Campaigning Wiki

5 min read

Summary

A Monitoring, Evaluation and Learning (MEL) framework decides what to measure at each level of the campaign’s results chain, how, and how the resulting learning feeds back into the next campaign.

Body

MEL is the discipline that closes the campaign-cycle feedback loop. Monitoring tracks continuously during the campaign; evaluation judges worth at phase-ends or close; learning feeds both back into the next round. The Community Tool Box names “planning an evaluation” as one of the core sections of its public-action model, alongside assessing community needs, engaging stakeholders and action planning [source: community-tool-box]. The People Power Manual closes its Campaign Strategy Guide with the Evaluation and success indicators chapter, treating MEL as a first-class part of strategy rather than an afterthought [source: people-power-manual].

The discipline is to plan all three before launch. The Community Tool Box warns explicitly that MEL planned after the action runs has no baseline to measure against [source: community-tool-box]. The ALA Frontline Advocacy Toolkit’s planning roadmap asks library advocates to decide what criteria can you measure, and how? — a question whose honest answer is rarely more than a handful of indicators [source: ala-frontline].

Monitoring indicators. Set indicators that are themselves SMART. Distinguish three categories that should never be confused:

  • Output indicators — what the campaign produced/did (events held, signatures, doors knocked). Easy to count, but low-value on their own.
  • Outcome indicators — changes in others (target’s position, public opinion, a policy step). Harder to count, but the actual point of the campaign.
  • Leading vs. lagging. Leading indicators predict (volunteers recruited this week); lagging indicators confirm (a vote won). Track both.

The Commons Library’s organising modules treat the dashboard as the operational form of these indicators — a small, owner-assigned set the team reads at a regular cadence so strategy shifts are triggered by signals rather than anecdote [source: commons-library].

Types of evaluation answer different questions, and the campaign should match the type to the decision being made:

TypeQuestionWhen
FormativeIs the design sound?before / early
ProcessAre we implementing as planned?during
OutcomeDid the intended changes occur?after a phase
ImpactWhat lasting difference did we make?well after
SummativeOverall, was it worth it?at close

The Community Tool Box’s evaluation chapters pair these types with the moment the team needs to decide — formative before launch, process weekly, outcome at each milestone, summative at close [source: community-tool-box]. The ALA Frontline Advocacy Toolkit asks the same question — when do we need to know? — and warns against skipping evaluation when the advocacy project “appears to be finished”, because the next project depends on the lessons [source: ala-frontline]. BetterEvaluation’s Rainbow Framework organises the full evaluation cycle into seven tasks — define, frame, understand theory, describe, understand process, explain, and synthesise — providing a structured map for designing a MEL system that doesn’t skip the theory-of-change foundations [source: betterevaluation].

ToC-based evaluation tests the if/then links of the campaign’s theory of change against what actually happened. The Commons Library treats the ToC as the falsifiable proposition the evaluation administers, and the indicators as the operational form of each if/then link [source: commons-library]. The People Power Manual pairs this discipline with SMART objectives: each objective has at least one named indicator, an owner, a baseline and a target [source: people-power-manual].

Contribution analysis is the honest answer to did we cause this? In advocacy, the campaign usually contributes to a change alongside many other actors; it rarely solely causes it. Contribution analysis builds a credible story: here was our ToC, here’s the evidence each link held, here are the other factors, and here’s why our contribution was plausibly significant. The Discuss Data 2024 BASEES panel — “Research Data Quality in Times of War” — and a 2025 Post-Soviet Affairs article by Aaron Erlich (McGill) on wartime survey methods are working examples of the methodological discipline that contribution analysis demands: how do you claim a contribution, and what evidence backs the claim? [source: discuss-data-civil-resistance].

Outcome Harvesting and Most Significant Change (MSC) are two qualitative methods built for messy, emergent advocacy where you cannot pre-define every result. Outcome Harvesting starts from observed changes and works backwards to ask whether and how the campaign contributed — well-suited to unpredictable advocacy and systems change. MSC systematically collects stories of change from those involved, then facilitates a structured selection of the most significant ones — surfacing impact that numbers miss. Both are paired with indicator-based monitoring rather than replacing it.

After Action Review (AAR) is the short, structured team debrief built around four questions: What did we expect? What actually happened? Why the difference? What do we do differently next time? The AAR runs after every milestone or action — not just at the campaign’s end. The Commons Library’s Campaign Accelerator trains campaigners to run an AAR after each action and to file the lessons back into the ToC and the risk register [source: commons-library]. The ALA Frontline Advocacy Toolkit asks the same discipline in the library-advocacy context: evaluation feeds the next project [source: ala-frontline].

Use it for

Designing the monitoring plan with the campaign plan; reporting to a funder, board or coalition; surviving a leadership transition; ensuring the campaign’s lessons feed the next one; answering the did it work? question honestly, including the contribution-not-attribution discipline.

Open Questions

  • 2026-06-23 — The locally fetched RAW corpus does not contain a deep chapter on contribution analysis, Outcome Harvesting, or Most Significant Change by name. Re-fetch a BetterEvaluation resource on each before this page can be promoted to established.

Sources & verification

  • sources/community-tool-box — grounding: secondary — RAW (833 chars)
  • sources/ala-frontline — grounding: secondary — RAW (13361 chars)
  • sources/people-power-manual — grounding: secondary — RAW (7977 chars)
  • sources/commons-library — grounding: secondary — RAW (5257 chars)
  • sources/discuss-data-civil-resistance — grounding: secondary — RAW (10564 chars)

Verified 2026-06-23 by llm-qc.

Graph View

Backlinks