The potential promise of Omnichannel is to unify data on all of a customer’s or prospect’s actions in multiple channels and then present that data in the form of targeted, timely offers for a positive user experience.
What is the motivating business factor besides prospect experience?
Simple. The engagement rate on omnichannel is 18.96% vs. 5.4% via a single channel.
Omnichannel demand gen is prospect-based, not channel-based, with goals to:
- Provide engagement options across the prospect experience
- Capture that engagement in various forms
- Unify those data streams so they can be fully monetized
These are very obtainable goals. To that point, I’ll be presenting four genuine use cases in omnichannel demand gen.
Reaching Finance, Marketing, and Human Resources Audiences
This blog will serve as the foundation for a 3-part series exploring key elements of said use cases.
For purposes of discussion, we’ll call them:
- BRAND 1 (Marketing Audience)
- BRAND 2 (Finance Audience)
- BRAND 3 (Marketing Audience)
- BRAND 4 (Human Resources Audience)
We’ll start in the middle with Brand 3 and a campaign targeted to a marketing audience. Here’s how that audience looked:
|BRAND 3 (Marketing Audience)|
|Report Start:||August 2, 2021, 12:00 AM|
|Report End:||August 31, 2021, 11:59 PM|
|Date Generated:||August 31, 2021, 4:40 PM|
|Campaign Duration:||30 Days|
|Campaign Goal1 attained:||50+ Leads – 72 Leads|
|Campaign Goal2 attained:||500,000 Impressions|
GPS Isn’t the Only Way to Find your Audience
Location segment data can get quite interesting in these use cases. We can identify the viewers’ geography down to specific regions in the U.S. We can learn more about our audience and prospective customers by analyzing that data against company name, industry, function/level, and job title. Here is an example of a location segment:
In addition, we executed a concurrent email campaign for this brand that resulted in 150 leads.
A Chart Tells a Thousand Words
What were our results with this marketing audience?
We saw a 50% increase in campaign spending by the brand with an ROI of 220 leads. We were able to attain deep intelligence on how the target group performed across 1+ million impressions, including company name, industry, location segment, function/level, job title, target audience, and ABM-specific criteria.
Were our results repeated? They were!
Use Case – Omnichannel Demand Gen for Finance
A different brand, Brand 2, wanted to approach a similar, though not identical, audience. In conjunction with the success of Brand 3’s campaign, a strategy for Brand 2 was created, but using omnichannel expectations. The campaign flight was three weeks in duration, and the outcomes sought were a minimum of 50 marketing qualified leads (MQLs) and “impressions intelligence” of the target audience.
Brand 2 (Finance Audience)
We exceeded the desired MQLs goal by 10% and drove 180% of the impressions we committed to them. Again, we can assemble this intelligence from company name, industry, location segment, function/level, and job title.
For this exact brand, we executed a concurrent email campaign that resulted in 100 leads. When looking at aggregate campaign outcomes with a 50% increase in client budget, we found 155 leads and impressions intelligence for nearly 1 million individual results.
|BRAND 2 (Finance Audience)|
|Report Start:||August 1, 2021, 12:00 AM|
|Report End:||August 31, 2021, 2:47 PM|
|Date Generated:||August 31, 2021, 2:47 PM|
|Campaign Duration:||16 Days|
|Campaign Goal1 attained:||50+ Leads – 55 Leads|
|Campaign Goal2 attained:||500,000 Impressions|
All About the Persona
Unlike Brand 1, where we applied an expansive ABM list, Brand 2’s campaign was persona-driven. We needed to reach finance and accounting professionals, specifically in financial services-related industries, at companies employing 200+ people.
Here are a few samples of the campaign’s performance. These data points are part of the unified information we obtain from an email channel and a LinkedIn Ads channel. How do you think we did?
Persona-driven Omnichannel Demand Gen Success
HIPB2B has successfully tested one omnichannel model, namely the use of email campaigns supplemented by LinkedIn B2B social media and ads, to give customers both leads and engagement data on an additive set of names that they can nurture or retarget.
A couple of items have been confirmed since our earliest days and still hold:
- Email is most often the primary channel in B2B demand gen
- Many companies use additional channels, but their tactics and customer views are siloed, meaning data from one channel doesn’t flow seamlessly to another to ensure the best customer experience
These are the main reasons I think omnichannel marketing, whereby marketers use multiple channels. Still, the data flows from various channels into a central system IS seamless, has so much potential for demand gen. Our experience shows that omnichannel is driving solid outcomes (and it has been quite successful).
Our testing process yielded a compelling use case for omnichannel demand gen: LinkedIn advertising that supplements lead-gen campaigns and delivers actionable engagement data within the filters our customers specify for their campaigns.
We leverage our customers’ campaign messaging in ads to drive those who engage with the ad to the same landing page as their email campaign while collecting advertising engagement data and additive form completions.
With LinkedIn ads, advertisers can build a target audience using demographic qualifiers or upload a list of their own with, for example, company domains and email addresses. We have used the latter approach because our database is a core business asset, but we expect most marketers will choose native LinkedIn targeting.
By advertising to LinkedIn members, our customers can reach a new audience with minimal overlap versus their email-generated leads; both sets of responders meet the campaign’s criteria such as company size, professional title, and industry.
With this information, our clients can secure a powerful blend of verified business card data and firmographics on leads, as well as data on those who engage with their ad targeting, data capture, and prospect responses.