Prioritizing Customer Journeys for Optimization
This skill teaches you how to score and rank customer journeys by business impact, customer friction, and strategic alignment so you can decide exactly where to focus improvement efforts instead of guessing or defaulting to the loudest stakeholder's request.
Score each journey on three dimensions: business impact (revenue, retention, or cost influence), customer friction (severity and frequency of pain points), and strategic alignment (fit with current company priorities). Multiply or weight the scores, then rank. Start with the journey that scores highest across all three, because it delivers the most value with the least organizational resistance. Revisit scores quarterly as data and strategy shift.
Outcome: A scored, ranked list of customer journeys with clear rationale for which to optimize first, second, and later, enabling your team to allocate design, engineering, and research resources with confidence instead of intuition.
Prerequisites
- A completed journey portfolio inventory (list of all journeys with basic metadata)
- Access to at least one source of customer experience data (NPS, CSAT, support tickets, session analytics, or qualitative research)
- Understanding of journey hierarchy levels (L0-L3) so you know which level of granularity to score
- Familiarity with current business strategy and quarterly or annual OKRs
Overview
Every organization that practices journey management eventually faces the same problem: you have dozens, sometimes hundreds, of mapped customer journeys, and each one has friction points begging for attention. Without a systematic way to decide where to invest, teams default to whichever journey the most senior stakeholder cares about, or whichever pain point generated the most recent support ticket spike. Prioritizing customer journeys replaces that pattern with a repeatable scoring model that surfaces the journeys where improvement will deliver the greatest combined value to the business and the customer.
This skill sits near the end of the Ecosystem Journey Framework workflow. You cannot meaningfully prioritize journeys you have not inventoried, so this skill depends on having a complete journey portfolio and a clear hierarchy of journey levels. Once you have those foundations, prioritization becomes the decision layer that turns your portfolio from a reference document into an action plan. The output is a scored and ranked backlog of journeys, each annotated with the reasoning behind its position. That backlog becomes the input for quarterly planning, resource allocation conversations, and cross-functional alignment meetings.
The artifact you produce is a journey priority matrix: a table or spreadsheet where each row is a journey (typically at the L1 or L2 level), each column is a scoring dimension, and the final column is a weighted composite score. A well-built matrix makes trade-offs visible. It lets you say, with evidence, 'We are optimizing the onboarding journey before the renewal journey because onboarding has 3x the friction volume and directly feeds the retention metric we committed to this quarter.' That specificity is what moves prioritization from opinion to strategy.
Done well, prioritizing customer journeys also creates organizational transparency. When stakeholders can see every journey's score and understand the weighting, they are far less likely to derail planning with pet projects. The matrix becomes a shared artifact that earns trust over time, especially when your first optimized journey delivers measurable results and validates the scoring model.
How It Works
The technique works by decomposing the question 'which journey should we fix first?' into three independent dimensions, scoring each one, and then combining the scores into a single rank. The three dimensions are business impact, customer friction, and strategic alignment. By separating these concerns, you avoid the common failure mode where a journey with high emotional salience but low business impact crowds out a journey that would actually move metrics.
Business impact measures how much improving this journey would affect outcomes the company already tracks: revenue, retention, cost-to-serve, conversion rate, lifetime value, or expansion revenue. The key insight is that you are scoring the improvement potential, not the current performance. A journey that already performs well has less headroom than one that is clearly underperforming relative to benchmarks or cohort expectations. When data is available, use actual numbers: the dollar value of a 1-percentage-point improvement in conversion at this step, or the cost savings from reducing support contacts in this journey by 20%. When hard data is unavailable, use informed estimates calibrated against journeys where you do have data.
Customer friction captures how painful the current experience is and how many customers encounter that pain. A journey with severe friction affecting 5% of users may score lower than a journey with moderate friction affecting 60% of users. Friction evidence comes from multiple sources: CSAT or NPS at journey touchpoints, support ticket volume and severity, session recordings showing abandonment or confusion, and qualitative research themes. The scoring forces you to combine severity and prevalence into a single number, which prevents teams from fixating on a single dramatic anecdote.
Strategic alignment reflects how well improving this journey supports the company's current priorities. If your company's annual plan emphasizes new market expansion, a journey that primarily serves existing enterprise customers may score lower on alignment even if it has high friction. This dimension is intentionally subjective. It acknowledges that organizations cannot pursue every good idea at once and that resource allocation must serve declared strategy. Including it in the model prevents the scoring from becoming a purely data-driven exercise that ignores organizational reality.
