Predictive Analytics
Predictive analytics and data models
We build models that turn the organisation’s data into forecasts that can be applied to management: demand volume, reference prices, customer behaviour and concentration of risk. Each model is trained on proprietary information and validated before being incorporated into decision-making processes.
Areas of application
Models that anticipate volume by product, channel and period, incorporating seasonality, promotions, calendar effects and the external variables relevant to each sector.
Price adjustment based on demand, competition, inventory and estimated elasticity, with the business rules and limits the organisation determines.
Models for probability of default, fraud or non-compliance, with the explainability and traceability their use before clients, internal bodies and supervisors requires.
Churn prediction, propensity to buy and expected customer value, applied to direct commercial effort towards the opportunities with the highest conversion.
Monitoring systems for the relevant metrics that report deviations as they occur, without relying on manual review of reports.
We deliver forecasts in directly readable dashboards, integrated into the reporting tools the organisation already uses.
Validated, explainable models
The value of a predictive model lies in the confidence with which its results can be incorporated into management. Our developments are validated on data independent of the training set, identify the variables that determine each prediction, and are delivered with the technical documentation evidencing their performance. This is the standard that allows decisions to be founded on their results.
Validation on independent data
Each model’s performance is evidenced on historical periods outside the training set, with the resulting metrics documented and reviewed jointly.
Explainability of results
Each prediction identifies the determining variables and their contribution, enabling technical review and substantiation before third parties.
Quantified contribution
The model is compared with the procedure currently in place, so that its contribution is quantified from the start of the project.
Sectors of specialisation
The robustness of a predictive model rests on knowledge of the field in which it is applied. We concentrate our activity in sectors where we have demonstrated experience, which allows the determining variables and the regulatory particularities of each environment to be identified from the outset of the project.
Banking and financial services
Risk scoring, fraud detection, arrears forecasting and portfolio analysis, meeting the traceability and explainability requirements of a regulated environment.
Energy
Demand and price forecasting in wholesale markets, procurement optimisation, and analysis of the impact of weather and regulatory variables.
Media and retail
Audience and consumption forecasting, price and advertising inventory optimisation, and turnover and replenishment models at point of sale.
Working methodology
Definition of the business objective
We establish which decision is to be improved, how often it is taken and which indicators will allow the model’s contribution to be evaluated.
Data preparation
We consolidate the information sources, harmonise criteria and build the history on which the model will be trained.
Training and validation
We evaluate different approaches and select the one demonstrating the best performance on independent data, documenting the metrics obtained.
Integration and monitoring
We incorporate the model into the workflow and establish the monitoring and periodic reviews that maintain its performance over time.
FAQs
Frequently asked questions about predictive analytics
What data is needed to build a predictive model?
We work with the information the organisation already generates: sales history, transactions, customers or operational records, supplemented where appropriate with external market sources. In the initial phase we establish which information is relevant to the stated objective.
How is the model’s reliability evidenced?
Through validation on historical periods independent of the training set and comparison with the procedure currently in place. The metrics obtained are documented and reviewed jointly before go-live.
Do you work with data spread across different systems?
Yes. Consolidating and integrating sources forms a routine part of the project scope, including data from spreadsheets, legacy systems and sector-specific applications.
Who takes the final decision?
The model issues a recommendation and the decision rests with the organisation. Our developments incorporate the controls, limits and activity logging required to maintain governance over the process.
Is maintenance of the model included?
Yes. We establish the monitoring metrics and review schedule appropriate to each case, and take on the updates required to maintain performance over time.
How are the forecasts integrated into our systems?
Forecasts are delivered in whatever format is operationally useful: dashboards, direct integration into the ERP or CRM, or API access for consumption from existing tools. Integration forms part of the project scope.
ALS Innovation
Tell us which management decision you want to place on a firmer footing and what information is available. We will analyse the most appropriate approach for your case.