DEEPINGSOURCE

Drugstores

Tester interaction analysis for drugstores

How to verify with data which tester locations actually lead to purchases.

Request a consultation

Signals to read first

Three signals to read before the result

Tester trial frequency unmeasured01

Tester trial frequency unmeasured

Operators cannot see which testers customers try, how many times they try them, or when interaction peaks.

No criteria for tester placement02

No criteria for tester placement

Testers are placed by intuition because there is no evidence showing which location generates the most contact.

Live preview

This is what you actually see

01 Detect · saai care

Automatic recognition of tester contact behavior

AI captures and tallies actions such as picking up specific products or testing on face. Also measures dwell time in tester zones.

ⓘScreens are illustrative and may differ from the actual service.

Before · After

From finding the result later, to receiving the signal first

Knowing first

saai care · saai insight · saai agent

How to verify with data which tester locations actually lead to purchases.

  • Automatic recognition of tester contact behavior
  • Tester contact vs. purchase conversion analysis
  • Optimal tester placement suggestions

Decision flow

Connect the evidence from signal to action

01 · 01 Detect · saai care

Automatic recognition of tester contact behavior

AI captures and tallies actions such as picking up specific products or testing on face. Also measures dwell time in tester zones.

02 · 02 Analyze · saai insight

Tester contact vs. purchase conversion analysis

Links with POS data to verify the proportion of customers who tested and subsequently purchased the product or nearby items. Compares effectiveness across time slots and tester positions.

03 · 03 Act · saai agent

Optimal tester placement suggestions

Identifies high-interaction but low-conversion tester products and suggests improvements (price adjustments, location changes, complementary product pairing).

Operational evidence

See the scene and the outcome together

Expected results

How to measure tester impact with data

In drugstores and beauty shops, testers can strongly influence a purchase decision. Yet teams often rely on subjective judgment to know which product is tried most, how often people interact with a tester, and how often that interaction leads to purchase.

+16%

Tester zone purchase conversion

+34%

Dwell time after tester contact

2배

Tester ROI measurement accuracy

* Figures are illustrative examples of real deployments. Actual results vary by site.

A drugstore scene with an anonymous shopper considering products at a shelf
Operational context image · not an actual analysis screen

Case studies

See how it worked in a similar environment

View case studies

More Drugstores solutions

+12% increase in high-margin zone basket value

VMD optimization for drugstores

How zone-level dwell-time data raises attention to high-margin product areas.

View detail

+20% improvement in blind-spot zone visit rate

Zone-by-zone performance analysis for drugstores

How to identify which in-store zones drive sales and which are blind spots.

View detail

3 Core SAAI Products

How three products help this space together

Detect, analyze, and act connected in one continuous operating loop.

Detect

saai care

Detects critical anomalies in real time.

saai care Learn more
Analyze

saai insight

Analyzes why it happened and reveals trends.

saai insight Learn more
Act

saai agent

Proposes next actions by priority; a person confirms.

saai agent Learn more
View all solutionsDrugstores industry detail
  • 103 patents
  • SOC 2 · PIPA certified
  • 1,700+ cameras connected
  • 8+ partner brands

Could this work in your space too?

Tell us about your current CCTV setup and space. We will review a practical starting point with you.

Request a consultation