A Complete Guide on Business Intelligence

In this article, we’ll explain business intelligence (BI) and why it’s essential for any company that promotes itself on the internet. Then, we’ll show examples of business problems that can be solved with the help of the best business intelligence software (BI) and describe the main steps for implementing it.

OWOX BI will help you easily merge data from the website, advertising services, and CRM systems and build cross-channel marketing analytics & reporting based on this data.

Note: This post was written in 2019 and has been completely updated based on the recent updates in July 2024.

Business intelligence (BI) integrates data from various customer touchpoints to view marketing performance and profitability comprehensively. BI enables organizations to make data-driven decisions that optimize marketing investments and drive business growth by analyzing completed sales, not just bids.

BI's predictive and prescriptive analytics capabilities empower organizations to forecast future outcomes and guide strategic decision-making more effectively than primary data aggregation alone.

To understand its importance, let’s look at the sales funnel of any e-commerce project in a simplified form:

Ad services → Website → Placing an order → Your profit

Users come to the site from different channels, get acquainted with your product, and place an order by filling in an online form, sending a message by chat or email, calling, or going to a physical store to make a purchase.

By default, Google Analytics gives all the value to the last indirect source (according to the Last Non-Direct Click attribution model), ignoring the contributions of other channels that participated in the sales funnel, which isn’t entirely fair.

To find out the real value of traffic sources, you need to determine the contribution of each session before placing an order. To do this, you need to consider all customer interaction points, including by phone, email, and offline. Business intelligence helps you solve this problem by providing actionable insights and leveraging business analytics to understand the full scope of customer interactions.

The second important thing that distinguishes business intelligence from standard analytics is the method of accounting for sales. Some people who place an order on the site might not complete the purchase; that is, they might cancel the order or simply not pay for the order (for instance, if they’ve chosen to pay on receipt or using an off-site payment method) or might return the goods.

Because of this, the number of orders in the web analytics system and the number of transactions in the CRM can vary significantly. It’s more accurate to evaluate advertising by profit and number of sales, not by order submissions and leads.

Can BI Improve Business Efficiency? BI can be done in many ways – it provides an integrated view of all your most relevant data and business records in real time. It provides business intelligence analysts numerous advantages, including enhanced analytical capabilities to increase competitive advantages. Some excellent business intelligence advantages are as follows:

1. Improved Decision-making: BI tools provide comprehensive insights by using data modeling and doing current and historical data analysis, which helps make informed and timely decisions, reducing the risks associated with guesswork.
2. Increased Operational Efficiency: By automating analytics, BI systems allow for more efficient data processing, which helps identify operational inefficiencies. This leads to streamlined processes and better resource allocation.
3. Enhanced Competitive Advantage: Access to detailed analytics on market trends and consumer behaviors allows business users to stay ahead of the competition. BI tools can pinpoint areas of improvement and opportunities for innovation.
4. Real-Time Data Access: With BI, organizations have real-time access to data, enabling quick responses to business dynamics. This agility can be crucial in rapidly changing markets.
5. Increased Customer Satisfaction: By analyzing customer data and feedback, business users can improve their products and services, tailor their marketing efforts, and ultimately increase customer satisfaction and loyalty.
6. Data Quality and Accuracy: BI tools promote high-quality, accurate data collection and management, reducing errors and ensuring decision-makers have reliable data.

Business analytics and BI function similarly and are commonly used interchangeably; however, BI must be considered a subset of business analytics. BI analyzes historical business data and knowledge to understand current knowledge and information about the current business. Whereas data mining enables business users to make decisions, business analytics involves looking ahead. It is also used as the basis for predicting the future of the data.

While Business Intelligence (BI) primarily focuses on the present and the past by using data aggregation, analysis, and reporting to inform and optimize current business operations, Business Analytics (BA) takes a more forward-looking approach. BA uses advanced statistical methods, artificial intelligence, predictive modeling, and machine learning to forecast future trends and potential outcomes.

This predictive capability not only helps in strategic planning but also enables businesses to address potential challenges and leverage opportunities proactively.

Business intelligence (BI) is a comprehensive process that involves several steps. Its goal is to transform raw data into actionable and valuable insights to improve organizational decision-making.

