Dwell Time and View Direction in Pharmacies: Did Customers Actually See the Vitamins?

Here is an uncomfortable finding that shows up again and again in pharmacy sensor data: the shelf directly beside the prescription counter — the one your merchandising team fought for — often records long dwell times and almost no views. Customers stand there for four, five, six minutes. Their bodies are next to the vitamins. Their eyes are on the counter, waiting for their name to be called. If your planogram decisions are based on foot traffic or proximity alone, you are paying premium placement rates for an audience that never turned its head.

This is the gap that pharmacy dwell time analytics — combined with view-direction measurement — was built to close. Prescription waiting time creates something almost no other retail format has: a genuinely captive audience, standing still for minutes at a time within arm's reach of your highest-margin OTC categories. But captive is not the same as attentive, and proximity is not attention. A customer can spend six minutes next to the vitamin bay and never see it. Until you can separate those two things, every "high-traffic zone" claim in your store design brief is an assumption, not a measurement.

Three behaviours your foot-traffic counter cannot tell apart

Modern overhead sensors — Xovis units with dwell and view-direction capabilities are one example, and Vemco Group's platform is sensor-agnostic, so existing hardware often carries over — distinguish three behaviours that a simple counter collapses into one number:

• Passing: the customer moved through the zone without stopping. Useful for flow mapping, useless as evidence of category exposure.
• Standing: the customer stopped in the zone — but their body orientation faced the counter, the queue display or their phone. This is the classic prescription-wait posture.
• Viewing: the customer's orientation was toward the fixture for a measurable duration. This is the only one of the three that correlates with consideration and conversion.

Once you split the data this way, familiar zones look very different. The waiting area may show enormous standing time but a view rate toward adjacent shelving of a small fraction of that. Meanwhile, a secondary aisle endcap — modest traffic on paper — can turn out to hold genuine viewing attention because customers approach it facing forward with nothing else competing for their gaze. Category managers who have only ever seen traffic counts routinely rank these two locations in the wrong order.

The waiting zone is an orientation problem, not a placement problem

A practitioner detail that rarely makes it into vendor slide decks: in most pharmacy waiting areas, customers orient toward the dispensing counter and the queue number screen, because that is where the information they care about will appear. Whatever sits behind them or beside them at ninety degrees is effectively invisible, regardless of how good the traffic number looks. Implementers who have run these projects learn to ask a different question. Not "where do customers wait?" but "what do waiting customers face?"

The fixes are often cheap once the view-direction data exposes the problem. Angling a vitamin gondola fifteen degrees so it sits inside the natural sightline to the counter. Moving the queue screen so the glance path crosses the immune-support shelf. Placing seasonal OTC — allergy in spring, cold and flu in autumn — on the fixture that waiting customers actually face rather than the one nearest to them. Store designers tend to think in adjacency; the data pushes you to think in sightlines.

Stop debating display locations. A/B test them.

The most productive use of this data is location A/B testing: move a display, measure exposure and dwell before and after, and keep what wins. It ends the perennial argument between the category manager who wants vitamins at the entrance and the pharmacist who wants them near the counter — because now there is a number attached to each option instead of two opinions.

A disciplined test looks like this: baseline the current position for two to four weeks, capturing pass-by volume, stop rate, view rate and average viewing duration. Move the fixture. Measure the same metrics for an equivalent period, ideally matching for day-of-week mix and any promotional calendar effects. Then pull the sales data alongside. The exposure metrics tell you why sales moved — or why they did not. A display that gained views but not sales has a range or price problem, not a location problem. Without view-direction data, those two failure modes are indistinguishable, and teams routinely fix the wrong one.

Because platforms like Vemco's deliver data with roughly two-second latency, you do not have to wait for a monthly report to know whether a Monday-morning fixture move changed behaviour. Merchandising teams can check the exposure numbers the same afternoon and abort a clearly failing move before it costs a full trading week.

Accuracy and privacy: the questions your compliance team will ask

Pharmacies are rightly cautious about anything resembling customer surveillance. Two points matter here. First, this measurement is anonymous and GDPR-compliant by design — sensors track body position and orientation as data points, not identities, and no images of individuals are stored or needed. Vemco processes its data on AWS in EU-Frankfurt, which simplifies the data-residency conversation with European compliance officers considerably. Second, on accuracy: the contractual minimum is 96%, and in practice accuracy typically lands at 98–99% when lighting, store layout and visitor behaviour allow. That honest framing matters, because a pharmacy with a low ceiling, a glass entrance throwing hard sunlight across the sensor zone, or heavy pram traffic will sit closer to the minimum — and a vendor promising a flat 99% in those conditions is telling you what you want to hear.

Scale is worth a sanity check too. Vemco Group processes more than 85 million counts per day across 2,000+ customers in 95+ countries — which means the methodology behind pharmacy dwell time analytics has been stress-tested far beyond a pilot store, across formats from compact urban pharmacies to large health-and-beauty layouts.

What to measure first

• Waiting-zone view rate: what percentage of standing time in the prescription area converts into views of adjacent fixtures? This is usually the biggest single surprise in the first month of data.
• Vitamin bay stop-to-view ratio: of customers who stop near the vitamins, how many actually orient toward them, and for how long?
• One A/B test per quarter: a single well-run fixture relocation, measured before and after, teaches more than a year of debate.

The prescription queue is minutes of undivided customer time that most pharmacies currently waste on the back of someone's head. The question was never whether customers were near the vitamins. It was whether they saw them — and now that is measurable.

Want to know what your waiting customers actually look at — and run your first display A/B test with real exposure data behind it? Talk to Vemco Group about dwell time and view-direction analytics for your pharmacy locations.