies.
• Data normalization. OWOX BI helps you with cleaning, deduplicating, and updating data as well as converting costs into one base currency and monitoring the relevance of data. Also, OWOX BI can define mistakes in UTM tags such as unsupported dynamic parameters, syntax errors, or an outright lack of obligatory UTM tags.

The greatest option for marketing needs is Google's safe BigQuery cloud storage, where you may gather marketing data for reports of any complexity using OWOX BI.

Surveys show that 84% of marketers find it challenging to obtain data and insights, and 86% believe they require better tools to do so. To put it plainly, marketers desire access to digital marketing reports as fast as possible.

When insights are implemented by the marketing team instead of being extracted by the analytics team, the business benefits. It should also be simple to obtain insightful data, given the increasing amount of user activity data that marketers are collecting. In actuality, though, things frequently operate in the exact opposite manner.

Even after overcoming challenges with data preparation and collecting, there are still certain challenges with report development, like:

• Deciding which attribution model to use. There’s no right answer, as different models are required for different tasks. Also, you should keep in mind the peculiarities of your business. A detailed overview of all attribution models on the market can help you choose the one that’s best for you.
• Deciding which report building service to use. Choose a service that can easily provide understandable data visualizations and can update reports automatically. Please note that while data visualization services such as Google Data Studio can work with more than two data sources, it’s still not possible to use them for merging and transforming data. If you want to create a report based on many data sources, you have to first collect all the necessary data into a data lake (e.g. Google BigQuery).

The system fails more easily the more complex the overall reporting environment gets, particularly for large firms, and the more reports and SQL queries are created. In addition to genuine data mistakes, there could be other issues that surface and result in further errors, broken SQL queries, or misinterpretation and abuse of the gathered data.

• Choosing an attribution model: There's no one-size-fits-all model - different tasks require different models, and your business specifics matter. Review all available options to find the best fit.
• Selecting a report building service: Opt for tools that offer clear data visualizations and automatic updates. Services like Google Data Studio support multiple data sources, but require a data lake (e.g. BigQuery) for merging and transforming data.
• System complexity and failures: As reports and SQL queries multiply, especially in large firms, systems become prone to data errors, broken queries, and misinterpretations.
• Frequent report edits: Constantly changing SQL queries disrupt consistency, making it hard to track changes and ensure edits are applied across all datasets.
• Evolving requirements: Changing definitions (e.g., conversion now means a visit, not an order) lead to inconsistent data across reports, complicating analysis.
• Slow reporting process: Overloaded analysts delay reports, with dashboards taking days and costing thousands, leaving marketers to make decisions based on incomplete data.
• Data confusion: Inconsistent interpretations of metrics (e.g., "user" meaning different things in different reports) lead to decisions based on mismatched data definitions.

Whisper down the lane is a game that serves as an example of all these variables. Even though it appears that your input data is accurate and pertinent, the outcome is nevertheless completely different from what was intended.

Making data and reports reliable and useful is a big job. The following are some of the main challenges and roadblocks in accomplishing this goal:

• Having access to up-to-date, complete, high-quality business-ready data to free analysts’ time to look for risk and growth zones and find new approaches to working with data.
• Being able to generate marketing reports within minutes so that marketers can test hypotheses fast and search for insights.

Thankfully, trustworthy reports and data are now available to marketers and analysts thanks to contemporary cloud analytics tools. Try using OWOX BI if you want to produce reports you can rely on- reports that don't break and don't take days to generate. It's a service created especially to meet marketing reporting requirements using comprehensive, superior data.

Analysts may test ideas and discover insights five times faster with this methodical technique. OWOX BI produces monthly marketing reports that make it simpler to assess campaign performance and return on investment by showcasing marketing results and performance parameters over a 30-day period.

OWOX BI democratizes data access for all users, irrespective of their level of technical expertise. With the help of the service, you may generate and modify reports quickly by using automated procedures for gathering and processing data that is ready for commercial use. Additionally, since OWOX BI ensures that your reports are always up-to-date, you can stop worrying about inaccuracies in reports that you modify!

With OWOX BI, it's now simpler than ever to release the insights hidden in your data and produce reliable reports. To learn more about OWOX BI's data quality assurances and how fully automated data management may help you, schedule a free demo now!

A company gets the most value from its data when that data is high-quality and when decision-makers can quickly act on data findings.

When an analyst can quickly answer business questions with data, work with data that is ready for business use, and provide actionable insights, they are at their most valuable. Writing SQL queries and spending time gathering, preparing, and harmonizing data are no longer the main responsibilities of an analyst.

If a business wants to use marketing insights to expand quicker than its competitors, it needs to use automation technologies with modeled business-ready data. Every report that is created and every management decision that is made should be based on high-quality data, including search engine optimization indicators.

• Prevent errors when collecting data, as it’s easier and cheaper to stop errors from happening in the first place than it is to mitigate their consequences.
• Avoid the very chance of an error by using as much automation as possible in workflows. Not to mention that automating manual and repeatable tasks enables specialists to focus on more value-added activities, such as developing actionable data insights.