The weighting across dimensions is a conscious design choice. A common starting point is 40% business impact, 35% customer friction, 25% strategic alignment. You should adjust these weights to reflect your organization's actual decision-making values. If your company is in a growth phase, you might weight business impact at 50%. If you are in a retention-focused phase, customer friction might get 45%. The weights themselves become a useful leadership conversation, because they make implicit priorities explicit.
The model works best when scores are generated independently before being discussed in a group. If a team scores together in real time, anchoring effects compress the range and high-confidence individuals dominate. Independent scoring followed by calibration discussion produces more accurate and defensible results. After calibration, multiply each dimension's score by its weight, sum the weighted scores, and sort descending. The top of the list is your starting point for optimization work.
Step-by-Step
Step 1: Select the journey level and scope to score
Decide which level of your journey hierarchy you are prioritizing. For most organizations, L1 journeys (end-to-end experiences like onboarding, purchasing, or renewal) are the right unit. L2 journeys (sub-stages within an L1) are appropriate only when you have already committed to optimizing a specific L1 and need to decide which sub-stage to tackle first. Pull your journey portfolio inventory and filter to the selected level.
Remove any journeys that are clearly out of scope for the current planning horizon, such as journeys owned by a different business unit or journeys already mid-optimization. The output of this step is a clean list of 10-30 journeys ready for scoring.
Tip: If your list has more than 30 journeys at the same level, you likely have a hierarchy problem. Revisit your L0/L1/L2 structure before scoring, because evaluating too many items at once degrades scoring quality.
Step 2: Define your scoring dimensions and scales
Document the three scoring dimensions (business impact, customer friction, strategic alignment) and the scale you will use. A 1-5 scale works well for most teams because it limits false precision. Write a rubric for each score on each dimension. For business impact: 1 means negligible revenue or cost influence, 5 means directly drives a top-line metric by an estimable amount.
For customer friction: 1 means rare and minor, 5 means frequent and severe with measurable abandonment or escalation. For strategic alignment: 1 means no connection to current OKRs or strategic themes, 5 means directly advances a stated company priority. Share the rubric with all scorers before anyone begins scoring. The rubric is your calibration tool.
Tip: Include concrete examples in your rubric from your own business. 'A 4 on business impact looks like the checkout journey, which influences $2M in quarterly revenue' is far more useful than an abstract definition.
Step 3: Gather evidence for each journey
Before scoring, compile the data each scorer will need. For business impact, pull revenue or conversion data associated with each journey: funnel metrics, cohort retention rates, average order values, or cost-to-serve figures. For customer friction, aggregate NPS or CSAT scores at the journey level, support ticket counts tagged to journey stages, session replay highlights, and any recent qualitative research findings. For strategic alignment, distribute the current strategic plan, quarterly OKRs, or leadership priorities document so scorers can reference it directly.
Organize this evidence into a brief per journey, ideally one page or one slide. Scorers who lack context produce unreliable scores, so this step is not optional.
Tip: If you have no quantitative data for a journey, note that explicitly in the brief. An informed estimate marked as low-confidence is better than skipping the journey or guessing without acknowledging uncertainty.
Step 4: Score each journey independently
Distribute the journey list, rubrics, and evidence briefs to each scorer. Ask each person to assign a 1-5 score on each of the three dimensions for every journey. Scorers should work alone and not discuss scores with colleagues before submitting. Provide a simple spreadsheet or form where they enter their scores and, critically, a brief written rationale (one to two sentences) for each score.
The rationale requirement slows down gut reactions and surfaces reasoning that will be useful during calibration. Collect all submissions before moving to the next step. Aim to include 3-7 scorers who represent different functions: product, design, engineering, marketing, support, and leadership.
Tip: Set a deadline of 2-3 business days for scoring. Longer timelines invite procrastination, and shorter ones pressure people to skip the evidence briefs.
Step 5: Calculate initial averages and identify disagreements
Aggregate all individual scores into a single spreadsheet. For each journey and dimension, calculate the mean score and the standard deviation (or simply the range). Journeys where all scorers agree within 1 point are low-controversy and unlikely to need discussion. 2.
These disagreements are the most valuable part of the process because they reveal different assumptions about the journey's importance or different levels of familiarity with the evidence. List the flagged disagreements as the agenda for your calibration meeting.
Tip: Sort the disagreements by their potential to change the final ranking. A 3-point spread on a journey that is already clearly in the bottom half matters less than a 2-point spread on a journey near the top.
