When one display underperforms, the cause could be the product, the location, or the display method. You can only separate those causes by breaking the funnel into visit, exposure, exploration, and pickup.
Each row is one display. A row with high visits but a pickup near zero at the far right is the display to address first. Names that would identify the store or its products are masked. The product UI shown is the Korean build.
What it shows
Shelf analysis quantifies the four-stage funnel, from visit to exposure, exploration, and pickup, at the display and shelf (tier) level. You can compare the top, middle, and bottom shelves against the same benchmark, side by side.
High exposure-to-exploration but low exploration-to-pickup means people looked at the product but their hands didn't follow. Start by reinforcing the POP messaging.
When to use it
Use it to compare performance across displays and decide SKU management and merchandising strategy.
A worked example
Here's an example. Say one display converts 68 percent from exposure to exploration, but only 4 percent from exploration to pickup. Reinforce the POP messaging, then compare pickup rate on the same funnel before and after to verify what was actually driving it.
Which features to read it with
One feature alone rarely gets you to the cause. The pattern that repeats is: overlay a second feature to narrow the cause, act, then re-measure with a third.
- Measure — Overlay the low-scoring shelves with the shelf heatmap to re-read the table's numbers as color.
- Act — Move products into the golden zone or reinforce the POP messaging.
- Verify — Use purchase conversion to follow that display through to a purchase.
High exposure with low pickup means you should suspect the display method first. To scan shelf-level attention and pickup in color first, continue with the shelf heatmap. Check out shelf analysis in saai insight.