Here’s a broader view of how the BI process typically works:

• Data Collection: The first step in the BI process is collecting and analyzing data from various sources. This can include both internal and external systems sources like ERP systems, CRM platforms, and financial software, as well as external sources like social media, market research, and public data sets.
• Data Integration: Once data is collected, it must be integrated into a coherent structure. This often involves complex data cleansing and transformation to ensure that data from different sources is accurate and consistent. Integrating data visualization tools and middleware can help automate this process, ensuring that all data is compatible and usable for analysis.
• Data Storage: After integration, the data is stored in a central repository, such as a data warehouse, data mart, or data lake. This storage solution depends on the volume, variety, and velocity of the data being processed. Data lakes, for instance, are increasingly popular for their ability to store large volumes of unstructured data.
• Data Analysis: With the data organized and stored, analysts and business users of company data can begin to extract insights and explore data. This step might involve complex queries, data mining, predictive analytics, or machine learning algorithms to identify trends, patterns, and anomalies.
• Data Reporting: The final step is to report the findings in an accessible format. Tools like dashboards, visual analytics, and detailed reports present the data clearly and effectively. This allows stakeholders to make informed decisions based on the data science and insights derived from the BI process.

Enterprise BI applications typically are used in specific use situations, and applications vary across various industries.

1. Financial companies also use data mining and analytics tools to determine the risks and benefits of loan and policy decisions and find new product lines to provide customers.
2. BI helps retail marketers manage campaigns and promotions, while manufacturers rely on it for both historical and real-time data.
3. BI is crucial for optimizing logistics and supply chain operations. Companies use BI to monitor supply chain performance, anticipate inventory needs, and identify potential disruptions. This helps reduce costs, improve delivery times, and enhance service levels.
4. Telecom companies use BI to analyze call data records, customer service interactions, and network performance. This data helps understand customer behavior, improve service quality, and plan network expansions. BI also supports churn analysis to identify at-risk customers and develop retention strategies.
5. Online retailers use BI to optimize website interfaces and personalize marketing strategies. Insights into customer purchase patterns, product preferences, and cart abandonment rates drive targeted campaigns and improve sales.

To objectively assess marketing efforts, businesses need comprehensive data on user acquisition costs, customer interactions, order placements, and actual profitability by channel. Google Analytics provides critical metrics like traffic sources, user behavior, conversion rates, and online transactions. Combining this data with financial information allows companies to determine each marketing channel's actual return on investment and optimize their strategies accordingly.

However, other information required for business intelligence isn’t available in Google Analytics:

1. Advertising costs. Google Analytics doesn’t collect information about expenses for advertising services. If you don’t import this data, you won’t be able to calculate and compare the ROAS for each channel.
2. Accounting for all customer touchpoints. If customers always completed transactions on the site, that would be perfect. Tracking the user's path would make it easy to simply relate each order to the source that brought it. But in reality, people call by phone to clarify details or place orders, message by chat and email, go to offline stores, etc. Google Analytics doesn’t record these actions on its own. To get this data, you have to integrate with other services: for example, a call tracking system records all call data and links each one to a specific channel and user, then transmits this information to Google Analytics.
3. Execution of orders. In Google Analytics, you only see bids, not actual sales. Customers can place an order, change their mind, buy the product in another store, or return it. Google Analytics also records test orders. As a result, revenue and transactions in Google Analytics may differ significantly from the actual picture in your CRM system. It’s difficult to conclude the effectiveness of an advertising channel if you don’t know what kind of profit it’s brought.

Incorporating systems that offer advanced analytics capabilities is essential to truly leverage the full spectrum of data analytics for enhancing business intelligence. These systems go beyond the basic analytics provided by tools like Google Analytics, enabling more sophisticated analysis, predictive insights, data discovery, and a comprehensive understanding of business data.

When you collect data from all sources in a single system, you have data analysts who can analyze your marketing in more detail and increase its effectiveness. Business intelligence data visualization tools will help you:

1. Automate the management of advertising campaigns. You can adjust spending by taking into account the ROAS for each channel, campaign, and phrase, reducing your advertising costs.
2. Create an automated report to analyze the important marketing KPIs for you: clicks, expenses, income, conversions, sales, ROI, etc. With a computerized report, you can monitor metrics for advertising campaigns in real-time and measure the effectiveness of all advertising channels on different platforms.
3. Find out which channels, campaigns, and keywords make a profit and which work at a loss. Based on the calculated ROI, you can then redistribute your budget to increase revenue. This is what Answear did.
4. Segment customers based on their buying activity. Then, you can personalize communication with these customer segments to reduce advertising costs, extend customers' life cycles, and increase the lifetime value (LTV) of your customer base as a whole.
5. Perform ROPO analysis to discover how your online marketing affects offline sales. Business intelligence will also help you understand why your customers are looking at products on the site and buying in a physical store, and find out what prevents them from ordering online.
6. Conduct cohort analysis to calculate LTV and understand how to increase this indicator.
7. Conduct A/B tests of various advertising campaigns and channels to identify the most effective. For example, our client experimented to compare individual offers by phone and email. As a result, they managed to increase their email conversion rate to 15%.
8. Utilize data analysis to generate actionable business insights through advanced reporting and querying capabilities, enhancing decision-making processes.
9. Employ predictive analytics to model future outcomes based on historical and real-time data, aiding in strategic planning and forecasting.
10. Leverage data visualization to create intuitive charts, graphs, and visual representations of data, facilitating easier understanding and interpretation of complex information.

