Targeting

First PublishedMay 24, 2026Last UpdatedMay 25, 2026ByAtif Alam

The decision this page enables: which of your defined segments deserve real investment — and which you’ll explicitly not pursue.

What targeting is

Targeting is the second step of STP. Once you’ve segmented the market, targeting answers a single question:

Of the segments we identified, which ones will we actively invest in, and which will we explicitly leave to competitors or to the future?

Targeting is the most consequential decision in marketing strategy because it’s the only one with an obvious cost: the segments you do not pursue. Every founder, marketer, and salesperson has limited attention and budget. Targeting is the discipline of admitting that out loud and choosing.

A targeting decision usually produces three artifacts:

A primary target list — the 1–3 segments getting full investment.
A secondary / tier-2 list — segments you’ll serve opportunistically but won’t build campaigns around.
An anti-target list — segments you’ll explicitly not pursue, even if they show interest.

If you finish targeting without an anti-target list, you haven’t really targeted. You’ve expressed a preference.

Why it matters

Targeting determines:

Where your acquisition spend goes — paid channels, content topics, partnerships.
What your sales motion looks like — SMB self-serve vs. mid-market hybrid vs. enterprise outbound.
What your product roadmap prioritizes — features that serve target segments get built first.
What you say “no” to — every “yes” to a non-target segment is a “no” to your primary target.
How your team is structured — Tier-1 ABM accounts need named SDR/AE coverage; Tier-3 segments get email drip and nothing else.

Bad targeting (too broad, no anti-target list, no scoring) is the single most expensive failure mode in early-stage GTM. It shows up as low win rate, long sales cycles, generic positioning, and a product roadmap pulled in five directions by five customer types.

The four targeting strategies

Marketing theory recognizes four classic targeting strategies. Most companies use exactly one at a given stage; some use a hybrid that combines two.

Strategy	What it means	When to use	Example
Undifferentiated (mass-market)	One mix, one message, all segments.	When the product genuinely serves everyone identically and price is the main differentiator. Rare today.	Commodities (table salt, bottled water, retail electricity in deregulated markets)
Differentiated	Multiple segments, each with its own mix (product variant, price tier, message, channel).	When you have the resources to maintain distinct go-to-market motions per segment, and segments differ enough to justify it.	Mature SaaS (different motions for SMB / mid-market / enterprise); large consumer brands (different product lines for different demographic segments)
Concentrated (niche)	One segment, one mix, full focus.	Early-stage default. When you don’t yet have the resources or evidence for differentiated, or when you’re deliberately playing in a beachhead.	Most pre-Series-B startups; specialist consultancies; vertical-SaaS players
Micro-targeting (one-to-one)	Individual or hyper-niche treatment, often with bespoke product or sales motion. ABM at the extreme.	When the segment is small but each customer is enormously valuable.	Enterprise sales with named-account ABM; ultra-luxury goods; bespoke B2B integrations

How to choose between them

flowchart TD
    A[How many segments<br/>have you validated?] --> B{1 strong<br/>segment?}
    B -->|Yes| C[Concentrated<br/>'one focused bet']
    B -->|No| D{Multiple<br/>validated segments<br/>+ resources to staff each?}
    D -->|Yes| E[Differentiated<br/>'parallel motions']
    D -->|No| F{Segment is<br/>tiny but each<br/>customer worth $1M+?}
    F -->|Yes| G[Micro-targeting / ABM]
    F -->|No| H[Go back to<br/>segmentation; not<br/>ready to target]

If you’re in doubt, default to Concentrated — at almost every stage, doing one thing well beats doing three things adequately.

The beachhead strategy (Crossing the Chasm)

Geoffrey Moore’s “Crossing the Chasm” framework is the most influential targeting model in B2B technology and is worth understanding even if your product is B2C. The core idea:

You don’t try to win the whole market at once. You pick a beachhead — one specific, narrowly-defined segment — and dominate it before expanding to adjacent segments in a deliberate sequence. Moore called this the “bowling alley” because you knock down one pin (segment), and the adjacent pins (segments) fall easier afterward.

