5 Friction Points Hurting Your Category Performance

Discover hidden friction points hurting your category performance and depleting shoppability, plus how AI tools can help.

In an era where convenience and choice reign supreme, shoppers demand more from their retail experience — and those expectations directly influence category performance. They want experiences that are effortless yet enjoyable, intuitive but engaging. Yet many stores unknowingly sabotage their own success with cluttered layouts, confusing signage, and misaligned product groupings. These friction points quietly erode shoppability—the ease with which shoppers navigate, browse, locate products, and convert—costing brands millions in lost trips and baskets.

The good news? With advancements in behavioral science and AI-driven analytics, you no longer need to guess where your category experience falls short. We’re here to share the most common (and costly) shoppability gaps—and how to fix them to win with shoppers.

1. Product Availability Blind Spots

• The Problem:

  Shoppers don’t just want products in stock—they expect them in the right format, condition, and location. A cereal brand stocked on the top shelf might go unnoticed by hurried parents; a warm six-pack in the beer aisle fails to meet "grab-and-go" expectations. Worse, inconsistent restocking creates pockets of emptiness that frustrate loyal customers who are looking for an easy path to purchase.

• How VideoMining Helps:

  By analyzing millions of anonymized shopper journeys, we identify patterns in shopper behavior, including how and where customers search for products, how they engage at the shelf, and what causes them to walk away. Our AI tools flag inconsistencies and experiential friction —like incomplete merchandising execution—leaving no element of the category experience to chance.

   

2. Aisle Flow That Ignores Shopper Logic

• The Problem:

  Aisles organized around internal logistics (like vendor agreements) rather than the shoppers’ mental model of the category can create unnecessary complexity. Imagine an OTC medicine aisle where medicine is grouped by brand rather than ailment —shoppers waste time jumping between shelves to compare options on cold medicine, increasing the likelihood of abandonment. Getting to the bottom of how shoppers navigate, browse, and decide is paramount to building aisle experiences that land.

• How VideoMining Helps:

  Heatmaps generated from AI video analytics reveal traffic patterns where shoppers pause, backtrack, or walk away. We provide the concrete insights needed to rationalize and verify aisle redesigns and planogram optimizations - based on true shopper reactions. Curious what happens when shoppers seek products across different categories (like crackers and chips) to address specific need states? Wondering how large packs of carbonated soft drinks impact shopper interactivity with bulky paper goods, like toilet paper and paper towels? Just ask. We have over a decade of historical shopper behavior data at our fingertips to power fast and precise answers to your most specific questions.
  

3. Experiential Clutter

• The Problem:

  Overstuffed endcaps, displays blocking shelves, and traffic bottlenecks all contribute to sensory overload. These distractions pull focus from high-margin products and make aisles feel chaotic—like a maze shoppers rush to escape. What’s more, aisle traffic jams and physical barriers to a smooth aisle navigation can hurt total store performance and decrease trip profitability.

• How VideoMining Helps:

  Patented computer vision technology detects congestion points and "visual noise" that deter engagement at the shelf. VideoMining helps brands identify these points of friction and identify changes to rectify and improve the aisle experience from start to finish. 

4. Assortment Misfires

• The Problem:

  Missing a single high-demand product attribute—be it plant-based, fair trade, free-from, or specific pack sizes and packaging types—can derail an entire category for certain subsets of shoppers. Shoppers arrive with specific needs, and if those aren’t met immediately, they might assume the retailer doesn’t understand (or care about) their preferences. Plus, if shoppers are hardwired to believe a certain packaging type is the only option that will satisfy their needs, they’ll walk away with nothing. This can be true in both functional cases, such as parents wanting yogurt pouches for their children and strongly deselecting messy yogurt cups; or it can be preconceived notions about packaging efficacy and benefits, like a shopper who believes an aluminum beer bottle will keep their beverage colder than its traditional glass counterpart would.
  

• How VideoMining Helps:

  By correlating purchase data with in-aisle behavior (e.g., prolonged label reading or repeated shelf scanning), we provide category solutions and data to pinpoint those unmet needs. Proprietary category decision trees - rooted in behavioral data – can also help identify those missing pieces across the board. This allows for rapid iterative improvements on retail stimuli, such as assortments, pricing, and promotions to align with what shoppers actively seek.

5. Shelf Flow That Fails the "5-Second Test"

• The Problem:

  Planograms often look flawless on your screen, but can fall short in real life. For example, snack aisle organized by brand attribute instead of flavor leaves shoppers squinting at labels and giving up when they can’t find the product segment they seek; a skincare section sorted by product size (not skin type) forces unnecessary hunting and low shoppability. The more time shoppers spend searching without success, the more likely it is that leakage will occur, reducing shoppability and category performance. Planogram optimization can pack a heavier punch if it takes into account the inherent way shoppers shop the shelf.
  

• How VideoMining Helps:

  Our Behavioral Scorecard evaluates shelf performance using proven shoppability metrics like:

  • Closure Rates: How many browsers convert after engaging with the shelf?
  • Dwell Time: Are shoppers pausing due to interest or confusion?
  • Product Interactions: Are shoppers actively engaging with products? What does it tell us when they pick an item up and put it back down?
  • U-Turns: How many laps up and down the aisle does it take for the shopper to find what they’re looking for? At what point do they abandon the shelf mid-search?

Building a Shoppable Store: 3 Action Steps

1. Audit Shoppability and Experiential Design: Use AI-powered traffic and behavioral analytics to see your store through shoppers’ eyes. Where do they linger, rush, or walk away? Where do you win, and where do you lose when it comes to delivering shoppable experiences?
   
   
2. Test & Refine: Pilot small changes (e.g., re-sorting a single aisle by use case) and measure shifts in engagement and conversions.
   
3. Embrace Shopper-Centric Design: Align every element — from signage to restocking schedules — with observed shopper behavior, not assumptions.
   

Why VideoMining?

We combine decades of behavioral science research that is grounded in retail truths with cutting-edge AI to decode the "why" behind shopper decisions. Unlike traditional market research solutions, our analytics reveal:

• How aisle layouts influence navigation patterns and impulse buying.
• How specific demographic profiles and shopper segments interact differently with a given category
• Which product attributes drive quick purchasing decisions.
• Where small tweaks (like moving a premium brand eye-level) yield stronger returns.
• How brand-led decisions are made at the shelf.

Contact VideoMining to uncover your store’s hidden revenue leaks, improve category performance, and turn every aisle into a conversion engine.