Do you know who your customers are? Do you know what compels customers to buy your goods or use your services? Do you know how many customers are unhappy and looking at competitors or alternatives? In what way are you going to keep them? Customer insight is essential to answering these queries.
Customers are the lifeline of any business. To be successful, you need loyal customers who will support, market, and promote your business. In today’s market, the ratio of supply to demand is ever-changing. Customers today have more options to choose from.
Although this is wonderful for customers, it puts duties and pressure on businesses to maintain and satisfy customers. Numerous things influence a customer, including product quality, variety, pricing, and after-purchase services. Sometimes, all it takes to lose a consumer is one competitive competitor’s unique offer.
Businesses can now fully utilise their CRM systems, improve client experiences, obtain insightful data, and automate processes with never-before-seen intelligence because of artificial intelligence (AI). Also, businesses that use AI CRM in their customer journey saw an increase of up to 20% in customer satisfaction.
In this blog, we will delve into what customer insights are, how AI powered customer insights are changing the CRM landscape, and the future of customer insight.
Table of Contents
What are AI-Powered Customer Insights?
Customer insight is the comprehensive analysis of diverse data sources to gain a deep understanding of customer behaviours, preferences, needs, and expectations, thereby informing strategic business decisions and enhancing customer experiences.
For example, consider a retail brand that uses AI CRM to understand how customers shop online. The AI looks at what they (customer) buy and what they’re interested in. If someone often buys eco-friendly products and shops during sales, the AI remembers this. AI then predicts what this customer might want to buy next. Based on this insight, the brand sends them special offers on eco-friendly items, especially when there’s a sale. This way, shopping becomes more personal and convenient for the customer, and it helps the business too.
Let’s delve deeper into how it helps:
Sentiment Analysis
AI CRM tools can analyse large datasets from various sources like surveys, social media comments, call centre notes, and chatbot conversations, to discern overall customer sentiment. This analysis helps identify customer satisfaction levels and common issues that need to be addressed.
Demographics and Customer Segmentation
AI algorithms can efficiently process large sets of data to extract key demographic attributes and segment customers based on these attributes. This segmentation allows businesses to tailor their products and services to specific customer groups.
Personalisation
AI in CRM enables the creation of personalised customer experiences by analysing individual behaviours and data. This can include personalised product recommendations, email sequences, and customer journey customisation to enhance satisfaction and conversion rates.
Real-time and Predictive Insights
AI-driven systems can analyse real-time data and predict future customer behaviours based on historical data and current trends. This predictive insights capability allows brands to proactively address customer needs and preferences.
Efficiency in Market Research
AI in CRM enhances traditional customer experience (CX) analysis methods, providing rapid and highly granular insights. It significantly speeds up the process of data collection and analysis, making market research more efficient.
Significance of Customer Insights For Businesses
Understanding and leveraging customer insights has become crucial, particularly in the realm of Customer Relationship Management (CRM) is essential for every organisation.
Pre-Targeting
In the pre-targeting phase, customer insights are crucial for understanding market needs and consumer behaviour before a product launches. This involves analysing customer data to gain insights into preferences, purchasing patterns, and potential areas for product improvement or marketing strategy adjustments.
The growing CRM market in India, set to reach about $2.3 billion in 2023, is a clear indicator of the increasing reliance on CRM systems for such insights. We expect large enterprises to drive significant growth in the CRM applications segment. Technological advancements like AI/ML and analytics platforms increasingly support this growth by enhancing customer experiences through CRM applications.
Post-Targeting
In the post-targeting phase, businesses transition from making a sale to nurturing a long-term relationship. This phase is of paramount importance as it offers a window into the customer’s journey with the product or service.
AI in CRM systems become instrumental here, diligently tracking customer interactions, collecting feedback, and gauging satisfaction levels after a purchase. This wealth of data serves as the foundation for personalisation. Businesses can use this information to craft tailored communications, provide customised solutions, and swiftly address any concerns.
The result? Customer satisfaction and unwavering loyalty.
How Amazon Utilises AI Powered Customer Insights for Enhanced Customer Experience
Did you feel your Amazon feed looked different from your friends’? It is because of the amazing Amazon algorithm. So when you visit Amazon’s website or app, it creates a personalised storefront just for you. The products and recommendations you see are carefully curated to match your preferences. This customization is based on your past browsing and purchase history, as well as the items you’ve shown interest in. These personalised recommendations and product placements aren’t static; they’re updated in real-time.
For example, if a consumer buys socks, Amazon might not recommend socks alone in the future. Alternatively, their technology might examine a user’s past browsing activity, detect that they’ve seen superhero movies on Prime, and then suggest Marvel brand t-shirts.
For Amazon, the ability to quickly and accurately evaluate vast amounts of conflicting data is essential, and their recommendation engine generates an astounding 35% of their total revenue.
Now, you might be wondering how Amazon leverages AI in CRM to get enhanced customer insights. Here’s an overview of how they do it:
Collaborative Filtering
Amazon employs item-based collaborative filtering, which suggests products based on the purchasing and browsing history of the customer and others who bought similar products. This approach makes each customer’s homepage unique, displaying items based on their interests and previous purchases​​.
Visual Search
Utilising computer vision technology, Amazon’s AI algorithms can analyse the visual characteristics of an image and match them with relevant products. This simplifies the search process, allowing customers to find products based on their visual preferences​​.
