How an AI Observer Conducts Competitive Intelligence (Using a Shoe Retail Chain as an Example)

Introduction

A shoe retail chain — 80 stores in Central Russia. It competes with Kari, Respect, Lamoda, and thousands of sellers on Wildberries/Ozon. Every day: competitors change prices, launch promotions, receive reviews, and appear in news and social media. All of this is unstructured data scattered across dozens of sources. The company has neither the resources nor the time to monitor this manually.

This is exactly where an LLM agent is needed. Not for if...else on sensors, but for reading, understanding, and cross-referencing hundreds of sources in natural language.

This is an observer agent: it doesn't wait for a question — it reads the world and reports what matters.

Why an LLM Is Needed Here, Not a Script

What the AI Observer Monitors

1. Competitors: Prices, Assortment, Promotions

The agent daily crawls the websites of 15 competitors and marketplaces. But it doesn't just parse numbers — it understands what they mean.

What the agent sees:

Example insight (unavailable to a script):

Agent: 📊 Competitive summary for August 20

       Kari: launched a "3 pairs = 2,000 ₽" promotion.
       This isn't a seasonal sale — only the
       Spring-2025 collection is included. Likely reason: clearing
       inventory before the fall drop. Expected launch of the new
       collection — September 1–5.
       Recommendation: launch our fall collection
       on August 25–28 to get ahead by a week.

       Respect: raised prices on men's boots by +12%
       across all items. Reason unclear. Possibly supply
       issues with leather. Worth checking — if this is a trend,
       we have a price advantage.

2. Reviews and Social Media: What People Say About Us and Competitors

Not just counting stars. The agent reads the text and understands the essence.

What it analyzes:

• Reviews on Wildberries, Ozon, Lamoda, 2GIS, Yandex.Maps
• Comments on VK and Telegram
• Brand mentions on social media (via API / open data)

What it outputs:

Agent: 🗣️ Review analysis for the week (1,240 new)

       About us (480 reviews, average rating 4.1):
       😊 Praised: "comfortable last" (34%), "quality leather" (22%)
       😟 Complaints: "sizing runs small" (18% — specifically
           model art. 8821, in size 42), "sole is slippery"
           (7% — model art. 7750).
       🔥 New: 12 complaints this week about heel counter
           rubbing on model art. 8821. This complaint didn't
           exist before. Possibly a defective batch. I recommend
           checking warehouse stock.

       About competitors (760 reviews):
       Kari: growing complaints about "sole came off after a month"
           (+40% vs. average). Likely a problem with their
           adhesive supplier. This is an opportunity for us — we can
           emphasize "90-day warranty" in advertising.

3. News and Trends: What's Happening in the Industry

The agent reads industry news and cross-references it with the company's context.

Sources: FashionUnited, Shoes Report, RBC, Kommersant, industry Telegram channels, supplier news.

Example insights:

Agent: 📰 Industry digest

       🔴 IMPORTANT: Turkish factory "DeriTek" (leather
       production) announced a temporary shutdown due to
       an earthquake in the region. According to our data, "DeriTek"
       supplies leather for 40% of our assortment
       (through the importer "Kozha-Import").
       
       Recommendations:
       1. Contact "Kozha-Import" — confirm the status
          of supplies for September-October.
       2. Consider alternatives: Italian leather
          (+25% in price) or Russian leather (limited
          range of colors).
3. If the delay is >2 weeks — prepare communication
          for retail about a possible shortage of the autumn collection.

       🟡 TREND: The query "shoes made from recycled materials"
       grew by 340% over six months (Yandex.Wordstat). None
       of our competitors has yet launched an eco-line.
       An opportunity to occupy the niche first.

4. Internal anomalies: what requires attention

The agent analyzes internal data, but not just "sales fell" — it looks for the cause in context.

Agent: 📉 Anomaly: sales of model art. 7750 (women's
       boots) in Voronezh fell by 35% relative to
       other cities.

       Possible causes:
       1. Wildberries lowered the price on this same model by 22%
          (competitive monitoring data).
       2. In Voronezh, the model has a low rating (3.7 versus
          4.4 on average) — local buyers complain
          about the "slippery sole" more often than others.
       3. Kari opened a new store in the same mall — possible
          traffic outflow.
       
       Recommendation: temporary 15% discount in Voronezh +
       replacement of the batch with the problematic sole.

User scenarios

Scenario 1: Morning briefing for the director

The director opens the chat at 08:30:

Agent: ☕ Good morning! Summary for August 21.

