Analytics dashboard development
When data on advertising, sales, requests, and finances is stored in different systems, regular reporting becomes too time-consuming. The marketer downloads metrics from advertising accounts, the analyst consolidates spreadsheets, the sales team works in the CRM, and the manager has to compare multiple reports before making each decision.
What is an analytical dashboard and why does a business need one?
Dashboard development helps gather relevant metrics in a single analytical panel and set up regular data updates. As a result, the team can see relevant KPIs, compare periods, track deviations, and quickly understand which channels, products, or departments are delivering the desired results. Seo-Gen creates dashboards for specific business tasks, including projects in Power BI and Looker Studio.
An analytical dashboard is a panel with key company metrics compiled from connected sources. Instead of several separate files, users see graphs, tables, KPI cards, charts, and other visualization elements in a single interface. The dashboard's composition depends on the task: a marketer might need expenses and leads, a manager might need sales and profit, and a finance specialist might need plan, actual, and marginality data.
Data can be retrieved from Google Analytics 4, CRM, advertising accounts, Excel, Google Sheets, SQL, BigQuery, and internal company systems. With the right architecture, information is updated automatically, eliminating the need for employees to manually enter data each time. This type of business analytics is especially useful for companies whose decisions depend on multiple data sources.
What tasks does an analytical dashboard solve?
A dashboard is essential where metrics need to be regularly reviewed, compared, and analyzed across multiple dimensions. A manager can see the overall business dynamics, a marketer can see the cost of customer acquisition, and a sales team can see the progress of leads through the funnel. All users work with a consistent set of metrics and use the same calculation rules.
Most often, analytical dashboards solve the following tasks:
- monitor key business indicators and help to quickly notice deviations from the plan;
- reduce the time employees spend on manually preparing regular reports;
- combine marketing analytics, sales, financial performance and website data;
- show the dynamics of indicators for a selected period and help compare different periods;
- make it possible to analyze results by channels, managers, regions, products, and other sections;
- reduce the number of errors that occur when transferring information between tables and reports.
Once configured, the dashboard can be used for daily monitoring or periodic reporting. The set of metrics depends on the specific decisions the user needs to make based on the data.
How is a dashboard different from a regular report?
A standard report is often created for a specific date or period and then sent to a manager as a spreadsheet, presentation, or file. If the period needs to be changed, a filter added, or a different aspect checked, the employee must re-collect the data. An analytical dashboard works with connected sources and gives the user more opportunities for independent analysis.
| Criterion | Analytical dashboard | Regular report |
|---|---|---|
| Updating data | Can be performed automatically | Often requires manual updating |
| Working with periods | The user changes the period independently | Typically set when preparing a report |
| Data sources | It is possible to combine several systems | Often data is summarized manually |
| Filters | Interactive filters and sections are available | Capabilities are limited by file format |
| Data visualization | Graphs, tables, KPI cards, diagrams | Static visualization is used more often. |
| Purpose | Regular monitoring and analysis | Recording results for a certain period |
When choosing between a report and a dashboard, consider the frequency of data processing. If metrics are checked regularly and there are multiple sources, an automated analytics dashboard typically reduces the amount of manual work.
What goes into creating a dashboard in Power BI?
Development involves working with sources, data models, calculations, visualizations, and access rules. The scope of the project depends on the state of the source data. If the information is already prepared and stored in a unified structure, the work proceeds more quickly than if several incompatible tables need to be cleaned and compared.
Before launching, it's also important to determine which metrics should be calculated within Power BI and which are best prepared at the database or data warehouse level. This impacts update speed, project maintenance, and the complexity of future changes.
Connecting data sources
Power BI can work with tables, databases, cloud services, and other sources with a suitable connection method. Projects can use Excel, SQL, Google Analytics 4, CRM, advertising systems, BigQuery, and data retrieved via APIs.
Before connecting, it's necessary to check the field format, update frequency, and data quality. If one source stores dates in one format and the other uses a different structure, the data should be pre-converted to a consistent format. This preparation is especially necessary when integrating multiple systems.
