Data Analytics and Machine Learning Experts
Retail organizations need predictive analytics and optimization in today’s world because the landscape is so competitive. Companies able to target specific consumers with offers based on their current location, past purchasing history, and demographic information in real-time with offers steeped in pricing revenue optimization and aligned long-term customer experience management (CEM) objectives will gain significant marketplace separation. Look at the success of companies like Amazon.
Many organizations have embraced analytics. Living in such a diverse, data loaded enterprise with elements of logistics, marketing, pricing, supply chain, manufacturing, and strategic planning, can leave an executive’s head spinning. With so many decisions to make it is no wonder data analytics is quickly becoming a necessity in the retail world.
Mosaic, a leading analytics consulting firm, is a great strategic partner for companies in the retail vertical. We bring world-class big data analytics consulting and model development capabilities to be more data-driven in all facets of the business. Our data science consultants bring cutting edge machine learning, statistical, and data engineering techniques to transform business questions into analytically-fueled solutions that bring competitive advantage.
Sometimes figuring out how to get value from your data is a lot like solving a maze-good thing a partner like Mosaic is here to help!
We work with some of the World’s leading Retail brands.
Leading Outdoor Retailer – Inventory Optimization for Size Profiles
- This retailer wanted to turn towards a more predictive data-driven strategy for making seasonal inventory purchasing decisions.
- Previously decisions were made based on historical sales data.
- Mosaic used predictive analysis techniques to discover and quantify insights into product-level demand patterns, which the inventory team was than able to translate into making better buying decisions.
- Mosaic delivered an automated model which recommends product specific order distributions across sizes, styles and colors.
- This tool decreases markdowns and increases revenue.
- Case study available here!
Leading Brick and Mortar Retailer – Price Elasticity of Demand
- Mosaic provided innovative pricing for >160 million seasonal products.
- We explored and modeled different promotional pricing approaches to increase seasonal clothing revenue, decrease stock-outs, and minimize close-out pricing.
- Mosaic calculated how elastic demand is for individual items and geographies, what seasonality patterns drive demand for each type of item.
- Our data scientists developed nonlinear regression techniques to predict product-level demand and to provide confidence intervals from longitudinal and cross-sectional data.
- We delivered open-source developed R Shiny decision support dashboard.
- ‘What-If’ scenarios and A/B testing features implemented so it is easy to understand impact of potential changes.
- Case study available here!
Neiman Marcus – Online behavior modeling from a cold start
- A Proof of Concept to enhance customer experience, predict demand patterns and sell more product at the right price for individual customers.
- Our data scientist explored and implemented non-parametric choice models to predict online customer product preferences.
- The data analyzed included web browsing data, brand-specific product data, and transactional data.
- The results of this model were useful in showing how different consumer types and their browsing behavior can indicate product preferences.
Leading Global Fashion and Luxury Conglomerate – Customer Lifetime Value Analysis
- Mosaic applied advanced analysis and modeling techniques to improve the company’s understanding of customer purchase patterns and customer lifetime value (CLV.)
- Our data scientists mined a database consisting of 250,000 customers and prospects to:
- define a customer value metric.
- develop a forward-looking model predicting customer spend over a specific time period.
- We optimized performance within specific customer segments with innovative Xgboost methods.
- Delivered model significantly outperformed competing approaches, particularly in identifying the core customer segment most likely to respond to direct marketing outreach.
J. Crew – Customer Sentiment Prediction
- Mosaic analyzed customer contact center interactions.
- We predicted the sentiment via natural language processing algorithms, and how the sentiment of a customer interaction will impact that customer’s likelihood to buy in the future.
- Mosaic extracted text data sources from call logs, social media and surveys
- We built custom predictive models for sentiment, and tied it back to customer lifetime value metrics.