Predictive Analytics Solutions | Consulting Services

Client Challenges

Forecasting And Risk Detection Challenges Affecting Business Decisions

olbeg is typically engaged for predictive analysis services when planning and risk decisions still rely heavily on manual estimates, static rules, or backward-looking reports. As decision volume grows, these approaches become more difficult to scale consistently across teams and operational units.

Service Overview

Predictive Analytics That Enables Better Business Decisions

Solbeg’s predictive analytics services cover the analytical, data, and software engineering work required to produce forecasts and scores that operational teams can use in day-to-day decisions. An engagement can begin with one defined use case or with an assessment of several candidate areas.

Consulting and feasibility assessment

Predictive analytics consulting helps identify which decisions can benefit from prediction, whether the available historical data contains a usable signal, what baseline currently supports the decision, and what improvement would justify further investment.

Demand and business forecasting

Forecasting of demand, sales, transaction volumes, workload, revenue, cash flow, or other measurable outcomes at the level of detail required by the planning process, including by product, location, customer group, or time interval.

Customer and risk scoring

Models that estimate outcomes such as churn, conversion, late payment, default, fraud, or other relevant events for individual customers, accounts, transactions, or cases.

Early warning and failure prediction

Identification of equipment, process, customer, or transaction patterns associated with increased risk, allowing teams to prioritize cases that may require attention before an unwanted outcome occurs.

Predictions in everyday workflows

Delivery of forecasts, scores, and related information into the systems where planning, sales, finance, and operations decisions are already made. This allows predictive analytics solutions to become part of the operational process rather than remaining a separate analytical report.

Problem & solution

Predictive models do not always translate into better decisions

Predictive initiatives are sometimes scoped around a dataset or algorithm instead of a specific business decision. A team may optimize a technical metric without first establishing who will use the prediction, how far in advance they need it, which action should follow, and what different types of prediction errors cost the business. A churn model, for example, can be statistically accurate but still provide limited value if the warning arrives too late for the retention team to act.

Validation is another common challenge. Historical datasets may contain information that would not actually have been available at the time a prediction was supposed to be made. Random train-test splits can also produce optimistic results when production conditions change over time. Models evaluated this way may perform well during development but lose quality once they encounter later data.

Ownership after deployment also needs to be defined. Data distributions, customer behavior, pricing, seasonality, and operational conditions can change. Without an agreed process for comparing predictions with actual outcomes and monitoring model inputs, performance can deteriorate without becoming immediately visible.

Each model is designed around a decision, baseline, and defined owner

We begin by defining the decision the prediction is intended to support: who uses it, when they need it, what action follows, and how the consequences of missed cases compare with false alarms. The prediction horizon, output format, thresholds, and evaluation metrics are derived from those requirements. The existing forecasting method, rules, or manual process becomes the baseline the new approach is measured against.

Datasets are prepared to reflect the information that would realistically have been available at each point in time. Validation is then performed using later periods or other appropriate holdout methods that resemble the way the model will operate after release. As part of our predictive analytics consulting services, results can be compared with the agreed baseline together with relevant uncertainty and error measures.

Before predictive analytics solutions move into production, monitoring and ownership are defined. The engagement establishes how predictions will be compared with actual outcomes, which changes in input data or model behavior should trigger review, and when retraining or reassessment may be required. Responsibility for those activities is agreed before launch.

When this service fits

Business Decisions That Benefit From Predictive Analytics

Predictive analytics services are most useful when a planning, prioritization, or risk decision can be defined clearly and supported by relevant historical data. Solbeg evaluates the decision context, available signal, required prediction horizon, and operational use of the result before the modeling approach is finalized.

Offering discounts or proactive outreach to every customer can be expensive, while a simple list of at-risk accounts does not show where intervention is most likely to create value. Churn probability can be combined with customer value and other business criteria so retention teams can prioritize accounts more selectively.

