3 Ways Machine Learning is Shaping The Retail Industry

The retail industry is dealing with exciting yet challenging times. Consumers enjoy more product options, price points, and purchasing modes than ever, and the global retail market is expected to surpass $31 trillion by 2025.

However, meeting an unprecedented level of customer expectations and fending off competition from all angles are no picnics. For retail firms to foster a healthy long-term business outlook, a retail store analytics program is essential.

Successful retail business intelligence tools combine integrate every data source into an ad-hoc search format. But where platforms like ThoughtSpot take it further is by using artificial intelligence (AI) and machine learning (ML) to offer end users more personalized answers and hidden insights they might have missed.

Let’s discuss the three ways machine learning is shaping the retail industry.

  1. Wider Data Access
    Shoppers are sharing more information about themselves than ever before. But without easy ways to access and analyze data, the information just isn’t actionable enough. A search-driven analytic platform like ThoughtSpot allows employees to analyze important data sets — like sales and transaction volumes by region or by store — in seconds to accurately forecast employee staffing and customer demand against company benchmarks.

    When data access is extended to all employees and not just a select few data employees’ knowledge is shared among the business and better decisions are made because of it.

  2. Reduce Customer Churn
    AI and ML applications have already begun to revolutionize customer service. Per Forbes, 75 percent of enterprises using AI and machine learning enhance customer satisfaction by more than 10 percent. One way this is happening is through the use of chatbots to handle common customer questions and issues. But on an overarching level, reducing customer churn and boosting customer satisfaction lie in the data.

    Using ThoughtSpot’s retail analytics platform, a retailer can not only easily determine their average order value, but also dig into a variety of metrics that affect your AOV. For instance, simultaneously looking at your revenue per visitor is a good way to delineate who’s spending money, on what, and from where. You might find that only some segments of your customer base are underperforming and that tailoring your efforts to increase their purchasing dramatically boosts your AOV.

    Where do you start? Anywhere you want. That’s the best part of using relational search technology with an AI-driven feature like SpotIQ built on top of it. Why did subscribers drop in Q3? Search ThoughtSpot for something like, “unsubscribe by reason Q3” and be spoon fed insights through data visualizations.

    SpotIQ also detects the stuff users aren’t looking for such as key indicators, anomalies, causal relationships and more to ensure retail firms stay on top of their dynamic analytics.

  3. Better Demand Forecasts
    Every retail organization has their peak and low seasons. Unfortunately, warehouse, distribution, staffing and more have their ongoing costs. When any of these business areas start to fluctuate, the entire company becomes unstable.

    Machine learning tools like ThoughtSpot help clarify the picture, allowing users to gauge demand, determine pricing elasticity, and set an overall product line strategy heading into each season. Using ThoughtSpot, you can quickly build liveboards of insights and share with team members to fuel brainstorms, for example, how to reduce out of stocks and overstocks while keeping fulfillment running strong.

Want to learn more about how ThoughtSpot’s AI and machine learning platform can improve the effectiveness of your retail marketing plan?

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