Unlock consumer insights from POS data! What are Lazuli PDP feature tags?

Improving consumer LTV amid domestic market decline and diversifying values has become a top priority for all manufacturers and retailers. Understanding the psychology behind consumer purchases is essential for product development and marketing strategy. As a product information platform, Lazuli PDP uses AI to go beyond basic product information, converting consumer purchase psychology hidden in POS data into data and attaching it to each individual product as feature tags. In this article, we explore how feature tags work and what impact they have on business.

The biggest challenge of POS analysis is that you can only know 〇〇

The biggest challenge facing POS data analysis is that POS data can only tell us “what was bought, where, and when,” but not “why it was bought.” Purchase history shows specific product choices and their timing, but it does not reveal psychological factors behind them, such as consumer emotions, motivations, or brand loyalty.

Without understanding these invisible aspects, moving forward with marketing strategies and product development may fail to lead to a true understanding of customer needs. Interview surveys and questionnaire surveys are effective for collecting consumers’ raw voices, but they also have drawbacks, such as small sample sizes that can skew results, and the fact that they can only provide image-based research at the brand level rather than for individual products.

Therefore, it can be said that the method of using AI to augment POS data—the largest big data source in Japan—to capture consumer psychology will become a new trend in future business strategy.

Make consumer psychology and competitor research easy with feature tags

The feature tags generated by Lazuli PDP turn POS data into more than just purchase history; they become a powerful tool that simplifies consumer psychology analysis and competitor product research. This system can automatically collect information needed for marketing and sales activities, such as online reviews and competitor product prices on e-commerce sites, and attach it to products.

The information enrichment performed by AI provides valuable data to support product development and marketing strategies, dramatically reducing the time-consuming work of traditional research. As a result, businesses can respond to market needs more quickly and accurately, while deepening analysis and promoting strategic decision-making. Feature tags are set to become the new standard for consumer analysis going forward.

Three analysis patterns to make 120% effective use of POS data

1. Customer clustering based on values

By analyzing purchase data together with feature tags, you can achieve segmentation not only by customer attributes such as age, gender, and household composition, but also by customer values. This lets you gain insights such as: “This person bought this product at this time and place,” and also “The feature tags of these seemingly unrelated products share common factors such as health, dieting, low calories, and natural ingredients. Maybe this person prefers health-conscious and natural products.” This enables more refined marketing strategies and customized recommendations and campaigns tailored to each cluster’s needs.

2. Estimating the characteristics of high-LTV customers

For the challenge of not knowing the characteristics of customers who will be important in the future or those likely to churn, you can understand the traits of high-LTV customers and use them to prevent future-LTV decline and churn. By analyzing the values and purchasing behavior of high-LTV customers from their past product purchase histories and finding common elements, you can identify differences in data between loyal customers and those likely to churn, plan retention measures, and create initiatives that further turn loyal customers into enthusiastic fans.

3. Mapping competitor products

By comparing and analyzing products from competitors in the market with your own products, you can clarify competitive advantages and use the insights to improve products and identify new market opportunities. POS data may allow you to obtain purchase histories not only for your own products but also for competitors’ products. By mapping competitor product features and selling prices to reveal your company’s unique positioning in data and applying that to product development, you can shift from product development based on intuition and experience to data-driven product development.

Summary

POS data has value beyond just purchase history. By leveraging the feature tags generated by Lazuli PDP, you can perform customer clustering, LTV estimation, and competitor product mapping in ways that were not possible before, greatly improving the accuracy of your marketing strategy. Lazuli PDP can solve challenges related to data analysis that uses product information, such as providing new analytical dimensions and data cleansing.

Rather than treating POS data merely as purchase history, turning it into a company asset as purchase big data allows you to use it in product development and marketing as a powerful tool for deeply understanding consumer psychology.

At Lazuli, we develop and provide “Lazuli PDP,” a product data generation and supply solution that provides the product data needed for organizing and processing product master data in data analysis. If you are struggling with POS data analysis or the use of internal data, please contact us here.

Learn more about Lazuli PDP here: https://corporate.lazuli.ninja/feature/