Step 6: Run a calibration session
Bring the scoring group together for a 60-90 minute session. Walk through only the flagged disagreements. For each one, ask the high scorer and the low scorer to share their rationale. Often the disagreement resolves when one scorer had access to information the other lacked, or when the rubric was interpreted differently.
After discussion, each scorer re-submits their score for that dimension. Do not force consensus. If two scorers still disagree after hearing each other's reasoning, keep both scores and let the average reflect the genuine uncertainty. After resolving disagreements, recalculate means.
This session also builds shared understanding of journey conditions across functions, which pays dividends beyond the scoring exercise.
Tip: Timebox each disagreement to 5 minutes. If the group cannot resolve it in that window, table it and assign one person to gather additional evidence before the next session.
Step 7: Apply weights and calculate composite scores
With calibrated scores in hand, apply your chosen weights. 76. Calculate this for every journey. Sort descending by composite score.
The result is your ranked priority list. Review the top 5 and bottom 5 for face validity: does this ranking match what you know intuitively about your business? If a journey's position feels deeply wrong, investigate whether the evidence briefs were incomplete or whether a dimension weight needs adjustment.
Tip: Present the weights to leadership before finalizing the ranking. The conversation about whether impact should be 40% or 50% is actually a strategy conversation in disguise, and getting buy-in on weights makes the final ranking much harder to challenge.
Step 8: Segment the ranked list into action tiers
Divide the ranked journeys into three tiers. Tier 1 (Optimize Now) includes the top 3-5 journeys that will receive active optimization work this quarter. Tier 2 (Prepare) includes the next 5-8 journeys that should receive deeper research or data instrumentation so they are ready for optimization in the following quarter. Tier 3 (Monitor) includes everything else, which you will track but not actively improve.
Assign each Tier 1 journey an owner: the person accountable for defining the optimization scope, assembling the team, and reporting on progress. Document the tier assignments and owners in a format that can be shared broadly, such as a wiki page or a shared dashboard.
Tip: Resist the temptation to put more than 5 journeys in Tier 1. Spreading effort across too many simultaneous optimizations dilutes impact and slows all of them. Fewer, deeper investments outperform broad, shallow ones.
Step 9: Schedule quarterly re-scoring
Put a recurring event on the calendar to re-score the portfolio every quarter. Between scoring cycles, capture new evidence as it arrives: updated NPS data, a spike in support tickets for a specific journey, a change in company strategy. When re-scoring, you do not need to start from scratch. Review the evidence that has changed, re-score only the affected dimensions, and recalculate composites.
Journeys that were in Tier 1 and have been successfully optimized should drop in friction score and may move to Tier 3. New journeys that emerged from product launches or market changes should be added to the portfolio and scored fresh. Over time, the scoring model becomes more accurate as scorers calibrate against observed outcomes.
Tip: After each quarter, compare your predictions (which journeys would deliver the most improvement) with actual results. This feedback loop is what transforms the scoring model from a rough heuristic into a trusted planning instrument.
Examples
Example: B2B SaaS company with 15 mapped journeys
A 200-person B2B SaaS company serving mid-market customers has completed its journey portfolio inventory and identified 15 L1 journeys, from initial awareness through renewal and expansion. The company's annual OKR emphasizes net revenue retention. The CX team has NPS data at the journey level and access to support ticket categorization. They have one quarter to choose which journeys to optimize.
The CX lead selects 15 L1 journeys for scoring and distributes evidence briefs containing journey-level NPS, quarterly ticket volume, and revenue attribution estimates. Five scorers (CX lead, head of product, VP of customer success, a support team lead, and the CFO) independently score each journey on business impact, customer friction, and strategic alignment using a 1-5 rubric. The team uses weights of 35% impact, 35% friction, 30% alignment, reflecting the equal emphasis on retention (friction) and revenue (impact) with meaningful strategic input. After independent scoring, four disagreements are flagged.
The largest is on the 'implementation/onboarding' journey: the support lead scored friction at 5 (citing 320 monthly tickets), while the CFO scored it at 2 (seeing low churn correlation). Calibration discussion reveals that most onboarding tickets are resolved quickly and do not predict churn, but they do predict slower time-to-value, which correlates with lower expansion revenue. Both scorers adjust to 4. 7).
These three become Tier 1. The next five journeys (including upsell and annual review) become Tier 2, with instrumentation work planned to fill data gaps before next quarter's scoring. The ranked list is presented to the leadership team alongside the weights, and the VP of Sales challenges the renewal ranking, but the documented rationale (high friction score driven by 22% CSAT drop at the contract-review touchpoint) resolves the objection.
