A production-ready Azure-based Retail analytics solution delivered within 8 weeks - enabling organizations to make better business decisions, faster.
Motifworks’ Azure-based Retail Analytics offering is an accelerated route to achieve your defined business outcomes, as uncovered during the pre-assessment solution stage, utilizing services such as:-
* Azure Data Lake,
* Azure ML,
* Power BI.
Organizations can Accelerate sales, Control margin abrasion, and improve customer loyalty by focusing their retailing strategies on world-class analytics, artificial intelligence, and best-in-class enterprise data architecture provided by Motifworks.
The 8-week engagement aims to explore any one business scenario at your organization and leverage Azure Data Platform and Data Science to improve results. It’s the best starting point for evaluating the potential of Analytics and Artificial Intelligence to boost your business.
Motifworks’ AI and advanced analytical capabilities empower retailers with real-time data/insights, fetched/mined from multiple touchpoints in the retail value chain.
Retail Analytics Use Cases that can be considered for the Implementation:
1) Market Basket Analysis (MBA):- Motifworks help retailers by accessing basket-level insights and using the product’s transactional data to increase sales and customer satisfaction.
2) Demand Forecasting Solution:- We leverage Power BI capabilities to capture insights on interactive dashboards—empowering category managers to make decisions evaluating revenue, sale, and profitability of the product.
Motifworks also offers its Retail expertise in analyzing store sales, transactions, and product data. Motifworks enriches the data with:
* Price positioning
* Media spending
* In-store assortment
* Demand forecasting
* Consumer loyalty
* Customer Segmentation
* Product Recommendation
* Note:- The 8 weeks Implementation is only for one (1) specific use case. The estimate is based on the use case with 4 different data feeds with medium complexity. Firm time and price to be provided after further review of the use case and data sources.