This article provides an overview of the functionality and use cases of the Reveal Element.

1. Introduction

The Reveal Element helps you to analyze your marketing data to detect trends, anomalies, and gather campaign optimization recommendations. The data analysis capabilities of the Reveal element work out of the box. There is no setup necessary.

After you authorize an account from a supported advertising system, Adverity constantly analyzes its data to detect marketing insights and pro-actively display them to you.

There are three major parts of the Reveal element:

• Proactive Analytics
• Forecasts
• Channel Mix Optimization

To see the value proposition and the use cases for different aspects of the Reveal element, read the Use Augmented Analytics on our website.

See the article Is Augmented Analytics the Ultimate Digital Marketing Tool for more information.

2. Proactive Analytics

With Proactive Analytics, you can visualize important information about:

• Anomalies
• Trends
• Segment Analysis
• Budget Suggestions

The main dashboard of Proactive Analytics shows widgets that report developments based on your marketing data. Adverity updates dashboards once a day.

Every widget is rated on an impact score on a scale:

• low
• medium
• high

The impact score shows you how much attention you should pay to the displayed information.

2.1. Filtering widgets of Proactive Analytics

Filtering results of Proactive Analytics can help you to see only the information you are interested in. For instance, you can filter out only the results for the previous week. By default, you can see all of the widgets. To filter and sort results:

1. Go to the Proactive Analytics tab of the Reveal element.
2. Use the right panel to filter the results. You can filter for the following:
   • Date range
   • Widget types
   • Impact score
   • Datasources
   • Funnel steps
   • Accounts
3. Select your preferred sorting method in the Sorting section.
4. Click the Show widgets button.

3. Forecasts

Forecasts help you to monitor your spendings against your planned budget. This feature can provide you with forecasting data for up to 30 days in the future.

To set up your budget:

1. Go to the Forecasts tab of the Reveal element.
2. In the Forecast Range field at the top of the page, select the time range.
3. In the Budget at the top of the page, enter the planned budget for the selected period.

3.1. Filtering and sorting results of Forecasts

To filter information in the budget forecast:

1. Go to the Forecasts tab of the Reveal element.
2. Use the right panel to filter the results. You can filter the following:
   • Datasources
   • Accounts
   • Campaigns
3. Click the Apply button.

4. Channel Mix Optimisation

Channel Mix Optimization helps you in finding your optimal budget allocation in order to increase your Return on Investment (ROI). This is provided by forecasting the output of thousands of different budget allocations based on your historical data. Channel Mix Optimization can help you determine what type of marketing channels you need to use to achieve the maximum revenue for a defined budget.

In the right panel of the Channel Mix Optimization tab, you can:

• Change the Historical time range.
• Change the Channels.

4.1. Calculating optimal ROI with Channel Mix Optimization

There are two essential pieces of data necessary for optimal functionality of Channel Mix Optimization ROI calculation:

1. Revenue - The revenue data of Google Analytics.
2. Costs - The costs data from any advertising datasource which includes them.

To start your Channel Mix Optimization:

1. In Adverity, go to Connect Element.
2. In the left navigation panel, click Connections.
3. In the upper right corner click the + Add button.
4. Search for Google Analytics.
5. Click Google Analytics. Add and authorize it as a connection.
6. Repeat this process with any advertising datasource which includes costs. For example Google Ads.

Note: Some historical data is needed for a data model:

• At least 2 years of historical data for an accurate data model.
• Ideally 5 years of historical data for a very accurate data model.

5. Appendix - Capabilities and limitations

5.1. Supported data sources

5.1.1. Proactive Analytics

Proactive Analytics supports a limited set of data sources:

• Google Ads
• Display & Video 360
• Facebook Ads
• Microsoft Advertising
• Linkedin Ads
• Google Search Ads 360
• Apple Search Ads
• Twitter Ads
• Amazon Ads
• Criteo Marketing
• Instagram Ads

5.1.2. Forecasts

The Forecasts feature supports every datasource available in the Connect element.

5.1.3. Channel Mix Optimization

To use the Channel Mix Optimization feature, Adverity needs to verify if your data sources are compatible on a case-by-case basis. Contact your Account Manager for details.

5.2. Getting valuable insights

For Proactive Analytics and Forecasts, there is no limit for metrics to generate widgets.

To get valuable insights, use the Proactive Analytics and Forecasts features for:

• High-volume accounts (high amount of impressions, clicks, costs).
• Campaigns that track conversions.

5.3. Anomalies - conversion lag

In digital marketing, conversion lag refers to the amount of time between ad engagement and conversion. As most users do not immediately convert after initially engaging with an advertisement, conversion lags can regularly appear. Read more in this blog post on our website.

Adverity uses a regression model to forecast the effect of the conversion lag. This smoothes the time series which is then subjected to outlier detection.

5.4. Trends - time range

The purpose of trend detection is to detect long-term trends. The time range is determined dynamically and can differ between trend widgets. A trend always ranges from the last turning point until yesterday.

5.5. Historical data necessary for the accurate data model

For an accurate data model, some historical data is needed. The data you can provide to optimize the data model includes:

• Advertising costs
• Revenue

The time range of historical data which you can provide is:

• At least 2 years of historical data for an accurate data model.
• Ideally 5 years of historical data for a very accurate data model.