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SAS Retail Solutions

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.”

RETAIL media

Boost your retail media revenue with one, easy to use platform.

Challenges
  • Serving 100% demand for vendor marketing, while now it takes 20 emails to set one campaign and only 30% demand can be served.
  • Providing to vendors advanced offer optimizing various metrics.
  • Improving ROI of vendor marketing.
Solutions
  • Unique combination of self-service booking platform and ad-server driving process effectiveness and advertising performance.
  • Monetizing 1st-party data for driving ROI.
  • Advisory combining know-how from retail, vendor marketing and online media with technology built for retailers.
Benefits
  • Growth of revenue - vendors commit 2-4% of turnover generated from retail sales to advertise their products at retail advertising space.
  • Highly profitable revenue – even 85% margin.

strategic financial management & loss prevention

Drive better and integrated financial forecasts for planning, budgeting & report consolidation.

Challenges
  • Enterprise planning, capital planning for investment, budgeting, consolidation and reporting in the volatile economic environment (aka VUCA). 
  • Need for effective optimization of operational business model.
  • Accurate planning for well informed decision taking.
Solutions
  • Automate processes.  
    Example: Automatic data collection, validation, conversion and consolidation. Flexibility in preparation of the allocation process and its timetable with automatic notifications for timely delivery.
  • Work within a flexible algorithmic environment.   
    Example: Ability to define your own algorithms. Completion of data flexibly in MS Excel or via web browser. Manual adjustments of source and output data.
  • Full transparency & governance.
    Example: Coherence & auditability through multiple levels of acceptance of planning data and generated results.
  • Utilize interactive exploration and visualization of data. 

    Example: Integrated reporting processes within the organization based on a single consistent data source and reporting platform and a unified chart of accounts. No-code approach for report preparation & distribution. Alerting and notifications to keep each stakeholder informed.

Benefits
  • Classical Financial Management processes can be extended and strengthened with forecasting, analytics, stress testing and risk modeling to make your organization more resilient. Governance & transparency is ensured for well-informed decisions.

Retail loss prevention

Detect anomalies and patterns quickly – and prevent loss before it’s too late.

Challenges
  • Retail fraud, waste, and abuse become more widespread and sophisticated.
  • Fraud activities occur across many areas and often remain undetected for long periods.
  • Lack of clear ownership and a common approach to leading loss prevention processes in the organization.
Solutions
  • Connect siloed data sources across the business to provide a single view of key entities such as stores, people, and products.

    Example: Track transactions outside of business hours.

  • Analyze aggregated patterns of behavior rather than just isolated events and anomalies.

    Example: Use known fraud cases to predict future fraudulent activities.

  • Detect evidence of organized collusive activities between employees, customers, and suppliers.

    Example: Eliminate employee - customer collusion.

    Benefits
    • Automated monitoring of high-risk processes and quick response based on ready-to-use anomaly detection models and scenarios.
    • Real-time fraud detection across multiple risk areas, including point of sale, digital channels, and inventory.
    • Full return on investment within months.

    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.
    Contact Us
    Being able to activate data in SAS Customer Intelligence 360 while applying AI and machine learning techniques, such as customer journey optimization, allows us to continue as customer engagement leaders.
    We are incredibly grateful to have a flexible, experienced partner like SAS. They’ve helped us start our digital transformation and face daunting challenges with confidence.​​
    With this system, we can manage campaigns from A to Z. For the first time, more complex campaigns and flexible control become possible.
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    Contact us to find out how we can scale your business.

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