Marketing Automation & AI Report 2024. Tactics & tools of AI-based lead generation
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2024
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05 - Research report or working paper
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Zürcher Hochschule für Angewandte Wissenschaften ZHAW
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Winterthur
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Abstract
Three hundred and eighty-five companies from Switzerland and Germany participated in this study on the maturity of lead management. The study, which focused on business-to-business (B2B), was conducted between November 2023 and January 2024, and shows that using artificial intelligence (AI) in lead management is becoming increasingly important. In particular, the areas of lead research, lead generation, lead engagement, lead scoring, and lead automation were analyzed. Based on the results, the Lead Automation Maturity Index (LAMI) was calculated for each company, and recommendations for action were derived.
In the area of lead research, LinkedIn was found to be the most important source of contact data, with 58 percent, followed by events and personal recommendations. This finding underlines the importance of social networks and direct interactions in the B2B sector for data collection. Regarding lead generation, 38 percent of companies rate their strategies as successful but see further potential for optimization. In terms of lead engagement, it is striking that 41 percent of companies prefer personal interaction with interested parties. For lead scoring, it is worth noting that 56 percent of companies are still evaluating manually, indicating significant potential for efficiency gains through automation and AI. An analysis of the current situation reveals that despite advancing digitalization and AIsupported tools, personal interactions and networks still play a central role in lead management.
The findings in Section 3 underline the high transformative potential of artificial intelligence and automation for marketing strategies and customer communication. Companies that use AI technologies can significantly increase lead generation and qualification efficiency through more precise customer data analysis, improved predictions of customer behavior, and personalized marketing communication.
The most frequently mentioned AI use case in lead management is the automated personalization of emails and
follow-ups for contact lists, which are increasingly being generated with AI. Most respondents see the recognition of a lead’s readiness to buy through AI as an important use case. Almost half consider it beneficial to increase the deliverability of emails and engagement in social media using AI. Forty-three percent of B2B companies consider it worthwhile to adapt website content dynamically based on user behavior or, in 27 percent of cases, even create it individually. For 37 percent, it would be helpful to pass on ready-to-buy leads to the sales team automatically in response to AI-based lead scoring.
Almost half of the companies surveyed are still in the “learning” phase concerning AI. These companies want to
understand how to utilize AI in their business and are exploring initial use cases. The other half is already in the “testing” phase. These companies carry out smaller pilot projects for specific AI applications. Only five percent of the companies are advanced in AI and already in the “scaling” phase. They have implemented AI tools widely in their business processes and are scaling up. Just seven percent do not have any confidence in AI.
The study results suggest that only a few companies currently benefit from the advantages of AI and automation in lead generation, although most respondents recognize the potential. The average LAMI is only 36 points on a scale of 0 to 100. Individual sectors – such as technology providers and consultants – and several other companies have progressed. However, Section 4 shows that the majority still have room for improvement in the five dimensions of LAMI. Limited resources and skills are by far the biggest obstacle to introducing AI in marketing.
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English
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Zumstein, D., Rettenmund, M., Gasser, M., Cantin, V., Thüring, U., & Kölle, D. (2024). Marketing Automation & AI Report 2024. Tactics & tools of AI-based lead generation. Zürcher Hochschule für Angewandte Wissenschaften ZHAW. https://irf.fhnw.ch/handle/11654/48326