Inside Investor Expectations: Lessons from Series A[I] Companies
I often receive a surprised reaction from founders when I tell them I get paid to learn. While the archetypal ‘banker’ might rely on historical financials and warehouses full of collateral, Silicon Valley Bank leans heavily into the symbiotic relationship between a founder’s vision and their investors’ expectations. It’s through listening and learning from these relationships that we can become a meaningful provider of minimally-dilutive growth capital alongside equity investors for early stage companies.
Pretty dang cool, right? While pioneers in tech like Mark Suster have written about both sides of the table — founders and investors — we’re the fortunate party to pull up a third chair and join the conversation. So what can we do with the valuable insights we gain along the way?
Today, I’d like to share some of those learnings with our early stage clients and the entrepreneurial community more broadly.
In any venture debt opportunity, one of the most critical steps in SVB’s lending process is speaking with key investors. We’ll seek to understand their investment thesis, their expectations over the next 12–18 months and how confident they are in the ability of the management team to execute. On its own, a single investor call can provide insight into a specific company at a specific place and time. In aggregate, many investor calls can paint a broader canvas of investor expectations for a particular space and stage of company. Today, we’ll look at a summary of investor call notes for Series A companies who are either building vertically-focused AI or function-specific AI products.
Lastly, we pressure tested our findings with Kellan Carter and Cameron Borumand of Ignition Partners, a venture capital firm that invests in vertically-focused enterprise start-ups. Their insight into the space was critical to distilling our findings into shareable learnings.
We hope you enjoy.
Learning #1: Who You are as a Founder
It may not come as a shock, but passion paired with aptitude for the problem/space was the most important aspect of founder-related feedback (e.g. founder market fit). While passion and aptitude are hard to tangibly measure, these two qualities can be more apparent in the case of vertically and functionally focused AI start-ups. A few examples that highlight tangible outcomes from these qualities are:
Aptitude for Workflow Nuance: Do you understand how information and process flow within a customer organization? Do you know where to plug into a workflow effectively? Do you have this understanding for not one, but many potential customers? Without the ability to identify and integrate with customer workflows, founders run the risk of losing early inertia and elongating time to value.
Aptitude for Data Nuance: It’s easy to identify the types of data you’d need to unlock AI-driven functionality in your solution, but once you’re inside the doors of a potential customer, do you know where that data resides? Do you know who owns it? Is there a seamless way and time to gather it and organize it? How will the organization grant access to their data and on what terms? What does this mean for where your solution is hosted and how does that change your strategy?
Without the ability to source data and plug into existing workflows, AI-driven start-ups will face an incredible challenge when attempting to turn pilots into paying contracts. As Cameron Borumand of Ignition Partners puts it, “domain expertise, especially for vertically-focused AI companies, is incredibly important. Deep understanding of end-user and buyer behavior (which are often two different people) allows the company to better navigate customer buying journeys.”
Learning #2: What You’ll Deliver
So you’ve found a path into enterprise customers and you’re working through your sales cycle to land pilot programs. What do investors expect here? Much of the feedback was related to securing paying enterprise customers. But if we dig deeper, there are a few key points to highlight:
Value of your workflow: Does your solution provide 10x lift for your users’ workflow? Or as Borumand framed it, “are there early adopters who would be livid if you took the product away from them?”. The answer to this question is undoubtedly critical. While you as a founder might sell into your personal network as the first approach to acquiring paying customers, subsequent organizations will have less personal connection and therefore will be less likely to pay your company if the value isn’t explicit and material. This can be exasperated when AI-driven solutions are sold to non-technical users; they’ll need to be able to grok your solution and realize value right away or they likely won’t buy.
Product standard deviation: It’s natural to work creatively with early customers to deliver use cases and features that meet their needs — in the early days, this can be necessary to find your 10x value mentioned above. However, the more you take on custom work across early customers, the less you’ve ultimately proved from a product market fit perspective — and investors will queue on this as well. Your goal should be to prove that a “use case is repeatable among a broader customer set” per Kellan Carter, with a small standard deviation in how customers use it. “Conducting numerous customer interviews before putting pen to paper is critical to dictating the v1 product roadmap”.
Paths to product maturity: Here’s where we heard two types of feedback that highlighted different longer-term approaches between vertical AI technology and function-focused AI technology. For a vertical AI company, investors focus on deepening the use of your product at each customer you acquire (e.g. land and expand). This is especially important for vertically-focused companies because the TAM is inherently smaller, i.e. there are fewer companies to sell to — perhaps one of the most common reasons why investors pass on vertically-focused startups. Carter put a finer point on this, stating that Ignition is looking for start-ups that can show demonstrable ROI with the initial product, with an obvious roadmap to Act 2 and Act 3. A key enabler for adding “layers to the cake” is access to unique data. For function-specific companies, investors highlighted a long-term strategy of growing the pie via market adjacency. Are you able to apply your product from one vertical to another? Like Act 2 and 3 noted above, function-specific start-ups will also need to deliver a second and third product down the road to become stickier as a suite of products.
Learning #3: How You’ll Grow It
The last topic we’ll cover today is on growth. As noted above, a founder may rely on her/his personal network out of the gate to win earlier paying customers. However, founders will eventually look to ramp beyond their personal rolodex and will require key organizational structure to do so quickly/efficiently. A few key themes we heard from investors:
Sales < > Customer Success Balance: We focus primarily on sales in this post, but a critical element noted from investor calls is having a strong hire in customer success to support early customers. CS is a start-up’s first line of defense for churn and will also gather critical feedback from customers — both to improve product in the near-term and for the longer-term product road map e.g. acts 2 and 3 as discussed above. Carter sees CS as “absolutely critical in this day and age, especially with the number of venture backed companies in each category” — further highlighting that your customer success team is your closest point of relationship development with customers post-conversion. A start-up must be able to find balance between their sales efforts (e.g. acquisition) and customer success (e.g. retention and expansion) in order to build a sustainable business.
Start with a Motion, then Build a Machine: It’s critical to figure out a rhythm, or motion, when it comes to selling and supporting your first few paying customers. In the early days, you’re looking to do enough to learn from customers, keep them around and grow your product. But as you look beyond the first few customers, that motion will need to become a machine. More customers will require a bigger team, and a bigger team will require standardization to scale successfully. One particular area that this paradigm was highlighted was in the hiring of key leadership members. As Carter noted, generally the founding team will need to learn the early sales motion and deep customer pain points. As the company begins to scale and replicate execution on customer needs, building out the GTM and product teams post-Series A becomes necessary to scale the playbook. Lastly, many investors underscored their portfolio companies’ abilities to build a deep technical team with functional expertise. It may go without saying, but the ability of a founder to recruit engineers who have relevant AI experience (ML, NLP, computer vision, etc.) is critical to building their company’s ‘machine’, especially in a talent constrained world.
Interested in learning more?
The teams at SVB and Ignition Partners would love to chat with you!
At SVB, we work with startups at all stages of development with the goal of increasing their probability of success. Send your blog post feedback and questions to Matt Rubright at mrubright@svb.com.
If you’re a brave entrepreneur building a vertically focused enterprise business, Ignition would love see if they can be helpful. Reach Kellan Carter at kellan@ignition.vc and Cameron Borumand at cb@ignition.vc.
Stay tuned for future posts!
NOTE: The views expressed in this article are solely those of the author and do not reflect the views of SVB Financial Group, Silicon Valley Bank, or any of its affiliates. Companies referenced throughout this document are independent third parties and are not affiliated with SVB Financial Group.
