How to Effectively Use GenAI to Personalize Customer Experiences

When it comes to AI in customer engagement and brand-building, there is no room for debate. Brands need to embrace AI wholeheartedly unless they want to risk oblivion.

76% of consumers today expect brands to understand their needs and expectations. At the same time, 84% of customers say they want to be seen and treated as a person, not a mere statistic. Customers will likely avoid brands that don’t give them a tailored personal experience. It is no secret then that personalization takes the cake when it comes to winning customer hearts, attention, and wallets.

Personalization is a win-win for both brands and customers. A 2020 study showed that 70% of companies using advanced personalization have already earned 200%+ ROI from their efforts.

Personalization plays a critical role across the full customer lifecycle journey—whether it is acquisition, customer engagement, purchase frequency, ticket size, up-selling and cross-selling, or retention.

Personalization begins with understanding your customer—which means embracing the data you have and analyzing it thoroughly. The idea is to stay relevant and make the customer feel important.

But, here’s the catch. As the business grows, it can be really hard to effectively utilize the ever-growing troves of data. That’s where personalizing at scale comes in.

Need for Personalized Customer Experiences at Scale

As contextual and relevant personalization is essential, brands must know that accurate insight-driven analytics can best enhance customer experiences. At the same time, businesses must have the capability to personalize at scale.

This is the area where there is a huge gap between leaders and laggards. While leaders today are assimilating data at scale, laggards are entangled in ways to collect quality data.

Some challenges with traditional personalization for brands today:

• Lack of personalization in real-time, making it pre-programmed and not automatic
• Poor customer segmentation as niche customer segments are not created at a granular level
• Limited capabilities of content creation which affects personalization
• Customized content creation is typically involves high volumes and content customized for each customer segment
• Difficulty in organizing varied data from many data sources
• Difficulty in combing through data assets quickly and effectively to find actionable trends

The need of the hour for these businesses is to operate at scale—that is, analyze large amounts of data from individual customers to offer a highly personalized customer experience.

An example of personalization at scale is that of Spotify.

Spotify has about 550 million monthly active listeners, as of August 2023. Of these 220 are premium subscribers and the rest are ad-supported. Despite this massive customer-base, the leading music streaming service is able to provide hyper-personalized recommendations and create playlists for individual customers.

How?

Through personalization and real-time data analysis.

The app analyzes the listening history of each customer segment to understand their tastes and preferences. It is then able to make music and podcast recommendations closely aligned with the customer’s taste, making it a sticky and satisfying personal experience. To get to this level of personalization, the company uses several forms of artificial intelligence (AI) to recommend content. It hyper-personalizes the listener experience using behavioral data to recommend music, podcasts and playlists to listeners. It also uses Natural Langugage Processing (NLP) tool to enhance searches and uses AI to create playlists suited to each customer. Similarly, brands can tap opportunities that technologies such as AI offer for understanding the audience better, faster insight-to-action, and creating personalized experiences at scale.

GenAI for Achieving Personalized Experiences at Scale

No doubt, AI has ushered in an era of hyper-personalization beyond traditional personalization experiences. Hyper-personalization refers to developing a deep understanding of customer behavior, preferences, and needs for devising tailor-made journeys. The result is a stickier and more engaging experience for customers.

Clearly, data is the fuel for driving this level of personalization and also needs the right AI and machine learning (ML) tools. AI and ML models when trained using data sets have the ability to sift through huge volumes of data, analyze it, and create real-time contextual interactions.

So, when customers are purchasing or browsing your website, app, or social media, these algorithms can incrementally adjust to behavioral data in real-time based on every new interaction. The result is even smarter marketing efforts across more channels and customer segments.

Using this information, marketers can understand a customer’s expectations and potential future actions with better accuracy. These can be acted upon for recommending a product the customer may need or like, informing them about a relevant upcoming launch or event, or even making personalized offers.

With the proliferation of generative AI, brands have access to new capabilities for customer-facing teams, customized communications across channels, automation, AI-generated content and images, and more, to provide even greater personalization at scale.

At the very top of the funnel, generative AI has superseded traditional AI-driven lead generation with advanced algorithms that identify patterns in data to segment and engage a relevant audience. This has resulted in tailored lead-activation campaigns. Further, GenAI can optimize marketing strategies with A/B testing of page layouts, ad copies, SEO strategies and so on. It also offers the warmth of personalization with customized communications and human-like customer assistance.

How Brands are Harnessing GenAI to  Tailor Customer Experiences

In 2021, PepsiCo collaborated with Synthesia for the “Messi Messages” campaign featuring famous footballer Lionel Messi for their Lay’s brand. The brand created personalized video messages using only 5 minutes of Messi’s actual footage with an AI model. The model generated nearly 650 million personalized video variations in 8 languages. Now, that’s personalization at scale!