Preparing the data model
After connecting the sources, the data is cleaned, transformed, and linked. Power Query, SQL, or a separate processing layer can be used for preparation. At this stage, duplicates are removed, date formats are adjusted, identifiers are verified, and relationships between tables are created.
The model must align with the logic of the future analysis. For example, advertising costs can be linked to channels and campaigns, and CRM data to requests and sales. If there is no connection between the systems, this limitation is identified before visualization development.
Setting up calculated indicators
Calculated indicators are created after formulas are approved by the client. In Power BI, DAX can be used for this, and some calculations can be prepared at other levels of the model. Particular attention is required for metrics that are calculated using different rules by different departments.
For example, ROMI depends on which expenses and revenues are included in the formula. A similar situation arises with marginality, conversion, and customer acquisition cost. Before visualization, these definitions need to be aligned so that users see the same values.
Creating visualizations
Data visualization is built around user questions. Cards are suitable for summary KPIs, line charts for dynamics, bar charts for category comparisons, and tables for detailed values. The chart type is chosen based on the meaning of the metric, not for the sake of interface variety.
Filters allow you to change the period, channel, region, product category, or other context. Drill-down allows you to drill down from a general indicator to more detailed data. The number of elements on the screen is limited to make it easy for the user to find the information they need.
Setting up updates and access
The update frequency depends on the sources, project architecture, and business requirements. For some reports, daily updates are sufficient, while others require more frequent data retrieval. This parameter is agreed upon before launch, as it may impact the technical connection scheme.
If employees at different levels are working with the report, access rights are configured. Users can see the entire dashboard or only the portion of data they have access to. After launch, the stability of the update and the correct display of indicators are also checked.
I recommend starting dashboard development with questions the business needs to answer regularly. First, we define KPIs and data sources, then we check their quality, and only then we design the visualization. This process helps avoid a situation where the dashboard looks neat, but users can't make concrete decisions based on the metrics.
How does dashboard development work?
Development begins with the business objective and then moves on to data and technical implementation. This process helps avoid unnecessary graphs and calculations that go unused after launch. Before creating the interface, it's important to understand who will be using the report and what decisions they make regularly.
The project is divided into sequential stages: goal analysis, source audit, technical specification preparation, design, integration, visualization development, and testing. After launch, the structure can evolve as the business develops new sources or reporting requirements.
Analysis of the problem and business goals
In the first stage, we identify the users of the future dashboard and the questions they want to address. For example, a marketing director might need to monitor ROMI across channels, while a sales manager might need to monitor plan execution and conversion rates between stages.
Next, a list of KPIs and required sections is compiled. It's immediately determined which metrics should be on the first screen and which can be moved to more detailed sections. This approach helps maintain a clear structure and avoid interface clutter.
Audit of data sources
Before development, we check the location of the required metrics and how reliably they can be obtained. We analyze CRM, GA4, advertising accounts, tables, databases, and other systems involved in the future model.
Field formats, identifiers, and data quality are checked separately. If data cannot be linked between systems, this is identified before the visual portion of the project begins. At this stage, the appropriate update frequency and the need for intermediate storage are also determined.
Formation of technical specifications
The technical specifications specify sources, indicators, formulas, filters, sections, and access rules. Calculation logic is specified for each KPI, especially if it is generated from multiple tables or depends on additional conditions.
The page structure of the future report is also described. For example, the first page might contain a general summary, the second – marketing channels, the third – sales, and the fourth – financial indicators. This layout gives both the developer and the client a common understanding of the results.
Designing the dashboard structure
Before full development, the structure of the future dashboard is created. At this stage, the arrangement of KPI cards, graphs, tables, filters, and navigation elements is determined. Key metrics are placed at the top, while detailed information is moved below or to separate pages.
The visual hierarchy is built around the user scenario. The manager should quickly see the main changes, and the analyst should be able to drill down to the details. If the report requires too much explanation, the structure is revised before technical implementation.
Data integration and processing
Once the structure is agreed upon, the sources are connected and information preparation begins. Data is cleaned, transformed, and linked according to agreed-upon rules. If necessary, an intermediate storage or separate processing layer is created.
