Traditional analytics services provide information only about the number of impressions, clicks, and website visits from various ad campaigns, along with the total amount spent. However, they don’t reveal how many actual purchases were made. This happens because advertising data resides in ad platforms, while sales data is stored separately in the CRM system, meaning these data sources are fragmented.You need to view all this information in one place to accurately assess the return on ad spend. 

Automated end-to-end analytics can help with this by showing whether a particular ad campaign is driving sales in real time, with report updates as frequently as every hour. Based on this data, you can reallocate your ad budget towards effective campaigns for scaling, while turning off unprofitable ones. It tracks the entire customer journey in the funnel, from the moment an ad is displayed to the moment of actual purchase.

Screenshot of an actual marketing report sheet

Thanks to the high level of data granularity, it's also possible to evaluate performance across ad accounts, ad groups, products, sales managers, and timeframes (specific days, periods, months, years). This allows for precise and targeted decision-making regarding corrective actions exactly where problems are identified. The reports are enriched with various filters for deeper insights.

Screenshot of an actual marketing report sheet

Screenshot of an actual marketing report sheet

Screenshot of an actual marketing report sheet

Screenshot of an actual marketing report sheet

Screenshot of an actual marketing report sheet

End-to-end analytics is essential for making timely management decisions for business owners, marketers, targeting specialists, sales department heads, and others. It is also convenient if the company does not have an analyst position temporarily or at all.

• Identify Data Sources: Determine where the data will come from.

• Run API Scripts: On the server, set up API scripts to collect data from the data sources according to a schedule.

• Consolidate Data: Integrate the collected data into a cloud database.

• Data Modeling: Model the data to structure it for analysis.

• Create Report Templates: Design report layouts with dynamic metrics in Power BI.

• Publish Reports: Publish the reports in Power BI Service and provide access to users.

• Configure Data Updating: Set up automatic data updates to keep the reports current.

A general diagram of the process of creating automated analytics

• CRM Systems

• Ad Platforms: Google Ads / Facebook Ads Manager  

• Google Analytics (GA4)

• Websites

• Google Sheets, Local Excel Files

• Google forms / Telegram bot forms

• Telephony Systems: Binotel, etc.

🔄 The data update process, according to a set schedule, occurs both in the database and within the Power BI report itself. 

The update frequency can vary based on your organization's needs and your subscription plan.

Power BI Mobile is a free app from Microsoft that gives you access to your analytical reports and dashboards anytime, anywhere. Available for both iOS and Android smartphones and tablets, it allows you to view, interact with, and analyze data in real-time directly from your mobile device. 

The app synchronizes with your Power BI platform, enabling you to see updated data and changes in real-time.

• Advertising Spend: The total amount spent on advertising.

• Revenue: The total income from sales.

• Impressions: The number of times an ad is displayed.

• Cost per Impression (CPI): The cost of one ad impression.

• Clicks: The number of times users clicked on an ad.

• Cost per Click (CPC): The cost of one click on an ad.

• Click-Through Rate (CTR): The percentage of people who clicked on the ad after viewing it.

• Leads: The number of potential customers who left their contact information.

• Registration Conversion Rate (Regs%): The percentage of people who registered.

• Cost per Lead (CPL): The cost of acquiring one lead.

• Buy-Out Orders: The number of completed sales.

• Conversion Rate to Purchase (CVP): The percentage of leads that made a purchase.

• Cost per Order (CPO): The cost of one paid order.

• Return on Advertising Spend (ROAS), Return on Marketing Investment (ROMI): Revenue generated per unit of advertising spend.

• Average Revenue per Paying User (ARPPU): The average amount spent by a customer per order.

• Order Cycle Time in Days: The time from lead to order completion.

• Share Metrics: The proportion of individual metrics within the overall process.

• Percentage Changes in Metrics Over Time: The percentage changes in metrics over time.

• Number of Calls and Successful Calls: The total number and success rate of calls.

• Lifetime Value (LTV): The total revenue from a customer over their entire relationship with the company.

• Sales Plans: Planned sales volumes for a specific period.

• ABC-XYZ Analysis: Categorization of products based on sales performance.

• Other: As per your specific needs.

🕒 Project Duration: 2-6 weeks.  

Creating automated analytics is a complex process that requires careful checking at each stage. A few weeks to build a system that simplifies your work and provides quality analytical data is the optimal timeframe.  

In addition to the standard automated analytics creation process, the project timeline also includes additional stages:  

• Audit and Requirements Gathering: Studying your data and business processes to build a system that meets your needs.  

• Communication with Your Technical Experts: As needed.  

• Additional settings of your CRM system for high-quality data accounting (mandatory fields, including linked to stages, drop-down lists, auto-update fields, calculated fields, etc).

• System Testing: Ensuring all systems are checked for guaranteed seamless analytics operation.  

• User Training: Providing training for users.

As mentioned above, comprehensive user training and technical support are provided to ensure the effective operation of your analytical system.

Why is user training important for interacting with automated reports?

• Understanding Reports: Insufficient understanding of reports can lead to incorrect conclusions and decisions.

• Maximizing System Use: Without proper training, users may not utilize all the features of the system, reducing its effectiveness and benefits for the business.

What does user training include?

• Familiarization with Reports and Dashboards: I explain how to interpret data and use various report filters in Power BI.  

• Training Materials: I provide detailed video instructions for ongoing use of the reports.

• What does technical support include?

• Data Update Support: Regular checks and adjustments of automated data updates.  

• Error Correction: Prompt response to technical issues and their resolution.

💰 The cost of creating an analytics project starts from $2000. 

The final price depends on the project's complexity and requirements, as well as the chosen option for ongoing technical support: premium or budget.