Challenges

1. Managing the volume and variety of data: Facing the challenge of handling large amounts of qualitative data from diverse sources such as:

• 2 million CSAT survey responses per year
• Social media comments
• Hundreds of thousands of complaints and contact centre messages

2. Lack of Insight into Organic Feedback: Other analytics tools required pre-defined categories, which limited the ability to uncover unexpected themes or customer sentiments. Additionally, there was a need for a deeper understanding of shifting customer trends that isn’t immediately visible in quant data.

3. Disparate Data Sources: The different feedback sources were housed in separate areas of the business, making it difficult to consolidate and analyse them in a unified way, leading to fragmented insights and data siloes. 

4. Underutilised Qualitative Data: Many qualitative customer comments were not fully utilised for decision-making, limiting Sainsbury’s ability to pain the whole picture of their customer insights. 

Solutions

Sainsbury’s implemented Wordnerds to address these challenges. With a whole host of benefits:

1. Efficient Data Categorisation: Wordnerds allowed Sainsbury’s team to categorise and organise feedback from all sources (surveys, social media, complaints) into consistent themes easily. This enabled a unified view of customer sentiment across various touchpoints.

2. Uncovering Organic Trends: One of the standout features of Wordnerds was its ability to extract organic topics from open-ended survey responses. This capability meant Sainsbury’s could identify their customers' concerns and desires without predefined categories, making it easier to uncover emerging issues, like shifts in shopping behaviours due to the cost of living crisis.

3. Sentiment and Trend Tracking: The tool enabled the team to monitor changes in sentiment and trends over time. For example, they could track whether certain customer concerns were increasing or declining and react proactively, such as noticing changes in how customers viewed the store experience or the relevance of certain products.

4. Integration of Diverse Data Sources: By consolidating all feedback from various channels into one platform, Wordnet provided Sainsbury’s with a holistic view of customer opinions. This allowed the team to provide the business with clear insights, highlighting key issues and shifting customer expectations.

5. Empowering Teams with Qualitative Insights: Wordnerds helped the CSAT team present qualitative data alongside quantitative survey results, making the findings more relatable and actionable. Powerful customer quotes became key moments in internal discussions, because the story of qualitative data resonates more than quantitative data.

Outcomes

1. Better Proactive Decision-Making: With the ability to identify emerging themes and track shifting trends, Sainsbury’s could anticipate changes in customer behaviour and needs, leading to more proactive adjustments in business strategy, marketing, and customer service.

2. Enhanced Customer Understanding: Wordnerd’s ability to uncover the “why” behind survey results provided a deeper understanding of customer mindsets. For example, when survey scores fluctuated, the team could quickly identify which areas of the business were influencing those changes and take corrective action.

3. Improved Internal Collaboration and Communication: By sharing qualitative insights with senior leaders and retail colleagues, Sainsbury’s ensured that the feedback was not just seen but acted upon. Regular meetings with key stakeholders allowed for deeper conversations about what was driving customer satisfaction, fostering cross-departmental collaboration.

4. Increased Engagement with Customer Feedback: The tool helped bring qualitative data into the spotlight, ensuring that customer comments were no longer sitting unused but were integral to decision-making. Stories and direct quotes from customers became central to communications within the business, creating more engagement and focus on customer satisfaction.

5. Streamlined Reporting and Actionable Insights: By automating the process of categorising and analysing qualitative data, the team was able to spend less time sorting through responses and more time making informed decisions. This increased the efficiency of feedback reporting and made the process more scalable as the volume of customer data grew.