Finding the right consultant should be this easy.
1
Tell us about your project
2
Interview candidates
(We’ll provide bios within 48 hours on average)
3
Select your consultant and start work!
What Is Predictive Analytics?
Predictive analytics uses historical and current data to estimate what is likely to happen in the future. It may involve statistical modeling, machine learning, forecasting, scoring, segmentation, simulation, or other analytical methods that help organizations anticipate outcomes and make better decisions.
Predictive analytics can be applied across many business questions, such as demand forecasting, customer churn, sales conversion, pricing, inventory planning, maintenance needs, credit risk, operational performance, workforce planning, and marketing effectiveness. A predictive analytics consultant can help define the business problem, assess available data, build or improve models, interpret outputs, and translate results into practical recommendations.
When Clients Seek Predictive Analytics Support
Clients often seek predictive analytics support when they have data available but need help using it to anticipate future performance, identify risks, prioritize opportunities, or guide decisions. This support can be especially useful when leadership wants to move beyond descriptive reporting and use analytics to support planning, targeting, forecasting, and execution.
Common situations include:
- Forecasting support: Developing forecasts for demand, revenue, sales, volume, inventory, staffing, capacity, costs, financial performance, or operational needs.
- Predictive modeling: Building models that estimate future outcomes, probabilities, risks, performance changes, or business impacts based on relevant data.
- Customer analytics: Predicting customer behavior, churn risk, lifetime value, purchase likelihood, retention opportunities, or customer segment performance.
- Demand prediction: Estimating future demand by product, customer segment, geography, channel, season, market condition, or other business driver.
- Risk scoring: Developing scoring approaches to identify customers, accounts, suppliers, projects, assets, or transactions that may carry higher risk.
- Segmentation: Grouping customers, markets, products, assets, or behaviors into meaningful segments that can support targeting, planning, service design, or decision-making.
- Machine learning use cases: Identifying, designing, or supporting machine learning applications where predictive techniques can improve business outcomes.
- Model-driven decision support: Translating model outputs into dashboards, recommendations, decision rules, prioritization tools, or executive-ready materials.
Consultant Profiles Umbrex Can Identify
Umbrex can help identify consultants with experience developing predictive analytics approaches, forecasting models, customer analytics, segmentation, risk scoring, machine learning use cases, and model-driven decision support.
- Former McKinsey, Bain, BCG, or other top consulting firm consultants with experience applying predictive analytics to strategy, commercial performance, operations, finance, customer experience, risk, or transformation.
- Consultants who can define the business question, assess data availability, identify relevant modeling approaches, and translate analytical outputs into practical recommendations.
- Consultants with experience supporting CEOs, CFOs, CMOs, COOs, CIOs, CDOs, analytics leaders, business unit leaders, operations teams, finance teams, or private equity sponsors.
- Consultants who can work with business stakeholders, analytics teams, data scientists, data engineers, and technology teams to move predictive analytics from concept to usable decision support.
- Consultants with experience in forecasting, statistical modeling, machine learning, segmentation, scoring, scenario analysis, and performance measurement.
- Consultants who can communicate predictive analytics results clearly to non-technical executive audiences and connect model outputs to business decisions.
Illustrative Engagement Models
Predictive analytics support can take different forms depending on the business question, data maturity, modeling complexity, and implementation needs. Some clients need help identifying the right use case. Others need hands-on support building a model, interpreting results, or embedding predictive insights into decision-making.
- Predictive analytics use case definition: A consultant helps identify and prioritize predictive analytics opportunities based on value, feasibility, data availability, and business relevance.
- Forecasting model development: A consultant develops or improves forecasting models for demand, revenue, sales, volume, inventory, staffing, or financial planning.
- Customer analytics support: A consultant helps analyze and predict customer behavior, churn, retention, lifetime value, purchasing patterns, or segment-level performance.
- Risk scoring model support: A consultant develops scoring approaches to help prioritize risks, accounts, transactions, suppliers, customers, assets, or projects.
- Segmentation analysis: A consultant develops customer, product, market, operational, or behavioral segments to support strategy, targeting, planning, or performance improvement.
- Machine learning opportunity assessment: A consultant helps evaluate where machine learning may create business value and what data, process, technology, or governance requirements must be in place.
- Model interpretation and business translation: A consultant helps leadership understand model results, limitations, implications, and recommended actions.
- Decision-support tool development: A consultant helps translate predictive outputs into dashboards, reports, prioritization tools, decision rules, or executive materials.
Connect with the right consultant
Umbrex rapidly connects you with independent professionals who combine top‑tier consulting experience at firms such as McKinsey, Bain, Boston Consulting Group with hands‑on roles.