B2C Lead Generation

The client promotes educational projects and research to improve the learning and development of children and young people worldwide. It supports innovative approaches and scientific findings to bring about sustainable positive change in education.

Among other things, I managed a specific platform for the client, which is a multimedia platform that highlights scientific findings on the development and learning of children and young people. It features contributions from researchers, practitioners and journalists on topics such as educational technology, psychology and neuroscience to stimulate discussion and improve educational opportunities for children worldwide.

The aim of my work was to generate more qualified registrations. The client was looking for an increase in newsletter subscriptions (on the parent side) and more teacher registrations (on the teacher side). There is one subpage for teachers and one for students, both of which were advertised in the same ads account.

The main pain point was that the previous agency had used unspecific broad match keywords, which led to expensive and irrelevant traffic. The conversion rate was low and a lot of budget was wasted. It was necessary to fundamentally optimize the campaigns to reduce costs while increasing the number of qualified conversions.

What have I achieved:

Significant improvements since taking over the campaigns:

• 1163% more conversions (196 in the previous year to 2481 in the new year)
• 24% lower costs compared to the previous year

These results reflect the success of the restructuring of the Google Ads campaigns, which has significantly improved efficiency and ROI.

How did I achieve it?

3.1 New campaign setup and segmentation

The old agency used large, diversified ad groups with many keywords, which led to unspecific ads and high wastage.

• Detailed segmentation: One of the first measures was to divide the campaigns into several ad groups in order to place more targeted and relevant ads. This made it possible to control bids more precisely and to display ads that were a perfect fit.
• Ad customization: Each ad group was equipped with specific ads that were precisely tailored to the keywords searched for and the target group. This led to a significant improvement in ad relevance, which had a positive impact on the quality score.

3.2 Keyword optimization and negative keywords

• Keyword research: Phrase match and exact match were used instead of broad match keywords in order to control traffic more precisely and only capture relevant search queries.
• Negative keywords: By entering negative keywords, irrelevant search queries could be excluded. This not only reduced costs, but also increased the conversion rate as the traffic became more targeted and qualified.
• Regular review: The keyword set was regularly adjusted and expanded to take account of new search trends and continue to achieve a high proportion of qualified clicks.

3.3 Switching off inefficient networks

• A significant proportion of traffic came via the display network and Google search partners. Both network extensions led to a large volume of poor and unqualified traffic, which did not deliver the desired conversion rate and increased costs.
• Network deactivation: To qualify traffic and reduce costs, both the display network and search partners were deactivated. This helped to significantly increase the quality of traffic, as only targeted search queries via the Google search network were taken into account.

3.4 Quality score optimization

One main focus was on improving the quality score through several measures:

• Ad Relevancy: by targeting and matching the ads more closely to the respective keywords, the relevance of the ads increased, resulting in better ad positions and lower CPCs.
• Landing page experience: The ads no longer only led to the general homepage, but to individually created landing pages that were specifically tailored to the respective campaign and search intention. These targeted landing pages improved the user experience and significantly increased the conversion rate.
• Expansion of ad texts: To further increase ad relevance, the text ads were regularly expanded and enriched with additional call-to-actions and useful information.

3.5 Bidding strategies and bidding adjustments

• Manual bid control: Initially, a manual CPC approach was used to precisely control bids and optimize CPCs according to performance.
• Transition to Smart Bidding: After the initial optimization phase, we switched to Smart Bidding to achieve the best bids for conversions through machine learning.
• Bidding adjustments: Regular bid adjustments were made during the campaign period in order to react to changes in competition and performance. This made it possible to support peak times and particularly high-converting keywords with higher bids.

3.6 Optimization of the ad assets

• New ad assets: All assets, especially image ads, were completely recreated and regularly reviewed. Weekly iteration of ad assets ensured that only the best and highest performing assets continued to be used.
• Removal of underperforming assets: Underperforming assets were consistently removed to improve the overall result.

My conclusion

Through an in-depth restructuring and optimization of the Google Ads campaigns, we were able to significantly increase lead generation for both Bold and the Digital Museum of Learning:

• 1163% more conversions at 24% lower costs.

• Increased quality score through higher ad relevance, more targeted keyword selection and better segmentation of ad groups.

• More efficient bidding strategies, which led to lower costs per conversion.

These measures have made traffic more relevant, efficient and cost-effective, resulting in a significant improvement in ROI and a massive increase in sign-ups.