Measurement
Part of Appointment setting agencies measurement explained
Five checks for judging benchmark research on appointment setting agencies
Most teams read appointment setting agency benchmark research as a ranking table. Five named checks, sample, date, definition, funding and baseline, show whether the numbers deserve your budget.
What can a benchmark tell me about an agency?
A benchmark can show whether an agency result looks unusual, but it cannot rank providers or predict your pipeline by itself. Treat a published number as evidence about the businesses studied, then check whether their market and sales conditions resemble yours.
Research based on 40 respondents at software firms selling £5,000 contracts offers little direct guidance to a manufacturer selling £80,000 systems. The metric name may be the same while the sales conditions differ. Reviewing the benchmark method helps you see what evidence supports the figures.
| Check | What to establish |
|---|---|
| Sample | Who took part, how many, and their sectors and contract sizes |
| Date | When fieldwork took place and whether the market may have changed |
| Definition | What counts as a meeting booked or qualified appointment |
| Funding | Who paid for the study and whether they sell what the results favour |
| Baseline | How the figure compares with your own results using the same definition |
The five checks are sample, date, definition, funding and baseline. The seven numbered steps further down put those five checks into a working order, adding a check on sector fit and a final comparison with your own results.
Why might an average give me the wrong impression?
An average can conceal very different outcomes within one sample. If one half books one meeting per 200 dials and the other half one per 40, the mean may describe neither half. Ask for quartiles when available, so you can see more of the spread.
Averages hide the distribution
Half the sample
- Dials per meeting
- 1 per 200
- Mean describes
- Nobody
- What to ask for
- Quartiles
Other half
- Dials per meeting
- 1 per 40
- Mean describes
- Nobody
- What to ask for
- Quartiles
Definitions can distort a comparison too. A meeting booked might mean a diary entry, a confirmed attendee or a qualified opportunity, so studies can use the same label for different things. Check measurement definitions and agree what counts internally before setting an external figure beside your results.
What should I inspect before using a study?
Read the source notes in a consistent order: identify the publisher and funder, inspect the sample and any sector detail, record the fieldwork date, copy the metric definition, and check whether results include a range or quartiles rather than one average.
Seven checks before you quote a study
- Find the method note; if none, stop
- Note sample size and recruitment route
- Note fieldwork dates against your offer
- Check sector and contract value mix
- Ask who commissioned and whether they compete
- Write down the exact metric definition
- Compare with your own last two quarters
- Name the publisher and find out who funded the research.
- Check the sample size and the sector breakdown.
- Note when the fieldwork took place.
- Copy the study definition of a meeting booked or a qualified appointment.
- Look for a range or quartiles instead of relying on a single average.
- Compare the sample sector and contract size with your own.
- Set the result beside your own last two quarters using the same definition.
Worked example: testing an agency's benchmark claim
For example, an agency brochure says it books one meeting per 120 dials, against "an industry average" of one per 200. The example is illustrative, not a real study. Run the five checks on the claim.
Sample: how many calls and which clients produced the 120? Date: which quarter? Definition: is a meeting a diary entry or a qualified opportunity? Funding: who paid for the "average", and do they sell outsourced calling? Baseline: what is your own dials-per-meeting figure on the same definition?
If the brochure cannot answer the first four, treat the claim as marketing. If it can, the fifth check turns it into a hypothesis you can test in a short pilot.
A sample with fewer than 50 respondents and no sector breakdown is better treated as anecdote. Once a study passes, set the agency beside the quality checklist. The final comparison is a hypothesis about your business, not a verdict on the team; pipeline measures and a reporting dashboard help put outcomes and spend in context.
Which results can I compare across teams?
Conversion rates can be more useful for comparison than activity totals, provided the definitions match. Dials per day and emails per sequence depend on list quality, territory and sector norms, so treat those counts as descriptions of a sample rather than targets for your team.
Connect rate and meeting-to-opportunity conversion are steadier comparisons across teams than raw activity counts, but they still require a shared definition. Cost per appointment also needs context because salary, list cost and prospect seniority affect it. Compare a qualified-meeting cost with spend beside the outcome.
Outreach rules also differ by channel: consent requirements for email are not the same as for telephone calls, so consent-based and cold outreach results may not be comparable. Note the statistical quality of the source as well as its headline figure.
Should I compare my results with competitors or with myself?
Start with your own trend because it uses the same team, market and definitions, and you can inspect the factors behind it. External research is a sense check: if your meeting-to-opportunity rate sits far below a credible published range, investigate the difference rather than panic or copy another provider.
Review the gap each quarter and record whether it narrows. Revisit the comparison sooner if your offer, territory or channel mix changes.
For example, a team paying £400 a month for list and tooling could compare its cost per qualified meeting with a published range, then track the figure for two more quarters. Keep the definition stable and read spend beside the result, rather than treating the number as an agency league position.
What should I do first?
Start with the study notes, then use your own results as the final test. Keep the first actions simple:
- Check the sample, date, definition and funder.
- Prefer a range or quartiles to a lone average.
- Compare like with like, then record your own baseline.
- Refresh the comparison at least annually, or sooner if your offer, territory or channel mix changes.
