How Siftwell Analytics Positions at the Intersection of Predictive Analytics Without Being Just Another Analytics Company

How Siftwell Analytics Positions at the Intersection of Predictive Analytics Without Being Just Another Analytics Company

Every B2B founder faces the same positioning dilemma: position too broadly and you’re invisible in a crowded market. Position too narrowly and you miss buyers searching for solutions in adjacent categories.

In a recent episode of Category Visionaries, Trey Sutten, CEO and Co-Founder of Siftwell Analytics, a healthcare technology company that’s raised over 5 million in funding, revealed how his company navigates this tension with a positioning strategy that’s both pragmatic and sophisticated. “We broadly fall under the category of predictive analytics, but we’re an entirely different animal,” Trey explained—a statement that captures the essence of positioning at category intersections.

The Problem with Pure Category Plays

Healthcare predictive analytics isn’t a new category. Dozens of vendors claim to use machine learning to identify high-risk patients. Most health plans have already evaluated or deployed at least one predictive analytics solution. The category exists, the demand is proven, and the budgets are allocated.

But here’s where most analytics companies fail: they compete on the terms the category dictates. Better accuracy. More sophisticated algorithms. Larger training data sets. They’re playing the same game, just trying to score more points.

Trey recognized this trap from his years as a health plan operator. “I think a lot of people, a lot of technologists have entered the healthcare industry and thought that, you know, it was a better model, a better mousetrap that was going to do it. They wanted to talk about accuracy measures like area under the curve or something,” he explained.

From the operator’s chair, these technical differentiators missed the point entirely. “What I can say, being a former operator, is, yeah, I want you to do that as best as you can. But what way more important to me is getting the results that I’m trying to get. This isn’t about how accurate your model is, it’s about your model telling me that an individual needs an intervention and so that their use of an emergency department or their readmission rates go down.”

The Three-Layer Positioning Framework

Siftwell’s positioning works on three distinct levels, each serving a different purpose in the GTM motion.

Layer 1: Category Inclusion

First, Siftwell explicitly claims membership in predictive analytics. This isn’t accidental—it’s strategic. When health plan CFOs or CMOs allocate budget for predictive analytics solutions, Siftwell appears in that consideration set. When procurement teams issue RFPs for predictive analytics platforms, Siftwell qualifies.

“We broadly fall under the category of predictive analytics,” Trey stated plainly. This category membership solves the fundamental discoverability problem. Buyers searching for predictive analytics solutions find Siftwell. The company captures existing demand rather than trying to create new demand for a category that doesn’t yet exist in buyers’ minds.

Layer 2: Differentiation Declaration

But immediately after claiming category membership, Trey draws a sharp distinction: “but we’re an entirely different animal.” This isn’t vague positioning fluff—it’s a clear signal that evaluation criteria used for traditional predictive analytics vendors won’t work for Siftwell.

This differentiation matters because it changes how buyers evaluate the solution. Instead of comparing model accuracy scores on a spreadsheet, buyers need to understand what makes Siftwell different. That forces a conversation, which is exactly what Siftwell’s sales motion requires.

Layer 3: Value Reframing

The third positioning layer is where Siftwell’s operator DNA becomes a competitive weapon. Rather than competing on technical capabilities, they compete on operational outcomes.

“We’re taking really deep and battle tested managed care experience, combining it with these advanced Technologies to not only point out who they should focus on, but why they should focus on and even what they should do in order to better engage them,” Trey explained.

This reframing transforms the evaluation. It’s no longer “which vendor has the most accurate predictions?” It becomes “which vendor helps us actually improve health outcomes?” The first question invites technical comparison. The second question requires understanding the buyer’s operational challenges—which plays directly to Siftwell’s strengths.

Positioning in Budget Cycles

Category positioning isn’t just marketing messaging—it determines where your solution shows up in budget discussions. Trey deliberately keeps this flexible based on the buyer.

“This is often showing up in either a chief medical officers or maybe a CFO’s budget,” he noted. The same solution, positioned slightly differently based on whether the conversation centers on clinical outcomes or financial performance.

This flexibility is only possible because Siftwell straddles categories. A pure-play predictive analytics vendor lives in one budget line. A solution that delivers clinical outcomes through advanced analytics can live in multiple budget lines, expanding the potential buyer base.

The Operator Credibility Multiplier

What makes Siftwell’s positioning credible isn’t just clever messaging—it’s the team’s operator background. Trey and his co-founders spent years sitting in the seats their buyers now occupy.

“What gets us into the door oftentimes is the fact that we are, you know, a number of the members on the team are former managed care operators, and we speak the language, we know the problems,” Trey explained.

This creates a positioning reinforcement loop. Siftwell claims to be different from typical analytics vendors. Buyers take meetings and discover the team actually understands their operational challenges. The positioning promise is validated through direct interaction, making the differentiation claim credible rather than marketing hype.

The Demand Capture vs. Demand Creation Balance

Most positioning advice falls into two camps: either ride existing category demand or create an entirely new category. Siftwell’s approach is more nuanced—capture existing demand, then educate buyers on different evaluation criteria.

When a health plan decides they need predictive analytics, Siftwell gets included in the RFP. But during the sales process, Trey isn’t just demonstrating better predictions—he’s educating buyers on why outcome-focused analytics delivers more value than accuracy-focused analytics.

“100%,” Trey responded when asked if this positioning captures predictive analytics demand. “In fact, what gets us into the door oftentimes is the fact that we are, you know, a number of the members on the team are former managed care operators, and we speak the language, we know the problems and the know, quote, unquote use cases. But we know folks are struggling with. And what we’re doing is we’re immediately bringing in this union of really powerful analytics with the ability to speak the language and empathize and solve the problems that these people are having.”

The Actionability Wedge

Where Siftwell truly differentiates isn’t in the predictions themselves—it’s in what comes after the predictions. Traditional analytics stops at identifying high-risk populations. Siftwell explains why they’re high-risk and what to do about it.

Take the cancer screening example Trey shared: “We had a client that wanted to better understand how to connect more of their members to cancer screenings. We ran the analytics, identified the 12,000 that were unlikely to go, but we go deeper and we help them understand for different cohorts within that 12,000, for example, there might have been a group of 80% chance of non compliance. And that was related to their issues with transportation, affordability, distance to the screening facility, et cetera.”

This actionability wedge lets Siftwell command premium pricing while technically competing in the same category as cheaper predictive analytics tools. They’re not selling predictions—they’re selling the operational improvement that comes from contextualized, actionable predictions.

Lessons for Category Positioning

Siftwell’s positioning offers a framework for founders navigating similar challenges:

Claim category membership explicitly. Don’t be afraid to say “we’re in category X” even if you’re different. Capture that demand.

Immediately differentiate. Right after claiming membership, explain why you’re different. Make it a sharp distinction, not a subtle variation.

Change the evaluation criteria. Don’t just claim to be better at what the category values. Introduce new criteria that favor your unique strengths.

Make differentiation credible. Back up positioning claims with team experience, customer proof points, or demonstrable capabilities that competitors lack.

Stay flexible on buyer alignment. Let your solution live in multiple budget categories if the value proposition supports it.

The hardest part of this positioning strategy isn’t the messaging—it’s having something genuinely different to offer. Siftwell can claim to be a “different animal” because they fundamentally deliver different value than traditional predictive analytics vendors. The positioning works because the product backs it up.

For founders building in crowded categories, Siftwell’s approach offers a path forward: don’t abandon existing categories, but don’t let them define your terms of competition either. Position at the intersection, capture the demand, then win on different criteria.