Particular attention is paid to dates, identifiers, channel names, and references. Even a slight discrepancy in spelling can split a single source into multiple lines. Therefore, data is normalized before being used in final calculations.
Creating visualizations
Graphs, tables, KPI cards, diagrams, and filters are built on the finished model. A clear format is selected for each indicator. Dynamics are displayed as a graph, structure as a diagram, detailed values as a table, and the final metric as a separate card.
The complexity of the visualization should match the user's task. If a standard table provides an answer faster, there's no point in replacing it with a fancy diagram. After assembly, the screen's readability is tested with different filters and data volumes.
Testing
Before launch, figures, formulas, periods, and filter logic are verified. Dashboard values are compared with source systems to ensure data is transferred and calculated correctly. Particular attention is paid to metrics aggregated from multiple sources.
Updates, access rights, and the display of different periods are also tested. If the user changes the filter, related elements should be correctly reorganized. Any discrepancies identified are corrected before the report is submitted.
Launch and maintenance
After testing, the dashboard is handed over to users. If necessary, the team receives clarification on the structure, filters, and logic of the metrics. For complex projects, a technical diagram of the sources and update rules is separately documented.
Over time, a business may add a new advertising channel, CRM field, or financial metric. In this case, the existing model is refined to accommodate the new task. The more carefully the initial architecture is prepared, the easier it is to maintain the report after launch.
What we actually did
Dental clinic · Kyiv and Chernihiv
+44% clicks from search
A domain with no history and a site on a website builder. We built the semantic core for both cities, reworked the landing pages and built the link profile from zero. In four months: 34.8k clicks, impressions 1.32 → 1.76M, DR 0 → 41.
E-commerce · international
+96% clicks in two months
A catalog of digital 3D models. We clustered the semantics, rebuilt the hub pages and fixed duplicates and indexing errors. Google users 247 → 532, CTR 2.4% → 4%.
Medical center · Ukraine
+68.75% visibility in the first month
Narrow visibility and a small semantic core at the start. Semantics, landing page structure, metadata and internal linking, then gradual link building.
How long does it take to create a dashboard?
Development time depends on the readiness of the source data. If the required indicators are already in a single table and do not require complex processing, dashboard creation takes less time. Integrating multiple systems, setting up APIs, and preparing the model increases the scope of work.
The number of pages, filters, estimated metrics, and access scenarios also impact the timeframe. Before assessing a project, it's important to review the sources and agree on the structure. Afterward, you can determine the sequence of work and determine which stages require more preparation.
How much does it cost to develop a dashboard?
The cost depends on the volume of data processing and the complexity of the proposed report. A dashboard with a single, pre-built source requires less time than a system that requires integrating CRM, advertising accounts, web analytics, and financial data. Therefore, ordering a dashboard at a fixed, universal price without analyzing the task is usually impossible.
The assessment is influenced by the number of sources, API complexity, history size, number of pages, calculation metrics, access model, and the selected BI platform. Dashboard creation may also involve preparing the warehouse and resolving issues in the source data structure.
Why is it more profitable for a business to order dashboard development?
With regular manual reporting, employees waste time downloading, copying data, and updating formulas. The more sources involved in a report, the higher the likelihood of errors and discrepancies. Commissioning dashboard development makes sense when this type of work is repeated weekly or monthly.
With automation, the team receives up-to-date metrics faster and can focus on analysis. A unified model also reduces disputes over numbers if formulas and sources are agreed upon before the project launches.
The benefit depends on the quality of the source data. If the CRM lacks statuses, analytical events are configured incorrectly, or employees maintain multiple unrelated tables, these limitations must first be addressed.
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Answers to your questions
How much does it cost to order an analytical dashboard?
The price depends on the number of sources, the complexity of integrations, the volume of data, and the structure of the proposed report. The cost is also affected by the number of pages, calculation indicators, the need for storage provisioning, and user permissions.
To estimate, you need to briefly describe the task and list the systems from which data needs to be retrieved. After verifying the requirements, you can determine the scope of the development and select a suitable technical solution.
How long does it take to develop a dashboard?
