Product Sunset Rate

Goal of the analysis:

The Product Sunset Rate measures the proportion of products discontinued (end-of-life/end-of-sale) within a defined period relative to the active portfolio. It helps executives assess whether the company is pruning its portfolio at a healthy pace, balancing innovation with operational simplicity, and minimizing customer disruption. Too low a rate suggests a bloated portfolio, rising complexity costs, and diluted commercial focus. Too high a rate indicates instability, wasteful development churn, and potential revenue leakage. This analysis supports product lifecycle governance, cost-to-serve optimization, and strategic capital allocation.

Data required:

• Product master and lifecycle data:
  • Unique product IDs/SKUs, hierarchy (category, family, platform), and attributes (version, options).
  • Lifecycle statuses and dates: launch date, end-of-sale (EoS), end-of-support (EoL), deprecation announcements.
  • Flags for seasonal/limited editions, regional variants, and replacements/successor links.
• Commercial performance data:
  • Revenue, gross margin, and volume by SKU/product and period.
  • Customer count, contracts tied to product, renewal/churn outcomes (for SaaS/recurring).
  • Price list history and discounting.
• Operational and support data:
  • Inventory levels and aged stock by SKU and channel.
  • Manufacturing and supply data: MOQs, lead times, tooling/obsolescence costs.
  • Support tickets, defect rates, maintenance obligations, spare parts commitments.
• Governance and policy references:
  • Product lifecycle policy definitions (what constitutes “sunset”, required notices, obligations).
  • Regional regulatory constraints, warranty terms, and partner agreements.
• Historical and benchmark data:
  • Historical portfolio counts, introduction rates, prior sunsetting decisions and impact.
  • Industry benchmark ranges; internal best-performers (business unit, region, product line).

Detailed step-by-step instruction on how to conduct the analysis:

1. Define scope and period:
   • Agree the unit of analysis (product family vs SKU). For executive view, use “sellable product” or family; maintain a SKU-level appendix.
   • Select a period (e.g., last 12 or 24 months) and cadence (monthly/quarterly).
   • Define what counts as sunset: end-of-sale date, end-of-support, or both. Typically use end-of-sale for commercial rate; track support separately.
2. Extract data:
   • From PLM/PIM (e.g., Teamcenter, Agile, Salsify): product IDs, lifecycle statuses, key dates, successor relationships.
   • From ERP/BI (e.g., SAP, Oracle, Snowflake): revenue, margin, volume, inventory, and pricing by product and period.
   • From CRM/CS tools (e.g., Salesforce, Zendesk): contracts affected, support commitments.
3. Clean and standardize:
   • De-duplicate products and map regional variants to a parent product where appropriate.
   • Tag and exclude short-term seasonal/packaging refreshes if policy dictates (or report separately).
   • Align calendars and fill missing dates; if only announcement date exists, estimate EoS using policy (e.g., announcement + 90 days).
4. Compute portfolio baselines:
   • Active products each period: count products with status “active/sellable”.
   • Average active products for the analysis period = average of monthly active counts (more accurate than simple start/end average).
   • New introductions each period for context (to assess net growth vs pruning).
5. Identify sunset events:
   • List products with an end-of-sale in the period; capture EoS date and any successor product.
   • Classify sunsets: planned (per roadmap), reactive (quality, supply), or commercial (performance threshold).
   • Compute counts and revenue/margin associated in the 12 months pre-sunset.
6. Calculate core metrics:
   • Count-based sunset rate = number of products sunset in period / average active products in period.
   • Revenue-weighted sunset rate = revenue from sunset products (pre-sunset window) / total revenue.
   • Margin-weighted rate (optional), and split by planned vs reactive.
   • Net portfolio growth = introduction rate − sunset rate.
7. Segment and compare:
   • By product line, platform, region, channel, price tier, and customer segment.
   • By vintage (years since launch) to see lifecycle patterns; optionally compute survival curves of product longevity.
   • By profitability cohort (top/bottom quartile margin) to ensure pruning focuses on low-value tail.
8. Assess impact and quality of sunsets:
   • Attach customer/contract impact (number affected, ARR exposure, migration rates to successors).
   • Track inventory write-downs, supply chain simplification (SKU count reduction), and support cost changes.
   • Compare gross margin mix before/after sunsets to confirm value accretion.
9. Time-series and benchmark view:
   • Plot rates over time and versus introductions; highlight deviations from policy targets.
   • Benchmark against internal best-performing BUs and external ranges.
10. Quality assurance:
    • Reconcile sunset counts with governance logs (stage-gate/EoL approvals).
    • Spot-check high-revenue sunsets to validate status and dates.

