CXOToday has engaged in an exclusive interview with Rajiv Lamba, Founder, SurveySensum.
How does personalization in customer engagement build stronger emotional connections and foster repeat business?
While engaging with the customers, personalization advertises that you understand your customers, their needs, and their expectations, and this makes them feel valued. And when you tailor your strategies and marketing communications as per their expectations and requirements, they perceive care and relevance—this builds stronger emotional ties, fosters loyalty, and boosts satisfaction.
These satisfied customers tend to purchase again and spread positive word of mouth, increasing customer life value and referrals.
How can businesses effectively leverage AI to tailor and deliver messages that resonate with customers based on their behavioural patterns?
Personalization has become a thing of the past, and ‘hyper-personalization’ is the new personalization! Activities like personalized emails or targeted ads are no longer meeting customer expectations. Customers expect businesses to take one more step and proactively offer what they are looking for, even before they realize it themselves. And yes, brands like Amazon, Netflix, Tesla, and YouTube are successfully doing so.
With AI tools like Text Analytics, BigQuery, and SageMaker, brands are learning from past customer behaviours (with browsing history, support interactions, demographics, and more) and strategically launching more targeted campaigns that directly hit the customers’ pain points and expectations.
How can businesses leverage AI to analyze large volumes of customer data effectively, turning it into actionable insights?
AI tools like ‘Text Analytics’ streamline this process and make it seamless. Let’s take an example, Uber must be receiving thousands (or lakhs) of customer feedback across different channels and touchpoints every day in Delhi alone. How do you think they’d analyze such massive data?
Traditionally, resources were allocated for it. On spreadsheets, they used to manually tag and categorize this data and create multiple reports on it, which were later shared with the stakeholders. This tentatively took 5-7 days at least (depending on the data). This not only costs money but time as well.
Now, with AI-enabled Text Analytics, the entire process is automated, and these relevant insights are extracted in real-time. Text Analytics leverages NLP and machine learning and interprets complex datasets in no time, giving you emerging trends, top customer complaints, and a lot more. All you need to do is integrate your system with it.
How are machine learning algorithms transforming businesses by predicting customer behaviour and preferences, and how does this capability enable proactive, personalized recommendations?
Machine learning has taken personalization to a whole new level. Machine learning algorithms continue to learn from customer data—their browsing pattern, what they like or don’t like, how much time they spend on something, their demographics, their interactions with the brand, and a lot more. Based on this data, it helps in predicting customers’ behaviour and what they will like or dislike. This enables businesses to offer targeted solutions, provide personalized recommendations, and proactively address their concerns in a timely manner. It not only boosts customer satisfaction but also drives engagement and loyalty, which directly impact the bottom line.”
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