In July 2023, Virgin Voyages used AI to create a personalized cruise invitation tool featuring Jennifer Lopez as the “Chief Celebration Officer.” The tool allows prospective customers to generate personalized AI-driven video invites from the celebrity popstar. The videos mimic JLo’s voice and appearance. According to Virgin Voyages, the campaign already generated over 1,000 bookings within a month of launching, and engagement rates were 150% more than in previous campaigns. Imagine a celebrity personally inviting you to a cruise? Would you not go?

Another brand that tapped GenAI for truly personalizing at scale is Mondelez. In an AI-powered Cadbury campaign, the brand turned Bollywood superstar Shah Rukh Khan into a personal brand ambassador for local stores. With this campaign, Mondelez India generated more than 130,000 social media ads customized for local stores using existing footage of the actor and AI-generated scripts. The result: it garnered over 94 million video views for little ad spend and generated massive ROI for the brand.

GenAI Impact on Customer Engagement, Retention, and Revenue

The potential of GenAI is clear, seeing how some of the biggest brands are leveraging it. GenAI can directly impact growth and profitability by enhancing customer engagement, retention, and satisfaction.

As per Draup, a 5% increase in customer retention can result in a 25% to 95% profit growth. Moreover, McKinsey’s research found that companies investing in AI are seeing a revenue uplift of 3-15% and a sales ROI growth of 10-20%.

From the top-of-the-funnel activities such as lead identification to nurturing, optimizing communications, predictive analytics, intelligent recommendations to customer support and more, generative AI can take marketing activities up a notch.

Giants in traditional industries too are using AI to implement marketing automation. For example, Walmart uses GenAI to create customized marketing content for customers based on their interests and needs, which helps with retention.

How to Overcome Challenges of AI-based Solutions

While hyper-personalization with GenAI can take your marketing ROI to the next level, it does come with challenges with implementation.

• Access to data and technology: The foremost task for marketers is to have access to high-quality customer data. Scarcity of data can be a major roadblock for AI models to work for your brand. Often, brands deal with siloed and poor-quality data. There are also challenges related to availability of a skilled workforce in AI/ML applications.
• High investment: It is no secret that AI demands high investments. Marketers would need to do a thorough cost-benefit analysis to see if AI can really augment their future growth and where it fits in their overall strategy.
• Ethical concerns: With the increasing use of AI, marketers must consider ethical concerns regarding the transparency of the AI-generated content they use. Perhaps, adding disclaimers for AI-driven content can help viewers to exercise discretion.
• Potential for Biases: AI is prone to biases, and the analytics and the content it generates may have biases from the datasets fed into the models. Marketers must ensure that diverse and inclusive datasets are used.
• Privacy vs. Personalization: When it comes to personalization, marketers need to draw the line between personal and intrusive. This includes using data responsibly, asking for content where necessary, and being transparent about data usage.

Future Potential of AI-based Personalization Tools

The AI revolution is here to stay. And, as far as AI-based personalization goes, we seem to have only scratched the surface.

While marketers are currently leveraging generative AI for producing human-like content, brainstorming, and tailored messaging, it can be a game-changer in many other areas of marketing.

Use-cases of AI and Allied Technologies:

• GenAI offers the scope of Integrating with other technologies like natural language processing (NLP) and computer vision. These can be used to generate reports, offering refined conversational support, context-appropriate content, and translation.
• In the limitless digital world, Web3.0 and the metaverse present a huge opportunity for brands. Generative AI can help enhance virtual reality and augmented reality experiences. This includes real-time spaces, objects, and character generation for a more immersive experience.
• Generative AI models especially designed for CRM can leverage the power of AI-generated content for sales, service, marketing, commerce, and IT support.
• Sentiment analysis can further optimize the use of AI for customer service. It can identify how customers “feel” about certain products or brands by analyzing conversations with chatbots. This can allow marketers to refine their strategies, and in turn, deliver a better customer experience.
• The scope for personalized product development based on customer requirements and better demand/supply planning is also immense.

TLDR? Here’s a quick summary:

• The power of personalization cannot be overstated when it comes to customer acquisition, engagement, and retention.
• Generative AI is equipping brands with a powerful stack of tools that allows personalization at scale.
• AI is being used by an increasing number of brands to make a tangible difference in business outcomes.
• Some of the world’s largest brands are already building compelling narratives, personalizing messaging for the relevant target audience, and driving conversion using generative AI tools.
• While some challenges to implementation remain for the uninitiated, brands can begin by analyzing how and where AI fits into their business.
• With the prowess of AI that we have seen so far, it seems that incumbent brands will need to jump onboard the GenAI train to stay relevant and competitive.
• As generative AI continues to evolve, it is finding wider applications in the business world. Its potential in marketing holds a lot of promise. For the world of GenAI and marketing, it seems like the best is yet to come.