Quick Summary Customer success data creates value only when your team acts on it. Focus on one outcome-driving anchor, triangulate customer truth, and steer the clearest path to value.
Staring at a new dashboard and wondering what to do with that data? Stop.
In today’s metric-driven world, it’s easy to mistake information for insight. Customer teams collect, display, and peruse data as the numbers pile up, and the clock ticks away. Like milk in the fridge, data has a shelf life, and smart leaders know there’s nothing to gain from simply admiring it.
Enter the fresh approach of customer success data alchemy, coined by Andrew Tjaden, chief partnership officer at Stractix, and Steve Hershberger, managing partner with [Amplify]FLO, and shared in a hit workshop at ZERO-IN.
It’s a framework for acting on the data you have, in a clear, decisive way, to effect stronger customer outcomes in every scenario. Data alchemy aims to help you break up with bad habits, start with anchors, and uncover the action items within your data, which are often hidden in plain sight.
Here’s how it works.
“Data is required for insight, [but] more data is more noise.”
1: Find a data anchor to align on customer truth
Before you pick, prod, and present metrics, start with the end in mind. Pick an outcome, a “win condition” (no surprises here: retention, adoption, expansion, etc.), then start looking for the single anchor: the one attribute that most reliably predicts that outcome.
Identifying the anchor requires looking past the “SKU” or specific product to the attributes that truly matter.
Let’s zoom out. In a field like sports betting, for example, the anchor might be a team’s “on-base percentage” rather than their fame. In travel, it might be the delay rate percentage of a specific flight—a small datapoint that might be the most important story for a business traveler who can’t afford to miss a meeting.
2: Listen, analyze, triangulate, and confirm.
Once you have an idea of what the anchor is, it’s time to triangulate. Going back to customer success, imagine there are three buckets of truth:
- What the customer says.
- What you think the customer says, and
- What the data tells you.
Conversations give color, internal beliefs give hypotheses, and behavior gives proof. When those three line up, the anchor tends to glow in the dark.
Often, the anchor will be smaller than you expect: a subtle friction in onboarding, or a specific feature threshold that, once crossed, predicts renewal. A small formatting tweak in a message that changes comprehension.
Remember: the point isn’t to assemble a cathedral of analytics. It’s to surface a single, decision-driving detail that’s “hidden in plain sight.”
“Who here has done a survey and rushed right through it? Can you really trust only what the customer says?”
Data alchemy case studies: the intermediary strategy and the compact model
Speakers Andrew and Steve used two disparate but relatable case study examples to demonstrate how to better tell your data’s story and organize it.
Example 1: A spirit company’s intermediary strategy.
Let’s start with the connection between a bestselling Chardonnay and an apple bourbon whiskey, the latter a new product for its owner’s marketing team.
Superficially, the two products appeared unrelated, yet graph data structure revealed they shared an identical flavor profile (sweet peach, honey, and tart green apple).
Instead of going out and pleading with wine drinkers to switch to spirits, the marketing team introduced an intermediary: marketing a cocktail next to the wine display. They targeted specific ZIP codes where Chardonnay sales were high and introduced the bourbon to a demographic that typically avoids it by sharing a QR code for the recipe, plus a first-purchase discount.
The result? Sales took off. It didn’t require re-platforming or a twelve-initiative roadmap. It simply took identifying a smarter bridge from what customers already enjoyed to something new.
The company simply changed the “music” of the marketing story, said Andrew and Steve, without replacing the “instruments” of delivery mechanism.
So how can we apply this consumer goods success story to our own CS strategy? The principle is the same: find the anchor that holds the desired outcome…
- Achieve value within X days
- Adopt a particular workflow
- Stakeholder reach within first 30 days
… with clarity. The organizations that win are not necessarily the ones with the most metrics, but the ones who see the metrics that matter.
Example 2: KenPom rankings and the effectiveness of a compact model
If you’re a fan of March Madness (get on the bandwagon, it’s storytelling in real time, at its finest), you may already know the infamous KenPom rankings.
It’s a perfect example of a compact model for a complex system, which uses a simple framework (tiers and short list of criteria) to predict tournament outcomes. It’s not fancy, but it’s clear and accurate.
Such an approach is just as effective and applicable for CS playbooks: a lean set of rules that everyone can remember, apply, and iterate. When the model is lightweight, your team can run it often, learn quickly, and adjust without any drama.
While it’s fun to consider new categories (why is our instinct always to add more features, collateral, dashboards?) it also creates a classic paradox of choice. That saturation is friction, say Andrew and Steve, and “friction kills”.
The winning model: restraint. Clarify the anchor, remove unnecessary steps and data, and reduce the decisions between intent and value. Customers don’t want every knob and dial; they simply want the one dial that makes the music better.
The five pillars of customer success data alchemy.
To summarize Andrew and Steve’s presentation, five quick steps to incorporate a data alchemy approach into your CS programs this quarter:
1: Interrogate intent. Ask customers what they’re trying to accomplish, and verify it in behavior.
2: Pick an anchor. Choose the one attribute most predictive of your outcome. Make it memorable. Make it everyone’s job.
3: Build a bridge. If the jump is big, add an intermediary (a workflow, offer, or placement) that carries customers across.
4: Kill the friction. Remove one step per sprint. You’ll be shocked how quickly momentum compounds
5: Tell the story with receipts. Narrative plus embedded facts beats a pile of disconnected charts.
Always remember to revisit, review, and revise along the way.
Andrew and Steve reminded everyone that data storytelling is “an ongoing process…a living document.” Your anchors will shift as products change and markets move, so give yourself permission to evolve.