Creating a business intelligence strategy is crucial for leveraging data analytics to inform business decisions effectively. Here are the steps to consider when crafting your strategy:

Depending on their size and budget, businesses may choose to hire new staff specifically for analyzing BI or might select representatives from various departments to form a BI team. This team should gather and analyze social insights and data, as 53% of business leaders highlighted in The 2023 State of Social Media report.

Understanding your business objectives is essential. Determine what you aim to achieve with your BI strategy—whether it's improving benchmarks, streamlining operations, or enhancing competitive analysis. Knowing these goals will guide the choice of resources, tools, and integrations needed for your BI initiatives.

Identify all necessary stakeholders across multiple departments, such as IT, to ensure secure data access and management. Establish who needs to be involved in building a business case for new BI tools and who will receive the insights generated.

Choosing the right BI tools is crucial for extracting meaningful insights from data and driving informed, customer-centric decisions. Key factors to consider include the tool's data integration capabilities, visualization features, predictive analytics, and ease of use for non-technical users.

Evaluating BI tools based on your business needs and goals will help you select the most suitable solution to support your organization's growth and success.

Integrate data from various sources, such as social media analytics, CRM systems, and customer service channels, to enhance the value of your BI. This cross-functional approach ensures a comprehensive view of your business landscape.

Capitalize on BI to identify and analyze market trends. With 97% of business leaders expecting an increase in the use of social data for trend analysis, staying ahead in identifying relevant trends is crucial for proactive business planning.

Utilize data visualizations to present your findings clearly and effectively. Tools like OWOX BI offer visual reports that connect social activities to website traffic, helping to demonstrate the impact of social media on business operations.

Business intelligence software or tools are used to collect large quantities of data and process it from internal systems and data analytics. Their main purpose is to search and report complex business information. BI software is intended mainly for making business decisions. Business intelligence tools combine information from various sources, e.g., databases and spreadsheets, to provide analysis.

When selecting tools, knowing which features will help your organization achieve its goals is essential. BI tools can automatically identify and eliminate inaccurate data if it is incomplete or duplicated. Data standards and rules also apply across different systems, providing consistency and accuracy while lowering the risk associated with manual data entry. Businesses can gather various data through business intelligence tools.

Business Intelligence is an integrated set of analytics software designed for different information needs. Most support BI software as well as traditional BI platforms. Several Business Intelligence tools available to organizations include the following-

OWOX BI is a business intelligence and data analytics platform that integrates various data sources to create a comprehensive data pipeline. It provides users with automated data collection, consolidation, and processing, facilitating accurate and accelerated decision-making.

OWOX BI is particularly effective in e-commerce analytics, helping companies optimize marketing campaigns and forecast sales. Its robust tools support advanced analytics like attribution modeling and ROI analysis, making it a valuable asset for businesses aiming to enhance their analytical capabilities and insights.

Microsoft PowerBI is an integrated analytics solution that excels at visualizing information. Moreover, a brand-new integration helps improve a campaign's effectiveness. Microsoft PowerBI's web interface makes it easy to connect to a variety of applications. The software can provide real-time dashboards and reports.

Tableau excels in data visualization and is easy to use for non-technical users with numerous data connectors and interactive dashboards. It offers real-time data integration, allowing users to visualize and analyze live data.

Tableau Public provides a free platform for learning and improving BI skills, supported by various educational resources. It employs a tiered subscription pricing model, making it accessible for teams of all sizes.

Looker Studio is a free, upgradeable tool that offers customized, interactive reports and dashboards. It integrates seamlessly with Google services like Google Analytics and Google Sheets. The platform features a drag-and-drop report builder, supporting real-time collaboration and easy report sharing. It is designed to make data accessible and understandable for users at any skill level.

Domo is a cloud-based BI platform known for its user-friendly interface and extensive data integration capabilities with over 1,000 pre-built connectors. Its Magic ETL feature simplifies data transformation for users without technical expertise. Domo offers a free version and multiple pricing tiers based on usage credits, allowing unlimited users but varying by data consumption.