The timeframe depends on the state of the source data and the number of integrations. A dashboard based on a single prepared source is created faster than an analytics system with CRM, advertising, GA4, and financial reporting.
Before work begins, the sources, formulas, and structure of the future panel are verified. Afterward, the sequence of stages can be assessed and which data require additional preparation.
Is it possible to order dashboard creation in Power BI?
Yes, creating dashboards in Power BI is suitable for projects with multiple sources, linked tables, and complex calculations. Power BI allows you to configure filters, drill-down, data model, and access control.
Before choosing a platform, review the project's requirements. If the task is simpler and primarily involves marketing reporting, Looker Studio or another architecture may be suitable.
What data can be combined in one dashboard?
A single report can combine GA4, Google Ads, Meta Ads, CRM, Excel, Google Sheets, SQL, BigQuery, and other available sources. The specific list depends on the technical capability of obtaining and linking the data.
For proper merging, common identifiers or clear matching rules are also required. If there is no connection between systems, this limitation is identified during the audit phase.
Is it possible to combine advertising, website, and sales from a CRM?
Yes, if the systems store the data necessary to correctly link the user journey. Advertising costs can be correlated with website visits, inquiries, lead statuses, and actual transactions.
The quality of the result depends on the analytics and CRM settings. If sources or identifiers are transmitted with errors, you will first need to correct the data collection.
Is it possible to automatically update data in the dashboard?
Automatic updates can be configured if the selected sources and architecture support regular data retrieval. The frequency depends on the platform, connection method, and business needs.
Some companies only need to update the report daily, while others require more frequent synchronization. This parameter is agreed upon during the system design.
What do I need to provide to start development?
For an initial assessment, it's sufficient to describe the task, the users of the future report, the main KPIs, and a list of sources. It's also helpful to show current tables or reports if the company already uses manual reporting.
Access is granted after the scope of work and security requirements have been agreed upon. During this initial phase, it's usually possible to evaluate the architecture without granting full access to all systems.
Is it possible to improve the existing dashboard?
Yes, an existing dashboard can be reviewed and refined. First, an audit of the sources, formulas, data model, filters, and current visualization structure is performed.
After verification, errors can be corrected, new metrics added, additional sources connected, or the interface redesigned. The scope of this work depends on how suitable the current architecture is for further development.
If data is distributed across advertising accounts, CRM, GA4, spreadsheets, and other systems, it can be consolidated into a single analytical structure. To begin, simply define which metrics users need and which sources are used in the calculations.
Seo-Gen will analyze the task, review the available data, and propose a suitable architecture. To order a dashboard, please submit a list of sources and key KPIs – this is sufficient for an initial assessment of the development scope.
In short: a good dashboard reduces manual reporting, provides unified metrics for the team, and helps quickly identify changes in marketing, sales, or finance. Submit a development request to determine the appropriate platform, integrations, and report structure.
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More on: Analytics dashboard development
What analytical dashboards do we develop?
The dashboard's structure depends on the processes the company wants to monitor. One project might focus on advertising metrics, another on sales, finance, or e-commerce. Before development, the users, business metrics, data sources, and decisions to be made based on the report are determined.
Creating dashboards doesn't require displaying every available metric. The more precisely the user's questions are defined, the easier it is to select appropriate KPIs, filters, and visualizations. A manager might only need a few key metrics, while an analyst can work with a more detailed data model and a greater number of cross-sections.
Marketing dashboards
The marketing dashboard aggregates metrics across advertising channels, your website, and lead generation. In a single dashboard, you can compare costs, clicks, impressions, leads, CPL, CPA, CAC, ROMI, and ROAS. If you've also integrated a CRM, the report can be expanded to include data on qualified leads, deals, revenue, and actual advertising ROI.
This structure helps quickly identify campaigns where acquisition costs are rising and sales are declining. Users can select a period, advertising channel, campaign, or other context and monitor metric trends without creating a separate spreadsheet. If necessary, marketing analytics can be supplemented with planned values and a comparison of actual results with established KPIs.
Sales dashboards
The sales dashboard is built around data from the company's CRM and internal systems. It can show the number of new requests, qualified leads, deals, average order value, revenue, and conversion rates between stages. Department managers can also conveniently display plan fulfillment and individual manager metrics.
