domain
Retail
Retail
Retail
Retail
Dynamic Pricing
Dynamic pricing involves adjusting prices in real-time based on various factors such as market trends, competitor pricing, and customer demand. By using advanced methods, businesses can optimise pricing strategies to maximise revenue. Respond swiftly to changes in the market and consumer behaviour, be sure that prices accurately reflect current demand and inventory levels.
Competition monitoring
Competition monitoring services track competitors' pricing and promotional strategies in real time, giving businesses better understanding of market dynamics. By analysing competitor pricing, product offerings, and promotional activities, we help retailers detect changes as they happen.
Competition monitoring
Competition monitoring services track competitors' pricing and promotional strategies in real time, giving businesses better understanding of market dynamics. By analysing competitor pricing, product offerings, and promotional activities, we help retailers detect changes as they happen.
Competition monitoring
Competition monitoring services track competitors' pricing and promotional strategies in real time, giving businesses better understanding of market dynamics. By analysing competitor pricing, product offerings, and promotional activities, we help retailers detect changes as they happen.
Stock clearance
We create stock clearance solutions that use data-driven strategies to optimise the sale of excess inventory. By analysing historical sales data, seasonality, market demand, and customer behaviour, we identify the best timing and pricing strategies for clearing stock.
Stock clearance
We create stock clearance solutions that use data-driven strategies to optimise the sale of excess inventory. By analysing historical sales data, seasonality, market demand, and customer behaviour, we identify the best timing and pricing strategies for clearing stock.
Stock clearance
We create stock clearance solutions that use data-driven strategies to optimise the sale of excess inventory. By analysing historical sales data, seasonality, market demand, and customer behaviour, we identify the best timing and pricing strategies for clearing stock.
Market research analytics
With market research analytics, we provide a deep understanding of consumer preferences, market trends, and competitive landscapes. By analysing a wide range of data sources, we identify key factors driving customer behaviour and emerging market opportunities.
Market research analytics
With market research analytics, we provide a deep understanding of consumer preferences, market trends, and competitive landscapes. By analysing a wide range of data sources, we identify key factors driving customer behaviour and emerging market opportunities.
Market research analytics
With market research analytics, we provide a deep understanding of consumer preferences, market trends, and competitive landscapes. By analysing a wide range of data sources, we identify key factors driving customer behaviour and emerging market opportunities.
Price optimisation
We develop technologies that integrate various data sources, such as historical sales data, competitor pricing, and market demand, to adjust prices dynamically in real time. Solutions continuously learn and adapt to market changes, optimising pricing strategies for different customer segments and sales channels.
Price optimisation
We develop technologies that integrate various data sources, such as historical sales data, competitor pricing, and market demand, to adjust prices dynamically in real time. Solutions continuously learn and adapt to market changes, optimising pricing strategies for different customer segments and sales channels.
Price optimisation
We develop technologies that integrate various data sources, such as historical sales data, competitor pricing, and market demand, to adjust prices dynamically in real time. Solutions continuously learn and adapt to market changes, optimising pricing strategies for different customer segments and sales channels.
Causal impact measurement
When A/B testing is not feasible, there is still a need to measure the impact of specific interventions, such as price changes, on business performance. Our causal impact measurement services evaluate the effects of pricing changes and promotional strategies on sales.Using statistical modelling techniques, such as structural Bayesian time-series models, we isolate the impact of specific actions from other external factors.
Causal impact measurement
When A/B testing is not feasible, there is still a need to measure the impact of specific interventions, such as price changes, on business performance. Our causal impact measurement services evaluate the effects of pricing changes and promotional strategies on sales.Using statistical modelling techniques, such as structural Bayesian time-series models, we isolate the impact of specific actions from other external factors.
Causal impact measurement
When A/B testing is not feasible, there is still a need to measure the impact of specific interventions, such as price changes, on business performance. Our causal impact measurement services evaluate the effects of pricing changes and promotional strategies on sales.Using statistical modelling techniques, such as structural Bayesian time-series models, we isolate the impact of specific actions from other external factors.
Retail
Retail
Retail
Demand Forecasting
Demand forecasting uses historical sales data and market insights to predict future product demand. We can identify seasonal trends and shifts in consumer behaviour. Accurate demand forecasting enables retailers to optimise inventory management, reducing the risk of stockouts or overstock situations.
Stock management
Stock management solutions use advanced forecasting techniques to maintain optimal inventory levels. By analysing historical sales data, market trends, and external factors, we accurately predict future demand to minimise stockouts and overstock situations.
Stock management
Stock management solutions use advanced forecasting techniques to maintain optimal inventory levels. By analysing historical sales data, market trends, and external factors, we accurately predict future demand to minimise stockouts and overstock situations.
Stock management
Stock management solutions use advanced forecasting techniques to maintain optimal inventory levels. By analysing historical sales data, market trends, and external factors, we accurately predict future demand to minimise stockouts and overstock situations.
Location coverage optimisation
By integrating data from various sources, including sales performance, demographic trends, and geographic factors, we build systems that provide better awareness into the best placement for stores and distribution centres. Identify underserved areas and potential growth regions, ensuring that your retail footprint aligns with market demand and maximises profitability.
