Analyze store sales not just by how much was sold, but also by why and how it happened.
Sales analytics is more than just reporting transaction totals. When product, customer, store, channel, employee, campaign, appointment, offer, inventory, delivery, and return data are considered together, the true reasons behind performance can be understood.
Minerva Sales Analytics enables the analysis of company and franchise stores through common definitions and comparable indicators. Operational dashboards support daily decision-making, while detailed analyses provide input for product, store, and customer strategies.
Sales and profitability indicators
Gross sales, net sales, quantity, and number of orders.
Gross profit, contribution margin, and store profitability.
Average basket size, average order, and revenue per product.
Discount rate, campaign effect, and price variations.
Product, collection, category, and variant performance
Store, region, company, and franchise comparisons.
Sales consultant, team and shift results.
Customer and sales process indicators
Visitor numbers and sales conversion rate
Appointment scheduling and conversion of appointments into sales.
Number of offers, offer value, and offer-to-order conversion.
New customer, repeat purchase and customer lifetime value.
Cross-selling, complementary product, and product binding rates.
Return, cancellation, complaint, and satisfaction indicators.
The contribution of pre-sales contacts and different channels
Inventory and operations analytics
Inventory availability, turnover rate, and inventory aging.
Display item, damaged item and outlet conversion
Sales lost or delayed due to stock shortages.
On-time delivery and installation performance.
Reasons for return include damage, incorrect product, and service costs.
Cash difference, cancellation, authorized discount and transaction exceptions.
Use artificial intelligence and advanced analytics in a controlled manner.
Prediction, anomaly detection, product recommendation, and customer segmentation models can provide decision support. The data used by the models, the measurement definition, the confidence level, and potential biases must be known. AI outputs should not be used as absolute truth, but rather as signals to support human evaluation.
Make data quality a part of the indicator.
Conversion and person performance can be misinterpreted if visitor counters, customer matching, or employee assignments are missing. Source data coverage and quality level should be visible for each indicator.
Transform sales analytics from mere reports describing the past to reliable decision-making support that identifies the store's next correct course of action.