A sales funnel helps you see at which stage you're losing the most potential customers. If the data allows you to link leads to traffic sources, you can further compare the quality of leads from different advertising channels. This report provides the sales department with a unified control framework without the need for regular manual data collation from the CRM.
Financial dashboards
A financial dashboard is used to monitor revenue, expenses, profit, margins, and plan performance. The set of indicators depends on the company's accounting model and available sources. A single dashboard can display totals, period-by-period trends, and breakdowns by division, category, or other area.
During development, formulas and calculation rules for financial metrics are carefully checked. If different departments use different definitions of profit or expenses, these discrepancies must be resolved before creating the visualization. This approach helps avoid situations where the chart appears correct, but users receive different values for the same indicator.
Dashboards for e-commerce
E-commerce analytics typically includes data on traffic, advertising costs, orders, products, and actual revenue. Dashboards can show transactions, average order value, conversion rate, sales by category, revenue per user, ROAS, and advertising channel profitability.
For an online store, product and category breakdowns are useful, especially if advertising costs are distributed unevenly. For example, a user can compare high-turnover categories with high-margin categories. If repeat purchase data is available, the model can be supplemented with customer retention metrics and repeat order patterns.
Dashboards for managers
A manager's dashboard typically contains fewer metrics than an analyst's or marketer's. The primary goal here is to quickly monitor the business's status. The main screen can display revenue, profit, expenses, sales volume, plan fulfillment, and a few metrics related to key growth drivers.
Detailed information can be accessed through filters or drill-down, if needed. This approach maintains a clear structure and avoids cluttering the screen with dozens of graphs. Managers receive key KPIs, while specialists continue to work with detailed information within the same BI system or in separate analytical views.
End-to-end analytics dashboards
The end-to-end analytics dashboard links advertising costs to user actions on the website, CRM requests, and actual sales. With the correct IDs, you can trace the path from the advertising channel to the transaction and calculate metrics that are not available from the advertising account alone.
This model is especially useful for businesses with long sales funnels, where a lead doesn't necessarily translate into revenue. The dashboard allows you to compare advertising channels by cost per lead, lead qualification, number of transactions, revenue, and ROMI. Before development, it's important to verify the quality of the source data and the ability to correctly integrate systems.
Creating a dashboard in Power BI
Power BI is suitable for projects that require connecting multiple sources, building a data model, and setting up complex calculated metrics. The platform is used for corporate reporting, marketing analytics, sales and financial monitoring, and other tasks where standard spreadsheet exports are no longer sufficient.
Creating a dashboard in Power BI begins with reviewing the sources and logic of the future report. Then, the data is cleaned, linked, and prepared for visualization. This process reduces the risk of formula errors and helps ensure consistent KPI values across all report sections.
When should you choose Power BI?
Power BI is worth considering when a company works with multiple tables, CRM, advertising systems, databases, or corporate sources. The platform is suitable for processing large data sets and enables the creation of connected models where metrics are calculated according to consistent rules.
Power BI is also convenient if users require different access levels or detailed drill-downs. A manager can see a general overview, while a departmental employee can see metrics specific to their area of responsibility. However, it's best to choose a platform after analyzing requirements, as the simpler architecture in Looker Studio is sufficient for some marketing tasks.
What data sources can be connected to the dashboard?
The set of sources is determined by the company's infrastructure. Marketing metrics may be in GA4 and advertising accounts, sales in the CRM, financial data in the accounting system, and additional calculations in Excel or Google Sheets. To produce a single report, these sources are aligned to a consistent structure.
Before beginning integration, we check the data acquisition method, API availability, field structure, and the ability to link records between systems. A technical connection alone does not guarantee accurate analytics. If the advertising system and CRM use different identifiers, additional matching logic will be required.
Web analytics systems
Google Analytics 4 is used to analyze website traffic, user behavior, events, and conversions. GA4 provides data on channels, landing pages, devices, and other parameters, which can then be combined with advertising costs or CRM data.
