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, demographic information in real-time with offers steeped in pricing revenue optimization 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 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.
We work with some of the World’s leading retail brands:
Leading Outdoor Retailer – Inventory Optimization
- This retailer wanted to turn towards a more predictive data-driven strategy for making seasonal inventory purchasing decisions.
- Made decisions only based on historical sales data.
- Mosaic used predictive analysis techniques to discover insights, which the inventory team was than able to translate into making better buying decisions.
- Delivered an automated model which recommends product specific order distributions across sizes, styles and colors.
- Decreases markdowns and increases revenue.
Leading Brick and Mortar Retailer – Price Elasticity of Demand
- Innovative pricing for >160 million seasonal products.
- Explore and model different promotional pricing approaches to increase seasonal clothing revenue, decrease stock-outs, and minimize close-out pricing.
- Calculate how elastic demand was, what seasonality patterns drive demand for each type of item.
- Developed nonlinear regression techniques to compute demand and to provide confidence intervals from longitudinal and cross-sectional data.
- Delivered open-source developed R Shiny decision support dashboard.
- What-If scenarios feature easy to understand impact of potential changes.
Neiman Marcus – Online behavior modeling from a cold start
- Proof of Concept to enhance customer experience, predict demand patterns and sell more product at the right price for individual customers.
- Explore and implement non-parametric choice models to help client understand consumer demand patterns.
- 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 preference rankings inform buyer decisions.
Global Leader Luxury Goods Conglomerate – Customer Lifetime Value
- Objective: to better understand customer lifetime value and influence customer behavior
- Our data science consultants mined a database including transaction data from 250,000 customers and prospects to develop a forward-looking metric predicting individual customer spend over the next quarter.
- Current methods for making such predictions do not provide flexible means to incorporate covariates beyond the transactions.
- Developed a XGBoost method to improve CLV metric accuracy
- The client proceeded with the results to provide segmented customer lists to each store.
- The methods and models developed by Mosaic doubled the hit rate of the previous method used by the client.