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4 Ways Brands Leverage AI and ML for Compelling Customer Interactions


Brands are facing increasing pressure to adapt as customer behavior changes and economic conditions become more complex.

As a result, many companies are embracing new applications of familiar technologies like artificial intelligence (AI) and machine learning (ML) to become more agile, competitive, and responsive.

These technologies offer valuable insights into customer behavior, allowing companies to better understand when and what customers are likely to purchase, and when they will engage.

A Deloitte survey found that 79% of respondents have fully deployed three or more AI technologies, a 15% increase from the previous year. As AI and ML technologies become more prevalent, more businesses are looking to leverage them, with three-fifths intending to increase spending on digital transformation by the end of 2024. However, success is not guaranteed by simply investing in the latest tech trends.

The key is to use data, which is a company’s most valuable resource, to directly enhance AI and ML solutions that impact core KPIs at the enterprise level. These systems can help companies achieve two main objectives: increase revenue and reduce costs by enabling new efficiencies.

Here are four ways companies can use AI and ML to stay agile and create compelling customer interactions in 2024 and beyond.

#1 Collecting the Right Data & Collecting it with Consent

Many companies struggle to convert the large volume and complexity of customer data they collect into actionable customer interactions. A survey found that nearly three-quarters of CIOs and senior IT leaders are struggling with data management, leading most companies to discard the majority of the data they receive.

Effective AI and ML implementation requires accurate, actionable, and timely customer data. Brands can leverage various data sources, including transactional data, customer-collected data, and loyalty data, to obtain valuable information. Obtaining explicit consent from individuals before collecting data, and ensuring transparent data collection practices, can help build trust and lead to increased customer engagement and purchases.

#2 Compiling a “Single View of the Customer”

Compiling a complete and accurate understanding of a customer’s needs, preferences, and behaviors based on all the data a company has collected about them can be achieved through multi-platform infrastructures. This involves storing, tracking, and analyzing customer data from various sources, such as sales, marketing, and customer service.

#3 Creating Real-time Interactions

Real-time interactions can move customers through the buying process by delivering the information, insights, and promotions needed to convert leads into sales. To meet customers’ expectations for real-time, hyper-personalized interactions, companies can use AI and ML solutions to power real-time interaction management systems.

#4: Creating Hyper-Personalized Experiences for Customers

Consumers expect personalized experiences, and brands can use AI to deliver targeted advertising content and create personalized experiences at scale.

AI can be used in marketing, commerce, analytics, and merchandising to present targeted content to prospects and customers through various means. This can help move brand marketing away from static content towards real-time, customer-centric, generative content that converts buyers.

Truly Data-Driven

Leveraging AI and ML is crucial for brands to remain relevant and competitive in a digital-first world. By using data to impact core KPIs, businesses can increase revenue and reduce costs. However, it’s important to understand that simply investing in these technologies is not enough. As AI and ML adoption continues to rise, companies implementing these strategies will be well-positioned to stay ahead of the competition.

Featured Image Credit: Pixabay; Pexels; Thank you!

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