Example: E-commerce brand with heavy seasonal variation
A direct-to-consumer e-commerce brand selling outdoor gear has 22 L1 journeys. The brand's Q4 strategy is maximizing holiday season revenue. They have Google Analytics funnel data, Zendesk ticket categorization, and post-purchase NPS surveys. The CX team has three people and limited engineering support.
The team filters the 22 journeys to 18 (excluding 4 internal operational journeys outside their scope). Given the seasonal urgency, they weight the scoring 50% business impact, 25% friction, 25% alignment. Evidence briefs are compiled from the prior year's Q4 data: conversion rates by journey stage, cart abandonment rates, return/exchange ticket volume, and NPS by journey. Three scorers (CX manager, e-commerce director, and head of support) score independently.
- as the top three. The team has capacity for only two simultaneous optimizations, so they place the top two in Tier 1 and the returns journey in Tier 2 with a plan to instrument better return-reason data for January re-scoring. 1 because its friction data is thin and its revenue attribution is lower than assumed. The documented score prevents an unproductive debate and redirects the conversation toward getting better gift purchase data for the next cycle.
Example: Small startup with limited data
A 15-person fintech startup has mapped 8 L1 journeys but has only 6 months of product data, no NPS program, and limited support ticket categorization. The CEO wants to know which journey to optimize first with their single product designer and two engineers.
The CX lead acknowledges the data limitations and adjusts the approach. Instead of quantitative evidence briefs, each journey gets a one-paragraph summary based on three sources: the 12 most recent customer interviews (transcribed), Intercom chat logs from the past 90 days, and session recording observations from Hotjar. The rubric is simplified to three levels (Low/Medium/High, mapped to 1/3/5) to avoid false precision. Three scorers (CEO, product lead, and the designer) score independently.
Weights are 40% friction, 35% impact, 25% alignment, reflecting the startup's belief that reducing friction is the fastest path to retention at their stage. 2), driven by a friction score of 5 (7 of 12 interviewees mentioned confusion at this stage) and a high impact score (the journey directly drives the activation metric tied to their Series A targets). The team assigns all three Tier 1 resources to this single journey and schedules a re-scoring in 8 weeks once they have post-optimization data. 6 because customers found it annoying but not blocking.
This realization prevents a misdirected engineering sprint.
Example: Large enterprise with cross-functional journey ownership
A financial services enterprise with 5,000 employees has 40+ L1 journeys spanning retail banking, wealth management, and insurance divisions. Each division has its own CX team. The enterprise CX office wants to create a unified priority ranking to allocate a shared optimization budget of $2M per quarter.
The enterprise CX office convenes a cross-divisional scoring panel of 9 people (3 per division). They select 35 L1 journeys (excluding 8 that are legally mandated and cannot be redesigned). Evidence briefs are standardized across divisions: each journey includes customer effort score (CES) data, complaint volume, revenue-at-risk estimates from the finance team, and a strategic alignment assessment from each division's VP. Weights are set at 40% impact, 30% friction, 30% alignment, with the strategic alignment dimension scored against enterprise-level priorities, not divisional ones.
Independent scoring produces 11 flagged disagreements. The largest is between the retail banking and wealth management teams on a shared 'account opening' journey: retail scores it high on friction (high volume, moderate severity), while wealth management scores it low (low volume, high per-customer value). Calibration reveals they are thinking about different customer segments using the same journey. The journey is split into two variants for scoring purposes.
After calibration and recalculation, the top 5 journeys span three divisions. The $2M budget is allocated proportionally: $900K to the top-scoring journey (insurance claims), $500K to the second (retail account opening), and the remainder split across the third, fourth, and fifth. The scored matrix is published on the enterprise intranet, and division heads agree to use the same model for their internal L2 prioritization, creating a consistent decision framework across the organization.
Best Practices
Score each dimension independently in writing before any group discussion. Shared conversation anchors scores toward the first number spoken and compresses the range of opinions. Independent scoring followed by structured calibration produces scores that better reflect the group's actual knowledge. Teams that skip independent scoring consistently over-weight the journeys that the most vocal participant cares about.
Use the same rubric across all scoring cycles. Changing the definition of a '4' between quarters makes longitudinal comparison impossible and erodes trust in the model. If you need to refine the rubric, document what changed and why, and note that scores before and after the change are not directly comparable.
