Predictive Analytics with AI that forecasts demand, churn and revenue
We turn your historical data from 1C, Kaspi, CRM and WhatsApp Business into models that predict what customers will buy, who is about to leave, and how much revenue next month will bring — with accuracy you can plan around.
- Demand, churn and revenue forecasts on your own company data
- Recommender systems that lift average order value and repeat sales
- Live dashboards and API — forecasts plug into 1C, Kaspi and CRM
Predictive analytics uses your historical business data to forecast future outcomes — demand, customer churn and revenue — using machine learning. A-LUX builds these systems for companies in Kazakhstan and abroad on data from 1C, Kaspi and CRM, starting from ₸1,200,000, with delivery in about six weeks.
What predictive analytics gives your business
Stop reacting to the past — plan against a data-backed view of the next weeks and months.
Demand forecasting
Per-SKU, per-store and per-channel demand forecasts that cut stockouts and overstock. We learn from seasonality, promotions, weather and price changes, then feed the numbers straight into your 1C procurement.
Churn prediction
A model flags customers likely to stop buying before they actually do — with the reasons behind each risk score. Your retention team gets a daily list to act on through CRM or WhatsApp Business.
Revenue forecasting
Rolling revenue and cash-flow forecasts with confidence ranges, broken down by product line, region and sales rep — so budgeting and hiring decisions rest on evidence, not gut feel.
Recommender systems
Personalized product recommendations on your store, app and Kaspi listings that raise average order value and repeat-purchase rate, trained on real basket and browsing history.
How we build forecasting models on your data
A disciplined pipeline from raw company data to a model you can trust and retrain.
1. Data audit & integration
We connect to 1C, Kaspi, your CRM, website analytics and WhatsApp Business, then clean and unify the history into a single feature store. Most of the value — and most of the work — lives here.
2. Modeling & validation
We test gradient boosting, time-series and deep-learning approaches, then validate against held-out periods so the accuracy you see in the demo is the accuracy you get in production.
3. Deployment & API
The chosen model ships as an API and a live dashboard. Forecasts write back into 1C and CRM, so your team uses one familiar screen instead of a separate analytics tool.
4. Monitoring & retraining
We track drift and accuracy in real time and retrain on a schedule. As markets shift, the model keeps up — no silent decay six months after launch.
Where it pays off fastest
Predictive analytics delivers the clearest ROI where inventory, customer base or pricing move fast.
Retail & e-commerce
Forecast demand per SKU and location, automate replenishment, and recommend the right products on-site and on Kaspi to grow basket size and margin.
Banking & fintech
Predict churn, default risk and next-best-product. Score customers daily and trigger offers through the channels they already use.
Subscriptions & services
Spot at-risk subscribers weeks ahead, model lifetime value, and forecast recurring revenue with confidence ranges your CFO can plan against.
Logistics & distribution
Anticipate order volumes by region and route, balance fleet and warehouse load, and reduce the buffer stock that quietly ties up your cash.
Why companies choose A-LUX
19 years, 400+ delivered projects, and engineers who ship models into production — not slide decks.
Local data fluency
We speak 1C, Kaspi and the realities of the Kazakhstan market natively, and we work with international clients too. Integration that derails most projects is routine for us.
Production-grade ML
Every model is built to run live: versioned, monitored and retrainable. You get a working system with an API, not a notebook that breaks on new data.
Honest accuracy
We validate on real holdout periods and report the true error, then agree on a target you can act on. No inflated demo numbers that collapse in production.
Clear pricing & ownership
Projects start from ₸1,200,000 with a fixed scope, and you own the code, the models and the data. Talk to CEO Ivan Vostrikov on +7 705 966-25-25 or WhatsApp.
FAQ
Answers to common questions before ordering from A-LUX.
What is predictive analytics and how is it different from regular reporting?
Regular reporting tells you what already happened — last month's sales, this week's churn. Predictive analytics uses machine learning on that same history to forecast what will happen next: future demand, which customers are about to leave, and expected revenue. You move from looking backward to planning forward with quantified confidence.
How accurate are AI demand forecasts?
Accuracy depends on data quality and how stable your market is, but for retail demand we typically reach a level that meaningfully cuts both stockouts and overstock versus manual planning. We validate every model on real held-out periods and report the true error before launch, so you know exactly what to expect in production.
What data do you need to get started?
Ideally one to two years of transaction history from 1C, Kaspi, your CRM or e-commerce platform — orders, customers, products, prices and dates. We also use website analytics and WhatsApp Business interactions where available. If your data is messy or scattered, our first phase is exactly to audit and unify it.
Will the forecasts integrate with 1C, Kaspi and our CRM?
Yes. We deliver the model as an API and a dashboard, and write forecasts back into the systems your team already uses — 1C for procurement, your CRM for retention lists, Kaspi and the storefront for recommendations. The goal is one familiar workflow, not another tool to log into.
How much does a predictive analytics project cost?
Projects start from ₸1,200,000. The final price depends on the number of forecasting tasks (demand, churn, revenue, recommenders), the state of your data, and integration scope. We agree a fixed scope and price after a short data audit, with no surprises later.
How long until we see working forecasts?
A first working model — an MVP you can evaluate on your own data — typically takes about six weeks. Timeline depends mostly on data readiness; clean, accessible history moves fast, while heavy integration and cleanup add time. We share a concrete plan after the initial audit.
What happens after launch — does the model stay accurate?
We monitor accuracy and data drift in real time and retrain the model on a schedule. Markets, prices and customer behavior shift, and a model left alone decays quietly. Ongoing monitoring and retraining keep your forecasts reliable long after go-live.
Do we own the models and the code?
Yes. You fully own the code, the trained models and your data. We can hand over a self-contained system your team runs, or continue under a support and retraining agreement — your choice.
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