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Paid search strategy drives 78% more MQLs in under 6 months

For Winmo, we concentrated on creating a seamless user experience, proper campaign segmentation, & working with CRM-data-layered targeting to bring in high-priority conversion actions attributed to positive MQL/SQL growth.

Client overview

Winmo is the industry-leading advertising database that makes selling to brand marketers, agencies, and the technologies they work with easier. It streamlines the sales process and drives more profitable connections for sales professionals. From reliable decision-maker information to accurate sales predictions, Winmo raises the bar for prospecting.

 

The challenge

Previously, Winmo worked on paid search internally and wanted to reach their niche target audience even further. Most importantly, they aimed to upgrade lead quality and volume.

Winmo teamed up with Directive to craft a cohesive paid search strategy to improve MQL and opportunity volume, cut CPA in half, and shorten their sales cycle from paid search leads.

The strategy

To increase lead volume while decreasing the cost-per-marketing-qualified-lead,
we concentrated on creating a seamless user experience, proper campaign segmentation, and worked with CRM-data-layered-targeting to target high priority conversion actions attributed to positive MQL/SQL growth.

Drilling down buyer intent

The Directive team identified an opportunity to optimize and create a seamless user experience from the keyword searched to the ad clicked, leading to a relevant landing page offer to users with a buying intent.

Upgrading this strategy resulted in a 681% lift in conversions and a -52% decrease in Cost/MQL, their highest MQL generating campaign to date.

The strategy was executed by identifying gaps in the high-converting campaigns that were yielding unqualified leads. While the campaign had a tremendous opportunity but lacked the qualification, we wanted to determine which keywords were pulling in conversions and examine the intent behind the converting users. 

We started at the keyword level. We looked for keywords with conversions and took a more in-depth look at their search terms. Our objective was to identify trends with searches that stood out as irrelevant.

Our analysis concluded that the majority of converters had a “personal” search intent. Still, we were able to identify a subset of keywords that resonated with users who were searching for “business or sales” purposes.  

Once we identified the intent behind the search terms, we looked at our ads to see if they satisfied a personal or business intent. The language in the headlines (“corporate”, “sponsorship”, and “event”) tells the searcher that these sponsorships are for companies who may be on a corporate level. 

Our ads were checked off to prequalify users for business intent, which matched our keyword targeting. 

We added consumer-intent keywords such as “how to get a sponsor”, “sponsor”, “sponsored”, and “sponsor” as negatives in the account and expected conversions/CPA to decrease. 

Ultimately, we were no longer showing up for searches that gave us irrelevant traffic and unqualified conversions. Google was able to show our ads to highly -targeted people converting for business sponsorships and not consumers looking for sponsors.

Landing page intent optimization

Once our keyword intent analysis was completed, we audited the landing page to study the language used to ensure it reflected the needs for users looking to purchase.

The landing page did show the intent for “sponsorships” and not “sponsors” or “sponsored.” The keyword “sponsorship” was mentioned ten times, while sponsors/sponsored was not mentioned once. The landing page reflected people searching for personal intent, which was not aligned. 

We needed to match our intent with our target audience, ASAP.

Campaign segmentation

After further analysis of campaign performance and landing page experience, we shifted focus to separating these highly-qualified keywords into their own campaigns to maximize visibility. 

The strategy used is called the ALPHA/BETA campaign structure. The benefit of this campaign structure is that by splitting keywords into one campaign (ALPHA) with high-converting keywords and another campaign (BETA) with low-converting keywords, we would be empowered to focus a majority of our monetary resources in the ALPHA campaign.

This way, our budget was efficiently prioritized for high-converting terms, while a smaller budget would suffice the low-performing and experimental campaign. 

Next, we divided the keywords into their respective campaign segment. The ALPHA campaign included keywords that historically had higher conversion rates, a below-average cost-per-acquisition (CPA), and were bucketed under MQL/SQL conversions. The same process took place for the BETA campaign; only this time, we filtered for keywords that had low conversions, higher than average CPA cost, and categorized under recycled or disqualified conversion actions.

Within the first few weeks, we saw a 91% conversion difference and a 465% conversion difference between the campaigns. The ALPHA held 117 conversions versus BETA’s 10 conversions.

The CPA in the ALPHA campaign achieved the best CPA in the account at $23 versus the BETA at $130 CPA.

CRM data-layered -targeting

Directive values collaboration and transparency. Using these principles, we teamed up with Winmo to optimize the account, based on qualified conversion actions and internal data reports attributed to positive growth.

Results were astonishing as we saw a 96% decrease in the sales cycle and a 78% lift in MQLs quarter-over-quarter.

With their Raw Lead Data reports, we accessed their CRM lead data that provided granular insights into Lead Status, Qualification, Market Category, Job Title, Company Size, Geolocation, Campaign, and Keyword attribution.

Looking through the list of these reports, we segmented user data by campaign level to identify qualified and disqualified leads.

This method helped our team find any stand-out trends to help us exclude specific keywords in an ad group, increase bids on an ad group level, target by company size, and segment campaigns by high priority geolocations. 

Using the six-month raw data reports, we found keywords attributed to a bulk of the qualified conversions, which we increased bids on. Also, we segmented these keywords into their own ad groups to better monitor their performance while tailoring landing pages with the keyword-intent focus. We also identified and excluded keywords in the account that did not show opportunity-qualified leads. 

Using the geolocation data report based on converted Thank You Page users, we were able to create segmented campaigns targeting the highest MQL/SQL converting states.

96 Decrease in sales cycle, in just 6 months
78 Increase in MQLs, with lower spend QoQ
681 Increase in conversions, QoQ
52 Decrease in cost/MQL, QoQ

The results speak for themselves

Adding these layered targeting on our existing high-performing campaigns, we achieved a 96% decrease in the sales cycle and 78% lift in MQLs quarter-over-quarter.

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