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Podcast Shownotes
Retailers can lose millions to empty shelves, and the fix is rarely a math problem. In this episode of the Knowledge Hub podcast, we break down how predictive analytics works in retail and why the data, not the algorithm, is the real challenge.
Welcome to the Knowledge Hub podcast, where we turn complex business and technology topics into practical, actionable ideas. Picture a $50M retailer losing 4% of sales to stockouts. That's $2 million gone, and recovering just a quarter of it would pay for the whole program. So why aren't more retailers doing it? Let's get into it.
What You'll Learn
- Why your POS, inventory and CRM systems already hold most of what a demand model needs, and why joining them is the first step
- Why you should start with one scheduled decision, not a platform, and capture a baseline before you begin so you can prove payback
- Why data preparation takes about half of a typical 8-14 week pilot, and how one UAE retailer cut stockouts and overstocking by 80% just by unifying its inventory data
- How to stop model drift: name an owner, retrain quarterly, check weekly, and publish the accuracy number
- How forecasts support your merchandising team instead of replacing them, and how they can feed the Power BI or Tableau dashboards you already use.
Your Turn
Before you shop for tools, ask yourself: which single decision would you make better with a forecast, and is the data behind it actually in one place? If this episode helped, follow the show, leave a review, and share it with a retail or analytics colleague.
Podcast Agenda
- 00:00 - The $2 Million Question: Why Are Shelves Still Empty?
- 00:25 - The Data You Already Have (But Can't Use)
- 01:03 - Takeaway 1: Start With One Decision, Not a Platform
- 01:26 - What's the Real Payoff? The McKinsey Numbers
- 01:45 - Takeaway 2: The Hard Part Is Data, Not Algorithms
- 02:22 - Takeaway 3: Why Models Drift and Trust Fades
- 02:49 - Will This Replace Your Merchandisers?
- 03:14 - The One Question to Ask Before Buying Tools
