Case study
Bookseller
Context and goal
Client
Bookseller (B2C and B2B)
The client manages a network of physical bookstores, an online store, and B2B partnerships.They need better visibility into sales, more accurate inventory management, and advanced analytics and pricing tools.
Goal
To create an intelligent application that centralizes sales data, automates inventory management, predicts demand through AI, and optimizes commercial decisions in real time.Challenges (before)
Diverse sales across different channels
Need to unify data from physical stores, online platform and B2B customers.
Lack of detailed analysis of sales by items and periods
Manual queries slow down decision-making.
Risks of running out of popular titles
Bestsellers often go out of stock, resulting in lost sales.
Lack of optimal supply planning
Orders are placed without forecasting based on seasonality and dynamics.
Difficulty in maintaining competitive prices
There is no automatic comparison with other retailers or distributors.
Lack of cross-selling tools
Merchants have no information about potential additional offers to customers.
Solution:
Centralized sales management application
Tracks daily sales by item and by location in real time.
Dynamic reports by period
Allow detailed analysis of trends and seasonality.Intelligent inventory management
Tracks minimum quantities and generates automatic delivery requests.
AI prediction of future demand
The models analyze sales dynamics and determine which books will be in demand.
AI price optimization
Tracks prices of distributors and online competitors and offers optimal selling values.
AI recommendations for cross-selling
Recognizes patterns in customer behavior and suggests complementary headlines.
Implementation
Duration
10 weeks (analysis → integration → automation → AI functionalities)
Scope
20+ retail locations, online store, multiple B2B partners;integrated with warehouse and management software
KPI
Defined and monitored in real-time (see "Achieved Results")
Achieved results (5 months after implementation)
+46%
Higher accuracy of sales forecasts
The models predict seasonal peaks and genre trends.
-38%
Fewer out-of-stocks for searched titles
Automated queries and AI predictions have reduced shortages of popular books.
+24%
Higher employee efficiency
Simplified reports and automated reporting replaced manual processing.
Lower average cost of purchasing books
AI offers the most suitable conditions and suppliers.
+17%
Increase in turnover
Better availability and dynamic pricing policy increased daily sales.
-31%
Lower costs for warehouse operations
Optimized reloading eliminated overstocking.
+33%
Better conversion through cross-selling
AI recommendations led to more additional purchases.
+17%
Revenue growth YoY
Better availability of sought-after titles, accurate AI predictions, and a dynamic pricing strategy led to approximately 17% annual revenue growth.
What we learned / next steps
Automation eliminates a large portion of operational errors
The system proactively signals risks and suggests actions.
AI is a critical factor for managing fast-moving items
Forecasts support more accurate inventory planning.
Next steps
Expanding AI recommendations, integrating with personalized customer profiles, optimizing promotional campaigns.
Are you ready to manage your retail business more intelligently and based on real data?
Our solutions give you more accurate demand forecasts, automatic delivery requests, and dynamic price optimization.
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