Store-visit objective benchmarks matter for multi-location retail, healthcare, and service brands where digital media needs to drive measurable local foot traffic or bookings. Store-visit rate, cost per visit, local intent efficiency, and location-level lift.
Use these labeled KPIs together instead of judging store visits performance from one headline number. Conversion-sensitive metrics update when you change the conversion type above.
| Metric | Median | Top Quartile | What It Tells You |
|---|---|---|---|
| CTR | 2.4% | 4.1% | Creative and message-to-audience fit |
| CPC | $2.80 | $1.65 | Click acquisition efficiency |
| CVR | 3.4% | 6.2% | Landing-page and offer effectiveness |
| CPA | $82 | $45 | Cost to generate the selected conversion |
| CPM | $12.40 | $7.80 | Auction pressure and reach efficiency |
| ROAS | 3.1x | 5.2x | Revenue efficiency where purchase value is tracked |
Store-visit rate, cost per visit, local intent efficiency, and location-level lift. Benchmarks should be interpreted with contextual commentary, not as standalone averages.
| Objective | Average | Median | Top Quartile | Bottom Quartile |
|---|---|---|---|---|
| Store Visits | 7.6% | 6.2% | 11.4% | 2.7% |
These are the main drivers that typically explain why the same headline metric changes across channels, industries, and conversion contexts.
| Factor | Why It Matters |
|---|---|
| Location density and map visibility | Changes how store-visit rate, cost per visit, local intent efficiency, and location-level lift. |
| Mobile intent and local search demand | Changes how store-visit rate, cost per visit, local intent efficiency, and location-level lift. |
| Offer relevance and visit-to-booking or visit-to-sale quality | Changes how store-visit rate, cost per visit, local intent efficiency, and location-level lift. |
Store-visit objective benchmarks matter for multi-location retail, healthcare, and service brands where digital media needs to drive measurable local foot traffic or bookings.
Store-visit benchmarks should be tied to geography, device, and location density because the same media mix behaves very differently in different local markets.
Store-visit benchmarks should be tied to geography, device, and location density because the same media mix behaves very differently in different local markets.
Store-visit benchmarks should be tied to geography, device, and location density because the same media mix behaves very differently in different local markets.
The Benchmarketing 4-Band Method. The Benchmarketing 4-Band Method reads every marketing metric against four percentile bands — P25 (bottom quartile), median, P75 (top quartile), and elite (top ~10%) — for a specific industry and channel, instead of a single cross-industry average. Averages blend brand and non-brand campaigns, $500/month and $500,000/month accounts, and unrelated industries into a number almost nobody actually has.
Where the numbers come from. The figures on this page come from the Benchmarketing benchmark dataset — thousands of curated benchmark observations across channels, industries, and US metro areas. Every statistic traces to a named source: WordStream Google Ads Benchmarks (2024), Meta Business Insights (2024), HubSpot Email Marketing Report (2024), Unbounce Conversion Benchmark Report (2024), Databox Marketing Benchmark Report (2024), Benchmarketing Platform Data (2023–2024). Benchmarketing does not publish anonymous "studies show" figures.
The Benchmarketing position. Beating the cross-industry average is a vanity milestone, not a target. Compare your number to the P25–P75 band for your specific industry and channel; if you are above average but below your industry's P75, you are leaving performance on the table.
Strong benchmarks usually come from markets with high local intent, strong map visibility, and a clear handoff from click to visit.
They be judged at the location and market level because blended performance can hide weak local execution.