Our Services
Analytics Strategy
- Identify relevant use cases and estimate potential business value
- Prioritize use cases in the context of your company and industry
- Define initiatives and develop analytics roadmap
- Scope initiatives prior to starting data science efforts
Data Science Execution
- Translate business problems into the relevant data science problem
- Identify best modeling approach for your company's objectives and data availability
- Develop tailored algorithms, leveraging proven open source and industry standards
Project Management
- Define and execute pilots and proof-of-concepts
- Develop implementation programs, both technical and change management
- Monitor, support, and update analytical models as needed
Our Approach
Focus on business impact
- Every project is custom tailored to your company and industry, focused on business value and accelerating time-to-value
- Our project teams combine business acumen with data science and technical expertise
- We apply data science to core drivers of business value (e.g., customer segmentation, price optimization, demand forecasting)
Ensure project success
- Our expertise in the real-world application of data science ensures practical solutions in the context of your business
- Our talent and experience can help you avoid pitfalls and accelerate ideas through to successful execution
- Throughout the entire project lifecycle, we partner with you to clear hurdles and ensure project success
Support your end-to-end analytics journey
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PLAN
- Which use cases align to my business needs?
- Which use cases will have the biggest impact?
- Which use cases should I prioritize?
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MODEL
- What is the best analytical model for this business problem?
- What data is required for this type of model?
- Do I have sufficient data quantity and data quality?
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BUILD
- How do I integrate data science models into my existing IT?
- How will this change future workflows?
- How can I ensure successful adoption?
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RUN
- How do I measure business impact for pilots and after launch?
- How do I establish data pipelines and self-updating models?
- How do I monitor model performance over time and compare with new models?
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GROW
- How do I leverage data sets and learnings for additional use cases?
- How do I evaluate the ROI for a portfolio of analytics initiatives?
- How do I develop an analytics roadmap that grows capabilities over time?
Example use cases
Custom tailored for your company and industryPricing & Promotion
- Price optimization for each product (B2C)
- Price optimization for each customer (B2B)
- Predict impact of promotions and optimize ROI
Demand Forecasting
- Forecast by segment, geography, and product
- Leverage descriptive and behavioral data, from internal and external sources
- Scenario forecasting (e.g., post-COVID demand)
Customer segmentation
- Next-best-action for sales and service teams
- Digital marketing targeted messages and offers
- Customer journey analytics
Customer churn prediction
- Behavior and preference modeling
- Sentiment analysis
- Engagement scoring
Product recommendation
- Identify look-a-like customers
- Likelihood based on purchase history
- Context-dependent suggestions
Supply chain and procurement
- Inventory planning
- Suppler selection and risk management
- Spend optimization
Mike has advised business leaders at the intersection of strategy and technology for more than a decade. His experience in business transformation comes from three perspectives: business strategy at McKinsey & Company, machine learning implementation at IBM, and as a business executive responsible for growth at a global conglomerate.
Prior to Tandem Analytics, Mike led teams of business consultants and data scientists to support IBM clients in developing analytics and AI transformation programs. He partnered with clients from concept to realization, including algorithm development, pilot program design, technical integration support, and overall program management. Mike has advised business leaders on AI and analytics strategy across industries, including telecommunications, finance, industrials, retail, and health care.
Before joining IBM, Mike was a Director of Strategy & Innovation at Wolters Kluwer, where he led digital transformation for a B2B portfolio company. To improve customer centricity, he launched a portfolio of initiatives, including machine learning for segmentation and customer behavior insights. Early successes with pricing strategy and customer segmentation fueled transformation efforts across digital marketing, as well as increased personalization of sales and service.