Oracle University shows us how to build better lists – Behavioral Segmentation Test
Facts are our friends. You would be hard pressed to find anyone who doesn’t agree that the better targeted your list, the better your results will be. Oracle University teaches us how to use Eloqua to create smarter segmentation, increasing our conversion rates and reducing audience fatigue. So why do we still see many marketers who seem to rely on “spray and pray” list building? We speculated that demonstrating just how much better the results could be would convince our program managers to invest the extra time in refining their lists.
Pressure to go to market quickly with e-mail resulted in marketers using heavily fatigued “house” lists that were poorly segmented. This was driving down our open and click through rates and ultimately our generation of marketing qualified leads (MQLs) and pipeline. We needed to demonstrate the power of behavioral segmentation in order to justify taking the time to build better lists.
We elected to focus our test on one of the nine business lines in our company. It happened to be one that sends out a tremendous amount of e-mail each month, often with lists of 50,000 records or more. We set up five different segments to compare:
- Segment One: Visited relevant web pages
- Segment Two: Persona and Industry segment pulled from data captured in Eloqua. Primarily captured via form submits. This would be a good test of how well behavior follows self-reported demographics.
- Segment Three: Persona and Industry segment from an external list provider. Since these names were all incremental to our house list, we expected to see better performance.
- Segment Four: Sales provided list built by: Industry, Geography, Annual Revenue, Number of Employees, Title and Job Role. This list had grown over decades and had historically poor engagement. However it was the go to list for our marketers.
- Segment Five: E-mail respondents who opened, clicked through, or submitted a form in the last six months.
To make sure our sample size was sufficient, we scoped our test with the following parameters:
- Total number of people in all lists: 213,702
- Number of lists: 17
- Number of batch sends: 146
- Number of total e-mails sent: 557,663
The behavioral segments significantly outperformed the segments based on demographic data. The segment based on previous engagement with e-mail had the highest open rate performing 5.4 times better that the house list. The second best performing segment for opens was page views that had not previously engaged with e-mail. Our external list came in third. This suggests that our traditional house list was suffering from e-mail fatigue. We were somewhat surprised at the poor performance of our user submitted demographic information stored in Eloqua. In fact these results strengthened our resolve to put resources into data cleansing. Our data cleansing efforts and results will be covered in future blogs.
Our results for click through rates saw the top two open rates segments swap places, with Page Views by tagging being the clear winner with almost 4% click throughs compared to 1.6% for our e-mail engagement segment. The page views segment was too small to meet the MQL goals alone however. We were pleasantly surprised at our externally supplied demographic list also showing a click through rate of 1.6%. Our traditional house list and our user submitted demographic segments were the clear losers with the house list being outperformed by the e-mail engagement segment by a factor of 8!
Our test validated our assumption that behavioral segmentation will drive significantly higher conversion rates. Using the Eloqua platform, we could identify potential prospects by their behavior rather than our assumption of their interest based on traditional demographic information. The inclusion of behavioral segments nearly doubled our generation of MQLs with significant impact on pipeline, and our database marketing team always looks for the opportunity to include behavioral segments in campaigns now. In some campaigns, we could significantly reduce the use of generic house lists, further improving our e-mail conversion metrics. Our test also pointed out the need for additional focus on data cleansing, which we pursued vigorously. We validated the investment in external data sources, and reduced our dependence on our heavily fatigued house lists. While your results may vary I highly recommend going through the process.
While all the Oracle University B2B classes are helpful, this project relied most heavily on material covered in the fundamentals course and the B2B targeting course. Eloqua’s ability to track and collect digital body language gives you a powerful tool to better understand were your prospects are in their buyer’s journey, better target them, and communicate with them appropriately.
- B2B: Advanced Segmentation
- B2B: Data Cleansing
- B2B: Web Profiling
- B2B: Fundamentals of Segmentation
 "Eloqua B2B Targeting - Segmentation." Oracle University Accessed 14 May 2017.
 “Eloqua B2B Targeting – Page Tagging.” Oracle University Accessed 11 May 2017.
 “Eloqua B2B Data Cleansing.” Oracle University Accessed 19 June 2017.