When working with web analytics, it's important to determine in advance which events actually reflect business actions. If duplicate or incorrect conversions are configured in GA4, the error will also be reflected in the dashboard. Therefore, before integration, it's sometimes necessary to audit data collection and verify the existing analytics setup.
CRM and sales systems
CRM stores information about requests, funnel stages, managers, deals, and revenue. Data on lead statuses and actual sales is particularly useful for an analytical dashboard, as it helps evaluate the quality of requests and the effectiveness of advertising channels.
The connection depends on the specific system and the available method of obtaining information. This could be an API, a database, or regular data export. During development, the correctness of CRM data entry is also checked, as missing statuses and incorrect amounts directly impact the final metrics.
Advertising platforms
Data from Google Ads, Meta Ads, and other advertising systems is used to monitor costs, clicks, impressions, conversions, and acquisition costs. A single marketing dashboard allows you to compare channels using agreed-upon metrics and track trends over a selected period.
Advertising statistics become more useful when compared with CRM. A channel with a low CPL may generate many inquiries but few sales, while a more expensive source may generate higher revenue. Therefore, if the necessary data is available, it's better to build a report taking into account the subsequent stages of the funnel.
Tables, Databases, and APIs
Excel and Google Sheets are often used as additional sources for plans, reference books, financial calculations, or data not available in the primary systems. SQL and BigQuery are suitable for storing and processing large data sets, especially those with regular updates and complex relationships.
An API is used when data needs to be retrieved directly from an external system. Before development, interface limitations, available fields, and query frequency are checked. If direct integration is not possible, an intermediate download can be considered, but the method must meet the requirements for report currency.
What metrics can be displayed on the dashboard?
The number of available metrics is usually significantly greater than the number of indicators the user actually needs. Therefore, before development, a list of KPIs related to specific management or operational tasks is defined. An overloaded dashboard slows down analysis and makes it difficult to find the information you need.
For one department, the primary metric might be customer acquisition cost, while for another, it might be profit, number of deals, or plan fulfillment. If the dashboard is used by different teams, separate pages or views with the appropriate level of detail can be created.
Marketing metrics
The marketing section of the dashboard can include advertising costs, impressions, clicks, CPC, CTR, inquiries, CPL, CPA, CAC, ROAS, and ROMI. The specific set depends on the acquisition channels and how far the user's journey can be traced after submitting a request.
If CRM data is available, it's best to supplement marketing metrics with sales and revenue. This allows the team to see the difference between the number of inquiries and their actual quality. To monitor trends, you can add comparisons with the previous period, plan, or target.
Sales figures
For the sales department, the dashboard typically displays new requests, qualified leads, deals, conversion rates between stages, average order value, revenue, and plan fulfillment. Additional sections help compare managers, regions, business lines, or referral sources.
The funnel diagram might look like this:
Advertising contact → website visit → inquiry → qualified lead → deal → revenue
This view helps identify the stage at which the majority of potential customer losses occur. With historical data, you can further compare the current funnel with previous periods.
Financial indicators
The financial section of the dashboard may include revenue, expenses, gross profit, margin, profitability, and target values. Formulas depend on the company's accounting model, so the list of metrics is agreed upon with the responsible employees before development begins.
When financial data is combined with marketing and sales data, the report helps evaluate the contribution of individual areas to the bottom line. For example, high revenue does not necessarily indicate high profitability if advertising or operating expenses have also increased. This analysis requires accurate source data.
Power BI, Looker Studio, or another tool – which one to choose?
The platform is selected after analyzing the sources, data volume, and user requirements. Power BI is often suitable for corporate reporting with multiple tables and complex calculations. For some marketing tasks, Looker Studio is more convenient, especially when the primary sources are linked to Google services.
The choice also depends on the update frequency, number of users, and access rules. Therefore, dashboard creation begins with the task at hand, not with a pre-selected program. This approach reduces the likelihood of migrating the project to another platform once development begins.
Power BI
Power BI is suitable for complex data models, multiple linked sources, and large numbers of calculated metrics. Within a single project, you can create different report pages and assign access to users based on their roles.
