So? Many EdTech companies have not implemented a RevOps stack for brand building, demand generation, and nurturing prospects. This is a huge mistake, but I understand day-to-day survival can hamper more strategic initiatives. My recommendation is to document your customer success activities, implement systems to collect customer data, and build a dashboard. Take what you learn and build a RevOps tech stack that automates sales-related activities and supports A/B experimentation.

Also see Customer Success Best Practices

Also see Customer Success Playbook

Idea-market fit is an important early indicator where potential buyers are wildly enthusiastic about your proposed solution.This requires a trained user research ear. You are not just looking for agreement that a problem exists and your solution would provide utility. Listen for the excitement.

Messaging-market fit is about clear messaging, positoing and customer-friendly language. The goal is to resonate, educate and motivate.

Problem-market fit is when your solution resonates with customers who quickly and consistently see value.

Product-market fit is about customers finding value in the product to the point where they would be sad if they had to stop using it. New customers are “pulled” toward your product and rave about it. You have built a product that speaks for itself. Customers and prospects talk about it organically. This can take anywhere between 6 months (rare) and 4+ years, depending on market noisiness, problem severity and amount of education to differentiate and position the product.

GTM-market fit is when you can cost-effectively locate potential customers (ICP and beyond) and close deals. You have a well-defined, data-driven sales and marketing process that evolves over time. At this point, you can scale the business with AI agents ahead of hiring additional people.

For example, let’s say you want to host a webinar to bolster your company as a trusted authority. The goal is a minimum of 50 attendees. How many impressions will you need? Given that getting seen online is difficult due to competition for eyeballs, and the time a person actually spends on social media. There is also the likelihood that a person responds to the impression (e.g., registers for webinar). In this case, my guess is that only 10-20% of people sign up for webinars. At that rate and the relative importance of a webinar (relative to everything else in a person’s world), you would likely need 1000–2000 impressions (see funnel graphic below).

Several factors impact how many impressions are required for an outcome. Clear messaging and positioning improve the impact of an impression. A person who is familiar with your company and product is much more likely to respond. Familiarity with the problem improves response rates, and rapid time-to-value increases the impact of interactions with marketing content.

The intended messaging and positioning should inform product development. If “no training required” is a value proposition of the product, then how will it be manifested in the product? How will the UI/UX and capabilities make this easy for prospects to see? If “personalization” is a value prop, then how will it be manifested in the product? How will the UI/UX and capabilities make personalzition easy for prospects to see?

Almost all EdTech tools lack any support for pedagogies (e.g., PBL, inquiry-based learning, constructivism, experiential learning, 5E, UDL, etc.) These are complicated and comprehensive concepts, but there is no reason EdTech tools should not support a range of learning activities and elements of master lesson plans. EdTech tools should have support for individual and group learning activities (see figure 2), such as debate, think-pair-share, Frayer model, K-W-L charts, concept mapping, etc.

EdTech tools should also have elements of high-quality lesson plans that we see from master teachers (see figure 3; not an exhausive list). This includes making connections to previous lessons and helping improve motivation through clear and achievable learning goals, scaffolding and differentiation, and not leaving students to wonder, “when will I use this?” Tools should make sure students are ready to learn by probing for background knowledge and skills and layig the groundwork for long-term transfernce through metacognitive reflection.

What to do instead of a pilot? First, create an RFP with requirements from all stakeholders. Discuss and assign weights to each requirement. Get personalized demos that showcase all relevant stakeholder capabilities, with emphasis on the highest-weighted criteria in the RFP. Each stakeholder group gets its own demo and IT is part of all demos. There will likely be several rounds of demos to synthesize the whole product experience. The RFP may need to be amended once the demos start. I often see capabilities that were not part of the original RFP and add them.

Get experiences from peers. Get feedback from peers at other schools. Don’t ask them to complete the RFP, but ask specific questions about the most important capabilities (e.g., integration, school-year rollover, etc.). Get feedback about the implementation. How well did it go, and how long did it take? What do they like and dislike about the product? How is customer support in terms of responsiveness and completeness? Get experiences from user groups. Join vendor-sponsored and independent user groups. Ask questions similar to those asked of peers.

Understand customer responsiveness. I am a fan of public roadmaps for #SaaS companies. This lets users request bug fixes and additional features. Users vote on features so the vendor can prioritize requests. How long does it take a request to “be considered”, “worked on”, and “implemented”? Yesterday, a major SIS vendor implemented a feature that was requested in 2015. This company is underinvesting in its product. This may not be a deal breaker, but it is good to know upfront.

Also consider social listening to analyze off-website content. Do you know where your problem is being discussed? How is your company being talked about? Is the messaging and positioning hitting home?

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We are a Google school. We gravitated to Google over Microsoft because the product line was simpler than Microsoft’s very broad product line. Over time, Google’s product line grew to include cloud-based documents, application development and a management infrastructure. It has grown into a broad product line. #AI can be found in multiple places. This may not be welcoming to new users. Google’s AI has grown smarter, but it’s usage is still much smaller than ChatGPT (see graphs).

Google should continue to leverage a minimal homepage for both search and AI. Most AI tools have a minimal homepage and Google should too. I’m envisioning a toggle button (see image). The button would automatically toggle based on what is typed. The search results and AI results would be different webpages. I don’t love the AI summary at the top of the search page. It takes me longer to sift through the list.

My newfound enthusiasm for AI Agents has led me to wonder about the future of software development. Full-stack development is much more productive than previous approaches. It suffers from a lack of metadata and documentation. New applications are written from scratch, or code is borrowed from existing applications. Wrapping your head around someone else’s code can be as hard as starting from scratch. The end result is that full-stack applications are challenging to modify due to their complexity and the integrated nature of the software layers. AI code development is spotty, but AI is very good at analyzing code and describing behavior. This documentation and creation of metadata will make modifying code easier, but it will not be as easy as using AI agents as applications.

AI agents don’t use JavaScript code and libraries like full-stack applications. AI agents consist of goals, tasks, backstories, tools, and delegation. Need a new capability? Modify an existing agent or create a new one that gets delegated the new task. The results not up to snuff? Create a new agent that is responsible for quality control and verification. They use plain text, making them easy to modify. At some point, software developers will make a decision about how to implement a new application — code-based or agent-based. In the meantime, agents will likely be able to replicate the functionality of code-based applications without the need to generate code.

There is a relationship between onboarding and Customer Success. A very productive onboarding will likely lessen the need for high-touch Customer Success. Beyond onboarding, Customer Success investment is necessary to continually drive product adoption and grow the value users derive. The most overlooked aspect of onboarding is collecting user profile information, such as JTBD and goals. Customer Support information is an important data source for CS folks. The most underutilized aspect of Customer Success is a product that is missing a persistent mechanism for users to volunteer what will make them more successful.

The graphic below is my playbook. Each stage can have a range of automation from low-touch to high-touch based on account size and their expectations. The overarching theme is a 💎 negotiated level of communication and check-ins, 💎 autonomy for choice of types of resources and service level, 💎 organization and project management, and💎analytics to identify worrisome types of usage. Most importantly, solicit and collect feedback about how buyers and users like the processes. This information may change your ICP.