Voice Commerce
Amazon’s AI-powered virtual assistant, Alexa, enables customers to make purchases using voice commands. Integrating AI and natural language understanding, Alexa processes customer requests, searches for products, and completes the purchase process, thereby making shopping more convenient and accessible​​.
Supply Chain Optimisation
AI is also used to optimise Amazon’s supply chain operations. By analysing data from various sources, AI algorithms identify bottlenecks, optimise routes, and streamline processes, resulting in faster delivery times and improved efficiency​​.
Sentiment Analysis
We use AI-powered sentiment analysis to understand customer feedback and reviews. By analysing the text of customer reviews, AI algorithms determine the sentiment expressed, which helps Amazon identify areas for improvement and enhance the customer experience​​.
Deep Learning
Deep learning, which is a subset of AI and machine learning, uses human behavior to understand and generate results. This helps Amazon recommend products based on a customer’s interests, their browsing history, reviews they’ve left, ratings they’ve given, and previous purchases​​.
Content-Based Filtering
Amazon’s recommendation engine also uses content-based filtering, where once a customer interacts with a product, it looks for other products with similar features and then presents them to the customer​​.
Bandit and Causal Inference in Recommendations
These are newer models used in filtering to identify the best algorithm for each user based on their interactions with recommended products. Causal inference helps in understanding the factors that might have caused a user to click a product link​​.
6 Ways AI is Enhancing Customer Insights
By now, you must have understood all the aspects of how AI powered customer insights are changing the CRM landscape. Now we’re going to explore the ‘8 Ways AI is Enhancing Customer Insights’, shedding light on how this innovative technology is transforming businesses’ approaches to understanding their customers.
Hyper-Personalisation
AI powered CRM is capable of analysing consumer data to produce an in-depth understanding of each individual, including social media activity, browsing patterns, and past purchases. AI is able to generate customised information, targeted offers, and personalised recommendations for goods and services by analysing this data.
For example, Netflix uses AI powered CRM to personalise its suggestions to each customer depending on their viewing preferences.
Customer Journey Mapping
AI can map out the customer journey by analysing touchpoints and interactions across multiple channels. Businesses can use this to identify pain points and areas where they can make improvements to enhance the overall customer experience.
Data Analysis and Processing
AI’s ability to process and analyse extensive customer data rapidly and accurately is pivotal for businesses. It encompasses structured data like purchase history and unstructured data like social media comments. For instance, when an e-commerce company analyses customer reviews and purchase history using AI, it may discover that positive sentiment in reviews correlates with higher sales, enabling the company to focus on improving customer satisfaction and increasing revenue.
Advanced Analytics
AI systems are able to filter through huge amounts of client data, finding trends and information that people might ignore. This can assist in determining what customers desire, predicting their needs going forward, and highlighting areas where the customer experience can be enhanced.
Customer Segmentation
AI can develop comprehensive categories of consumers based on demographics, behaviours, and preferences by analysing customer data. This makes it possible for companies to create marketing campaigns that are highly targeted and to increase the relevancy of their messaging.
Social Media Monitoring
AI CRM tools can monitor social media channels to track mentions, comments, and sentiment related to a brand. This allows businesses to respond promptly to customer feedback and adapt their strategies accordingly. For example, a restaurant chain can use AI to track social media mentions and respond promptly to customer feedback. This not only improves customer engagement but also helps adapt marketing and service strategies based on real-time insights.
Future of AI Enabled Customer Insights
The future of AI in customer insights is shaping up to be a transformative force across various industries, especially in customer service, marketing, and sales. The integration of AI CRM technologies is not only enhancing the efficiency of these sectors but also revolutionising the way businesses interact with and understand their customers.
Customer Data Platforms (CDPs)
Central to AI’s role in customer insights are Customer Data Platforms. These platforms create and continually update unified customer data profiles, allowing businesses to deeply understand customer needs across interactions. This unified data fabric is crucial for contextual understanding of customer interactions, combining past and real-time data​​.
Predictive AI and Generative AI
Predictive AI is gaining traction by actively learning from customer data. Predictive AI plays a significant role in gathering intelligence, analysing insights, and adapting actions based on real-time feedback from each customer engagement. Generative AI, already used in marketing, sales, and service applications, delivers individualised content in digital communications, thus enhancing customer support interactions and overall satisfaction. In the near future, these technologies will activate personalised customer interactions. These interactions will happen in real-time and on a large scale.
Impact on Marketing and Sales
AI, particularly generative AI, is poised to have a significant impact on marketing and sales. It enables hyper-personalized content and offerings based on customer behaviour, persona, and purchase history. AI can also boost sales effectiveness by automating mundane activities, allowing sales teams to focus more on customer interaction. Gen AI’s role extends to various stages of the customer journey, from lead identification to customer retention, providing personalised support and insights throughout​​.
Conclusion
The future of AI in customer insights is bright and promising. It’s not just about analysing data; it’s about understanding customers at a profound level and engaging with them on a personal journey. AI is the catalyst for this transformation, and businesses that embrace it are poised to thrive in the age of customer-centricity. As businesses leverage AI powered CRM technologies such as machine learning, natural language processing, and predictive insights, they gain unprecedented insights into customer behaviour, preferences, and trends.
Schedule a CRM check-up by AI experts to explore how AI CRM technologies can be seamlessly integrated into your business processes, providing you with the tools to deliver hyper-personalised experiences, advanced analytics, and innovative solutions that set you apart in the competitive digital landscape.