The platform is well suited for management reporting, sales, finance, and unified analytics. Creating dashboards in Power BI is also convenient when data comes from SQL, Excel, corporate systems, and other sources that require preprocessing.
Looker Studio
Looker Studio is often used for marketing reporting and data visualization from the Google ecosystem. The platform is compatible with GA4, Google Ads, Google Sheets, and other sources that can be connected directly or via an additional connector.
For relatively simple marketing reporting, this functionality is often sufficient. If the project requires complex relationships between large tables, numerous calculations, or a corporate access control system, it's best to evaluate the architecture separately.
Individual solution
Some projects require intermediate storage or a separate processing layer between the sources and visualization. For example, data may first be collected in BigQuery or SQL and then, after preparation, transferred to Power BI or Looker Studio.
This approach is used when a report must integrate multiple systems or regularly process large volumes of information. The specific design is determined after auditing the sources, as choosing a complex architecture in advance is pointless unless absolutely necessary.
Examples of tasks for which dashboards are ordered
A dashboard can be ordered for a specific department or for multiple business areas. One project helps a marketer manage advertising, while another aggregates sales and financial metrics for a manager. The structure always depends on the available data and the user's goals.
Below are typical scenarios to help you understand the possible project format. These are examples of tasks, not descriptions of specific Seo-Gen cases.
Monitoring the effectiveness of online advertising
The marketing dashboard combines Google Ads, Meta Ads, GA4, and CRM data. Users can see costs, leads, cost per lead, transactions, and revenue across individual channels. This structure helps compare advertising results.
If the CRM stores the source of the request and transmits the necessary identifiers, the report can be built down to the deal level. Then the channel is assessed based on sales and revenue, not just the number of leads from the advertising account.
E-commerce analytics
For an online store, you can collect advertising costs, traffic, transactions, revenue, average order value, and metrics for individual product categories. If the necessary data is available, the report can also be supplemented with margins and repeat purchases.
This dashboard helps compare sales across channels, categories, and periods. If some information is stored in the accounting system and some in GA4, the sources are linked at the level of the shared model.
Sales department control
The sales dashboard displays requests, funnel stages, deals, conversion rates, average order value, and plan fulfillment. Managers can compare metrics across managers and quickly spot changes in the quality of inquiries.
When marketing sources are included, the report is supplemented with lead acquisition cost and lead quality. This helps marketing and sales teams use the same data when discussing results.
Management analytics
A management dashboard can combine marketing, sales, and financial metrics into a single overview. The main KPIs are displayed on the first screen, after which the user can drill down to the desired area.
This format is suitable for managers who don't need to open separate advertising accounts, CRM systems, and financial tables daily. The source data remains stored in specialized systems, and the dashboard displays a consistent summary.
Why order an analytics dashboard from Seo-Gen?
Seo-Gen evaluates the dashboard in conjunction with the data collection system. During development, it takes into account GA4, Google Tag Manager, advertising sources, CRM, and other systems that impact the final metrics. This helps identify issues that could cause discrepancies in the figures.
We start with business objectives and a list of KPIs, then review available sources and select a suitable architecture. Dashboard creation is based on real-world use cases: advertising monitoring, sales, financial performance, or management reporting.
If existing analytics require refinement, this is documented before development begins. This process reduces the number of revisions after launch and provides a clear basis for future report expansion.
What is included in the price?
The basic development scope typically includes requirements analysis, source validation, model preparation, visualization creation, filter configuration, and testing. If the project requires additional integrations or a separate repository, these tasks are estimated after the technical analysis.
The cost also depends on the number of approvals and the complexity of the formulas. Before the project begins, the scope of work is defined so that the client understands which sources are being connected, which pages are being created, and what metrics will be available after launch.
The main benefits are usually associated with the following tasks:
- the volume of manual data transfer between advertising accounts, CRM and tables is reduced;
- the manager receives a single set of KPIs without preparing several separate reports;
- indicators can be compared by periods, channels, managers, regions and other sections;
- the team quickly notices rising costs, falling conversion rates, or deviations from the established plan;
- Automated reporting frees up specialists' time to analyze causes and prepare solutions;
- The dashboard can be supplemented with new sources and sections as the analytical system develops.