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Retail promotion is always a dicey game. It usually entails thousands (even millions) of dollars in promotional budget, and there’s no hard and fast rule to understanding which promotional strategy is the most profitable.
For instance, the retail promotion of a particular product may lead to a decrease in sales of its substitutes and an increase in sales of its supplements- a classic case of cannibalization, and complementarity. It may also increase the sales of unrelated products due to higher footfall- aka the halo effect.
Manually calculating the effectiveness of each promotion would require tens of thousands of statistical models, hence posing a real challenge for retailers.
AiLens’ Retail Promotional Optimization models the impact of promotions by using Machine Learning algorithms built on Big Data architecture. It then identifies features and develop models for calculating the sales and margins for each promotion. It sifts through billions of rows of transactional data to identify tens of millions of dollars in lost margins, thereby automating the assessment of each promotion.
Using AiLens’ Simulator tool and dashboards, retailers can allocate their budget for the right promotional methods and save millions of dollars. They can immediately stop unprofitable promotions and even renegotiate funding for such promotions with vendors.
1500+ clients i ncluding a Fortune 500 multinational global payments company, and the world’s largest beverage company chose KnowledgeLens to grow their business.
AiLens’ Enterprise AI Platform offers an experiment designer, modelling and feature engineering workbench, recommendation engine, and a huge AI assets repository to accelerate your AI transformation to a Smart Retailer company. By identifying features and developing models, it generates intuitive, actionable business insights for retailers.Collect data and create variables (for promotions, pricing, holidays and weather):
200+ sophisticated reusable assets including Machine Learning and Deep Learning algorithms, Data engineering components and connectors.
Unified graphical interface for building data engineering and AI/ML pipelines. Intuitive job submission, monitoring framework, with no admin intervention required, and a code authoring environment for advanced users.
Shared catalog of AI models, datasets and documentation. Users can provide feedback on AI models and contribute to newer AI models.
Model processes and trigger model execution on any runtimes like TensorFlow, SparkML, AWS, Azure, and more.
Robust encryption, built-in integrations with enterprise security systems like Kerberos, LDAP and AD.
Applying the right Optimization technique in combination with Machine Learning models goes a long way in solving complex business challenges, and transforming enterprise operations.
We have helped several enterprises solve complex challenges by developing their Optimization technique. For our retail clients, this means decreased computational time of mathematical models in assigning 300+ employees, 20+ vehicles to 100+ events and 100+ pick-up sites. Through Optimal utilization and routing, trucking costs can go down by 5-10% .