Unmess is a SaaS product that provides analytics and insights to profile and predict customer value. It integrates into your processes to offer transaction data analysis, customer value prediction, and actionable insights to enhance profitability at every customer touchpoint. It supports integrations for more accurate data.
Features
Customer Transaction Analysis
Analyze customer transactions to predict their value, helping to maximize profitability by focusing on high-value customers.
End-to-End Insights
Get complete insights into customers' behavior, preferences, and product usage to understand the full customer profile and optimize engagement strategies.
Integrations
Integrates with QuickBooks, Xero, and Google Sheets to provide more accurate and comprehensive data, allowing for better decision-making processes.
Tiered Pricing
Implementing tiered pricing allows customers to choose plans that best fit their needs, providing potential for increased revenue from higher-tier options.
Add-on Features
Customers can purchase additional features or services not included in the base tier, allowing for incremental revenue.
Usage-Based Pricing
Pricing model where customers are charged based on their use of the service, encouraging more consumption.
Customer Success Programs
Programs designed to ensure customer satisfaction and engagement, leading to higher retention and potential upsells.
Account Management
Having dedicated account managers can help tailor the product to customer needs, driving upsells and long-term engagement.
Product-Led Growth
Strategy focusing on using product experiences to drive growth, encouraging upgrades as customers experience more value.
Targeted Marketing
Using targeted marketing campaigns to reach specific customer segments with personalized offers.
Data-Driven Decisions
Utilizing customer data to tailor offerings and identify opportunities for upsells or expansion.
Automated Growth Planning
Analyzes customer journey, leading to a 46% increase in conversion rates and sustainable growth.
Data Integration Insights
Provides understanding of customer behavior to significantly improve retention and expansion rates.
Sales Efficiency Optimization
Enhances sales processes, reducing deal velocity by 26% and improving close rates.
Price Optimization Strategies
Emphasizes feature-based pricing over user-based pricing, increasing customer retention by 36%.
Customer Success Insights
Predicts customer expansion and interaction patterns, helping to prevent churn and support growth strategies.
Automated Monitoring
Automatically monitors customer behavior patterns and resource consumption to provide insights for cost tracking and optimization.
Advanced Analytics and Pattern Recognition
Uses advanced analytics to identify opportunities for cost optimization and translate complex usage patterns into actionable business strategies.
Behavioural Insights
Mixpanel tracks user actions like page views, add-to-carts, and purchases. It also shows user engagement with site elements.
Traditional Segmentation
Segments users based on metrics like session duration and visit frequency, but misses financial context.
Funnel Analysis
Shows user journey from browsing to purchase but lacks integration with financial data.
Integrated Analytics Platform
Combines behavioural data with financial metrics to reveal high-value customer patterns.
Unbiased Pattern Detection
Automatically discovers customer patterns without preconceptions. Segments all customers who frequently bought successful items, saving potential revenue.
Dynamic Updates
Automatically adjusts to customer behavior changes, ensuring segments are always up-to-date with real-time insights.
Scalability and Segmentation
Allows scaling from a small local shop to a multinational brand with personalized service levels based on customer expansion.
User Insights
Provides insights into customer behavior, such as those spending more time on guides being 45% more likely to make purchases.
Efficiency in Data Management
Processes vast amounts of customer data quickly, which would be unmanageable manually.
Automatic Customer Segmentation
Automatically segments customers into natural groups based on various patterns like spending habits, enhancing marketing strategies and decision-making.
Customer Data Analysis
Automatically analyzes customer interaction patterns to provide insights into valuable opportunities, helping in predicting churn and optimizing pricing.
Purchase Frequency Analysis
Evaluates customer purchase frequency to identify different customer segments and their buying habits.
Churn Prediction and Prevention
Identifies early signs of potential churn risks and provides strategies to reduce churn by engaging at-risk customers.
Enhanced Customer Segmentation
Helps in identifying valuable customer segments through subscription box service metrics and behavior analysis.
Historical CLV Calculation
This method uses past purchase behavior to calculate CLV. It involves analyzing customer transactions over time to predict future value.
Predictive CLV Calculation
This approach uses predictive analytics and statistical models to forecast future customer behavior and predict potential revenue over time.
Traditional CLV Calculation
Calculation involves using average order values, purchase frequencies, and customer lifespans to estimate value.
Purchase Behavior Metrics
Key metrics like Average Order Value, Purchase Frequency, and Retention Rate help in feeding into CLV calculations.
Cost Metrics
Metrics such as Customer Acquisition Cost and Retention Cost help in understanding and analyzing CLV.
Visualization and Tracking
Includes dashboards for customer value tracking and segmentation based on value.
Personalization
Using customer data to tailor marketing and communication efforts, aiming for increased customer engagement and retention.
Value-Based Segmentation
Segmenting customers based on their value and tailoring strategies accordingly.
ROI Analysis
Evaluates the impact of CLV improvements by comparing returns against marketing spend.