       📊 SALES
       Yesterday: 4.2 million ₽ (+8% vs. plan, +12% vs. last year).
       Leader: men's boots art. 4401 (62 pairs sold).

       👁️ COMPETITORS
       • Kari lowered prices on children's shoes by 15–25%.
         Looks like a planned seasonal sale — this is
         standard for August. Our prices are still competitive.
       • A new seller on Wildberries is dumping: women's
         sneakers at 1,200 ₽ (our equivalent — 2,400 ₽).
         But the volume is only 50 pairs — looks like a clearance
         sale of leftovers, not a systemic threat.

       🗣️ REVIEWS
       No critical changes. Model art. 8821 — continues
       to collect complaints about sizing. Over the week −0.3⭐.

       📰 NEWS
       Chinese suppliers raised prices on hardware +8%.
       It will affect our assortment in 2–3 months —
       time to prepare.

       Full report — [link].

Scenario 2: Detecting a hidden threat

Agent: 🔴 URGENT: Wildberries, seller "ShoesLand".
       Over the last 3 days has listed 12 of our models
       at prices 30–40% below ours. This is not a clearance
       sale of leftovers — the assortment matches by 80%.
       
       Analysis:
       - The seller is new (registered 2 weeks ago)
       - Most likely, one of our wholesale clients
         has started direct sales on the marketplace, bypassing
         the dealer agreement.
       - If not stopped, price undermining across all models
         within a month.
       
       Recommendation: check shipments to wholesale clients
       over the last month. Identify the violator. Prepare
       a legal claim.

Director: Thank you, forwarding to the lawyers

Scenario 3: Finding an entry point into a new trend

Agent: 💡 Trend alert: over the last month, interest in
       "clogs" (sanitary clogs) has sharply increased on social media.
       - Yandex.Wordstat: +520% queries
       - Wildberries: +340% sales in the category
       - Pinterest: +290% pins with the tag #clogs
       - None of the top-5 competitors is yet represented
         in the category
       
       Recommendation:
       1. Order a test batch of clogs from our Chinese
          supplier — minimum run of 200 pairs.
       2. Production time — 4 weeks.
       3. Potential niche for 3–5 months, until the giants
          enter.
       
       Prepare a business case?

Category manager: Yes, do the calculation
Agent: With a purchase of 200 pairs at 1,200 ₽ and a sale at 2,800 ₽:
       - Revenue: 560,000 ₽
       - Margin: 320,000 ₽ (57%)
       - Risk: 30% won't sell → write-off of 72,000 ₽
       - Even in the worst case, profit of 248,000 ₽.
       The risk is justified. Launch the order with the supplier?

Scenario 4: Seasonal readiness

Agent: 📅 3 weeks until September 1.

       Analysis of previous years: in the last week of August,
       children's shoe sales grow by 210%. This year:
       - Competitors have already started promotions (Kari: −25%, Respect: −20%)
       - We haven't started yet.
       - Forecast: if we launch the promotion tomorrow — we'll capture
         demand first, before competitors ramp up their advertising.
       
       Recommendation: start tomorrow with −30% on the children's
       collection + VK targeting to parents.

Marketer: Ok, let's launch

Economics of implementation: calculation for a shoe chain

Initial data

💡 Key difference: here we're not replacing people, but creating a function that didn't exist before. An average company with 80 stores can't afford a competitive intelligence department. An AI observer makes it possible.

How much it would cost to do this manually

To cover the same volume of monitoring as an AI agent, it would take:

Annual cost of the team: 4 × 155,000 × 12 = 7,440,000 ₽/year

But the reality is that a company with 80 stores simply doesn't hire these people — and loses money not on salaries, but on missed opportunities and delayed reactions.

Cost calculation with an AI observer

💡 6,000 "observations" per month is roughly 200 checks per day: crawling 15 sites, reading new reviews, scanning industry news. Each check is one request to the LLM, which reads the page and extracts meaning.

Comparison

Payback

Payback = 500,000 ÷ (6,810,000 ÷ 12) ≈ 0.9 months ≈ 3.5 weeks

What the observer provides beyond direct savings

Summary

An AI observer for a shoe retail chain is not a replacement for a script with if price < threshold. It's a function the company simply didn't have: reading 40 sources, understanding meaning, cross-referencing facts, and reporting to the director every morning on what's happening in the market. Previously, only giants with an intelligence department could afford this. Now — any chain of 80 stores can.