Traditional one-size-fits-all approaches in sales management often result in inefficient resource allocation and slow adaptation to market changes. Companies struggle to understand competitive dynamics, leading to suboptimal customer experiences and limited data-driven decision-making. Additionally, a lack of awareness regarding the balance between risk and opportunity can lead to inefficient spending. For instance, if a low-value outlet presents minimal risk but incurs high costs, unnecessary visits could result in losses.
Why we’re better
The shopeyeQ Risk-based Store Audit addresses these challenges by leveraging performance data and risk assessments to optimise resource allocation. For example, if a supermarket sells 200 fewer packs of tea, resulting in a €40 loss, but a visit costs €50, the visit is not worthwhile as it would lead to an additional €10 loss. Our system automatically calculates these scenarios. This is particularly useful for low-value outlets with a low seQ score, ensuring that resources are allocated only where the benefit outweighs the cost.
Empower Your Retail Success
Discover how our platform's advanced features—like dynamic clustering algorithms, real-time insights, and predictive analytics—empower you to drive smarter growth.
Data Utilised
This use case explores how POS clustering can reveal insights into retail performance by grouping similar points of sale.
Must be acquired.
Must be acquired.
Unemployment rate, inflation (provided by seQ technology).
Purchasing power index by postcode, age in the region (provided by seQ technology).
Map the customer journey
You provide us with your data, which we cluster in the background. We identify lookalikes for you and perform value-based segmentation.
1- Initial Data Collection
- Nielsen ID: The user provides their Nielsen ID.
- Data Review: We check if the correct data, such as Historic Sales Performance Data, is available or needs to be acquired.
- Data Provision: The data is provided to the customer’s cloud (S3, Azure, Snowflake, etc.).
2- Data Processing (Case 1)
- seQ Pipeline: Our seQ Pipeline processes, enriches, and clusters the data using machine learning, accessing current and historical performance data from the last two years.
- seQ Value-Based POS Clustering: Creation of a list of sales outlets with their respective cluster labels (value-based labels from 1 to 20).
- Ranking: This list is ranked based on our seQ score, which is reviewed weekly.
3- Risk KPI Definition and Risk Analysis
- Risk KPI Definition: KPIs are defined based on the likelihood and impact of sales changes.
- Assignment Creation: Based on these KPIs, assignments are made to efficiently direct resources.
- Risk-Based Analysis: Conducted to determine which actions are required based on risk assessment and the defined KPIs.
Explore Similar Solutions
One of the major challenges in sales management is the delayed response to market changes, leading to inefficient inventory management and inconsistent customer experiences. Our solution, the seQ Risk-based Store Audit, leverages shopeyeQ’s risk assessments to ensure your resources are deployed exactly where they deliver the most value.
shopeyeQ Risk-based Store Audit utilises current and historical sales performance data to calculate and monitor the seQ score for your sales outlets. Through this detailed analysis, we can precisely determine when and where your field team should take action. For instance, if a supermarket sells 200 fewer units of tea, resulting in a €40 loss, and a visit by your brand costs €50, the visit is not worthwhile as it would lead to additional losses. This is particularly valuable for sales outlets with a low seQ score, helping you avoid unnecessary expenses. shopeyeQ Risk-based Store Audit calculates these values for you.
Our technology enables you to reduce the costs of visits, plan tours based on risk, and simultaneously ensure that your key products are always available. By using shopeyeQ Risk-based Store Audit, you not only save money but also improve the efficiency of your sales team, enhance retailer satisfaction, and increase end-customer satisfaction.
Optimizing Retail Insights
This section presents a series of case studies that demonstrate the transformative impact of POS clustering on retail operations.
Explore Similar Use Cases
Turn Insights into Impact with shopeyeQ
