As a consumer, you would now be familiar with your favorite eCommerce portal sending an email to complete your abandoned cart purchase or the recommendations from the OTT platforms based on your watch history. This is being made possible by personalization through AI and analytics. All the major companies, including FAANG, today keep personalization at the core of their each service offerings.

According to McKinsey research, 71 percent of consumers expect companies to deliver personalized interactions. Nearly 35% of sales at Amazon today come directly from personalization efforts. Also, 56% of these shoppers are more likely to be repeat buyers. Personalization is slowly becoming a de-facto standard for customer engagement across touchpoints.

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With the advancement of artificial intelligence, personalization is becoming a significant aspect of online and offline marketing strategies. Mainstream adoption of AI and advanced analytics has allowed businesses to process and analyze large amounts of customer data. It helps them gain valuable and deep insights into customer behavior and preferences. This paves the way for personalization through AI and analytics.

Every customer interaction is designed based on individual customer behavior and preferences. Starting from offering products or content suggestions to targeted marketing and after-sales service. AI helps businesses better understand and serve their customers by raising the personalization level (or hyper-personalization, as it is now commonly referred to) for improved outcomes. Read on to understand how AI is used in personalization.

Investing in customer data

For companies, a lot of data (both internal and external) often remained sitting in siloed systems. They remained untapped and were not used to derive insights. Until recently, they found it exhaustive and expensive in terms of time and costs to aggregate all this data coming from different sources. It had then to be cleansed and analyzed to derive any patterns. The answer to how AI is used in personalization begins with businesses investing in customer data.

Artificial intelligence (AI), through techniques such as machine learning, natural language processing, and predictive analytics, addresses the above issues with data. AI now plays a significant role in helping businesses gain confidence to invest in customer data for valuable insights.

• Machine learning algorithms can be used to identify patterns and trends in customer data, such as purchasing habits. Businesses can leverage it to make personalized products or content recommendations.

• Natural language processing can assist in analyzing customer feedback and social media posts. This helps with understanding customer sentiment and identifying key issues or pain points.

• Predictive analytics can help forecast customer behavior, such as which customers are likely to churn or which products are likely to be popular. This allows companies to engage in customer retention activities proactively for increased sales.

Personalization through AI and analytics has multiple use cases, such as:

• Conversational AI chatbots - Chatbots can personalize the interactions based on the consumer's previous interactions, purchase history, demographics, etc. Chatbots can understand the intent and context behind customer questions. They can respond to provide personalized support and assistance.

• Personalized product recommendations - Based on the behavior, preferences, and even concerns of customers, AI Recommendation Systems can make personalized products or content recommendations for each customer.

• Curated suggestions and offers - Customer feedback and social media posts can be analyzed using natural language processing and computer vision algorithms. Based on this understanding of customers' requirements, businesses can offer customer-curated, individual suggestions.

• Predictive and prescriptive insights - AI models, together with behavior science, can predict customer journey maps and provide insights for up-sell/cross-sell or the next best product to buy. Thus improving customer satisfaction and reducing churn.

Offer Omnichannel experience

Never before have customers had many channels (and touchpoints) to engage with companies. These range from traditional offline mode (in-person at a store) to online digital channels such as websites, mobile apps, messaging apps, and voice assistants. Customers use different channels to engage and, based on convenience, often shift from one to another.

An omnichannel experience provides them with a seamless and consistent experience across all touchpoints and channels, including online and offline.

For businesses, the benefits of an omnichannel experience include:

• Increased customer engagement and loyalty

• Improved customer satisfaction

• Increased sales and revenue

• A better understanding of customer behavior and preferences

• Personalization through analytics

However, to offer an omnichannel experience, companies need to be able to track and analyze customer data across all channels. They then need to use that data to personalize the customer experience. AI and advanced analytics can be used to help companies offer an omnichannel experience by:

• Collation of data from multiple sources

• Analyses of consumer data to recognize patterns

• Personalizing the customer experience based on individual preferences

• Automating and streamlining communication and interactions across channels

• Predictive modeling for forecasting consumer behavior

Protect customer privacy

For all its usefulness, data still is risqué. There’s a thin line between what the customers share and what they actually want to share. While delivering personalization through analytics, overstepping customer privacy can occur when companies collect or use customer data without their consent or for purposes other than what was agreed upon. This can lead to customers feeling violated and mistrustful of the company. This can even result in legal and reputational consequences.

Protecting customer privacy is important for several reasons:

1. Legal compliances become simpler:

Companies must comply with laws and regulations related to customer privacy, such as the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States. Failure to comply with these laws can result in significant fines and legal repercussions.

2. Earn consumer trust and loyalty:

Customers are more likely to trust and remain loyal to a company that takes their privacy seriously. If a company is perceived as not protecting customer privacy and confidential data, it may lead to the loss of customers and damage the company's reputation.

3. Ethical considerations:

Companies have a moral and ethical responsibility to protect customer data. They must use it only for legitimate and agreed-upon purposes.