A Data Science Central Community
4 Big Challenges for Retailers, Solved with Predictive Analytics
The most successful retail companies use data science and predictive analytics to improve efficiency, improve marketing campaigns, and gain customer insights that give them a competitive advantage.
In the 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. Given the incredibly small margins, Big Data will also provide much needed efficiency improvements - from tighter supply chain management to more targeted marketing campaigns - that can make a big difference to a retail business of any size.
Making data-driven decisions is no longer about learning from the past; it means making changes to the business constantly based on real time 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.
Data science software maker, Dataiku, recently explored the types of data problems facing retail, the problems they solve, and the steps that any retail organization can take to become more data driven.
PROBLEM #1: 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.
SOLUTION: Complete, Real Time Customer Looks
Cutting-edge retailers look at customers as a whole, combining traditional data sources with the non-traditional (like social media or other external data sources that can provide valuable insight).
More accurate and targeted churn prediction.
Robust fraud detection systems.
More effective marketing campaigns due to more advanced customer segmentation.
Better customer service.
PROBLEM #2: 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.
SOLUTION: 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.
More efficient inventory management based on real-time data and behavior .
Optimized pricing strategies.
PROBLEM #3: Analysis Based on Historical Data
Looking back at shoppers’ past activity often isn’t a good indication of what they will do next.
SOLUTION: Prediction and Machine Learning in Real Time
Instead, real time prediction based 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-the minute signals.
The ability to adapt automatically with customer behavior.
PROBLEM #4: One-Time Data Projects
Completing one-off data projects that aren’t reproducible is frustrating and inefficient.
THE SOLUTION: 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.
While each organization is different, data challenges are the same. It takes a data production plan to guide any sized team to successfully producing a working predictive model that yields meaningful insights for the business.
How to Complete any Data Project in Retail
The most successful retail companies worldwide solve these four issues by 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:
DEFINE: Define your business question or business need: what problem are you trying to solve? What are the success metrics? What is the timeframe for completing the project?
IDENTIFY DATA: Mix and merge data from different sources for a more robust data project.
PREPARE & EXPLORE: Understand all variables. Ensure clean, homogenous data.
PREDICT: Avoid the common error of training your model on both past and future events. Train only on data that will be available to you when a predictive model is actually running. Choose your evaluation method wisely; how you evaluate your model should correspond to your business needs.
VISUALIZE: Communicate with product/marketing teams to build insightful visualizations. Use visualizations to uncover additional insights to explore in the predictive phase.
DEPLOY: Determine if the project is addressing an ongoing business need, and if so, ensure the model is deployed into production for a continuous strategy and to avoid one-off data projects.
TAKE ACTION: Determine what should be done next with the insights you’ve gained from your data project. Is there more automation to be done? Can teams around the company use this data for a project they’re working on?
There is no doubt that data science, machine learning, and predictive analytics combined with Big Data will become an even more fundamental part of both online and traditional retail in the coming years. All retail organizations will use it, but only the successful ones will have an effective data production plan that yields the most effective insights into their business that gives them an edge over the competition.
To learn more about how Data Science can play a role in Retail download the free guide from Dataiku, 4 Big Challenges for Retailers, Solved with Predictive Analytics.
Dataiku develops Dataiku Data Science Studio, the unique data science platform that enables companies to build and deliver their own data products more efficiently. Thanks to a collaborative and team-based user interface for data scientists and beginner analysts, to a unified framework for both development and deployment of data projects, and to immediate access to all the features and tools required to design data products from scratch, users can easily apply machine learning and data science techniques to all types, sizes, and formats of raw data to build and deploy predictive data flows.
Hundreds customers in industries ranging from e-commerce, to industrial factories, to finance, to insurance, to healthcare, and pharmaceuticals use DSS on a daily basis to collaboratively build predictive dataflows to detect fraud, reduce churn, optimize internal logistics, predict future maintenance issues, and more. Dataiku, whose headquarters are in New York City, also runs operations out of Chicago, LA, San Francisco, Paris, and London.
Dataiku raised a $14M Series A round led by FirstMark Capital in October, 2016.