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Built for Performance – Introducing Cohort Analysis & Look-Alike Group Tools

Here at Nanigans we are committed to designing and developing tools within Ad Engine, our Facebook advertising platform, that empower advertisers to make informed and ROI-based decisions. Our latest Ad Engine release contains two powerful new tools:

  • Cohort Analysis to compare the lifetime value generated by campaigns over time
  • Look-Alike Groups to discover new audiences with high probabilities becoming customers

Cohort Analysis

Cohorts are groups of people who share certain characteristics. Cohort analysis is a common and powerful methodology used by marketers to understand how different groups behave over the long-term. Nanigans has developed a Cohort Analysis tool designed specifically for Facebook advertising campaigns, empowering advertisers to understand and compare the value generated by ad placements and customers in the days, weeks and months after initial ads are delivered. The tool was designed to be both intuitive and highly customizable, enabling advertisers to quickly identify trends.

Visualization – Generating Tables and Graphs

In the example cohort table below, the advertiser is comparing the downstream revenue generated by weekly groupings of Facebook ad placements during the 15 days following their initial delivery. The advertiser has also elected to display the total number of purchases generated by each cohort. By simply clicking through a day, the advertiser can quickly surface details of the advertising strategies that contributed to its performance and success, such as what creative, targeting and frequency capping were employed.

Cohort Analysis Facebook Nanigans

A darker shaded cell represents a more mature or higher performing cohort, and the change in text color from black to white indicates the point at which these cohorts generated a positive return on ad spend (i.e., more revenue was generated than the cost of the media spend). This visualization enables an advertiser to quickly identify trendlines, such as that ad placements delivered greater return leading up to holiday shopping season and with this return occurring fastest the week of November 25th (Cyber Monday) and dramatically slower the prior week of November 18th.

In addition to the table, graphs of cohorts are created with a simple click of the graph button located at the top left of the table. From here, data can also be exported and downloaded into a .CSV file, or as a .PNG or .JPG for quick copy-and-paste into any presentation or report.

Customization – Defining Cohorts and Conversions

Creating these visualizations is simple and highly customizable. Advertisers define cohort groupings by day, week or month over a specific time period. The tool even allows advertisers to refine these cohorts by campaign strategy, such as if they only wanted to compare ad placements leveraging revenue optimization as opposed to optimization for earlier in the funnel events like registrations.

Advertisers next select the performance details they want to display for each cohort. Ad Engine tracks 80+ metrics, so advertisers can display everything from CPC and CTR to purchasing users and lifetime return. These performance details can by displayed by day, week or month. In an effort to display more information in the visualization, advertisers can also select to display multiple additional total metrics for each cohort (e.g., the number of purchase events in the example above).

Look-Alike Groups

For advertisers leveraging Custom Audience targeting, we are equally excited to release Look-Alike Groups within Ad Engine. This tool quickly surfaces new audiences to target that share common interests with current customers, and thus have higher probabilities of delivering return.

An advertiser defines the audience criteria to be used in the analysis by entering or pasting in keywords (precise interests or broad topics) and at least one country. They have the option to further refine their analysis to specific genders and age ranges. Next the advertiser selects audience(s) for comparison — either a single high performing audience, or both a high performing and low performing audience. In the example above the advertiser has defined “Top Purchasers” and “Non-Purchasers” for comparison.

The tool generates a stack ranking of keywords that have a high overlap with the highest performing audience and, if selected for comparison, low overlap with lowest performing audiences. To create a new interest group to be targeted from the analysis, advertisers simply check boxes next to each keyword row, name the group, and click “Create Interest Group.”

Overall, this Look-Alike Groups tool offers a more efficient way to find new audiences on Facebook than traditional “spray and pray” methods. Rather than put spend behind random interests, advertisers can generate new targeting groups based on commonalities with their current customers.

Contact us today to learn how these and other Ad Engine tools can help you find profitable customers and increase the ROI of your Facebook ad spend.

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