Mastodawn

# Managing Shifting Customer Expectations With a Diversified Investment Strategy

This is a framework for technology B2C teams running SAFe with 6–15 people who are struggling to keep up with multiplying customer demands. It borrows a portfolio allocation model from Li Ka-shing's approach to building CK Hutchison and applies it directly to how product teams choose what to build.

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## The Problem You Probably Recognize (1/30)

You run a fitness subscription app with two million active users. Your agile release train has twelve people: one release train engineer, one product manager, two Scrum masters, two Scrum teams, three QA engineers, two UX designers, and one data analyst. You work in ten-week program increments with two-week sprints. Six months ago, users wanted more workout variety. You built fifty new plans. Satisfaction went up. Three months ago, they wanted social features (2/30)

. You delivered group challenges and leaderboards. Satisfaction went up again.

Last month, everything changed. Users now want AI-powered personalization. They also want offline mode. They also want Apple Watch integration. They also want meal planning. Your product manager picks what feels most important, but there is no data behind the picks. The team builds features some users want but not all users need. Satisfaction scores are flat. Churn is up four percent this quarter. (3/30)

You cannot build everything. You need a framework for choosing. That framework is an expectation portfolio.

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## The Core Principle

Li Ka-shing never put all his capital into one sector. He spread investments across ports, retail, telecommunications, energy, and infrastructure. More importantly, he allocated based on growth potential. High-growth sectors got more capital. Low-growth sectors got less. He reviewed the data continuously and shifted money fast. (4/30)

Your team faces the same allocation problem. You cannot build every feature customers want. You must allocate capacity based on growth potential. Growing expectations get more capacity. Shrinking expectations get less. The allocation must be data-driven and reviewed every sprint. Create an expectation portfolio. Measure the growth rate of each expectation. Allocate capacity based on growth rate. Review and adjust every sprint.

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## Step 1: Build an Expectation Portfolio (5/30)

Li tracked every investment in a portfolio with sector, amount, growth rate, and risk. You should do the same with customer expectations.

Your product manager creates a simple spreadsheet with five columns: expectation, volume, growth rate, current investment, and recommended investment. Populate it using three data sources: app store reviews scraped weekly by your data analyst, in-app feedback categorized by topic, and support tickets tagged by topic. (6/30)

Based on your current data, the portfolio might look like this: (7/30)

- AI personalization. 4,200 requests last month. Growth up 32% month over month. Current investment 20%. Recommended 35%.
- Offline mode. 3,100 requests. Growth up 18%. Current investment 10%. Recommended 20%.
- Apple Watch integration. 2,800 requests. Growth up 12%. Current investment 5%. Recommended 15%.
- Meal planning. 2,400 requests. Growth up 8%. Current investment 15%. Recommended 10%.
- Workout variety. 1,900 requests. Growth down 5%. Current investment 25%. Recommended 10%. (8/30)

- Social features. 1,200 requests. Growth down 12%. Current investment 15%. Recommended 5%.
- Progress analytics. 800 requests. Growth down 3%. Current investment 10%. Recommended 5%.

The full picture is now visible. The team can see which expectations are growing, which are shrinking, and where current investment does not match reality. (9/30)

For a SAFe team of six to fifteen people, create this portfolio before your first program increment. It should take about four hours and pull from at least three data sources. Treat it as a planning input for PI planning.

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## Step 2: Review Every Sprint and Shift Capacity

Li reviewed his portfolio daily and shifted capital as soon as growth rates changed. Your team should do the same every sprint. (10/30)

At the end of each sprint, the product manager updates the portfolio. It takes about thirty minutes. The data analyst provides the latest numbers. The product manager compares current sprint data to the previous sprint. (11/30)

From sprint one to sprint two, AI personalization requests jumped from 3,200 to 4,200. Growth went from 25% to 32%. The product manager raises the recommended investment from 30% to 35%. Social feature requests dropped from 1,800 to 1,200. Growth went from negative five percent to negative twelve percent. The recommended investment drops from 15% to 5%. (12/30)

The updated portfolio goes to the sprint planning meeting. It takes ten minutes to present. The team discusses and agrees to shift capacity. In this case, ten percent moves from social features to AI personalization. The shift happens in the next sprint. (13/30)

Sprint two to sprint three, offline mode requests rise from 2,600 to 3,100. Growth goes from 12% to 18%. The allocation increases from 15% to 20%. Workout variety requests drop from 2,300 to 1,900. The allocation decreases from 15% to 10%. The team shifts five percent of capacity from workout variety to offline mode.

The review is fast, data-driven, and happens every sprint. The team is always investing in the fastest-growing expectations. (14/30)

The review should take no more than thirty minutes and result in at least one capacity shift. Make it part of your sprint review.

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## Step 3: Write a Thesis for Every Expectation

Li treated every investment as a thesis. He had a hypothesis for why it would grow and a kill switch for when to exit. Your team should do the same. (15/30)

For each expectation, the product manager writes a thesis with two parts. A hypothesis that explains why the expectation will grow. A kill switch that defines when to stop investing. (16/30)

AI personalization. Hypothesis: Users who receive personalized workout plans have a 25% higher retention rate than users who receive generic plans. If we build the recommendation engine, retention will increase by at least 15%. Kill switch: If the recommendation engine does not increase retention by at least five percent after three months of data, we stop and pivot. (17/30)

Offline mode. Hypothesis: Users who travel frequently or have unreliable internet are churning at twice the base rate. If we offer offline mode, churn in this segment will decrease by at least 20%. Kill switch: If offline mode does not decrease churn in this segment by at least ten percent after two months, we stop investing. (18/30)

Apple Watch integration. Hypothesis: Thirty-eight percent of our users own an Apple Watch. If we integrate, engagement among Apple Watch owners will increase by at least 20%. Kill switch: If integration does not increase engagement among Watch owners by at least ten percent after two months, we stop.

These theses give the team discipline. Everyone knows when to stop. The kill switches prevent throwing good effort after bad ideas. (19/30)

Every expectation in your portfolio should have a thesis before you start building it. Write them as part of PI planning. Treat them as planning artifacts.

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## Step 4: Diversify, Do Not Go All In

Li never put all his capital into one sector, even when one was growing fast. He kept investments spread across multiple sectors so that a decline in one would not sink the whole portfolio. Your team should do the same with capacity. (20/30)

The product manager allocates capacity across at least four expectations based on the portfolio:

- AI personalization: 35% (four people)
- Offline mode: 20% (two people)
- Apple Watch integration: 15% (two people)
- Meal planning: 10% (one person)

The remaining 20% is reserved for maintenance, bug fixes, and technical debt. (21/30)