Format of the output of analysis:

• Executive summary page with headline sunset rates (count-, revenue-, margin-weighted), trend line, and key insights.
• Portfolio waterfall showing starting active products, introductions, sunsets, and ending active products.
• Segmented tables by business unit, region, product line, and vintage with rate and impact metrics.
• Charts: time-series of sunset vs introduction rates; bar charts of rates by segment; survival curve by vintage.
• Impact exhibit: revenue at risk from sunsets, migration to successors, margin mix change, and inventory write-offs.
• Appendix: methodology, definitions, exclusions, data dictionary, and reconciliation to governance records.

How to interpret results:

• High count-based rate with low revenue-weighted rate suggests pruning of long-tail SKUs—typically healthy if complexity costs fall.
• High revenue-weighted rate indicates that major products are being removed; ensure successor readiness and customer migration plans are robust.
• Very low sunset rate over multiple periods may signal portfolio clutter, stagnant innovation, or weak governance.
• Rising sunset rate coupled with growing introduction rate can reflect experimentation; validate that margin mix and complexity are improving.
• Differences across regions/lines: higher rates in fast-cycle categories (e.g., fashion) are normal; persistent outliers merit root-cause review.
• Trend: improving when sunsets increasingly target low-margin SKUs and net portfolio growth aligns with strategy; deteriorating if reactive sunsets rise or customer disruption increases.
• Benchmark positioning should inform targets but be adjusted for lifecycle length, regulatory constraints, and channel dynamics.

Steps a company can take to improve on this measure:

• Governance and policy:
  • Define clear EoS/EoL criteria (e.g., margin/velocity thresholds, quality triggers) and a quarterly review cadence.
  • Institute a cross-functional portfolio council to approve sunsets and monitor customer impact.
  • Link product funding to lifecycle outcomes (sunset ratio, complexity reduction goals).
• Data, systems, and tooling:
  • Maintain accurate lifecycle status and dates in PLM/PIM; enforce successor mapping for all sunsets.
  • Build automated dashboards showing introduction/sunset rates, tail profitability, and EoL pipeline.
  • Tag seasonal/limited SKUs distinctly to avoid obscuring core portfolio signals.
• Commercial and product levers:
  • Rationalize variants and platformize to reduce SKU proliferation; consolidate low-velocity items.
  • Design customer migration paths (bundles, incentives, data migration tools) before announcing sunsets.
  • Use pricing and promotions to accelerate run-out and minimize write-offs; align with supply planning.
• Capability and incentives:
  • Train product managers on lifecycle economics and kill-criteria; celebrate effective pruning decisions.
  • Align sales compensation to support migration to successor products, not to resist sunsets.
  • Codify a repeatable “sunset playbook” (communications, legal, support, supply) with RACI and timelines.
• Scenario examples:
  • If the sunset rate is low and tail SKUs are unprofitable, apply stricter thresholds and launch a tail cleanup program.
  • If the sunset rate is high and churn is rising, slow sunsets, strengthen successor value propositions, and enhance migration incentives.
  • If reactive sunsets dominate, invest in quality, supply resilience, and earlier risk detection.

Benchmark comparisons:

General benchmarks:

• Mature, multi-year lifecycle portfolios (industrial equipment, B2B hardware): 1–5% annual count-based sunset rate.
• General technology/hardware with moderate cycles: 5–10% annually; top performers prune low-value variants while protecting core revenue (revenue-weighted rate <3–5%).
• Software/SaaS at major product level: 3–8% annually; feature/module deprecations can be higher but should be managed via deprecation policies.
• FMCG retail assortments: 10–30% annually; fashion/seasonal categories may see 20–40%+.

Segment- or industry-specific benchmarks:

• Retailers with strong assortment discipline: top quartile maintain sunset rate 15–25% with improving gross margin mix and reduced stockouts.
• Telecom devices/accessories: 10–20% annually given rapid refresh cycles; revenue-weighted rates ideally <10% due to planned transitions.
• Medical/regulated products: 1–3% due to compliance and long support obligations; sunsets tightly governed.

• If external benchmarks are sparse, build internal benchmarks:
  • Compare business units, regions, and product lines; set targets based on top quartile internal performers.
  • Track multi-year trends; aim for stable or improving margin-weighted outcomes as sunset rate evolves.
  • Benchmark by vintage (median product lifespan) and by tail profitability before/after pruning.