Industries
Retail
We turn store and product data into stock, price and allocation decisions.
Fashion retail is one of the few sectors where demand depends on both season and trend. A product’s life is short and there is no second chance: stock sitting in the wrong store mid-season melts away at markdown by the end of it.
Two halves of a season
Retail planning looks like two separate problems but is a single chain.
Before the season the range is built, the budget allocated, the initial price set, the stores clustered. Mistakes here are expensive because they cannot be fully corrected later. Good allocation cannot rescue a badly built range.
During the season the initial allocation goes out, replenishment runs, inter-store transfers are decided, markdown timing is set. Speed decides the outcome here: a week of delay turns sellable stock into markdown.
The whole chain is laid out on our retail planning map: 37 algorithms, each with the question it answers and the stage it belongs to.
The problems we work on
Allocation and transfer. How many units go to which store and when, under production, weather and store capacity constraints. Moving a slow-selling product to a store that can sell it, weighing logistics cost against sell-through probability.
Size and broken runs. Once a size run breaks in a store, that product effectively stops selling there. Getting the size mix right inside the pack matters for exactly this reason.
Price and markdown. From measuring price elasticity through to deciding which product gets which discount depth in which week, under end-of-season targets.
Lost sales. Quantifying, at SKU level, the sales missed during stock-out periods.
Customer side. Segmentation, churn prediction, lifetime value, recommendation systems and campaign audience building.
The part we publish openly
We publish some of our retail analytics work in the open. Each problem starts with a real business question, is set up with its mathematical model, and is worked through step by step in SQL and Python. Everything runs on synthetic data from Lumoda, a fictional 25-store fashion chain we built for the purpose.
The data, code and models are available at perakendeanalitigi.com without filling in a form.
Our work in this area
Derimod
Derimod: seven projects in three layers, from data warehouse to Optail
We brought data scattered across AX ERP, Excel and CRM into a single data warehouse, built more than 20 Power BI dashboards on top, and at the top placed Optail, which makes the initial allocation, replenishment and inter-store transfer decisions. Stock analytics (lost sales, size-run breaks, idle stock) and a CRM layer followed. This page is the overview; each of the seven projects has its own page.
- 20+ Dashboards
- 7,000+ Optail scenarios
Defacto
Defacto DSO: setting up a data science office and four years of consulting
We started in 2021 as mentors at the hiring Datathon. Through the end of 2024 we trained the team, built the DSO's database and server infrastructure, and wrote and put into production more than thirteen projects. Today the DSO is a directorate serving Defacto's retail analytics, AI and business intelligence needs.
- 4 years Duration
- 13+ Projects
Defacto
Defacto Norm Staffing: calculating the staff each store should have, from data
A model that takes the store staffing decision away from actual payroll and subjective requests and grounds it in measured workload and an efficient peer in the same segment. It produces a 2025 norm, a 2026 target and an increase / reduce / keep decision for every store × role. It runs entirely on BigQuery and is fully parametric.
- Store × role Decision unit
- Frontier Reference
Gürmen Group
Gürmen MATS: moving inter-store transfers from Excel to a system that decides at SKU level
The Inter-Store Transfer System that moves products not selling in one Ramsey or Kip store to a store that sells them, before markdown. It selects receivers and senders by cover at option level, derives need from sales velocity, and matches at SKU level. Block, single-unit and split-and-spread transfers run on the same infrastructure. It sits on Gürmen's server as a SQL project, and results go to the ERP.
- SKU Decision unit
- Cover Receiver / sender
Defacto
Defacto HR Analytics: dashboards, alerts and the Norm Staffing model
In a second period with Defacto after the DSO, we consult for Human Resources' own analytics team. The scope covers turnover, demographics, academy and HRBP dashboards, time-and-attendance and bonus automation, and an alert setup connected to the company's internal assistant, with the Norm Staffing model at the centre. Ongoing since December 2025.
- 2nd period Period
- 4 Layers
Karaca
Karaca: data analytics consulting, from the data warehouse to CustoMation
Consulting alongside the data analytics team in 2022–2023 for the store and e-commerce data of the Karaca group (Karaca, Karaca Home, Emsan, Homend, Kaşmir): the warehouse's analytics layer and cube, a product and idle-stock dashboard, store and e-commerce lost sales, a capacity system design, a transfer proposal, and CustoMation, which selects campaign audiences by RFM segment. This page is the umbrella; CustoMation has its own page.
- 6 Projects
- 5 Brands
Defacto
Defacto IofTheCustomer: an NLP system that classifies customer reviews and routes them to the right team
Every product review from defacto.com and the marketplaces passes through sentiment analysis within seconds; if negative, it is assigned one of nine categories and routed to the responsible team. Built in 2021, the system has run without interruption for three years and won a Stevie award in 2023.
- 9 Categories
- 700K+ Reviews processed
Defacto
Defacto Sorting: a scoring algorithm that sets product order on category pages
A dynamic scoring algorithm that decides the order in which products appear when a category page opens on defacto.com. It weighs sales, clicks, broken size runs, stock and shelf life together, with a cold-start solution for new products and scenario-based weighting.
- 5 Signals
- Cold start New products
Defacto
Defacto ELOSS: measuring the sales lost to stock-outs in e-commerce
A weekly calculation answering 'what would have sold had the product been there' for every SKU that runs out of stock on Defacto's own site and the marketplaces. It computes lost sales from pre-stock-out velocity with special-day and trend effects, then splits them by day using the daily sales distribution. An incremental design cut a calculation that took hours down to minutes.
