MKTG90026 Chap.5 Product Growth and Portfolio Effects
Product Growth and Portfolio Effects
Product and portfolio metrics convert stated trial intention into a transparent volume forecast, then test how growth affects the existing range. This chapter separates adjusted trial, first-purchase volume and repeat volume; compares year-on-year, same-store and compound growth; and measures cannibalisation.
The core habit is to expose every awareness, distribution, intent, repeat and purchase-frequency assumption rather than presenting a precise total as certain demand.
What this chapter covers
- 01
Trial and adjusted trial rate
- 02
Projected trial volume
- 03
Repeat buyers and volume
- 04
Year-on-year growth
- 05
CAGR and future value
- 06
Cannibalisation and portfolio value
Worked example · free
Build a trial and repeat forecast
- 1Trial population is 500,000 multiplied by 12.24%.
- 1Projected triers equal 61,200 people.
- 1Trial volume is 61,200 multiplied by two units.
- 1Projected trial volume equals 122,400 units.
Key terms
- Adjusted Trial Rate
- A weighted intention measure further reduced by expected awareness and distribution.
- Compound Growth Rate
- The constant periodic growth rate that links a starting value with an ending value.
- Cannibalisation Rate
- Sales lost from existing products divided by sales of the new product.
Product Growth and Portfolio Effects FAQ
Why is adjusted trial lower than stated intention?
Definite and probable intentions receive different behavioural weights, then awareness and distribution reduce the reachable opportunity. The calculation recognises that stated interest, brand knowledge and product access are separate conditions for a real first purchase.
When should I use CAGR instead of year-on-year growth?
Use CAGR to summarise the constant annual rate connecting a multi-period start and end. Use year-on-year growth for movement against the immediately prior period. CAGR smooths the path, so inspect interim volatility when it matters.
Is cannibalisation always bad?
No. A new product may defend the portfolio, retain customers or move demand toward a higher-margin offer. The rate identifies transferred sales, but the decision needs incremental volume, relative margin, strategic purpose and any competitor sales captured.
Why is a launch forecast better expressed as scenarios?
Awareness, distribution, trial and repeat assumptions are uncertain and multiply through the model. Low, base and high cases reveal decision exposure and identify which assumption needs evidence before capacity or spending is committed.
Exam move
Draw the flow from target population to adjusted trial, trial units, repeat buyers, repeat occasions and total volume. Recalculate a base case after changing distribution or repeat rate and distinguish percentage points from relative change. Compare total with same-store growth, then add cannibalisation and contribution so product sales are not mistaken for portfolio value.
Begin with a forecast tree rather than a single formula. Put target population at the left, then awareness, distribution, adjusted trial, trial units, repeat buyers, repeat occasions and repeat units. Label every branch as a rate, count or frequency. Multiply only compatible terms and use the emerging count as the base for the next transition.
This makes it easy to spot a stated intention percentage that has been applied directly to the entire population without behavioural weighting or product access. Work the base case once, then create a sensitivity table. Change awareness, distribution, definite intent, probable intent, repeat rate and repeat frequency separately. Predict the direction and identify which assumptions act on the largest base.
Distinguish a five-percentage-point increase from a five-percent relative increase before substituting. When multiple rates are uncertain, report a range or scenarios rather than adding false precision to the point estimate. A forecast is a transparent conditional model, not a promise of demand. For growth measures, write the comparison window before selecting the formula.
Year-on-year growth compares adjacent periods, CAGR connects a start and end through a constant compounded rate, and same-store growth holds the operating base more nearly constant. Recalculate total growth after adding new stores and compare it with same-store growth so expansion is not mistaken for underlying improvement.
If interim values are supplied, inspect volatility because the smoothed CAGR can conceal decline and recovery. Treat cannibalisation as a portfolio bridge. Separate new-product sales into volume transferred from existing products, volume captured from competitors and category expansion where the scenario supports those distinctions.
Calculate the rate using the stated lost existing sales over new-product sales, then convert the volumes to contribution. A high transfer rate can still support a decision when the new offer has better margin, protects customers or pre-empts competition, while low cannibalisation does not rescue an unprofitable launch.
For exam drills, sketch the tree, show one line per transition and reconcile trial plus repeat to total units. Add units to every intermediate result and avoid rounding early. Cover the answer options until you have a directional expectation. Keep an error log for incorrect bases, percentage-point confusion, repeat frequency applied to all triers and portfolio sales confused with incremental value.
Finish each case by naming the assumption with the greatest decision leverage and the evidence management should collect before scaling the launch.
Working through Product Growth and Portfolio Effects in MKTG90026? Sia is AskSia’s AI Marketing tutor — ask any MKTG90026 Product Growth and Portfolio Effects question and get a clear, step-by-step explanation grounded in how MKTG90026 is taught and assessed. Read this chapter free, then take your hardest questions to Sia.
