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Algomine and SAS Collaboration

Incorporated in 2015, Algomine was founded with a clear objective: to transform data into valuable revenue streams or cost reduction. Algomine is committed to the integration of top-tier, robust and fully reliable tools. The strategic collaboration between SAS and Algomine enables us to furnish our customers with unparalleled advanced analytics and a comprehensive AI lifecycle management platform, accessible via cloud.

Algomine’s credo: “We work with complicated processes and advanced mathematics, but our goal is to make the outcome as easy to use as possible.”

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Algomine expertise

digital insights

Understand how visitors interact with your digital channels.

Challenges
  • Improving user and customer experience based on insights from data collected in various channels.
  • Boosting response rates and optimizing campaign costs.
Solutions
  • Collecting data on the behavior of users in digital channels. Combining data provided through digital channels with off-line data.
  • Identifying customers across multiple digital and offline channels.
  • Driving CX improvements and engagement based on Insights.
Benefits
  • Intuitive GUI for governance of data collection rules.
    Example: on-line fashion retailer has 204 collection rules governed in GUI.
  • Advanced statistical analysis of large volumes of data.
    Example: on-line electronics retailer could identify 60% visitors vs 2-3% visitors previously.
  • Flexibility in the hands of business users – „build your own insights.”

DEMAND PLANNING & REPLENISHMENT

Accurately plan for customer demand – down to the individual store level.

Challenges
  • Rapidly shifting regional differences and channel preferences.
  • Variety of offered products and high service and product quality expectations.
  • Disruption - discontinuity of the supply chain.
Solutions
  • Visually analyze demand data to spot patterns and insights related to sales, shipments, pricing, promotions, etc.
    Example: Increase accuracy of forecasting by 10% - 33%.
  • Automatically generate forecasts and use out-of-the-box modeling strategies with predefined models.
    Example: Increase product availability by 20% - 30%.
  • Connect demand plans to products and freight on the move.

    Example: Reduce warehouse costs by 15% - 30%.

Benefits
  • Increase in product availability.
  • Increase in turnover and margin.
  • Optimize warehouse and transportation costs and reduce overdue losses.

receipt analytics

Track the transactions and optimize the sales process.

Challenges
  • Monetization of receipt data​ 
  • Relevant customer segmentation.
  • Nurturing customer loyalty.
Solutions
  • Increase the effectiveness of loyalty programs.
    Example: Predict future buying patterns and adjust loyalty programs accordingly.
  • Optimize campaigns and improve customer satisfaction with increased on-shelf performance.
    Example: Statistical segmentation of customers (based on purchase frequency and spend level) and points of sale.
  • Increase operational and pricing efficiency.

    Example: Analyze operational efficiency at the store level.

Benefits
  • RFM statistical analysis, customer segmentation, propensity models, assortment model, association analysis - basket analysis in one platform.
  • Extensive integration possibilities with almost any data source.
  • High performance - fast processing of large data volumes in the in-memory engine.

Retail Experts

Arkadiusz Wiśniewski
Chief Sales Officer and Partner,
Algomine

Cezary Głowiński
Chief Data Scientist,
Algomine

Igor Jakubiak
Lead Data Scientist,
Algomine

Patryk Iwanek
Data Scientist,
Algomine

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