The "Statistics" section helps you evaluate results in real time and identify the most successful channels and campaigns. For example, by selecting the appropriate parameter in the filter, you can see which ads generated the most registrations or sales , and quickly assess which channels are performing best.

Key statistics columns

• Number of clicks — the number of clicks on the tracking link on the landing page.
• Number of subscribers - the total number of dirty subscribers, excluding unsubscribes.
• CR (conversion rate) is the percentage of clicks that result in subscriptions.
• Number of net subscribers is the number of subscribers minus unsubscribes.
• Net CR is the conversion rate of clicks into net subscribers.
• Number of unsubscribes - the number of users who unsubscribed from the channel.
• % unsubscribes — the percentage of subscribers who unsubscribed from the channel.
• Bot activation count —the number of times commands are executed (e.g., /start, /link, /contact). Any word sent to the bot will also be counted.
• Number of dialogues – the total number of active dialogues with users.
• Number of registrations - total number of registrations.
• First Sales Amount - The total amount of first sales.
• Reg/Dep – the ratio of registrations to deposits.
• Number of repeat sales - number of repeat deposits.
• Repeat Sales Amount - The sum of all repeat deposits.

Grouping and filtering statistics

Grouping and filtering data in MVP Project allows you to analyze advertising campaigns in detail, identify patterns, and optimize your promotion strategy.

Filtering statistics

Filters allow you to select the required data based on various criteria:

• Date – specifies the time period for analysis.
• Advertising channel – choice of advertising platform.
• Facebook ID - select an advertising account using a unique identifier.
• Facebook (FB) Campaign – Filtering data by advertising campaigns.
• Ad Set FB – data selection by ad groups.
• FB Ad - Analysis of Specific Ad Creatives.
• Company Name – View statistics for individual companies.
• Adset name – details of data for specific ad groups.
• Ad Title – filtering by ads.
• Placement location – select placement platforms (feed, stories, etc.).
• Source name – division between paid and organic traffic.
• Landing – analysis of the effectiveness of individual landing pages.
• Spot – filtering by traffic sources.
• Spot type – segmentation of data by advertising formats.
• Country – highlighting statistics by region.
• Bayer - analysis of the work of the specialists who launched the advertising.

Filtering helps you focus on the data you need while eliminating unnecessary information.

Statistics grouping

Grouping is a way to categorize advertising results based on different criteria to better understand what's working and what's not. It helps you discover patterns, evaluate effectiveness, and make more accurate decisions. Available parameters:

• Date – analysis of dynamics by days.
• Month – evaluation of the effectiveness of advertising strategies on a monthly basis.
• Year – comparison of results for different years.
• Spot - analysis of the effectiveness of traffic sources.
• Advertising channel – details on placement sites within platforms.
• FB Campaign – Analysis of the success of individual advertising campaigns.
• Adset FB – evaluation of the effectiveness of different targeting segments.
• FB Ads – Identifying the most converting creatives.
• Company name - analysis of results for specific advertised brands.
• Adset name – comparison of different ad groups within campaigns.
• Ad Title – details on specific advertising messages.
• Placement – comparison of the effectiveness of different placements.
• Source – division of data by traffic acquisition methods.
• Landing – analysis of landing page effectiveness.
• Bayer – control over the performance of specialists launching advertising.
• Country – study of advertising effectiveness in different countries.

📊Weighted average statistics

A weighted average is a method of calculating an average in which each metric is given a weight (significance coefficient).
Unlike a simple arithmetic mean , where all metrics are given equal weight, a weighted average takes into account the relative importance of each metric in the overall sample.

⚙️How do I set up weighted average statistics?

1. Assigning weights to metrics

Go to the Statistics section and click on the pencil icon ✏️

2. Setting up grouping and weights

Select a grouping to display metrics.

⚠️ Grouping by date is unavailable - select the statistics date in the filters section.

Next, set weights for the most significant metrics. Each metric is assigned a weight reflecting its relative importance in the overall result (any numerical value can be specified).

Example:
Grouping by Spot is selected.

The metric "Number of subscribers" is assigned a weight 2 ;

Metric "Number of dialogues" - weight 3 ;

The remaining metrics are calculated using the arithmetic mean .

After that, click the "Save Rule" button.

💡 Tip: Choose the metrics that are most important for your analysis and assign them a higher weighting.

3. Activation of the weighted average

Turn on the "Weighted Average" toggle switch, and the system will calculate statistics based on the rule you created.

The final value of the metrics in the “Total” line is calculated using the formula:

Where:

metric_value — statistical data calculated using the arithmetic mean;

weight - the significance coefficient established for a given metric;

Σ(weights) is the sum of all weights.

📘 Calculation example

Total for the “Number of subscribers” column:

(2×2+0×2+0×2+1×2+6×2+0×2+4×2+0×2+1×2+4×2+2×2+21×2+0×2)/(2+3)=16.40

Total for the “Number of Dialogues” column:

(30×3+1×3+1×3+11×3+44×3+27×3+10×3+2×3+0×3+7×3+16×3+8×3+3×3)/(2+3)=96

🏁 Conclusion

Using a weighted average provides a more accurate and objective view of the results, especially when different metrics have different levels of importance .
By adjusting weights, you can flexibly manage the contribution of each metric to the overall result and focus on the truly significant indicators.
This makes statistical analysis more meaningful and tailored to your business goals .