Data that becomes decisions, and when needed, tools.

Verkkolynx builds analytics from business, customer, sales, inventory and process data: from KPIs and reporting to forecasts and decision tools. When an analysis needs continuous use, it can be built into an application.

Sunset over a frozen lake

Analysis does not have to end in a report. We identify the patterns and trends that improve decisions in inventory management, customer segmentation, pricing and market assessment.

Often a clear report is enough. When the same analysis is repeated over and over, it can become a forecast, a monitoring system or a decision tool your team uses itself.

  • Reporting and dashboards
  • KPIs
  • Forecasts
  • Data integration
  • Decision tools

What analytics answers

  • Data does not drive decisions

    Information accumulates but never turns into action.

  • Inventory does not follow demand

    Oversupply ties up capital and undersupply loses sales.

  • Customer groups are unclear

    The most valuable segments are not identified.

  • Processes are slow and costly

    Bottlenecks from product delivery to customer service go unnoticed.

  • There is too little data

    Datasets tailored to your needs can be built even from a small starting point.

What we analyse

  1. Sales trend analysis

    Identify what sells, when and why.

    Example

    Seasonal demand, so inventory levels are right.

  2. Customer behaviour tracking

    Understand your audience and identify high-value customer segments.

    Example

    Trends that fuel personalised campaigns and reactivation of dormant customers.

  3. Operational efficiency metrics

    Identify bottlenecks from product delivery to customer service.

    Example

    Smoother operations, lower costs and shorter delivery times.

  4. Market analysis

    Assess market dynamics in Finland and internationally.

    Example

    Untapped markets such as the United States: competition, regional trends and customer preferences.

  5. From analysis to tool

    When an analysis is needed repeatedly, it can be built into a decision-support application, a monitoring system or an algorithm inside the workflow.

    Example

    A forecasting tool, scoring model or automated monitoring that the team uses daily.

  6. Reporting and business intelligence

    Dashboards and reports that combine data from several sources and update automatically.

    Example

    For example a monthly management report whose figures are pulled straight from the systems.

  7. Operational analytics

    Monitoring of processes, inventory, pricing and performance with reliable metrics.

    Example

    For example tracking lead times, stock levels or the effect of price changes.

Where analysis can lead

  • Informed decisions

    Reports, visualisations and recommendations built on reliable data.

  • Decision support and forecasts

    Decision-support applications, forecasting tools and scoring models.

  • Automated monitoring

    Automated analysis and monitoring systems that detect deviations.

  • Part of the business

    Operational tools, interactive analysis applications and algorithms inside business processes.

The Verkkolynx difference

  • Analytics skills combined with experience of large multi-source datasets: product, transaction, customer and web behaviour data. Sports ecommerce data is where our experience runs deepest.
  • Analysis can be carried into software by the same team that did it: modelling and implementation stay together.
  • Recommendations based on your situation rather than generic assumptions.
  • Clear reports and visualisations.

Methods we use

  • Predictive analytics: statistical models and machine learning to forecast trends and risks
  • Dynamic pricing: algorithms that adjust prices to demand, competition and market trends
  • Data quality: cleaning, organising and standardising
  • Real-time metrics for sales, inventory and website performance
  • Training, so your team can interpret analytics tools on its own
  • KPI design and dashboards
  • Data cleaning, transformation and modelling
  • Combining data from several sources and APIs
  • Segmentation, anomaly detection and performance monitoring
  • Automating recurring reporting

What should the data tell you?

Tell us what data you have and what you would like to learn from it. We will say whether a report is enough or whether a tool is worth building.