Weight strategic alignment explicitly rather than letting it override business impact informally. Without a declared weight, strategy becomes a veto card that any senior leader can play to bypass the ranking. When it has a defined weight (typically 20-30%), it influences the result proportionally without dominating it.
Include at least one scorer from a customer-facing role such as support or customer success. Product and engineering teams often underestimate friction severity because they experience the product differently from customers. Support representatives see the failure modes daily and bring calibration data that desk research misses.
Document the rationale for each score, not just the number. A score of '3 on friction' is useless six months later. '3 on friction because average CSAT at step 4 is 3.2/5 and we received 140 tickets about this journey last quarter' is an artifact you can audit, challenge, and update. Undocumented scores decay into opinions.
Separate the scoring meeting from the resource allocation meeting. The purpose of scoring is to establish a defensible ranking. The purpose of allocation is to decide how many people and how much budget each Tier 1 journey receives. Combining these meetings creates pressure to inflate scores for journeys that already have a team assigned, or to deflate scores for journeys that would require hiring.
Validate rankings against at least one external data point before committing resources. If your scoring puts the 'account setup' journey at the top, check whether customers in churn interviews actually mention setup as a pain point. If there is a disconnect, investigate before accepting the ranking at face value. Internal consensus and external reality do not always agree.
Common Mistakes
Scoring journeys at the wrong level of granularity
Correction
This happens when teams mix L1 journeys (like 'onboarding') with L2 sub-stages (like 'email verification') in the same scoring exercise. The L1 journey will always look more impactful because it is broader, making the comparison unfair. The signal that you have a granularity mismatch is when some items on your list feel like 'themes' and others feel like 'tasks.' Before scoring, verify that every journey on the list sits at the same hierarchy level. If you need to prioritize within a specific L1, run a separate scoring exercise at the L2 level after the L1 has been selected for optimization.
Using the same score for business impact and revenue size
Correction
Teams often give a journey a high business impact score simply because it touches a large revenue stream, even if the journey is already performing well and has little room for improvement. The dimension is supposed to measure improvement potential, not current contribution. Catch this mistake by asking 'If we improved this journey by one level, what would change in dollars or percentage points?' A $50M revenue journey that is already at 95% conversion has far less improvement headroom than a $5M journey at 40% conversion. Score headroom, not absolute size.
Letting one anecdote drive the friction score
Correction
A single dramatic customer complaint or a viral social media post about a bad experience can inflate a journey's friction score far beyond what the data supports. This happens because anecdotes are emotionally vivid and easy to recall, while aggregate metrics are abstract. The diagnostic signal is when a scorer's written rationale references a single incident rather than a pattern. Counter this by requiring friction scores to cite aggregate evidence: ticket volume over a defined period, CSAT trend lines, or the number of session recordings showing the same behavior.
One story is a data point. A pattern is a score.
Never re-scoring the portfolio after the initial exercise
Correction
The first scoring cycle produces a ranked list, the team starts optimizing the top journey, and then the list is never revisited. Over two or three quarters, company strategy shifts, new journeys emerge from product launches, and friction patterns change as other teams ship improvements. The original ranking becomes stale but continues to drive resource allocation by inertia. Prevent this by scheduling quarterly re-scoring sessions on the calendar from day one and assigning a specific person to own the process.
A priority matrix that is 9 months old is not a planning tool. It is a historical artifact.
Skipping the calibration session to save time
Correction
When deadlines are tight, teams are tempted to average the independent scores and publish the ranking without discussion. This saves 60-90 minutes but sacrifices the most valuable part of the process: the disagreement resolution. Without calibration, a scorer who misunderstood the rubric or lacked key evidence distorts the average, and no one catches it. The result is a ranking that looks data-driven but is actually just averaged noise.
If you truly cannot schedule a full calibration session, at minimum review and discuss the top 3 disagreements asynchronously, with each dissenting scorer posting their rationale in a shared document.
Putting too many journeys in Tier 1
Correction
Organizations that are new to journey prioritization often place 8-12 journeys in the 'Optimize Now' tier, believing they can run improvements in parallel. In practice, each journey optimization requires dedicated research, design, and engineering cycles. Spreading those resources across a dozen journeys means none of them get sufficient depth, and after a quarter of work the team has started many improvements and finished none. The fix is a hard cap: no more than 5 journeys in Tier 1, and ideally 3.
If leadership pushes for more, ask them to specify which existing Tier 1 journey they want to deprioritize to make room.
Other Skills in This Method
Transitioning from Journey Mapping to Journey Management
How to evolve static journey maps into a dynamic, continuously managed journey management practice across teams.