The beachhead has to be:

Small enough that you can credibly dominate it (10%+ market share is the rough benchmark).
Large enough to be worth winning (≥ a few hundred customers at viable pricing).
Connected enough to adjacent segments via word-of-mouth or shared workflows that wins compound — winning Pin 1 makes Pin 2 easier.
Reachable with channels you can actually afford at your current scale.

The bowling-alley sequence usually looks like this — each pin makes the next one easier to knock down:

flowchart LR
    P1[Pin 1<br/>Beachhead<br/>dominate first] --> P2[Pin 2<br/>Adjacent vertical<br/>or adjacent stage]
    P2 --> P3[Pin 3<br/>One more adjacency<br/>now compounding]
    P3 --> P4[Pin 4 onward<br/>Mainstream<br/>scale motion]
    P1 -.-> R1[Why Pin 1 → Pin 2:<br/>shared workflow,<br/>referrals, brand recall]
    P2 -.-> R2[Why Pin 2 → Pin 3:<br/>case studies,<br/>broader category fit]

The dashed branches are the connection logic between pins — without those, you’ll dominate Pin 1 and stall. Write the connection logic down before committing to the beachhead, not after.

Where teams get this wrong: picking a beachhead that’s not connected to anything (you win the beachhead but can’t expand) or picking a beachhead that’s already commoditized (you can’t differentiate). The connection test is the most-skipped: write down why winning Pin 1 makes Pin 2 easier before committing to Pin 1.

Segment attractiveness scoring matrix

The workhorse tool for deciding which segments to target. Score each candidate segment 1–5 (or 1–10) on a set of criteria, weight the criteria, sum to a total — then look at the totals alongside the qualitative differences.

The seven criteria

Criterion	What it measures	Weight (typical)
Market size	TAM / addressable count of buyers in the segment	15-20%
Growth rate	How fast the segment itself is growing	10-15%
Accessibility	Can you reach them via channels you can afford?	10-15%
Competitive intensity	How crowded is this segment with credible alternatives?	10-15%
Fit with our strengths	Does the segment reward what we’re uniquely good at?	20-25%
Profitability potential	LTV / CAC and gross-margin potential	10-15%
Strategic durability	Will this segment matter in 3–5 years?	5-10%

Weights sum to 100%. The exact weights depend on your stage — early-stage usually weights fit with our strengths and accessibility the highest; later-stage weights market size and profitability more.

Scoring template

Segment: ___________________________________

| Criterion              | Weight | Score (1-5) | Weighted |
| ---                    | ---    | ---         | ---      |
| Market size            |  20%   |             |          |
| Growth rate            |  10%   |             |          |
| Accessibility          |  15%   |             |          |
| Competitive intensity* |  10%   |             |          |
| Fit with our strengths |  25%   |             |          |
| Profitability potential|  15%   |             |          |
| Strategic durability   |   5%   |             |          |
|                        |        |     TOTAL:  |          |

* Competitive intensity is scored inversely: 5 = uncrowded, 1 = saturated.

Qualitative observations (the score doesn't capture everything):
  Standout strength: [why this segment is great]
  Hidden risk:       [what the score doesn't show]
  Founder bias check: [does any founder want this for personal reasons?]

Decision: [primary / secondary / anti-target / re-investigate]
Why:      [one sentence]

Don’t treat the total score as the final answer. It’s a starting point for the conversation; the qualitative observations are often where the actual decision gets made.

Reading the scores

Total score	Interpretation
4.0+	Strong candidate for primary target.
3.5–4.0	Strong secondary; primary if no stronger candidate exists.
3.0–3.5	Worth serving if it shows up; not worth investing to create.
<3.0	Anti-target; explicitly do not pursue.

If 4+ of your candidate segments score 3.5+, you have too many viable segments — narrow your segmentation or sharpen your scoring criteria. If none of them score above 3.5, your segmentation is too broad or your strengths aren’t strong enough yet; go back to the drawing board.

