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DataFramed

#217 Data & AI at Tesco with Venkat Raghavan, Director of Analytics and Science at Tesco

Thu Jun 20 2024
data analyticsAIcustomer experiencespricing strategiescost managementcommunicationproject executionNLP techniquesAI capabilitiesdata fragmentation

Description

This episode explores how Tesco uses data analytics and AI to improve customer experiences, enhance pricing strategies, measure impact, and optimize cost management. It highlights the importance of collaboration between data engineering and analytics teams, effective communication in evolving environments, and aligning with commitments for successful project execution. The episode also discusses the value of data, future outlook, and building a culture of data-driven thinking.

Insights

Tesco uses data in various areas such as customer analytics, pricing, promotions, and logistics.

Data is used in strategic programs to improve customer service and drive efficiencies in the business.

Analytics plays a crucial role in improving sales, margins, and cash flow.

It provides insights for strategic planning and decision-making.

Tesco focuses on customer centricity and quality to enhance customer experiences.

They use data analytics and AI to gather information and improve product quality and store operations.

Two-way communication is crucial in evolving environments with varying speeds of competition.

Data engineering tasks facilitate communication between teams and avoid hindrances to business progress.

Pricing strategies have evolved to consider customer behavior, product interactions, promotions, and loyalty.

Personalization is key in pricing, using customer data to offer tailored promotions and prices.

Tesco's success in analytics is measured by impacting their Big 6 KPIs.

They align resources towards achieving impact on key metrics and receive feedback for continuous improvement.

Internal measures are crucial for reducing time to answer colleague and customer queries.

NLP techniques are used to understand and classify customer sentiments beyond general understanding.

Customizing AI capabilities can help solve problems faster and differentiate in the market.

Data science teams bridge organizational contexts and understand fragmented data meanings.

The speaker discusses the opportunity to add value to data by addressing its fragmentation.

They emphasize the importance of context, commerce, and culture in being successful in the industry.

The speaker is excited about the future of data science and building a culture of data-driven thinking.

They express enthusiasm for the shift towards JNI and ecosystem businesses.

Chapters

  1. Tesco's Use of Data
  2. Improving Customer Experiences
  3. Data Engineering and Analytics
  4. Effective Pricing and Cost Management
  5. Measuring Impact and Satisfaction
  6. Enhancing Customer Queries and AI Capabilities
  7. Value of Data and Future Outlook
Summary
Transcript

Tesco's Use of Data

00:00 - 07:18

  • Tesco uses data in various areas such as customer analytics, pricing, promotions, and logistics.
  • Tesco's Club Card loyalty program helps them understand and personalize experiences for their customers.
  • Tesco has strategic programs focused on customer loyalty, value propositions, convenience, and efficiency.
  • Data is used in these strategic programs to improve customer service and drive efficiencies in the business.
  • Analytics and data science play a crucial role in shaping Tesco's business goals and operational plans.

Improving Customer Experiences

06:57 - 13:43

  • Analytics plays a crucial role in improving sales, margins, and cash flow by providing insights for strategic planning and decision-making.
  • Tesco focuses on customer centricity and quality to enhance customer experiences through various initiatives like cleanliness in stores and efficient checkout processes.
  • Data analytics and AI are used to gather information from various sources like customer feedback, surveys, and product returns to improve product quality and store operations.
  • Tesco emphasizes the importance of collaboration between data engineering and analytics teams to extract meaningful insights from data for continuous improvement.

Data Engineering and Analytics

13:22 - 20:21

  • Two-way communication is crucial in evolving environments with varying speeds of competition.
  • Data engineering tasks are essential for organizing data and facilitating communication between teams.
  • Lack of data and analytics support can hinder business progress and AI initiatives may not transition beyond proof of concept without proper frameworks and methods.
  • Avoid solely relying on business requirements; focus on understanding commitments and aligning with them to drive projects forward.
  • Establishing the right governance, principles, and controls is necessary for successful project execution.
  • Pricing strategies have evolved from basic demand analysis to considering customer behavior, product interactions, promotions, and loyalty.

Effective Pricing and Cost Management

19:58 - 26:58

  • The pricing strategy involves considering a network of products and prices, not just individual items.
  • Personalization is key in pricing, using customer data to offer tailored promotions and prices.
  • Cost management is crucial for retailers, with a focus on predicting costs and negotiating with suppliers to maintain margins.
  • Using analytics and science at scale is essential for successful cost and price management in a large retail business.

Measuring Impact and Satisfaction

26:38 - 33:31

  • In order to negotiate effectively with suppliers, it is important to understand the product's ingredients and associated costs.
  • Tesco's success in analytics is measured by impacting their Big 6 KPIs, which include sales, margin, cash, colleague satisfaction, and customer satisfaction.
  • It is crucial for analytics teams to align resources towards achieving impact on key metrics and receive both quantitative and qualitative feedback.
  • Measuring data science team's impact on metrics like customer satisfaction involves initiatives that simplify processes and improve experiences for customers, colleagues, and suppliers.

Enhancing Customer Queries and AI Capabilities

33:01 - 39:32

  • Internal measures are crucial for reducing time to answer colleague and customer queries, with feedback scores being important.
  • NLP techniques, particularly neural network-based, are used to understand and classify customer sentiments beyond general understanding.
  • Customizing AI capabilities on top of generic frameworks like LLM can help solve problems faster and differentiate in the market.
  • Large-scale organizations like Tesco offer benefits and challenges for data scientists due to business fragmentation and data fragmentation.
  • Data science teams can provide horizontal intelligence by bridging organizational contexts and understanding fragmented data meanings.

Value of Data and Future Outlook

39:16 - 42:43

  • The speaker discusses the opportunity to add value to data by addressing its fragmentation.
  • They emphasize the importance of context, commerce, and culture in being successful in the industry.
  • The speaker is excited about the future of data science and the shift towards JNI for technique selection and code writing.
  • They express enthusiasm for building a culture of data-driven thinking and ecosystem businesses.
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