Using the Problem Identification Chart as a Target List

A Problem Identification Chart is usually taught as a discovery tool: three columns, built off the product before any outreach, that a rep references on a call to recognize a specific buyer’s problem fast. The same chart, built the same way, does a second job earlier in the process. It becomes the raw material for deciding which accounts and people are worth contacting at all.

The chart’s problems, impacts, and root causes get cross-referenced against firmographic filters, impact sensitivity, intent signals, and account viability, then layered with symptoms and catalysts that reveal which accounts are already suffering from those problems or are about to. That cross-referencing happens before a rep writes a single cold email. The chart itself isn’t rebuilt to do it — the same PIC an AE references on a discovery call is the one a BDR or SDR checks a target list against.

The PIC’s Two Jobs: Prospecting vs. Discovery

The three-column structure doesn’t change between the two uses, and neither does the chart itself — it’s built once, off the product and the company’s ICP, typically days, weeks, or months before either job happens, not during a call and not for one specific buyer. What changes is who uses it, when, and what they check it against.

What the Three Columns Capture

Keenan developed the Problem Identification Chart in 2018 as the first step to mapping a buyer’s current state. It’s built once, off the product, not per buyer and not during a call. Building one starts with three vertical columns. The first lists every problem a product or service actually solves, limited to problems in scope, not every problem a buyer might have. The second lists how each of those problems impacts the buyer; a problem that isn’t causing real discomfort usually isn’t worth targeting. The third lists the root causes behind each problem, such as an outdated process or the wrong vendor.

That third column is what makes the chart usable for targeting as well as diagnosis. A root cause like “managing invoices manually” or “no dedicated social media management” describes a condition that shows up in public signals before a rep ever talks to the company. That’s the bridge from the discovery tool to the list-building tool.

From Problems to Filters: Firmographics, Impact Sensitivity, Intent Signals, Viability

Once the problems, impacts, and root causes are mapped, the next step is deciding which accounts and people to reach, using four filters.

• Firmographics. Industry, company size, number of locations, geography, and relevant regulatory differences that make the mapped problems more or less likely to exist.
• Impact sensitivity. Who feels the mapped problems most and who absorbs the blowback if they go unmanaged, a different question from who holds purchasing authority. Reaching the person closest to the pain is often the fastest route to the people who hold the budget.
• Intent signals. Inbound actions, like downloading a related tool or resource, that suggest an account is already dealing with the mapped problem.
• Viability. Removing accounts and industries that rarely show the mapped problems or are known to be slow to adopt a fix, so time isn’t spent chasing low-probability fits.

Running firmographics, impact sensitivity, intent signals, and viability against the PIC’s problem list turns a generic ideal customer profile into a list sorted by how likely each account is to be living the specific problems the product solves.

Symptoms and Catalysts: Finding Accounts Before They Raise a Hand

Some accounts show obvious signs that a mapped problem exists. A symptom is an external, observable sign that a problem the PIC lists is already present. A company whose job postings for a role sit open for months without a hire is showing a symptom of a hiring problem. A flood of negative reviews about response times is a symptom of a customer service problem.

Other accounts show no symptoms yet, but a recent event makes the mapped problem more likely to intensify soon. That’s a catalyst: an event that increases the frequency or severity of a problem already on the PIC. A company that just hired 20 new sales reps is a catalyst for a ramp-time problem, even before that problem becomes visible in their numbers. A product launch is a catalyst for the kind of bugs a QA or bug-tracking tool catches. Common catalysts worth tracking against a PIC’s root-cause column include hiring surges, layoffs, leadership changes, funding announcements, rebrands, acquisitions, and new product launches.

Historical CRM data is the fastest way to find the catalysts and symptoms that matter most for a specific business: looking at what changed at an account shortly before it bought in the past turns that change into a repeatable signal for the next target list.

Feeding the PIC Into AI to Scale the List

Once the PIC and its matching symptoms and catalysts are mapped for known customers, the same structure can be used to generate new ones. A workable prompt for a chatbot, built directly from an uploaded PIC, reads: “Using this PIC, help me identify 50 catalysts (things/changes/events that can make these problems more frequent and/or severe) and symptoms (things/changes/events that suggest an account is already suffering from these problems) that I could observe online about a business as an outsider.” The output becomes a longer, testable list of signals a rep or SDR can sort by how easy each one is to observe from outside a company. Once prospects start responding, which of those replies are worth continued pursuit is a separate question from how the original list got built.

One Chart, Not Two

Checking a PIC against account and firmographic data happens with no specific buyer on the other end of the chart; the inputs are aggregated patterns across a market, not one person’s answers. Referencing that same chart in discovery happens with a specific buyer live on the call, confirming or correcting what the rep already suspected. Neither job rebuilds the chart — the PIC itself only changes when the product or ICP changes, not between uses. The two jobs reinforce each other. A target list built by checking accounts against a sharp PIC gets a rep into the room with buyers who are more likely to have the mapped problem, which makes the discovery conversation faster and more accurate. Building this muscle at scale, covered in Gap Prospecting, turns a static document into the engine behind a whole prospecting motion rather than a one-time diagnostic exercise.

Frequently Asked Questions

What is a Problem Identification Chart?

A Problem Identification Chart is a three-column framework, developed by Keenan in 2018, that lists the problems a product or service solves, the impact those problems have on a buyer, and the root causes behind them. It’s built once, off the product, before any outreach or any call — not per buyer and not live. The same chart is then used two ways: to build and prioritize a prospecting target list, and as a reference during discovery.

How is using a PIC for prospecting different from using it in a sales call?

Both jobs use the exact same chart, built once off the product — neither job rebuilds it. Before any outreach, a BDR or SDR checks the PIC’s problems and root causes against firmographics, intent signals, and public symptoms and catalysts to rank which accounts are worth approaching. During discovery, an AE uses that same chart as a reference, checking the buyer’s live answers against the already-mapped problems to recognize which one the buyer has and confirm it quickly. The prospecting job produces a ranked target list; the discovery job produces a validated case for one deal.

What is the difference between a symptom and a catalyst in account targeting?

A symptom is an external, observable sign that a problem already exists at an account, such as job postings that stay open for months. A catalyst is an event that makes an existing or likely problem more frequent or severe, such as a company hiring 20 new sales reps at once, which increases the odds of a ramp-time problem even before it shows up in performance numbers.

What is impact sensitivity, and how is it different from identifying the decision-maker?

Impact sensitivity identifies who at an account feels a mapped problem most directly and who takes the blame if it isn’t fixed, a different question from who holds budget or signing authority. Targeting the person closest to the pain often creates the fastest path to the people who control the budget, because that person has the clearest motivation to escalate the conversation internally.

Can a Problem Identification Chart be used to generate a target list without existing customer data?

Yes. A PIC built from product knowledge alone, without historical CRM data, can still be uploaded to an AI tool with a prompt asking it to generate catalysts and symptoms tied to each problem and root cause. The output needs testing and filtering for which signals are realistically observable from outside a company, but it gives a starting list where no purchase history exists yet.