Gearing Up for Retail AI? Here’s What You Need to Do First

Good execution of retail AI depends on one key thing: good data. AI technologies require clean, accurate data to function at their best. Unfortunately, bad data is rampant and need to be addressed.

Gearing Up for Retail AI? Here’s What You Need to Do First

Cryptocurrency? So 2022. Today, it’s all about robot brains, grandpa.

That’s right, if you feel like tech innovations have been speeding up in the last couple years, you’re not wrong. Due to machine learning, advances in Artificial Intelligence (AI) are increasing at an exponential rate. So much so that today’s retail landscape is experiencing its own, concurrent revolution, propelled by new retail AI breakthroughs seemingly every other day.

Retail AI, with technologies promising to transform everything from customer service to inventory management to price optimization, is poised to break out over the next few years. Valued at $5.91 billion in 2022, retail AI is projected to reach nearly $90 billion by 2031. If you’re a retail brand and not looking into AI tools and upgrades, you may find your competition leaping over you very soon.

But good execution of retail AI that actually shows ROI depends on one key thing: good data. AI technologies require clean, accurate data to function at their best. Let’s dive into this new frontier, understanding the role of AI in retail, the challenges posed by poor data quality, and the critical steps needed to polish your data to a mirror shine.

What is Retail AI?

Retail AI encompasses a broad spectrum of applications, all designed to enhance the shopping experience, optimize operations, and boost sales. From chatbots that provide personalized shopping advice to algorithms predicting the hottest fashion trends, AI is the unseen force driving more and more of the innovations in retail. It's like having a crystal ball, one that not only forecasts the future but also anticipates the needs and wants of each and every customer. It can even handle drive-thru orders with 95% accuracy.

Retailers are also beginning to leverage AI to help streamline internal processes as well including supply chain management, manufacturing optimization, and even to help with staffing. In fact, the real ROI from retail AI may be less about the customer-facing aspects – more and more, customers are craving the human element – and more about the back-end logistics.

However, the magic of AI doesn’t lie in the algorithms alone; it's powered by data—vast quantities of it, from transaction histories and customer preferences to real-time inventory levels. Which is why any retailer hoping to engage with machine learning and AI needs to do one very important task.

The Achilles' Heel of Retail AI: The Prevalence of Bad Data

Imagine preparing a gourmet meal with subpar ingredients; no matter your culinary skills, that sea bass is still going to be rancid. Similarly, AI's potential is diminished by inaccurate, incomplete, or outdated data. Surprisingly, bad data is more common than one might expect, with businesses routinely grappling with data that's cluttered with errors or inconsistencies. According to IBM, poor data quality costs organizations an average of $15 million each year. This amount will only increase the more retailers rely on AI which in turn rely on the quality of the raw data.

Bad data only leads to AI applications making flawed recommendations and can hinder business growth. The dream of a seamlessly automated retail environment remains just that—a dream—until the data fueling these technologies is squeaky clean. That’s where us humans who are currently in charge of data have to put in the work of preparing and cleaning the numbers that will be fed into the retail AI algorithms.

Polishing Data for Retail AI

Enter the solution: advanced data reporting tools like those offered by 42 Technologies. Think of these tools as the ultimate data cleaning service, designed specifically for the retail industry's unique needs. Just as a jeweler meticulously cleans a diamond to enhance its brilliance, data reporting tools help you sift through your data, identifying inaccuracies, redundancies, and gaps. By employing sophisticated algorithms and analytics, they not only cleanse existing datasets but also continuously monitor data quality, ensuring any connected AI applications are always powered by the most accurate and up-to-date information.

With a clean data foundation, AI can truly work its magic, optimizing every aspect of the retail operation, from predicting stock levels to personalizing customer engagement. The result? A retail experience that’s not only more efficient and profitable but also more attuned to the desires of today’s discerning consumers.

So as we stand on the cusp of a retail revolution, powered by AI, the message is clear: the path to leveraging this technology begins with clean data. Just as no store manager can sell efficiently without a tidy show floor, no AI can thrive without pristine data. In embracing tools like those offered by 42, retailers can ensure their data is not just clean, but crystal clear, ready to reflect the full potential of AI in transforming the retail landscape.

What’s more, 42’s suite of data reporting doesn’t just stop at helping to clean up your customer data. They also provide insights and visualizations that help retailers make informed decisions, turning raw data into actionable intelligence even if your team isn’t quite ready to entrust retail AI with all the decision making. The future of retail may belong more and more to the machines, but today’s customers aren’t waiting around for that when they’ve got their credit cards out.