This content originally appeared on HackerNoon and was authored by Ruban Phukan
The first half of 2024 has seen major enterprise SaaS players like Salesforce reporting declining sales, reflecting the broader challenges businesses have collectively faced this year. Working against influences like high interest rates, the economic uncertainty of an election year, and existential questions surrounding the future impacts of AI, IT leaders are tightening their budgets.
\ Amidst such challenges, strategic partnerships play crucial roles in opening new revenue streams and enhancing software offerings – even longtime rivals Apple and Meta have recently flirted with the idea. Microsoft has long been an exemplar of this strategy, going so far as to claim that 95% of its commercial revenue is generated through partnerships.
\ A recent Forrester study supports this, with over half of surveyed companies reporting that partnerships generate 20% of their overall revenue.
\ The most lucrative partnerships are those that have invested time, money, and resources into comprehensive enablement processes including training, tools, knowledge, and support. But resource limitations dictate that not all partners are treated equally when it comes to enablement. Consequently, larger, top-tier partners get personalized, often hands-on support, while smaller, long-tail partners are more likely to be directed toward self-service portals or waiting on unfulfilled IT support tickets. This disparity hinders growth for both partners.
\ Luckily, generative AI makes it possible to provide tier-one treatment to long-tail partners affordably. Here’s how.
Personalize and Precipitate Partner Onboarding
- These smaller partners are often directed to generic portals containing vast amounts of documentation. They are expected to independently navigate this information overload to learn the necessary skills for selling and implementing solutions. This can be a significant barrier to entry, THUS Long-tail partners take considerably longer to become productive due to the time required to learn from impersonal resources.
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- On average, students forget 70 percent of what we teach within 24 hours of the training experience.
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- Companies need to bear in mind customizability and also focus on developing training programs that will help organizations achieve specific goals tailored to their unique needs.
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- According to a McKinsey report, employees spend 1.8 hours every day—9.3 hours per week, on average—searching and gathering information.
Increase Implementation and Sales
- It’s estimated that around 80 percent of product features are “rarely or never used.”
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- Without proper guidance, long-tail partners may struggle to grasp the nuances of the software, hindering their ability to convert leads and deliver successful implementations.
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On average, young companies (1-2 years old) start out with 29 SaaS apps. By the time they’re 3-6 years old, that number spikes to 103 apps. (Bettercloud)
With so many apps, most are NOT using them to max capacity
Over 50% of SaaS licenses are unused for more than 90 days. (Redline) because people don’t know how to use products!
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Dashboards can illuminate which features are being underutilized that if properly deployed could lead to improved results
Decrease Churn
54% of businesses want to optimize and find savings in their software spend. (Flexera)
Don’t be the area that gets cut due to inefficiencies
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25% of IT teams say that they spend the majority of their time managing third-party vendors and business solutions. (Productiv)
\ AI may not yet be ready to be an assistant taking on our working tasks, but it is perfectly suited as an agent that can cater to the needs of long-tail partners.
This content originally appeared on HackerNoon and was authored by Ruban Phukan
Ruban Phukan | Sciencx (2024-07-17T23:21:31+00:00) Playing the Long Game: How AI Can Optimize Enablement in Longtail Software Partnerships. Retrieved from https://www.scien.cx/2024/07/17/playing-the-long-game-how-ai-can-optimize-enablement-in-longtail-software-partnerships/
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