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Did you know that 95% of Microsoft's commercial revenue comes from its partner ecosystem?
This success hinges on effectively enabling a critical group within that ecosystem: long-tail partners.
Long-tail partners refer to a large group that contributes a smaller but still significant portion of the overall revenue of their head company to whom they will sell or provide services, for instance. Long-tail partners assist with customer acquisition and retention as well as market expansion, and their collective contributions represent a substantial portion of a company's revenue.
While industry giants like Visa or Mastercard dominate the fintech market, this influence is only possible with help from numerous regional payment processors that cater to specific areas or specialize in niche transactions. These smaller players are crucial long-tail partners for major fintech companies. In fact, in 2022, Visa announced its plans "to explore long-term partnerships with fintechs to provide solutions to the entire value-chain of corporates and B2B suppliers" in India.
The benefits of long-tail partners are well-reported: A Forrester study revealed that 77% of companies surveyed described "partnership development as central to their sales and marketing strategy." However, managing long-tail partners has its associated challenges due to their vast number, coupled with many fintechs struggling with limited resources.
But, there is another way. AI technology can assist with an automated and enhanced approach, unlocking the full potential of long-tail partners in the fintech industry. Let's explore the challenges of long-tail partners in fintech and how AI can revolutionize long-tail partner enablement and increase profits.
The challenges of long-tail partner Enablement
On the surface, long-tail partnerships are a win-win for everyone involved. However, for the fintech, managing the collaborations isn’t as straightforward as you might think.
Enabling long-tail partners presents unique challenges in fintech. While potentially numerous and diverse, smaller partners often don’t receive the same level of attention and resources as their top-tier equivalents.
Most fintechs would typically concentrate on their top 20 partners because delivering high-quality, white-glove service to a larger number is resource-intensive and expensive. This approach can involve assigning account managers to each partner, tailoring support to meet the needs of each partner, and maintaining regular communication.
Therefore, smaller long-tail partners are often not effectively enabled due to the high cost and complexity of managing numerous partners. This results in:
Low and varying performance: Partners may lack the necessary tools, training, and support to deliver optimal performance. This means the service quality provided by long-tail partners can vary significantly, affecting the overall customer experience.
Missed opportunities: Potential revenue from long-tail partners are often underutilized because they are not fully integrated into the fintech company's ecosystem.
Furthermore, Forrester reported that customers don't differentiate between partner and brand experiences. This highlights the necessity for a seamless, consistent customer experience across all partners and touchpoints, as the quality and performance of partner services must align with the brand's own standards. Put simply, negative experiences with partners can directly impact the fintech’s reputation.
While long-tail partners can generate significant revenue for fintech, the challenges associated with this partnership are clear. However, with the development of AI technology, a new, streamlined, and efficient long-tail partner enablement option is available.
The solution: Leveraging AI
In 2024, there’s no need for fintech and their long-tail partners to be trapped in an unproductive cycle.
In fact, Accenture estimates that 40% of working hours could be augmented and supported by generative AI, saving businesses and their employees time and money. Fintech can revolutionize its long-tail partner program by building its own AI-powered partner portal or using an autonomous knowledge management system. Here's how.
Scaled onboarding and enhanced training: Traditional onboarding for each partner can be time-consuming due to lengthy manual processes. For example, extracting data from a partner’s documents and manually entering it into a FinTech database can be a long and tedious system. However, AI-powered chatbots and interactive tutorials can automate the task, ensuring consistent and thorough training for all long-tail partners.
Smart automation: Manual tasks like data exchange and compliance checks can cause bottlenecks for long-tail partners. With human oversight, AI can automate some of these processes, helping the fintech company and its partners by freeing up resources. This allows them to spend time attracting new clients and providing an optimal level of customer experience.
Personalized partner engagement: Long-tail partners often miss out on customized support due to spreading resources too thin among numerous partners. However, AI can analyze partner data and advise on relevant resources, such as marketing materials and sales leads, ensuring there’s a personalized experience for each long-tail partner.
For instance, an AI-enabled mobile investment platform could automate the onboarding of regional advisors using an autonomous knowledge platform. The platform could generate up-to-date custom-made training content with step-by-step implementation guidelines, automate document processing, and recommend suitable products for their clients while taking into consideration client-specific compliances. This streamlines onboarding reduces costs, and empowers advisors to grow rapidly.
How AI-enabled long-tail partners boost revenue
Fintechs are already technology innovators. However, while AI can drastically improve long-tail partner management, it needs to reap monetary rewards to be worth investing in. Luckily, this is one of the significant benefits of AI-powered long-tail partner enablement.
AI simplifies and modernizes partner onboarding, automates tasks, and delivers targeted support — ultimately increasing partner productivity. This enhanced service empowers long-tail partners to become productive quickly, bringing new customers and revenue to the fintech ecosystem faster.
Furthermore, personalized engagement fosters a sense of value and support among long-tail partners. This translates to higher satisfaction, reduced churn, and a more stable network of partners contributing consistently to the revenue stream.
Accenture research found that companies prioritizing customer service as a revenue driver experience three and a half times faster revenue growth than those who see it solely as a cost. Therefore, if fintech uses AI to improve the customer service experience of long-tail partners, it can also help advance a key area of its revenue generation: Happy partners are productive partners.
One of AI's major benefits is its data consumption and comprehension abilities, which enable enhanced data-driven decision-making. For fintech this can be particularly useful for targeted marketing and sales initiatives. AI can analyze partner data and customer demographics to recommend suitable marketing materials and sales strategies for each partner's specific audience. Partners can then reach new customer segments, maximizing conversion rates and improved targeting leads to a higher return on investment for marketing, boosting overall revenue generation.
As AI technology continues to evolve, the possibilities for long-tail partner enablement develop also. By leveraging AI, fintech can unlock the hidden potential within their network of partners, transforming them from individual contributors into a collaborative and powerful ecosystem. Fintech that embrace this opportunity will be well-positioned to dominate the market, shaping a more inclusive and dynamic financial landscape.
This content is provided by an external author without editing by Finextra. It expresses the views and opinions of the author.
David Smith Information Analyst at ManpowerGroup
20 November
Konstantin Rabin Head of Marketing at Kontomatik
19 November
Ruoyu Xie Marketing Manager at Grand Compliance
Seth Perlman Global Head of Product at i2c Inc.
18 November
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