RETAIL STOCK OPTIMIZATION

A STEP-BY-STEP GUIDE TO INCORPORATING MACHINE LEARNING

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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:

  • A broader overview of the role of anomaly detection in retail (beyond stock optimization) and ways to integrate the process into existing workflows.
  • Code samples for a simple machine learning-based stock optimization model, along with ways to customize and improve it.
  • How to approach ROI calculations when determining the first steps towards machine learning integration.

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About

Hugo LE SQUERENP1030334
Hugo Le Squeren
AI Deployment Strategist, Dataiku

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.

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About the Authors

Christelle Mercier
Christelle Mercier
Account Executive, Retail

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.

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ABOUT

Claire Carroll
Claire Carroll
Content Specialist, Dataiku
Claire has worked in consulting and content creation around blockchain and big data for the last five years. At Dataiku, she fosters engagement around AI ethics and diversity & inclusion. She holds a dual degree in Computer Science and English from Yale University.
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About this Guidebook

Between flash trends, seasonal variations, and fierce competition, optimizing stock distribution will never be simple, but gains are possible. This guidebook contains:

 

  • An overview of anomaly detection applications in the retail industry.
  • The common challenges we’ve experienced in implementing anomaly detection systems, and best practice solutions.
  • A concrete step-by-step path to a machine learning-based stock optimization POC
  • And more!
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Excerpt from the Guidebook

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.

Get the Guidebook

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.

 

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