Optimizing Digital Advertising With Analytics

Digital advertising campaigns are an increasingly important element of most brands' marketing mix and are designed to achieve specific goals: increase brand awareness, drive traffic to the advertiser's website, and achieve consumer conversions. And although digital advertising generates a huge amount of data, not knowing how to interpret it could result in inefficient spending and missed opportunities. This course introduces the use of analytics and data to measure the extent to which the goals of digital campaigns are being achieved, and thereby provides a roadmap for you to make more informed spending decisions. Through the application of various analytical tools, such as effectiveness and efficiency metrics, attribution modeling, and the design of randomized controlled trials, you—as a buyer or seller of digital advertising—will be more successful at monetizing digital assets. You explore this content through a mix of input from industry experts, a hands-on course project, and the presentation of best practices your instructor will also help broaden your understanding of digital advertising analytics and its impact on your advertising strategy.

Schedule

Event Details

Level

Intermediate

Date

14-JUNE-2021

Duration

3 Hours

Venue

ONLINE

Fees

$500.00

·         Analyze how current internet advertising trends impact how you measure the success of a campaign

·         Manage your spending in sponsored search advertising

·         Use seven common metrics and randomized controlled trials to measure advertising effectiveness and efficiency

·         Make future budget allocation decisions by attributing sales outcomes to specific marketing channels


·Marketing managers and professionals who interact with customers or product data

·Product managers and engineers or those involved in customer service strategy

·An intermediate level of skill using Excel, including functions such as pivot tables and regression analysis, is strongly recommended

 

·Marketing managers and professionals who interact with customers or product data

·Product managers and engineers or those involved in customer service strategy

·An intermediate level of skill using Excel, including functions such as pivot tables and regression analysis, is strongly recommended