Fixed maintenance schedules may replace components that still have useful life while failing to anticipate failures that appear between service intervals. Sensor data, operating conditions, maintenance history, and failure records can be used to estimate which assets may require attention and help maintenance teams prioritize inspection or service.

Support centers, warehouses, service teams, and other operations may schedule capacity from averages that do not fully capture day-of-week patterns, holidays, campaigns, seasonal effects, or recent changes. Forecasting workload by day, hour, location, or another relevant level can provide a more responsive basis for staffing decisions.

Contractual payment terms do not always reflect actual payment behavior. Historical payment patterns and account characteristics can be used to estimate expected payment timing for open invoices, supporting cash-flow planning and helping collections teams prioritize accounts.

In regulated or high-impact environments, a score alone may not provide enough information for reviewers, auditors, employees, or customers. The level of required explainability should be established before model selection so the project can balance predictive performance, transparency, and operational requirements appropriately.

New products and markets do not have the long historical record used by conventional forecasting methods. Forecasts can draw on comparable products, related segments, external drivers, or early observed results, with uncertainty made explicit and estimates updated as additional data becomes available.

A model built by an internal team may show promising test results while the organization needs additional validation before connecting it to larger budgets, customer decisions, or operational processes. A review can examine training data, validation methodology, time stability, leakage risks, monitoring readiness, and the production operating model before a broader rollout.

Scope of Work

Roles And Responsibilities In A Predictive Analytics Engagement

Depending on the use case and the capabilities already available internally, Solbeg’s predictive analytics consulting services can combine business analysis, data engineering, modeling, ML operations, software integration, analytics, QA, and delivery coordination.

Business analysis

Definition of the target decision, the users who act on predictions, the current planning or scoring process, and the business consequences of different prediction errors. These findings are translated into measurable acceptance criteria for the model and its integration.

Why Solbeg

What Companies Gain From Working With Solbeg On Predictive Analytics

Solbeg treats predictive work as both an analytical and an engineering challenge. As a predictive analytics company with data engineering and software development capabilities, we can support the path from use case assessment and data preparation to predictions integrated into production workflows.

Data pipelines and models can be developed within the same engagement

Data engineers responsible for preparing historical and current data can work alongside the specialists developing and validating the models. This reduces handoffs between separate teams and helps keep the data used in production aligned with the assumptions made during model development.

Comparison

Built-In Platform Forecasting Vs. Custom Predictive Models

Before investing in custom predictive analytics solutions, companies often review the predictive functionality already available in their BI, CRM, ERP, or planning platforms. The choice depends on how specialized the use case is, where the required data lives, how results need to be validated, and how much control the organization needs over model behavior.

Option A

Suitable for relatively standard use cases that fit the platform's data model and available predictive functionality
Can provide a faster starting point when the required data already exists inside the same platform
May require less separate infrastructure and integration work
Control over input features, algorithms, validation methods, and thresholds depends on what the vendor exposes
Combining data from external systems may be limited or require additional integration
Commercial terms follow the platform's licensing and usage model
Model versions and feature changes may be controlled by the platform provider
Predictions are typically easiest to use within that platform's own workflows and interfaces

Built-in functionality is a practical option when the use case is relatively standard, the relevant data already lives in one platform, and the available level of control and validation is sufficient for the decision being supported.

Option B

Suitable for use cases that depend on company-specific business logic or data combined from several systems
Provides more control over feature engineering, model selection, validation, decision thresholds, and explainability
Requires additional time for data preparation, feature development, validation, integration, and production setup
Requires ongoing monitoring and, where appropriate, retraining by the internal team or external predictive analytics firms
Predictions can be delivered to different systems through APIs, scheduled outputs, events, or other integrations
Usually requires greater upfront engineering investment, while operating cost depends on workload and infrastructure choices
Data pipelines and production components created for one model may be reusable across additional predictive use cases

Custom models become more relevant when prediction has a material impact on revenue, cost, capacity, customer treatment, or risk, when inputs come from several systems, or when the organization needs greater control over how results are produced and validated.

