A STEP-BY-STEP GUIDE TO INCORPORATING MACHINE LEARNING
In the age of AI and eCommerce, traditional distribution solutions are not fast enough or adaptive enough to engage with customers’ ever-changing tastes. They often rely on broad-strokes predictions that can’t factor in any sort of geographic granularity.
Make the shift to machine learning-based models with this guidebook, which includes:
Hugo is a former entrepreneur with an IT engineering background, who joined Dataiku in 2015. His focus is in helping retailers, CPG, hospitality companies, technology players, and startups use tooling to create value around business projects, including churn prevention, conversion improvement, loyalty, content personalization, sales forecasting, dynamic pricing, and more.
Christelle has worked in the Big Data space for her entire career. Before joining Dataiku last year, she ran her own luxury retail company. Now, she focuses on encouraging AI adoption in the retail, luxury, and CPG spaces. She believes in informing data decisions with ethics of inclusion. She is passionate about advancing customer expertise, marketing optimization, fraud detection, and operational excellence.
Between flash trends, seasonal variations, and fierce competition, optimizing stock distribution will never be simple, but gains are possible. This guidebook contains:
It’s clear that minimizing waste and shipping times with anomaly detection is important to consumers and stakeholders in retail organizations, but the question of where to begin is a challenging one. Beginning with concrete, incremental goals—such as a 5 percent increase in flagging stock shortages, or a 10 percent increase in detection speed—demonstrates clear ROI and helps highlight future room for growth.
There’s no way to implement a complete stock optimization overhaul overnight, but by carefully selecting a use case and encouraging teams’ adoption, retail organizations are poised to offer enormous value and an improved customer relationship; it’s a memorable experience when the products customers want are right where they need them to be.
The main challenge when it comes to stock optimization and detecting anomalies is that trends and surges in demand - with varied demand and logistical requirements behind stocking different products, it's challenging to get enough data to build and train a machine learning model. This guidebook walks you step-by-step through everything that you need to know in order to start using machine learning based stock optimization techniques for ultimately more effective and responsive systems.