- Weekly Run
- ~30 min Runtime
Defacto
Defacto NoSales: measuring stock that has not sold for 28 days, every week, by the same rule
A weekly system that checks, for every option × store pair with stock on Sunday, whether there was a sale in the last 28 days, and reports idle stock by store, product and region. Scheduled queries in BigQuery, a five-page Power BI dashboard, a trend since 2021.
- 28 days Rule
- Monday 05:30 Run
Defacto
Defacto PAT: meeting analytics that survived the move from Teams to Meet
A dashboard producing attendance, talk-time and meeting-efficiency metrics from online meeting data. Built on Microsoft Teams' relational data; when the company moved to Google Meet, Meet's nested event data was mapped to the Teams schema and the dashboard continued unchanged. Row-level security means everyone sees only their own hierarchy.
- 2 Platforms
- Daily Refresh
Defacto
Defacto DISCO: if I give this product that price, how many units sell in 14 days?
A system that bases the markdown decision on a forecast. For every product whose price changes, we built an XGBoost model that learns the units sold in the next 14 days from stock, broken sizes, sales velocity, price history and calendar effects. The pipeline runs every day, the error is measured against actuals 14 days later, and in a demo interface the planner can try five psychological prices side by side. The MVP was completed; by management decision it was not taken into the pricing process.
- Price → units Question
- Two-way Direction
Derimod
Derimod data warehouse: from AX, Excel and CRM to a single analytics database
The layer the other Derimod projects sit on. We brought AX ERP's transaction-level data, the teams' Excel files and the CRM together in one analytics database on SQL Server: schema and naming standard, procedure registry and log mechanism, daily flow with SSIS, uninterrupted loading with partition switch, delta detection with RowHash. Both the dashboards and Optail are fed from this warehouse.
- 3 Source systems
- 7 Schemas
Derimod
Derimod business intelligence: 20+ Power BI dashboards fed from one warehouse
We turned the clean data in the warehouse into dashboards every Derimod team can open and interpret directly: sales, product, customer, budget, store and warehouse capacity, logistics, e-commerce operations, Optail success reports. The dashboards follow a four-step catalogue (reporting, deep analysis, trading, CEO) and are all fed from the same warehouse; no report has a separate data source.
- 20+ Dashboards
- 4 Catalogue steps
Derimod
Optail Initial Allocation: first distribution of new product with a master plan, store grouping and capacity
When a new product leaves the warehouse there is no sales data yet; the decision rests on similar products' history, store groups and capacity. For Derimod we built the Master Plan screen, where planning is done before and throughout the season, along with store clusters and fill and dispatch-priority management; footwear and bags went live first, then apparel. In 2026 Flow Through, open-quantity allocation and simulation are being added.
- 3 Categories
- Master plan Decision input
Derimod
Optail Replenishment: automatic warehouse-to-store feed for what sells
Replenishment, live since October 2024, manages the warehouse-to-store flow by rate of sale: how many weeks of cover, minimum lot, which warehouse, which store group. In the first two months two thirds of replenishment came from Optail; in 2025 it approached 95% in footwear. We measure whether every replenishment turns into sales; Optail's sell-through rate is higher than both manual replenishment and last year.
- ~95% Automation
- ~1.5× Sell-through
Derimod
Optail Inter-Store Transfer: size-run consolidation, out-of-collection and season-end transfers
Optail's first module. It started with block transfer in February 2024; on top came the consolidation transfer that gathers broken size runs, the out-of-collection transfer that moves product to stores that do not carry it, closing-store clear-out, and the class-based transfer that concentrates product in selling stores at season end. Transferred goods sell through within 21 days at five times the manual rate.
- ~25% Sell-through
- 5 Transfer types
Derimod
Derimod stock analytics: lost sales, size-run breaks, idle stock and the success of every shipment
The layer that measures whether Optail's decisions work. We defined lost sales, size-run breaks and non-selling stock and tied them to weekly dashboards; made stock in warehouses and stores and every product movement (in transit, on dispatch order, incoming) visible; computed the sell-through of initial allocation, replenishment and transfer with a first-in–first-out link. In one season footwear lost sales fell from 14% to 10% and size-run breaks from 10.6% to 8.4%.
- 14% → 10% Lost sales
- 10.6% → 8.4% Size-run breaks
Derimod
Derimod CRM analytics: customer 360, a campaign success datamart and dashboard
Once the stock side had settled, the customer's turn came. We connected customer, membership, points and campaign data from Dynamics CRM, Shopify and the ERP to the same data warehouse; in place of campaign results summarised in a single line, we built a star schema at receipt-line grain. It tracks MASS and loyalty campaigns and points earned and spent by channel, segment and product. Daily segment and points updates are live and the customer 360 view is a prototype; churn and next best offer are on the roadmap.
- Receipt line Grain
- 2 Campaign types
Karaca
Karaca CustoMation: campaign audience selection by RFM and behavioural segments
We took the question of who a campaign goes to out of Excel filters and into a single tool. CustoMation selects customers for the Karaca group's five brands and eleven channels by RFM segment, brand-level behavioural segment, product group purchased, brand and recency window. Include and exclude rules are defined together, every selection is saved as a scenario and can be compared with past runs, and the audience goes to the campaign tool as Excel.
- 10 + 19 Segments
- 8 Filter dimensions