Zoho Analytics is a cost-effective BI tool suitable for small businesses. It offers robust data preparation and visualization features. It integrates with over 250 data sources and supports direct connections to numerous popular apps. The platform also includes Zoho DataPrep for self-service data management and 'Ask Zia,' a natural language query feature for easy data inquiries.

Sisense is recognized for its robust single-stack architecture that integrates data preparation, modeling, and visualization. It features a fast in-chip data engine capable of handling large datasets and an intuitive no-code, drag-and-drop dashboard builder.

Sisense provides advanced analytics capabilities through AI and machine learning integration, available in both self-hosted and cloud-based options to suit organizational needs.

There are many ways to set up a business intelligence system with various BI tools. Still, the goal is the same: combine data from Google Analytics, advertising services, call tracking systems and a CRM. Business intelligence software solutions facilitate this integration, offering a seamless way to manage and analyze data. You can accomplish this with various business intelligence tools and data visualization software by collecting your data in various places.

This method is ideal for companies with large and complex datasets that want to analyze their data carefully. OWOX BI integrates data from all sources in Google BigQuery cloud storage.

With OWOX BI, you can build reports on complete unsampled data using any combination of parameters and key performance indicators.

Benefits of business intelligence with OWOX BI

1. All user actions are collected to Google BigQuery directly from the site using the server-side streaming, in real-time, without prior processing, with hit or event-level accuracy.
2. Get unsampled, raw events' data, regardless of the number of site visits.
3. See how much each session costs you: ad spending is collected daily from advertising platforms to BigQuery and attributed to sessions according to UTM parameters.
4. With the OWOX User ID, you can analyze user behavior across your sites, even if they aren’t linked directly. An additional identifier will help bypass the limitations of Google Analytics Client ID, User ID, and cookie storage time.
5. You don’t need to purchase expensive software, licenses, or equipment. Data is stored in your Google BigQuery project, and you control its access.
6. OWOX BI's capability to integrate with multiple data sources enables the processing and analysis of raw data from various origins, including spreadsheets, databases, and social media, to generate comprehensive insights.

Basic configuration steps:

1. Set up website user behavior data collection in Google BigQuery using the OWOX BI Streaming.
2. Collect advertising cost data from various ad services into Google BigQuery.
3. Upload data about your transactions, revenue, purchases, and returns to BigQuery from your CRM system.
4. Build reports in Google BigQuery using SQL queries, or use ready-made low- or no-code SQL templates.
5. Connect final data marts to the visualization tool of your choice: Looker Studio, Tableau, Microsoft Power BI, or Google Sheets.

This method is suitable for companies with a relatively small amount of traffic, no offline sales, and minor differences between the number of purchases in the CRM system and in Google Analytics 4 interface.

It allows for integrating various advertising data sources into GA4. This approach to data literacy saves money and is really easy to implement.

With Google Analytics 4 help, you can find comprehensive instructions on importing expenses, products, sales, and returns data. This process is crucial for analyzing historical and current data to understand past performance and make informed decisions.

The disadvantage of this method is that the data in your reports may be sampled, and there are restrictions on the number and combination of parameters and indicators in one report. In addition, if you manually upload data on expenses and income to Google Analytics, you need to do this every time you need a report.

Basic configuration steps:

1. Put UTM tags in all links from your ad campaigns, so Google Analytics collects accurate information about traffic sources.
2. Set up advanced e-commerce tracking.
3. Setup & transfer the Client ID to your CRM system with an event, for example, when a customer fills out a form on the website, calls, or writes in a chat. You can link all your data using this identifier.
4. Import expenses from all your advertising sources into Google Analytics. This can be done manually or automatically using OWOX BI.
5. Set up the transfer of revenue, sales, and returns data along with Client ID to Google Analytics via the Measurement Protocol.

The cost of implementing a cross-channel marketing analytics and reporting system depends on many factors, including site traffic, the number of advertising services you use, and tools and setting methods.

You can choose a shareware setup option and try to merge the data on your own in Google Analytics 4 or Google Sheets. It is shareware because you have to spend resources on developers, creating your connectors, regular processing, data updates, etc. You can also entrust this work to professionals with extensive experience and expertise in this industry.

In our experience, companies that use the help of specialists at the very beginning, start to receive additional value much faster. The OWOX BI team understands business tasks well and will willingly share knowledge and experience to solve any task. Implementing BI tools effectively enhances business operations by providing real-time visibility into processes, improving operational efficiency and decision-making.