Case Study · Finance
Kuveyt Türk: master–apprentice mentoring for two analytics teams — from digital marketing to retail customer analytics
It began in 2023 with training for Digital Marketing's analytics team: SQL, Power BI, Python, machine learning. The team then built its own projects under Lumtify's mentoring — digital score, target management, churn cause analysis, UI usage analysis, product recommendation and churn prediction models. From 2025, spending analytics, a luxury spend score and next-best-action prioritisation with the Retail Customer Analytics team. The team writes the code; Lumtify sets direction, brings method and checks the output.
- Master – apprentice
- Model
- 8+
- Digital projects
- 2025 –
- Retail analytics
- 4 modules
- Training
On-the-job training; the team writes the code
From descriptive to predictive
Spending, luxury score, NBA
SQL · Power BI · Python · ML
Context
In 2023 Kuveyt Türk’s Digital Marketing Group was building its own analytics team. The team was there, the data was there; what was missing was technical competence and a way of working that would carry projects end to end. Lumtify’s role was defined from the start not as “doing” but as “getting done”: the master–apprentice model, on-the-job training.
Training first, then projects (2023)
Three trainings were delivered between April and June: data awareness and an introduction to AI, SQL with reinforcement projects, data visualisation with Power BI. Project consulting continued from June to December and the team carried five projects to MVP with its own hands:
- Digital score — a score computed from each customer’s use of digital and physical channels; a dashboard that can be analysed at both customer and bank level.
- Target management — a sustainable data preparation that tracks customers’ digital activity over the last 3–6–9–12 months and allows audiences to be built from demographics and product use.
- Digital assistant insight — an analysis comparing customers who discussed specific products with the assistant against their activation on those products; which products activate after a conversation became visible.
- Churn cause analysis — the errors customers who left the digital channels hit at login; the profile of those who got an error on a given login method and never came back.
- UI usage analysis — time per screen, clicks, errors and daily trends; a dashboard measuring the effect of design changes.
On every project, the plan was the team member’s, the SQL was the team member’s, the dashboard was the team member’s; Lumtify alongside at every step. The goal was a usable MVP, and every project’s output was a Power BI dashboard — analysable, sustainable, documented, ready to be integrated into the core system with IT.
From descriptive to predictive (2024–2025)
2023 was the year of “why” questions; 2024 moved to “what will happen”. Python and machine learning training was delivered and the projects were taken to the next step: segmentation on the digital score, a product recommendation model from digital assistant data, a churn prediction model, at-risk customer detection producing alerts, a retention dashboard and DataAlert. A digital score for corporate customers began; a data model combining all projects into a single customer life story, and a concept for building audiences in natural language on top of it, entered the roadmap.
The team changed over time; shortened repeats of the trainings for new joiners became part of the engagement.
The second team: Retail Customer Analytics (2025–)
A new period began in 2025 with the Retail Customer Analytics team. A team built on intelligence, with a high technical level; the need this time was not competence but making projects speak with data and opening small models to use quickly. The approach: a small model to start for each person, short projects, release and grow.
Spending analytics roadmap: RFM-based segmentation of card spending with segment naming; usage frequency, day- and hour-based spending behaviour and its relation to the portfolio; spending distribution by merchant category group (MCG). Luxury spend score: a customer-level score derived from merchant categories.
Next-best-action (NBA) prioritisation: scores produced by dozens of the bank’s models were being combined in the same expected-value formula; the churn score was a real probability while the cross-sell scores were not, so one action always came out on top. Lumtify’s methodology note defined the two missing pieces: a calibration layer on top of the outputs without touching the models (isotonic regression), and the action’s own incremental effect entering the formula; the threshold is derived from cost, capacity is tied to an assignment problem, and the single red line is the measurement loop — logging and a control group. Naming the method the team had already tried, as it is known in the literature, was one of the most valuable parts of the work.
Customer Experience and Academy
In 2025 a training-and-mentoring programme was designed for the Customer Experience team, running from data literacy to GenAI use, with an applied project at the end of every module. In 2026 an evaluation of the 2023–2024 period and an expanded programme proposal were prepared for Kuveyt Türk Academy: pre-assessment, small groups, practice on the institution’s real data, post-training project mentoring, before-and-after measurement.
Outcome
In three years two teams walked a road from descriptive analytics to machine-learning models, from MVP to dashboard to productisation — and always wrote the code themselves. Lumtify’s job was to set direction, bring method and check the output. A consultant’s most lasting output at a bank is leaving a working team behind when they go.