The Gillia blog
Inventory, business management, customers and artificial intelligence: practical articles to better understand and run your store.
Cycle counting
A system shows 12 units in stock, but only 9 are left on the shelf: here is how cycle counting detects these gaps before they pile up.
Unpaid invoice
An overdue payment isn't always handled the same way: here is how to identify an unpaid invoice, check it and follow up with the customer at the right time.
Inventory discrepancy
Your software shows 25 units in stock, but you only count 22 in the store: here is how to understand and fix an inventory discrepancy.
Agentic AI in retail
Some AI systems go further than a simple answer: they can use tools, follow several steps and act to reach a goal. This is known as agentic AI.
AI and customer service
In a store, most customer requests aren't complex: what takes time is finding the information before answering.
Multi-store management
Running one store already means tracking sales, inventory, customers and operations. With several points of sale, getting a global view becomes a real challenge.
AI in retail
Retail artificial intelligence isn't limited to chatbots. Connected to store data, it helps manage the store day to day.
Automating your store
Checking sales, verifying inventory, preparing a report: taken separately, these tasks end up weighing on the merchant every week.
Re-engaging inactive customers
A customer who no longer comes back isn't necessarily a lost customer. You still have to notice their absence and choose the right way to bring them back.
Customer segmentation
Sending the same message to all customers is rarely the most effective strategy. Segmentation groups customers so you can tailor your marketing actions.
Identifying your best customers
Not all customers are worth the same to a store. Identifying your best customers helps you better understand your clientele and tailor your loyalty actions.
Increasing the average basket
Bringing in more customers is one way to grow revenue. Another is to increase the average value of each purchase.
Revenue forecasting
Forecasting a store's revenue lets you anticipate purchases, staffing needs and cash flow. A useful forecast isn't about copying the previous month: it relies on your data, seasonality and trends.
Detecting sales anomalies
An abnormal drop in a category, an average basket that swings sharply, a product that no longer sells: these signals are easy to miss. Yet they help you detect a sales anomaly early.
Supplier order
Ordering too little creates stockouts. Ordering too much ties up cash. A good supplier order answers one simple question: how many units do you really need before the next restocking?
Overstock
Having stock helps meet demand. But when available quantities exceed needs for a long time, stock becomes a cost.
Sales analysis
Your revenue dropped 8 % this week. That's a fact. But it's not yet a cause.
Store dashboard
A good retail dashboard shouldn't display as many figures as possible. It should quickly answer one simple question: what's happening, and where do you need to act?
Inventory turnover
Large inventory isn't always bad inventory. The real question is simple: how fast do products sell and get replenished?
7 practical uses of AI
Artificial intelligence for retail helps you read sales, inventory, products, customers and orders. With AI for commerce, the goal is simple: save time on useful tasks.
AI agent for retail
Artificial intelligence can already write a text or answer a question. In retail, an AI agent can go further.
Stockout
To avoid stockouts in store, you have to look at the risk before the shelf is empty.
Dead stock
Some items sit in inventory for several weeks, even months, without selling. Every tied-up product locks up money that could be used elsewhere.
Store inventory management
Managing inventory well isn't just about counting products on the shelf. You also need to know what to order, when to restock, and which items risk running out.
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