What this looks like plotted

The seven-criteria score collapses cleanly into a 2-axis picture: attractiveness (size × growth × profitability × durability) on the vertical axis, fit with our strengths (accessibility × competitive intensity × strength-match) on the horizontal. Plotting your candidate segments turns the spreadsheet into a decision:

flowchart TB
    subgraph TOP["High attractiveness"]
        direction LR
        Q2["Build for the future<br/>━━━━━━━━━━<br/>200+ enterprise"]
        Q1["Primary targets<br/>━━━━━━━━━━<br/>10-49p tech / agency<br/>10-49p consulting<br/>50-199p tech"]
    end
    subgraph BOT["Low attractiveness"]
        direction LR
        Q3["Anti-target<br/>━━━━━━━━━━<br/>Regulated FS / health"]
        Q4["Opportunistic only<br/>━━━━━━━━━━<br/>5-9p seed"]
    end
    TOP --- BOT
    AX["← Low fit             High fit →"]
    BOT --- AX

How to read it:

Top-right (high attractiveness × high fit) — primary targets. Concentrate budget, headcount, and roadmap here.
Top-left (high attractiveness × low fit) — build-for-the-future segments. The market is attractive but you can’t credibly win it yet. Watch, don’t pursue.
Bottom-right (low attractiveness × high fit) — opportunistic only. Serve when they show up; don’t spend to create demand.
Bottom-left (low attractiveness × low fit) — explicit anti-targets. Add to the anti-target list below.

The plotted picture is more honest than the score totals because it forces you to see the shape of your portfolio — too many top-right candidates = under-targeted; nothing in top-right = strategy gap.

The anti-target list

The half of targeting most teams skip. The anti-target list is a written, shared list of:

Segments you will not pursue, even if they raise their hand.
Deal characteristics that automatically disqualify a lead (e.g. “headcount < 10,” “regulated industry without our compliance,” “ARR < $1,500”).
Inbound shapes you will explicitly turn away (e.g. “single-user trial requesting enterprise SSO without a path to ≥20 seats”).

The anti-target list does two jobs:

It frees your team to say no. Reps stop pursuing the wrong deals; marketing stops accepting the wrong leads; CS stops over-serving accounts that will never expand.
It surfaces founder bias. When you write down “we don’t sell to ,” you’re admitting where the founder’s personal preference would otherwise pull the company off-strategy.

Anti-target template

Anti-target list — last reviewed: [YYYY-MM-DD]

Disqualifying firmographics:
  - Headcount: [e.g. <10]
  - Industry:  [e.g. regulated FS / healthcare without our compliance]
  - Geography: [e.g. outside US/EU]
  - Stage:     [e.g. pre-revenue]

Disqualifying behavioral signals:
  - [e.g. single-user trial with no team-invite after 14 days]
  - [e.g. price-sensitivity below our floor tier]

Disqualifying needs / jobs:
  - [e.g. "wants a CRM replacement" — not the job we do]
  - [e.g. enterprise procurement without champion]

What happens when an anti-target lead arrives:
  - Marketing: [no nurture; one polite "we may not be the right fit" reply]
  - Sales:    [hard disqualify in 5 minutes; no SDR follow-up]
  - Self-serve: [accept the signup but no paid acquisition spend]

Exceptions (and who approves):
  [e.g. "If the deal is >$25k ACV and a strong reference customer, founder
   approval can override anti-target; document the exception."]

Re-review the anti-target list quarterly. Companies grow, markets shift, your capabilities expand — segments that were anti-target last year may become targets next year, and vice versa.

How to run a targeting decision — step by step

Confirm your segments are real (MSADA-passed from Segmentation). You can’t target segments that aren’t real.
Pick a targeting strategy — Concentrated, Differentiated, Micro, or Undifferentiated. Use the decision flowchart above.
Score each segment with the attractiveness matrix. Do it together as a team; don’t let one person do it alone.
Identify the beachhead — if you’re playing Concentrated, this is the answer. If you’re playing Differentiated, the beachhead is your most-resourced motion; everything else gets less.
Write the anti-target list. This is non-negotiable. No anti-target list = no targeting.
Sequence the bowling alley. Even if you’re starting concentrated, write down the next 2–3 adjacent segments you’d expand to, and why the beachhead helps.
Translate targeting into operating decisions — paid budget allocation, sales rep quotas by segment, CS coverage tiers, product roadmap priorities.
Review the targeting decision quarterly. Win-rate by segment, ACV by segment, anti-target leakage — all of it.

