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Why You Should Tag Your Data

TABLE OF CONTENTS

 

Introduction

Tagging your data is one of the most powerful ways to move beyond basic feedback and unlock deep, actionable insights. By categorising your sales and products at the point of upload, you transform "raw data" into a structured resource for business intelligence.

 

The Core Benefits of Tagging

  • Highly Focused Reporting: Tags allow you to slice and dice your data. Instead of seeing a single "Satisfaction Score," you can see how specific regions, store branches, or product categories are performing.

  • Compare and Contrast: Easily compare performance across different segments—for example, comparing "Online" vs. "In-store" sales or "Menswear" vs. "Womenswear" departments.

  • Targeted Feedback Campaigns: Use tags to trigger specific feedback requests. If you use the Feedback Request Manager, tags can ensure customers receive a tailored survey based on what they bought or where they bought it.

  • Dynamic Personalisation: Sales tags can be used as "dynamic variables" within your feedback forms. This means you can personalise the form with the customer's specific sales advisor name or branch location, increasing engagement.

 

Sales Tags vs. Product Tags

It is important to understand which tag to use based on the level of data you are tracking.

Feature Sales (Service) Tags Product Tags
Applied To The entire order/transaction. Individual items (SKUs).
Example Use saleschannel:online, store:london colour:red, size:large, material:leather
Consistency Every product in the order gets the same tag. Different products in one order have different tags.
Goal To analyse service levels and channel performance. To analyse specific product attributes and trends.

 

Tags vs. Insight Labels: What’s the difference?

A common point of confusion is the difference between a Tag and an Insight Label. The main difference is when and how they are applied.

Sales & Product Tags (Pre-Feedback)

  • When: Applied before feedback is received (during the data upload).

  • How: Automated via your CSV/XLS upload or API.

  • Purpose: To categorize the source of the data (who, where, what).

Insight Labels (Post-Feedback)

  • When: Applied after the customer has left a review.

  • How: Manually applied by your team within the Feefo Hub.

  • Purpose: To track qualitative trends found in the review text (e.g., labeling a review as "Delivery Issue" or "Pricing Complaint") to identify emerging patterns in customer sentiment.

 

Best Practices

  • Use Key:Value Pairs: Tags must follow the key:value format (e.g., department:home).

  • Be Consistent: Tags are case-sensitive. Always use lowercase and avoid spaces where possible to ensure your reports stay clean.

  • Group Variants: Use the parent search code tag to group product variations (like sizes or colors) so that reviews for all versions appear on the same product page.

  • Automate: By including these tags in your regular data feed, you ensure that every piece of feedback is automatically categorised, saving your team hours of manual reporting work.