Engagement Process

Predictive Analytics Delivery Across Assessment, Modeling, And Production Rollout

A predictive analytics consulting engagement can be structured in stages so feasibility, model quality, and production requirements are reviewed before broader investment. Solbeg adapts the scope to the use case, data readiness, current processes, and systems already in place.

Use case and data review

Business and technical stakeholders review the decision to be supported, the current method, available data, required prediction timing, and known constraints. The result is a defined use case together with initial feasibility findings and identified data gaps.
Baseline and success criteria

The current forecasting, scoring, or decision process is measured where possible using historical data. Evaluation metrics, prediction horizon, acceptance criteria, and relevant error trade-offs are then agreed before model development begins.
Data preparation and modeling

Data engineers prepare the datasets required for training and validation, while data scientists develop and compare candidate approaches using validation methods appropriate to the use case. The stage produces evidence showing how the selected model performs relative to the agreed baseline.
Controlled pilot

Predictions can be introduced within a limited scope, such as one team, market, product group, or operational process, while the existing method remains available where appropriate. Business users and the delivery team review results under real operating conditions before broader rollout.
Production rollout

ML and application engineers deploy the model, connect predictions to the required systems, and configure monitoring and relevant operational controls. QA verifies scoring outputs, data flows, and integration behavior before the production process becomes dependent on the model.
Monitoring and handover

Predictions are compared with actual outcomes according to the agreed monitoring process, and retraining or review follows defined triggers where applicable. Documentation and operating procedures are transferred to the client’s team, or Solbeg continues supporting the solution under a separate agreement.

Completed projects

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Predictive analytics projects we've delivered

Forecasting models that help businesses anticipate demand, spot risks early, and plan resources with confidence.

Facility & Asset Management/Hospitality/Software Development/Support

ServiceChannel Facility Management Platform

Cloud platform for automating facility maintenance and work orders

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FAQ

Key Questions Before Starting A Predictive Analytics Project

There is no universal minimum. The amount and duration of data required depend on the target being predicted, how frequently that event occurs, the level of granularity required, seasonality, and the number of variables involved. Forecasting may require enough history to observe relevant cycles, while risk models need enough examples of the outcomes being predicted. The initial data review is used to determine whether the available history is sufficient for meaningful validation.

No specific accuracy level can be confirmed before the relevant data and use case have been evaluated. What can be agreed in advance is how performance will be measured: the metrics, validation periods, baseline, and acceptance criteria. This allows stakeholders to see whether a model produces enough improvement to justify moving into production.

The level of explanation depends on the model type and the requirements of the use case. Predictions can often be accompanied by information about influential factors, contributing variables, or other model-specific explanations. If explainability is required for operational, regulatory, or customer-facing reasons, that requirement should influence model selection from the beginning.

Models can lose predictive quality when the patterns represented in historical data change. Monitoring should therefore identify material differences between expected and actual outcomes or shifts in important inputs. Depending on the situation, the response may include retraining, changing model inputs, adjusting thresholds, or temporarily relying more heavily on established manual or rules-based processes.

Not necessarily. Initial feasibility work can often use controlled extracts or access to relevant source systems. A production model, however, requires reliable and repeatable delivery of the data used for scoring and monitoring, so additional pipelines or data-platform work may become part of the implementation scope.

Ask how model quality will be validated over time, what baseline results will be compared against, and whether evaluation reflects the way predictions will actually be used in production. When comparing predictive analytics consulting companies, it is also useful to understand how data engineering, software integration, monitoring, and handover are handled. Many predictive analytics companies can demonstrate a working model, so it is worth clarifying who is responsible for integrating predictions into business workflows and for monitoring and updating the model after launch.

Timing depends primarily on data readiness, the complexity of the use case, validation requirements, and how deeply the predictions need to be integrated into operational systems. Projects with accessible and well-understood historical data can reach model validation sooner than those that first require significant pipeline, data-quality, or integration work.

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