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In the fast-paced and dynamic realm of digital merchandising, being reactive to customer trends has been the norm. In the past, merchandisers would collect data after the fact and use it to inform decisions about what and how to sell. However, the digital landscape is constantly changing. Shoppers are becoming more demanding due to technology disruption and continuous innovation in digital merchandising platforms, according to McKinsey.

To meet this demand, merchandisers are now seeking ways to leverage AI tools to simplify their tasks and make better proactive decisions that can be automated. To stay ahead of the curve, merchandisers need to be able to predict customer preferences before they even know what they want. This is where AI tools can be a valuable asset. AI tools can help merchandisers to:

  • Predict customer preferences with greater accuracy.
  • Identify patterns and trends that would be difficult to spot with the naked eye.
  • Collect and analyze data more quickly and easily.
  • Deploy and execute strategies with human oversight.
  • Generate product descriptions and content, saving time and resources.
  • Optimize pricing strategies based on market conditions, demand, and competitor pricing data.

In this blog post, we’ll explore how our recent study on merchandiser data maturity sheds light on the transformative shift from reactive to proactive merchandising.

Ebook shopping experience

Understanding merchandiser data maturity and AI tool adoption

To understand how data maturity affects customer AI tool adoption, we recently surveyed 50 ecommerce customers involved in merchandising strategy from a mix of small, mid-market, and enterprise companies. The study revealed that a significant number of surveyed Algolia customers (76%) are already using mature data practices. These practices include consistent and informed data collection, cleaning, and analysis. We also found that data maturity is not determined by the size of a company; small and medium businesses can both be just as data mature as their larger counterparts.

data maturity

Interestingly, we also found that less data-mature companies are using Algolia’s AI tools at a high rate. In our study, over 80% of less data-mature companies reported using Algolia’s AI tools, such as Dynamic Re-Ranking, Dynamic Synonym Suggestions, and Query Categorization. This demonstrates that Algolia’s AI tools can help companies at all data maturity levels make better merchandising decisions, even if they are just starting out.

AI data maturity

Platform preferences and experimentation strategies

Our research found that data maturity has a considerable impact on the platforms and experimentation strategies that companies use. Data-mature customers prefer off-the-shelf commerce platforms because they want to get up and running quickly and don’t always have the resources to build their own platforms. Even enterprise companies, who are more likely to build custom solutions, are showing a preference for off-the-shelf products.

Data maturity also influences the selection of data analysis tools. Data-mature organizations have the resources to hire data scientists and engineers who can use programming languages like R, Python, and SQL to analyze large datasets. Enterprise companies, on the other hand, prefer spreadsheets and BI platforms because they are easier to use and don’t require as much technical expertise.

The choice of platform and experimentation strategy should be aligned with the company’s data maturity level. By choosing the right tools and strategies, companies can make better decisions and drive business growth.

data maturity pie charts

Here are some additional insights from other research studies we’ve conducted:

  • AI tools are becoming more affordable and accessible. Even small businesses can now afford to use AI tools to improve their merchandising strategies.
  • The use of AI in merchandising is still in its early stages. However, the potential benefits are significant, and we expect to see even more adoption of AI in the years to come.
  • The most successful merchandisers will be those who are able to combine AI with their own expertise and intuition. AI can provide valuable insights, but it is still important for merchandisers to have a deep understanding of their customers and their products.

Empowering merchandisers with AI tools

AI tools can help merchandisers make better decisions by providing insights into customer behavior and trends. Our research consistently shows that merchandisers on all search platforms often struggle to accurately measure the impact of their strategies across the wide range of tools they use. However, Algolia’s data mature customers exhibit an interesting contrast: while they excel in measuring the success of their merchandising efforts, they express less confidence in their ability to make data-driven decisions. AI tools can help businesses do both: track the success of their merchandising efforts and identify areas for improvement in decision-making.

Algolia’s AI-powered tools seamlessly combine multiple AI signals to optimize business metrics, helping merchandisers understand customer intent, optimize product catalogs, and deliver personalized experiences that lead to increased sales and improved customer experience. For example, Algolia’s Query Categorization tool automatically groups user queries into product categories without coding, helping merchandisers understand customer intent and reduce catalog maintenance. It also includes analytics that automate reporting by forming clusters from category-related search queries, giving merchandisers deeper insights to improve product performance.

Algolia’s Query Categorization helps merchandisers do both: leverage category predictions to improve data-driven decision-making and track the success of their category merchandising efforts through analytics and automated reporting.

AI does the heavy lifting

Here are some tips for using AI tools to track merchandising success:

  • Choose the right tools and metrics. There are a variety of AI tools available, so it is important to choose the ones that are right for your business. You also need to select the metrics that are most important to you.
  • Track your results over time. It is important to track your results over time so that you can see how your merchandising strategies are performing. This will help you identify areas for improvement and make adjustments as needed.
  • Rally your team. It is important to involve your team in the process of tracking the impact of AI tools on merchandising success. Start by sharing your goals and objectives with them, fostering a collective commitment to achieving these milestones. This will help to ensure that everyone is on the same page and that you are getting the most out of your AI tools.

By following these tips, you can use AI tools to track merchandising success and make better decisions about how to allocate your resources. This can help you to improve your business performance and stay ahead of the competition.

Embracing a data-driven future

In the ever-shifting world of merchandising, data plays a pivotal role in guiding your path ahead. Whether you opt for cutting-edge AI tools or stick to more traditional data analysis methods, one unwavering principle stands tall: the vital importance of integrating insights into your strategy. This integration automates the continuous improvement of AI models, making your merchandising efforts more agile and responsive, saving you time and money so you can focus on more strategic tasks.

By embracing AI tools and fostering mature data practices, merchandisers gain the ability to make more strategic decisions, accurately predict customer preferences, and ultimately drive business growth.

About the author
Lorna Rivera

Staff User Researcher

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