Sales & Marketing Analytics

“Maximize Your Sales and Marketing ROI with Advanced Analytics – Backed by KEPLER Consulting Expertise”

Sales and Marketing Analytics helps you make better decisions and get better results for your sales and marketing work by using data. We give you useful knowledge about how your customers act, how well your marketing works and your sales. This is done with data mining and business intelligence tools.

You need sound data in order to do good sales and marketing analysis. You also need the right tools to analyze the data. The way you analyze the data will give you the insights you need to make more money, keep your customers coming back to do more business with you and set your business apart in your market.

Effective sales and marketing analysis requires high-quality data and appropriate analysis tools. The insights gained from this approach can improve profitability, build customer loyalty, and generate competitive advantage in your market.

Our Core Expertise In Sales & Marketing Analytics

Dispersed customer data across various systems hinder a comprehensive view
Poor segmentation leads to ineffective targeting and personalized messaging
Incomplete understanding of customer journey impacts campaign optimization
Difficulty in identifying high-potential leads, leading to inefficient resource allocation
Inaccurate sales predictions hinder resource planning and revenue projections
Inefficient allocation of marketing resources across channels and segments

Unified Customer Data Integration Solution: Integrate data sources, creating a unified customer profile for segmentation and analysis
Personalized Campaign: Utilize advanced segmentation techniques for more accurate categorization and personalized campaigns
Enhancing Insights: Implement attribution modelling to trace touchpoints, enhancing insights for tailored strategies
Lead Scoring model: Implement lead scoring models to prioritize leads, ensuring optimal use of sales resources
Sales forecasting: Develop robust sales forecasting models based on historical data and market trends
Resource optimization: Analyze data to determine optimal resource distribution, ensuring campaign effectiveness

Difficulty in monitoring product performance and portfolio insights
Challenges in optimizing pricing, promotions, and ROI for diverse product offerings

Category Management Cockpits: Implement 360-degree monitoring and data mining tools for real-time insights
Experienced Expertise: Leverage strong experience in building category management cockpits for informed decisions
Data-driven Decisions: Utilize advanced analytics to extract actionable insights and enhance decision-making processes

Lack of comprehensive data for competitor analysis
Slow analysis due to manual data collection and processing
Expensive resources for data acquisition and analysis

Multi-Source Data Scraping: Utilize strong data scraping tools to gather diverse market information
Efficient Computing: Employ advanced computing techniques for quicker data analysis
Cost-Effective Approach: Reduce expenses by leveraging automated tools for data collection and analysis

Tailor loyalty offers based on purchase history and behavior analysis
Implement predictive analytics to segment customers and personalize rewards
Employ real-time analytics for timely adjustments and personalized engagement

Customer Preferences Insights: Leverage data insights to create personalized loyalty rewards aligned with individual preferences
Effective segmentation: Utilize predictive models to segment customers accurately and deliver targeted incentives
Real-time Insights: Deploy real-time analytics for instant feedback on loyalty program effectiveness and optimize strategies

Integrate data sources for a unified view of customer journey across channels
Align marketing messages across channels for a seamless customer experience
Use advanced attribution models to accurately measure channel contributions

Data Modelling: Implement a data integration strategy for holistic customer journey insights
Consistent Messaging: Develop omnichannel marketing strategies to ensure consistent brand messaging
Attribution Modelling: Employ advanced attribution models to allocate credit accurately to each channel for informed decisions

Measuring the impact of different marketing channels on conversions and sales
Identifying the best relevant targets for personalized marketing campaigns
Assessing the investment ratings of online advertising and Google Analytics data

Contribution Analysis: Implement advanced attribution models to accurately measure the contribution of each channel
Segment Analysis: Utilize predictive analytics to segment and target audiences based on behaviour and preferences
ROI Evaluation: Develop a unified dashboard that integrates and visualizes data for better ROI evaluation