Metrics to track

% revenue from target segments — what fraction of closed-won ARR comes from your defined Tier-1 + Tier-2 segments. Floor: 70%. Below that, you’re effectively not targeting.
Anti-target leakage rate — % of closed-won deals from explicit anti-target segments. Healthy: <10%. Above 20% is a sign sales is taking what it can get.
Win rate by segment — Tier-1 win rate should be 2–3x Tier-3 win rate. If they’re similar, your targeting isn’t predictive.
CAC by segment — cost-to-acquire should be lowest in Tier-1, highest in Tier-3. If your Tier-1 has higher CAC, you’ve either picked the wrong target or your channels are misaligned.
Payback period by segment — months of MRR to recover CAC. Healthy Tier-1: <12 months for SMB, <18 months for mid-market, <24 months for enterprise.
Target-segment NPS — should run 20+ points higher in Tier-1 than overall. Tier-1 is the segment whose unmet needs you’ve explicitly tuned the product to serve.
Beachhead share — your share of the named beachhead segment. Goal at maturity: 10%+ of the segment. If you can’t ever realistically get there, the beachhead is too big and you haven’t actually narrowed.

Worked examples

SaaS workspace (B2B — concentrated targeting with bowling alley)

After segmentation, the team has 7 candidate segments. They score each against the attractiveness matrix:

| Segment                                       | M   | G   | A   | Ci  | F   | P   | D   | Total |
| ---                                           | --- | --- | --- | --- | --- | --- | --- | ---   |
| 10-49p seed-A tech/agency teams         (★)  |  4  |  5  |  5  |  3  |  5  |  4  |  4  | 4.30  |
| 10-49p seed-A consulting/services        ( ) |  3  |  4  |  4  |  4  |  4  |  4  |  3  | 3.75  |
| 5-9p seed startups                       ( ) |  3  |  4  |  5  |  2  |  3  |  2  |  3  | 3.05  |
| 50-199p A-B tech                         ( ) |  4  |  4  |  3  |  3  |  3  |  4  |  4  | 3.55  |
| 200+ enterprise tech                     ( ) |  5  |  2  |  1  |  3  |  2  |  4  |  5  | 2.95  |
| 10-49p regulated (FS / healthcare)       ( ) |  4  |  3  |  1  |  3  |  1  |  4  |  3  | 2.55  |
| Bootstrapped agencies, any size          ( ) |  3  |  3  |  3  |  4  |  3  |  3  |  3  | 3.10  |

Weights: M 20%, G 10%, A 15%, Ci 10%, F 25%, P 15%, D 5%
(Ci = competitive intensity, scored inversely)

Decision:

Primary target (beachhead): 10–49 person seed-to-Series-A tech/agency teams. Score 4.30; passes every criterion.
Secondary: 10–49 person seed-to-Series-A consulting/services. Score 3.75; serve when inbound, don’t outbound yet.
Tier-2 watch: 50–199 person A-B tech. Score 3.55; build relationships for later expansion.
Anti-target: 5–9p (too small, low ACV, high churn); 200+ enterprise (procurement complexity vs. our motion); regulated industries (no compliance yet); bootstrapped agencies (price-sensitive, low expansion).

Bowling alley: beachhead → adjacent vertical (consulting/services teams, similar size) → adjacent size (50–199p tech) → mainstream mid-market.

Targeting strategy: Concentrated, transitioning to Differentiated when ARR crosses $5M and team can staff a second motion.

Anti-target rules:

Sales auto-disqualifies any lead under 10 headcount within 5 minutes.
Marketing does not run paid acquisition to regulated-industry domains.
CS does not invest in expansion plans for sub-10-seat accounts.

Consumer fitness app (B2C — differentiated targeting)

The team has resources for 2 distinct go-to-market motions and has validated 5 candidate segments (3 needs × 2 demographics overlap). After scoring:

| Segment                                          | Total |
| ---                                              | ---   |
| F1: Young urban × "feel less anxious"      (★)  | 4.10  |
| F5: Deadline parents × "weight loss"       (★)  | 4.05  |
| F3: Young urban × "stay in shape travel"   ( )  | 3.30  |
| F6: Older × "stay in shape travel"         ( )  | 2.40  |
| F7: 50+ × "weight loss"                    ( )  | 3.55  |