PREDICTIVE ANALYTICS AT SCALE IN RETAIL

In the booming retail business, big data is poised in the coming years to open up huge opportunities in the way stores (both physical and online) fundamentally operate and serve customers.
Just look at the way giants of the digital age (Google, Apple, Facebook, and Amazon -GAFA) have affected the retail space already with data-first business models.
Given the incredibly small margins in retail, efficiency improvements anywhere – from tighter supply chain management to more targeted marketing campaigns – can make a big difference to a retail business of any size.
TRANSFORM BUSINESS WITH PREDICTIVE ANALYTICS
Making data-driven decisions is no longer about learning from the past; it means making changes to the business constantly based on realtime input from all data sources across the organization. Making predictions and applying machine learning is based on traditional data but also on new and innovative sources like connected Internet of Things (IoT) devices and sensors or, going a step further with deep learning, unstructured data from things like static images or cameras monitoring stock in warehouses. Consumers can be fickle, so being able to accurately anticipate what they will do next and quickly react is what puts the most innovative and successful retailers above the rest:
SILOED, STATIC CUSTOMER VIEWS
Many retailers still struggle with siloed data – transaction data lives apart from web logs which in turn is separate from CRM data, etc.
SILOED, STATIC CUSTOMER VIEWS
Many retailers still struggle with siloed data – transaction data lives apart from web logs which in turn is separate from CRM data, etc.
More accurate and targeted churn prediction
Robust fraud detection systems
More effective marketing campaigns due to more advanced customer segmentation
Better customer service
TIME CONSUMING VENDOR & SUPPLY CHAIN MANAGEMENT
Supply chains are already driven by numbers and analytics, but retailers have been slow to embrace the power of realtime analytics and harnessing huge, unstructured data sets.
AUTOMATION AND PREDICTION FOR FASTER, MORE ACCURATE MANAGEMENT
Combine structured and unstructured data in real time for things like more accurate forecasts or automatic reordering.
Optimized pricing strategies
More efficient inventory management based on real time data and behavior
ANALYSIS BASED ON HISTORICAL DATA
Looking back at shoppers’ past activity often isn’t a good indication of what they will do next.
PREDICTION AND MACHINE LEARNING IN REAL TIME
Instead, real time prediction based off of current trends and behaviors from all sources of data is the key.
Anticipating what a customer will do next
A more agile business based on up-to-theminute signals
The ability to adapt automatically with customer behavior
ONE-TIME DATA PROJECTS
Completing one-off data projects that aren’t reproducible is frustrating and inefficient.
AUTOMATED, SCALABLE, AND REPRODUCIBLE DATA INITIATIVES
The best data teams in retail focus on putting a data project into production that is completely automated and scalable.
More efficient team that can scale as the company grows
With reproducible workflows, team can work on more projects
BUILD SUCCESSFUL DATA PROJECTS AT SCALE
Detecting Potential Churners in Retail with 77% Accuracy

In order to counter churn, Showroomprive (a leading e-commerce player in Europe with more than 20 million members) previously used static rules common to all customers to trigger marketing actions. They were not doing any prior qualification to determine the value of each individual client before triggering marketing actions.
Showroomprive chose Dataiku Data Science Studio (DSS) to develop a robust solution that predicts whether or not a buyer will return to the website to make a purchase:
Maximizing Sales and Profits with Optimized Inventory Management

A large brick-and-mortar retail chain was losing money due to lack of real-time order management and losing time placing orders on a large inventory. When items went out of stock, it was inefficient and time consuming to get them restocked quickly.
This retail chain used Dataiku Data Science Studio (DSS) to incorporate data from a large variety of sources to completely optimize their supply chain and automate it when possible:

Automated the integration and enrichment of a variety of data sources (including customer data, order and delivery data, web logs, etc.).
Created more than 690 features derived from this data depending on variables like clicks on sales, orders, customer profile, etc.
Tested multiple machine learning algorithms to achieve the best predictive model.

Incorporated geo-social data to predict big events in the area likely to result in out-of-stock items and stock additional, specific items when called for.
Automated product sourcing thanks to a real-time view of demand, sourcing, and sales combined with data on lead time and weather in the area that may affect suppliers.
Faster order picking by using data from multiple sources (like historical picking times and warehouse layouts) to ship and fulfill orders faster.

The most successful retail companies worldwide are able to efficiently leverage all of the data at their fingertips by following set processes to see data projects through from start to finish. They also ensure those data projects are reproducible and scalable so the data team is constantly able to work on new projects vs. maintaining old ones. This is as easy as following the seven fundamental steps to completing a data project

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