What's new?Retail Customer Analytics Software That Drives Loyalty and Profit
Datawiz BI transforms raw customer data into actionable insights. With RFM segmentation and loyalty analysis, the platform helps retailers understand shopping behavior, enhance engagement, and increase customer lifetime value.
Key Benefits of Retail Customer Analytics
Increase Average Order Value
With retail customer analysis, you can identify cross-selling and upselling opportunities, recommend complementary products, and design promotions that encourage customers to spend more per visit.
Personalized Shopping Experience
Retail customer analytics software enables precise segmentation and tailored offers, creating a more relevant and engaging shopping journey that strengthens loyalty and drives repeat purchases.
Demand Forecasting Accuracy
By tracking buying patterns and seasonal trends, retail customer analytics helps you predict demand with confidence, reducing overstock and preventing costly stockouts.
Smarter Procurement Decisions
Use insights from retail customer analysis to align purchasing with actual customer needs, optimize supplier relationships, and reduce waste in your procurement process.
Customer Lifetime Value Growth
Advanced retail customer analytics software allows you to track and improve CLV by identifying high-value customers, targeting them with exclusive offers, and enhancing retention strategies.
Data-Driven Marketing Optimization
Analyze the impact of campaigns using retail customer analytics to fine-tune messaging, choose the right channels, and maximize ROI from every marketing initiative.
RFM analysis for deeper retail customer insights
RFM (Recency, Frequency, Monetary) analysis is a proven method for segmenting customers based on their purchasing behavior.
The RFM Analysis
The RFM Analysis report in Datawiz is a powerful tool for segmenting and understanding your customer base. It groups every customer according to three key parameters:
- Recency – how recently the customer made a purchase.
- Frequency – how often they visit your store.
- Monetary – how much they typically spend.
Using RFM analysis in Datawiz allows retail analysts to:
Targeted Marketing
Focus marketing efforts on the most profitable customer segments, minimizing wasted time and resources.
Growth of Key Metrics
Increase key store metrics such as revenue and turnover.
Measurable Results
Measure the real impact of marketing activities with objective, data-driven results.
Customer Retention and Lifitime Value
Make informed decisions that improve customer retention and lifetime value.
Datawiz RFM Analysis provides clear, interactive visualizations, turning complex customer data into intuitive charts and heatmaps. Retail teams can quickly identify high-value and at-risk customers, track segment distribution, and monitor campaign effectiveness in real time without analyzing raw datasets or extensive tables.
Loyalty Program as a Part of Retail Customer Analytics
Datawiz delivers diverse loyalty program analytics to cover every aspect of customer engagement. Multiple reports work together to reveal buying patterns, track performance, and highlight opportunities for growth — from high-level trends down to individual SKUs.
Loyalty Program Effectiveness
This report measures the real impact of your loyalty program at every level — from region to store, category, and SKU. You can easily compare results across two periods, monitor the share of loyalty sales in total revenue, and visualize key performance trends.
These insights allow you to evaluate the success of marketing activities targeting loyalty members and adjust strategies for maximum ROI.
Loyalty Program Statistics
Focused on the current state of your loyalty system, this report highlights customer base growth, purchase frequency, average turnover, and conversion rates. Clear visual cards make it easy to spot changes instantly and respond proactively.
With just a glance, retail managers can see how the program is performing, which customer segments are most active, and where there’s potential to increase engagement.
Segmentation of Loyalty Program Clients
Go beyond general metrics with in-depth segmentation. This report allows you to group customers based on purchase frequency, average check, turnover, and more. You can analyze sales within each group, identify the most profitable segments, and see which products resonate most.
By targeting each segment with tailored offers, you can boost retention, grow revenue, and enhance overall loyalty program performance.
Business-critical issues resolved with retail analytics
- Solved with:
RFM Analysis report
RFM Analysis report — segments customers by recency, frequency, and monetary value, so you can target the most profitable groups, re-engage inactive ones, and deliver offers at the right moment.
- Solved with:
Loyalty Program Effectiveness
Loyalty Program Effectiveness report — shows the share of loyalty sales in total revenue, compares performance over two periods, and visualizes key trends across regions, stores, categories, and SKUs.
- Solved with:
Loyalty Program Statistics
Loyalty Program Statistics report — provides instant visibility into customer base growth, activity, purchase frequency, and conversion rates through clear visual cards for fast decision-making.
- Solved with:
Segmentation of Loyalty Program Clients
Segmentation of Loyalty Program Clients report — groups customers by purchase behavior, average spend, and turnover, enabling highly targeted offers that boost engagement and sales.
Datawiz - a single source of truth for your retail data
All your analytics in one place
From sales and inventory to staff performance — every key metric is centralized. It’s convenient for teams and gives managers full visibility to stay in control and react fast.
Accurate forecasting based on real data
Descriptive analytics help you plan ahead with confidence. Instead of relying on assumptions, you forecast using customer behavior, seasonality, and actual sales trends.
Local-level control for stores
Datawiz lets you break down performance by store, category, or manager. Spot problems early, address local issues fast, and support your regional teams with actionable insights.
Intuitive system — no tech skills required
With a user-friendly interface, built-in tips, and live chat support, your team can get started instantly. No analysts needed and no delays. Just fast onboarding and immediate value.
Q&A
In brick-and-mortar retail, analytics platforms like Datawiz BI collect a wide range of customer data to understand shopping behavior. This includes demographic details (such as age, gender, and location), purchase history, loyalty program activity, and detailed basket composition — for example, which products are bought together and how shopping carts are structured. By analyzing these data points, Datawiz enables retailers to uncover buying patterns, optimize product assortment, and design more effective promotions.
Customer behavior analysis allows retailers to understand what drives purchasing decisions and shopping patterns. By studying purchase history, basket composition, and product combinations, retailers can identify popular products and trends, optimize store layouts and product placement, tailor marketing campaigns to different customer segments, improve inventory management, and strengthen customer service. This data-driven approach enables more efficient operations and a shopping experience that better matches customer needs.
Maximizing CLV in offline retail requires a deep understanding of customer behavior across stores and product categories. Retail analytics helps track purchase frequency, basket size, and category preferences to identify high-value shoppers. With this insight, retailers can design targeted loyalty programs, personalize promotions, and adjust assortments to keep customers engaged. Predictive analytics further supports long-term retention by forecasting churn risks, highlighting at-risk segments, and suggesting actions to maintain loyalty. Ultimately, data-driven decisions enable retailers to increase repeat visits, boost basket value, and maximize customer profitability over time.
Segmenting customer data by location allows retailers to capture regional differences in shopping patterns and preferences. By comparing purchase history, basket composition, and product performance across stores, retailers can identify which categories or promotions resonate in specific areas. This analysis supports more precise assortment planning, tailored marketing campaigns, and optimized inventory distribution. In turn, location-based segmentation improves customer satisfaction while ensuring that each store meets the unique needs of its local audience.
