Harnessing Data for Enhanced Customer Strategy and Personalisation

Harnessing Data for Enhanced Customer Strategy and Personalisation

Learn how to gather, organise, and create data to improve your customer strategy and personalisation. Discover the importance of segmentation, personas, social media data, VOC data, sentiment analysis, and AI for a data-driven customer experience.

Introduction

Understanding your customers is paramount to business success in today's data-driven world. A successful customer experience (CX) strategy begins with a comprehensive understanding of the customer journey. Knowing what data is necessary requires insight into customer interactions at every touchpoint. This understanding helps businesses identify key metrics and capture relevant data, driving personalised and effective customer strategies.

This article delves into how businesses can gather, organise, and utilise data to enhance customer strategy and personalisation efforts. I will explore the creation of customer segments and personas, leveraging social media data and Voice of the Customer (VOC) data, as well as the importance of sentiment and emotional feedback.

Additionally, I will provide tips on the critical role of data-driven CX and the integration of machine learning and AI for strategic and operational improvements.

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“If you think that big data and analytics alone can help create meaningful and emotional experiences, think twice.

Customers are not numbers, and their experiences cannot be averaged…………”

Source: Forget the customer see the person; Author: @Alex Genov et al

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Understanding the Role of Data in Customer Strategy

Customer strategy revolves around understanding and anticipating customer needs and behaviours. Data plays a crucial role by enabling businesses to make informed decisions, personalise experiences, and build strong relationships.

According to a report by McKinsey & Company, companies that leverage customer data effectively see a 5-10% increase in sales and a 2-5% increase in customer retention.

Understanding the Customer Journey

A successful CX strategy starts with mapping the customer journey. This involves identifying all customer touchpoints, understanding their experiences at each stage, and recognising the data needed to measure these interactions.

For instance, a customer’s journey with an e-commerce site might include stages such as awareness, consideration, purchase, pre-purchase and post-purchase support. Each stage provides opportunities to collect valuable data.

Gathering Customer Data

To create a robust customer strategy, businesses must begin with gathering data from multiple sources. Key methods include:

• Surveys and Feedback Forms: Directly ask customers about their experiences and preferences. For example, HubSpot uses detailed surveys to understand customer satisfaction and areas for improvement.
• Website Analytics: Track user behaviour on your site to understand how they interact with your content. Google Analytics is a popular tool that provides insights into user demographics, behaviour, and acquisition channels.
• Purchase History: Analyse past purchases to predict future behaviour and preferences. Amazon, for instance, uses purchase history to recommend products tailored to individual customers.
• Social Media: Monitor conversations and interactions to gain insights into customer sentiment and trends. Tools like @Artiwise, @Hootsuite and @Brandwatch can track brand mentions and sentiment across multiple social media platforms.

Organising Customer Data

Combining all the data in a single customer view effectively is essential for making sense of the information gathered. This involves:

• Data Management Systems: Use #CRM / #Data platforms, to store and manage customer data efficiently.
• Data Cleaning: Regularly update and clean data to ensure accuracy. According to @Experian, 30% of data is believed to be inaccurate, impacting business decisions.
• Data Integration: Combine data from various sources to get a comprehensive view of the customer. For example, integrating CRM data with social media insights and purchase history can provide a 360-degree customer view. Creating a Single Customer View (#SCV) data platform helps end users to deep dive, report and build segmentation, personas, Predictive models, Customer Value (#CV) and Customer Lifetime Value model (#CLV).

Enhancing Customer Data with Third-Party Data

Over my career within Insight, I have used third-party customer data to enrich existing customer information. This helped to develop a better understanding of customers and their demographics. Techniques include:

• Data Enrichment: Add missing information by using third-party sources. For example, #Clearbit enriches customer profiles by adding firmographic and demographic data. You also have @CACI,@CAllCredit & @Experiean
• Predictive Analytics: Using third-party data and segmentation flags to existing data helps to develop predictive models for future customer behaviour by Channel and Product etc. Machine learning is a great way to develop hyper-personalised journeys using data to predict the next best offer for journey orchestration.

Netflix employs predictive analytics to recommend shows and movies based on viewing history.

• Behavioural Analysis: By studying patterns in your customer’s behaviour you can identify trends and opportunities, as well as understand the WHY, their struggles, aspirations, and needs.

Segmentation and Personas

With the hype of personalisation, my worry is companies don’t move away from developing segmentations as they see personalisation as a segment of ‘ONE’. Other than being an impossible task it is not a feasible approach. However, this is not the case Segmentation and persona development are critical for targeted marketing and personalisation.

• Segmentation: There is not one perfect segmentation model that answers all questions. That’s why it is necessary to develop multiple segmentation models and use them in a manner that helps develop a more personalised experience.
• The most common is grouping customers based on shared characteristics such as demographics, behaviour, or preferences. According to Bain & Company, companies with well-defined customer segments can increase profitability by 15%.
• Personas: Creating detailed profiles representing different customer segments helps to humanise and better understand your audience. Nevertheless, be careful not to build too much of an average view of a segment, better still select what typically looks like your most valuable cohorts. Adobe's use of personas helps tailor content and marketing efforts to specific customer needs and preferences.

Importance of Data-Driven CX

Implementing a data-driven CX strategy offers numerous benefits:

• Personalisation: Tailor experiences to individual customer preferences. According to Accenture, 91% of consumers are more likely to shop with brands that provide relevant offers and recommendations.
• Improved Satisfaction: Address customer needs more effectively.

A report by PwC found that 73% of customers point to experience as an important factor in their purchasing decisions.

• Increased Loyalty: Build stronger relationships by understanding and anticipating customer desires.

Research by Gartner indicates that 81% of companies expect to compete mostly or completely based on CX.

Common Challenges and Solutions

Data Privacy: Ensure compliance with data protection regulations like GDPR. Implement robust data security measures and obtain customer consent for data collection.

Data Silos: Integrate data from different sources for a unified view. Use data integration tools and platforms to consolidate information.

Data Quality: Implement regular data cleaning processes to maintain accuracy. Use data validation techniques to ensure the reliability of your data.

Conclusion

A data-driven approach to CX is essential for modern businesses. By understanding the customer journey, and gathering, organising, and analysing customer data, companies can create personalised experiences that drive satisfaction and loyalty. Leveraging AI and machine learning further enhances these efforts, ensuring that businesses remain competitive and successful.

My next article is about ‘Predictive AI & Generative AI in Building CX Maturity’

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