Analytics
The goal of analytics is to use data, statistical algorithms and machine learning techniques to provide the best assessment of what will happen in the future based on historical data.
Leveraging deep data, industry and technology expertise, Codifyd will deliver Information Management and Analytics Solutions that drive overall commerce revenue performance gains.
Our Analytics Service Offerings
SUPPLY CHAIN ANALYTICS
Reduce demand/supply uncertainties. Build model to tackle SKU complexity/cost complexity in a multi node supply chain.
-
- Demand Analytics
How is my forecast tracking with actual sales
- Procurement Analytics
How to achieve lowest landed cost and secure long-term high quality supplier partners
- Inventory Analytics
What stock should I hold and where should I position it? What, when and where should I ship?
- Logistics Analytics
How to Optimize transportation routes and loads
- Customer Service Analytics
How do I reduce cost on manpower without hampering customer service
SALES FORECASTING
Increase top-line growth Sales Operations by improving sales effectiveness, sales predictability and sales productivity
-
- Sales Forecast Analytics
What will be my sales in next week/month/year?
- Pricing Analytics
Am I selling my products at the right prices and the right time?
- Sales Lead Analytics
How do I maximize efficiency of my Salesforce?
- Product Analytics
How my products are selling? Will any addition of new products hamper by the performance of existing products?
How do I manage my backorders?
CUSTOMER INTELLIGENCE
Identify and analyze customer segment, maximize cross sell and up-sell opportunities and enhance Customer Lifetime Value
-
- Customer Segmentation
How do I understand my customer base & their profile?
- Churn Analytics
Why am I losing customers? How do I stop it?
- Loyalty Analytics
Who are my most loyal customers and how do I improve my loyalty customer base?
- Cross Sell / Up Sell
How do I identify cross sell or up sell opportunities within my existing customer base?
MARKET INTELLIGENCE
Help bring context to market insights using analytics techniques, domain expertise and technology.
-
- Market Mix Modelling Analytics
How do I maximize my marketing ROI?
- Attribution Modelling Analytics
How much is each channel impacting sales? How is our offline marketing impacting online and vice versa?
- Media Mix Optimization Analytics
If we increased our marketing budget, what is the most profitable place to make additional investment?
The next frontier of B2B sales is predictive analytics
89%
B2B marketers have predictive analytics on their roadmap for 2018
78%
Marketers see their prospects buying journeys becoming more complex and non-linear
62%
Marketers most often face challenges of ensuring data quality and managing data from a variety of sources in attempting to gain greater insights about customers and prospects
49%
Marketers who have integrated predictive analytics into their workflows selected two or more discover-stage activities among their top three best practices
Analytics 360
Analytics 360 is a discovery engagement that helps organizations identify problem sets that can be solved through analytics, prioritize them based on business impact and help lay the roadmap for solution building and implementation.
Analytics Centre of Excellence (CoE)
Analytics Centre of Excellence (CoE) builds on the Analytics 360 and puts together the team, technology and governance required to execute on the roadmap.
- Discovery
Working with key stakeholders to identify a comprehensive list of problems that can be solved with maximum impact
- Detail
Provide information specific to the identified problems
- Tools & Infrastructure
Identify tools and technology infrastructure required to solve problem sets
- Governance
Describe governance process required for short-term and long-term analytics success
- Identification of problem sets that can be solved by applying analytics
- Prioritization based on business impact
- High level solution architecture
- Identification of data sources and data transformation
- Tool and infrastructure recommendations
- Business Needs
Identify the drivers behind predictive analytics project, define the problem statement & set up expectations on outcomes
- Data Exploration & Consolidation
Data Sufficiency, internal & external data identification & consolidation to form a single source of truth
- Predictive Modelling
Building Predictive model(s) suiting business needs and industry compliances
- Evaluation & Acceptance
How good is the model performing & User Acceptance Testing?
- Model Deployment
Deploying the model to a production environment
- Business Intelligence
Turning results to actions by empowering business users with Interactive & Actionable Dashboards
- CoE cross-functional starter team
- Analytics best practices and robust Tools & Infrastructure
- Predictive Model build and deployment
- Actionable Intelligence delivered via Business Intelligence tools
- Business user training and operationalizing solution for ongoing use