Location coverage optimisation
By integrating data from various sources, including sales performance, demographic trends, and geographic factors, we build systems that provide better awareness into the best placement for stores and distribution centres. Identify underserved areas and potential growth regions, ensuring that your retail footprint aligns with market demand and maximises profitability.
Location coverage optimisation
By integrating data from various sources, including sales performance, demographic trends, and geographic factors, we build systems that provide better awareness into the best placement for stores and distribution centres. Identify underserved areas and potential growth regions, ensuring that your retail footprint aligns with market demand and maximises profitability.
Hierarchical forecasting
The hierarchical forecasting approach breaks down demand predictions across different levels of the product hierarchy, including categories, subcategories, individual items, and regional markets.By using advanced statistical methods and machine learning models, we provide retailers with detailed understanding of demand patterns at each level.
Hierarchical forecasting
The hierarchical forecasting approach breaks down demand predictions across different levels of the product hierarchy, including categories, subcategories, individual items, and regional markets.By using advanced statistical methods and machine learning models, we provide retailers with detailed understanding of demand patterns at each level.
Hierarchical forecasting
The hierarchical forecasting approach breaks down demand predictions across different levels of the product hierarchy, including categories, subcategories, individual items, and regional markets.By using advanced statistical methods and machine learning models, we provide retailers with detailed understanding of demand patterns at each level.
Probabilistic forecasting
The probabilistic forecasting service uses statistical models and Bayesian techniques to predict demand while considering uncertainty and variability. Instead of providing a single estimate, we generate a range of potential outcomes, giving retailers a more comprehensive view of future demand.
Probabilistic forecasting
The probabilistic forecasting service uses statistical models and Bayesian techniques to predict demand while considering uncertainty and variability. Instead of providing a single estimate, we generate a range of potential outcomes, giving retailers a more comprehensive view of future demand.
Probabilistic forecasting
The probabilistic forecasting service uses statistical models and Bayesian techniques to predict demand while considering uncertainty and variability. Instead of providing a single estimate, we generate a range of potential outcomes, giving retailers a more comprehensive view of future demand.
Attribute sales forecasting
Attribute sales forecasting analyses specific product attributes—such as colour, size, or other features—to predict demand trends more accurately. By examining historical sales data alongside these characteristics, we identify how different attributes influence purchasing behaviour.
Attribute sales forecasting
Attribute sales forecasting analyses specific product attributes—such as colour, size, or other features—to predict demand trends more accurately. By examining historical sales data alongside these characteristics, we identify how different attributes influence purchasing behaviour.
Attribute sales forecasting
Attribute sales forecasting analyses specific product attributes—such as colour, size, or other features—to predict demand trends more accurately. By examining historical sales data alongside these characteristics, we identify how different attributes influence purchasing behaviour.
Retail
Retail
Retail
Sales analytics
Sales analytics works with sales data to uncover important insights about performance, customer groups, and product trends. By examining key indicators, we can make you see what drives your sales, spot areas that need improvement, refine your sales strategies, respond quickly to market changes, and boost your overall profitability.
Volume forecasting
We use historical sales data and market trends to predict future sales volumes with high accuracy. By applying advanced statistical models and machine learning techniques, we help retailers anticipate demand fluctuations and plan accordingly.
Volume forecasting
We use historical sales data and market trends to predict future sales volumes with high accuracy. By applying advanced statistical models and machine learning techniques, we help retailers anticipate demand fluctuations and plan accordingly.
Volume forecasting
We use historical sales data and market trends to predict future sales volumes with high accuracy. By applying advanced statistical models and machine learning techniques, we help retailers anticipate demand fluctuations and plan accordingly.
Causal inference
The causal inference approach analyses the impact of various factors on sales performance, helping retailers understand what drives revenue growth. By using rigorous statistical methods, we isolate the effects of marketing campaigns, pricing changes, and other variables on sales outcomes.
Causal inference
The causal inference approach analyses the impact of various factors on sales performance, helping retailers understand what drives revenue growth. By using rigorous statistical methods, we isolate the effects of marketing campaigns, pricing changes, and other variables on sales outcomes.
Causal inference
The causal inference approach analyses the impact of various factors on sales performance, helping retailers understand what drives revenue growth. By using rigorous statistical methods, we isolate the effects of marketing campaigns, pricing changes, and other variables on sales outcomes.
Сross impact analysis
We examine the relationships between different products and how changes in one category affect others. By analysing sales data across various factors—such as promotional activities, product launches, and market trends—we identify patterns and correlations that inform inventory and pricing strategies.
Сross impact analysis
We examine the relationships between different products and how changes in one category affect others. By analysing sales data across various factors—such as promotional activities, product launches, and market trends—we identify patterns and correlations that inform inventory and pricing strategies.
Сross impact analysis
We examine the relationships between different products and how changes in one category affect others. By analysing sales data across various factors—such as promotional activities, product launches, and market trends—we identify patterns and correlations that inform inventory and pricing strategies.
Let's talk with our CEO
Email: andrii.rohovyi@postdata.ai
Let's talk with our CEO
Email: andrii.rohovyi@postdata.ai
Let's talk with our CEO
Email: andrii.rohovyi@postdata.ai
Postdata. All right reserved. © 2024
Postdata. All right reserved. © 2024
Postdata. All right reserved. © 2024