際際滷

際際滷Share a Scribd company logo

OMC - Intel Case Study_FINAL

Joel G. Polanco
Joel G. Polanco

Intel wanted to identify their top consumer segments and scale online audiences to target through programmatic display. They used Oracle Marketing Cloud's data management functionality and look-alike modeler to analyze first-party respondent data and build models to identify additional prospects with similar behaviors. When tested, Intel's look-alike audiences outperformed control groups with over 75% higher engagement and 48% lower costs, providing insights to inform future strategies.

1 of 1
Download to read offline
CUSTOMER SUCCESS STORY DATA MANAGEMENT Intel Boosts Engagement by Activating Look-Alike Models Copyright 息 2015 Oracle and/or its af鍖liates. All rights reserved. Because Intel is inside other brands, the company faces the challenge of attracting end users to visit their own websites. Intel wanted to identify their top consumer segments by technology attitudes and priorities, and to scale those segments online to target via programmatic display. Thats why the company looked for a way to create look-alike models from a key seed dataset of 27,000 surveyed consumers. From these models, Intel aimed to develop profiles of various technology-savvy consumers. CHALLENGES Scaling niche audiences based on survey respondents Increasing engagement with link activity, content expansion, and content browsing Driving a deeper level of onsite product research SOLUTIONS Data management Look-alike modeling RESULTS Look-alike audiences surpassed campaign benchmarks by over 75%. Look-alike audiences were more cost efficient than the campaign average by 48%. Look-alike audiences reported the most cost-efficient performance among all target groups. Intel decided to use Oracle Marketing Clouds data management functionality and look-alike modeler to efficiently scale their niche audiencespowered by Oracle Data Cloud. The company analyzed their first-party respondent data, identifying top technology-savvy consumers. From there, they brought those users online and built out high-value look-alike models to identify additional prospects who exhibited similar behaviors and traits to the top engagers. This project gave Intel the tools to model consumer segments and find millions of users who look like top technology-savvy consumers. From there, Intel deployed media to look-alike and non-look-alike (control) audiences simultaneously to compare engagement rates and costs. This approach enabled Intel to gather insights on segments for media weighting, publisher site placements, campaign ideation, and targeting. When the results came in, Intels look-alike audiences outperformed control audiences and showed the strongest engagement rate. Look-alike audiences surpassed the campaign benchmark by over 75 percent. They were also more cost efficient than the campaign average by 48 percent, reporting the most cost-efficient performance among all target groups. Just as importantly, Intel garnered behavioral insights and tendencies of top engagers to inform its future messaging and strategy. Learn more at: oracle.com/marketingcloud 75% Intels look-alike audiences created by Oracle Marketing Cloudsurpassed campaign benchmarks for engagement by 75%.

More Related Content

OMC - Intel Case Study_FINAL

  • 1. CUSTOMER SUCCESS STORY DATA MANAGEMENT Intel Boosts Engagement by Activating Look-Alike Models Copyright 息 2015 Oracle and/or its af鍖liates. All rights reserved. Because Intel is inside other brands, the company faces the challenge of attracting end users to visit their own websites. Intel wanted to identify their top consumer segments by technology attitudes and priorities, and to scale those segments online to target via programmatic display. Thats why the company looked for a way to create look-alike models from a key seed dataset of 27,000 surveyed consumers. From these models, Intel aimed to develop profiles of various technology-savvy consumers. CHALLENGES Scaling niche audiences based on survey respondents Increasing engagement with link activity, content expansion, and content browsing Driving a deeper level of onsite product research SOLUTIONS Data management Look-alike modeling RESULTS Look-alike audiences surpassed campaign benchmarks by over 75%. Look-alike audiences were more cost efficient than the campaign average by 48%. Look-alike audiences reported the most cost-efficient performance among all target groups. Intel decided to use Oracle Marketing Clouds data management functionality and look-alike modeler to efficiently scale their niche audiencespowered by Oracle Data Cloud. The company analyzed their first-party respondent data, identifying top technology-savvy consumers. From there, they brought those users online and built out high-value look-alike models to identify additional prospects who exhibited similar behaviors and traits to the top engagers. This project gave Intel the tools to model consumer segments and find millions of users who look like top technology-savvy consumers. From there, Intel deployed media to look-alike and non-look-alike (control) audiences simultaneously to compare engagement rates and costs. This approach enabled Intel to gather insights on segments for media weighting, publisher site placements, campaign ideation, and targeting. When the results came in, Intels look-alike audiences outperformed control audiences and showed the strongest engagement rate. Look-alike audiences surpassed the campaign benchmark by over 75 percent. They were also more cost efficient than the campaign average by 48 percent, reporting the most cost-efficient performance among all target groups. Just as importantly, Intel garnered behavioral insights and tendencies of top engagers to inform its future messaging and strategy. Learn more at: oracle.com/marketingcloud 75% Intels look-alike audiences created by Oracle Marketing Cloudsurpassed campaign benchmarks for engagement by 75%.