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Transforming Retail Execution: Harnessing AI and Machine Learning

Discover how AI and machine learning are revolutionizing retail execution. Explore the power of predictive, descriptive, and prescriptive analytics to anticipate and enhance customer experiences. Learn how these technologies are employed across industries and provide actionable recommendations to achieve business goals.

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Transforming Retail Execution: Harnessing AI and Machine Learning

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  1. How Data, Machine Learning, and AI Provide New Ways to Measure and Transform Retail Execution

  2. The Focus of Machine Learning Providing Predictions and Prescriptions Descriptive Predictive Prescriptive y A A x B z • Describe What Happened • Employed heavily across most industries • Anticipate What Will Happen (inherently probabilistic) • Employed in data driven organizations as a key source of insight • Provide recommendation on What To Do to achieve goal • Employed heavily by leading data and internet companies Focus of Machine Learning 1: An executive’s guide to AI, McKinsey Analytics, 2018

  3. Can you describe how Artificial Intelligence is changing the Enterprise? • 3 Major Technology Revolutions: Cloud, Mobile First, and now AI. It's happening; have to do something. • Abundance of Data. • Helping to solve the biggest problems. • Opportunity to re-focus on more value-add activities.

  4. Can you share some examples of how you see AI / ML being applied in the area of Field Sales? • Vast Amounts of Retailer Data • Large problem for both CPGs & Retailers - 8% of SKUs out of stock • Most of the problem happening in the last 50 ft. of supply chain • Translate these insights directly into actions to take in the store visit • Learn over time

  5. Can Machine Learning evolve how the Sales Person plans their route? • Providing suggestive or prescriptive outlets to visit. • Assess POS Data for OOSs, Promotion, and Compliance opportunities • Identify the biggest value opportunities for a visit

  6. Are you applying any of these techniques to Perfect Store? • Helping define what the Perfect Store is. • Mine the data, find correlations between events. • Right action - Identify the most value-added actions to take with limited time.

  7. What additional data sources could be considered to feed the algorithms? • Millenials have inspired this concept of the 'gig economy'. Offers opportunity to be flexible and make money with quick, short jobs. US - 34% of the Workforce • Consider how you can leverage these crowd source platforms to augment your sales teams. • Connect it to your ecosystem of data as another signal; in-store conditions, image recognition

  8. Can these techniques also apply in areas where we don't have centralized trade options? • Opportunities to apply similar models using distributor data. • Opportunities to engage with millions of outlets through automated marketing & portals.

  9. There is a lot of news around Image Recognition, can you share how you see the current state? • For a long time, accuracy, speed, and cost have big barriers. • As Vision AI systems are more accessible, many new players are entering improving across all 3 dimensions. • We need them to move beyond simply collecting data and actually show recommended actions; in seconds. • Still barriers with offline.

  10. Many organizations don't know where to start when it comes to AI; can you share some practical steps to get started? • Start small; demonstrate practical value. Do something standalone for a model. • It's nearly free to learn! Open Source, Azure - Play! • Understand what data is available. Research publicly available data. Identify the biggest problems and use an AI vendor to help you quickly identify opportunities

  11. Where do you see Retail Execution in 2025?

  12. Thanks!

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