Aligning Teams Around Journey Ownership
How to assign cross-functional ownership of journeys at each hierarchy level and establish governance for ongoing journey management.
Building a Journey Portfolio Inventory
How to catalog all customer journeys into a comprehensive portfolio that serves as the foundation for ecosystem-level analysis and management.
Structuring Journey Hierarchy Levels (L0-L3)
How to define and organize customer journeys into hierarchical levels from macro (L0) to micro (L3) for scalable journey management.
Identifying Cross-Journey Insights and Patterns
Techniques for analyzing interconnected journeys to surface systemic pain points, redundancies, and optimization opportunities across the ecosystem.
Mapping Touchpoint Interconnections Across Journeys
How to visualize and document the relationships between touchpoints, channels, and journeys to create a holistic ecosystem view.
Frequently Asked Questions
How do I prioritize customer journeys when I have very little quantitative data?
Replace quantitative metrics with structured qualitative evidence. Use customer interview transcripts, support chat logs, session recordings, and frontline team observations. Simplify your scoring scale to three levels (Low/Medium/High) to avoid false precision. The key is that every score must still have a documented rationale, even if that rationale is 'based on 8 customer interviews, 6 mentioned confusion at this step.' As you optimize your first journey, instrument better data collection so the next scoring cycle has stronger inputs.
Should I prioritize customer journeys before or after mapping touchpoint interconnections?
Prioritize first, then map interconnections for the journeys you select. Mapping touchpoint interconnections across all journeys is time-consuming and produces diminishing returns for journeys you will not optimize this quarter. Score and rank your portfolio, identify your Tier 1 journeys, and then invest in detailed interconnection mapping only for those top-priority journeys. This sequence ensures your deepest analytical work is focused where it will actually inform action. See [mapping touchpoint interconnections](/skills/mapping-touchpoint-interconnections) for the detailed process once you have selected your focus journeys.
How often should I re-score the journey priority ranking?
Quarterly is the standard cadence for most organizations. This aligns with typical OKR and planning cycles and gives enough time for new evidence to accumulate between scoring sessions. However, trigger an off-cycle re-score if a major event changes the landscape: a significant product launch that creates new journeys, a strategic pivot that shifts company priorities, or a sudden spike in friction data (like a service outage affecting a specific journey). Re-scoring does not mean starting from scratch. Review what changed, update affected scores, and recalculate.
How many scorers should I include in the prioritization exercise?
Aim for 3-7 scorers representing different functions. Fewer than 3 risks blind spots, because any single person's biases dominate. More than 7 makes calibration sessions unwieldy without proportionally improving accuracy. The essential perspectives to include are someone with customer data access (CX or research), someone who understands business financials (product, finance, or leadership), and someone from a customer-facing role (support, sales, or customer success). If you must limit to 3, pick one from each of those categories.
Why does my journey priority ranking keep changing every quarter?
Moderate quarter-over-quarter movement is healthy. It means your scoring model is responding to real changes in strategy, friction data, and business conditions. If rankings are volatile (top journeys completely reshuffling each quarter), the likely cause is one of three issues: your evidence base is too thin to produce stable scores, your strategic alignment dimension is shifting frequently because company priorities are unstable, or different scorers are participating each cycle without sufficient rubric calibration. Fix this by standardizing your scorer group, strengthening your evidence briefs, and checking whether your strategic priorities are genuinely changing or just being communicated inconsistently.
Can I use this scoring approach for prioritizing within a single journey (L2 sub-stages) instead of across journeys?
Yes, and the process is nearly identical. Once you have selected an L1 journey for optimization, list its L2 sub-stages and score each one using the same three dimensions. The main adjustment is that business impact at the L2 level is usually measured by contribution to the L1 outcome rather than top-line revenue. For example, if the L1 journey is 'onboarding,' the business impact of an L2 sub-stage like 'first integration setup' would be measured by its effect on time-to-value or activation rate, not total company revenue.
How do I handle a stakeholder who disagrees with the final ranking and wants to override it?
Invite them into the process rather than defending the output. Share the rubric, the evidence briefs, and the individual scores. Ask them to identify which specific dimension and journey they disagree on and to provide their own score with a documented rationale. If their rationale introduces new evidence the group did not have, incorporate it and re-score that journey. If their rationale is 'I just think this is more important,' point them to the weight on strategic alignment. The model is designed to absorb subjective judgment through declared weights, not through after-the-fact overrides. Over time, as the model's predictions prove accurate, these challenges decrease.