Como você pode usar os dados do sistema de PDV para identificar clientes de alto valor?

By having a dynamic CRM system integrated with your POS.. This should have a customer purchase history by Value and frequency both broken into date, season, style, fits , size etc. Remember not only the value of the purchase but the frequency of the visit is also important. Both of these define brands affinity and loyalty.. But the most important thing is, how you act to this information and create a great customer experience by improving your products, services and ambience !!

The collection of hard, reliable data in retrospect that provides insight into product performance and reveals consumer or other behaviors, such as elasticity of demand, etc. Not only do they help us observe the behavior and level of acceptance of our products at the point of sales, but they also help us plan for the future with greater effectiveness and certainty. Hence the importance of carrying out good and timely POS analyzes.

Beyond identifying high-value customers, the information we obtain from the POS system is very valuable, so it is important to know how to take advantage of it. In addition to the average ticket, we have real data on the profile of our consumers, the preference of our products, and the affinity with other categories. We can confirm this information with the direct sell out of retail (in this case) and confirm and deepen in the face of sales declines, capitalize on growth, to somehow pamper our consumers. This information is also of great help introducing new products, to check their efficiency or make improvements.

The best way to Unearth high-value customers hidden in your POS data: Mine purchase history: Analyze transaction data to reveal frequent buyers and big spenders. Leverage customer profiles: Store information like preferences and purchase history to recognize consistent high-value buyers. Track loyalty: Integrate with loyalty programs and identify customers actively engaged through points and rewards. Remember, regularly update segments and focus on actionable groups aligned with your goals.

POS system data encompasses transaction details, customer information, inventory status, payment methods, sales reports, employee performance metrics, refunds and returns, usage of promotions, customer preferences, supplier details, tax records, operational metrics, security data, and customer feedback. This information aids businesses in making informed decisions, optimizing operations, and enhancing customer experiences. Integration with other business systems ensures a cohesive and synchronized environment, contributing to overall business success.

Segmenting customers using POS system data involves analyzing demographic information, purchase history, product preferences, loyalty program participation, seasonal buying behavior, promotion response, new vs. returning customers, shopping channels, average basket size, inactive customers, geographic location, preferred payment methods, customer feedback, and time of purchase. These segments enable businesses to tailor marketing, optimize operations, and enhance the customer experience. Regularly updating and refining segments based on evolving behaviors ensures personalized strategies and sustained business growth. Integration with CRM tools enhances the depth of customer insights.

In my experience, The correct reading of POS data is very useful to classify different groups of consumers according to purchasing habits or lifestyles. Through these analyses, we can determine the frequency of consumption, ideal size of products, preferred price points, most effective discount percentages or even discover which products are regularly purchased together or as a complement to our products. With all this, it is possible to classify different consumer groups to offer families of products capable of penetrating different segments.

The benefits of using a POS system include increased efficiency in processing transactions, improved accuracy in inventory management, better insights into sales and customer behavior through reporting and analytics, and enhanced customer experience through features like faster checkouts and personalized service.

To create personalized offers and communications using POS system data, start by segmenting customers based on purchase history, demographics, and behaviors. Utilize demographic insights for targeted promotions, analyze purchase history for tailored discounts, and leverage loyalty program data for exclusive rewards. Craft seasonal offers, recover abandoned carts with targeted messages, and identify cross-selling opportunities. Personalize communication channels, use location data for proximity-based promotions, and integrate POS data with CRM for comprehensive insights. Conduct A/B testing and send personalized thank-you messages to enhance customer engagement and loyalty.

In my experience, through the analysis of data from a POS system, we can learn about shopper insights and preferences of consumer groups, as well as identify other products that are usually consumed along with ours. In this way, tailor made promotions can be designed for specific groups, for example, on the day of highest store traffic for these consumers in a cross promotion at the most effective discount percentage previously analyzed.

Improve customer service using POS system data by creating detailed customer profiles, offering personalized product recommendations, and providing real-time order tracking. Streamline returns and refunds handling by accessing transaction details. Analyze customer feedback for continuous improvement and acknowledge loyal customers with personalized benefits. Train representatives to interpret POS data effectively, proactively address concerns, and identify cross-selling opportunities. Integrate POS data with CRM for a comprehensive view and prioritize data security and compliance. This approach enhances customer satisfaction, loyalty, and overall service efficiency.

To anticipate demands, expedite service, and personalize client encounters, use data from POS systems. To improve overall customer happiness, analyze purchase history to make customized recommendations, spot trends to maximize inventory, and collect feedback for ongoing enhancements.

Perform POS data analysis upon completion of specific promotional activities, such as seccondary placement, discounts, cross-promotions, etc. They serve to verify the effect created through said activity and thus determine its profitability. You can compare the effectiveness generated on different days of the week, or compare the incremental sales according to the size of the discount offered and against the regular price. This is used for planning upcoming activities, volume estimates, repetition, periodicity or budget.