4 Ways Brands Leverage AI and ML for Compelling Customer Interactions

Brands are under immense pressure to advance and evolve as customer buying trends change, budgets shrink, and broad economic factors become increasingly complicated.

In response, many companies are turning to emerging applications of well-known technologies like artificial intelligence (AI) and machine learning (ML) to make their companies more agile, competitive, and responsive.

These technologies provide powerful buyer insights that allow companies to understand better when customers will make a purchase, what they will buy, and when they will engage.

According to a Deloitte survey, 79 percent of respondents have fully deployed three or more AI technologies, a 15 percent year-over-year increase. As AI and ML technologies become more ubiquitous as mainstream services soar in popularity and serve as proof of concept for many business leaders, everyone seems to want more. To accelerate AI and ML adoption, three-fifths of businesses intend to increase spending on digital transformation by the end of 2023. Of course, simply throwing money at the latest tech trends doesn’t guarantee business success.

The key lies in leveraging data, a company’s most abundant and valuable resource, to directly enhance AI and ML solutions that impact core KPIs at the enterprise level. These systems can help companies achieve two foundational objectives: increase top-line revenue and reduce overall costs by enabling new efficiencies.

Here’s how leaders can leverage strategic applications of this technology to remain agile and create compelling customer interactions with impact in 2024 and beyond.

#1 Collect the Right Data & Collect It with Consent

Many companies are overwhelmed by the volume, velocity, and complexity of customer data they collect. They are unable to convert this raw data into actionable customer-facing interactions.

One survey of CIOs and senior IT leaders found that nearly three-quarters of respondents said they were struggling with data management, and most companies are discarding the vast majority—up to 90 percent—of the data they receive.

Effective AI and ML implementation is predicated on accurate, actionable, and timely customer data, so companies must turn off the firehose of information instead of collecting the correct information at the right time to inform the right decisions.

Brands can leverage several data sources to obtain this information, including:

• Transactional data from credit card and other financial services
• Customer-collected data from surveys, research, and other buyer-centric sources
• Loyalty data from product offerings and other promotional opportunities

Specifically, focus on incentivizing customers to provide 20 percent of the data that provides 80 percent of the value.

The brands best positioned to receive the highest value data will acquire customers’ consent before collecting data, capitalizing on transparent data collection practices to solicit support and build trust.

The results of building customer trust with this approach can reach all the way to the bottom line. Eighty-four percent of consumers say they are more likely to share information with brands with transparent data practices and policies, 77 percent say it impacts their purchases, and 50 percent say they will purchase more from transparent brands.

The message for innovative brands is simple: obtain explicit consent from individuals before collecting data. Users should be able to opt in or out easily. Some consumers will undoubtedly opt-out, but those that remain, when properly nurtured, become the backbone of solid brands.

#2 Compile a “Single View of the Customer”

Compiling a “single view of the customer” means having a complete and accurate understanding of a customer’s needs, preferences, and behaviors based on all the data and interactions a company has collected about them.

This can be achieved through multi-platform infrastructures that allow businesses to store, track, and analyze customer data from various sources, such as sales, marketing, and customer service.

Such efforts focusing on the value exchange must gather the information to complete the 80/20 guiding principle, which relies on progressive profiling to provide a single customer view across all touchpoints.

#3 Create Real-time Interactions

Real-time interactions can propel people through buying by delivering the information, insights, and promotion needed to convert leads into sales.

While customers expect real-time, hyper-personalized interactions, many anticipate that brands won’t be able to deliver. One industry report found that 44 percent of Gen Z shoppers and 43 percent of millennials “expended more effort than expected to complete an interaction.”

In 2023 and beyond, time is a valuable currency. Companies can increase conversions by deploying AI and ML solutions to power real-time interaction management systems that foster emotional connections, identify potential pain points, and optimize the buying journey.

Many brands continue to rely on static content to entice buyers. AI and ML solutions let brands move beyond this, delivering real-time, personalized interactions at scale.

#4: Create Hyper-Personalized Experiences for customers

A McKinsey & Company report found that 71 percent of consumers expect brands to provide personalized experiences, and most are disappointed when they don’t deliver.

Customer data is key to personalizing customer experiences, but many brands are overwhelmed by the firehose of information, making the sheer data volume and information sprawl an impediment to progress.

AI is making sense of this information and using it to generate targeted advertising content that empowers personalized experiences at scale.

Marketing, commerce, analytics and data, and merchandising can use AI in different ways to present targeted content to prospects and customers through lightboxes, promotional links, special offers and discounts, and platform onboarding efforts.

AI is moving brand marketing away from content repositories that present plausibly engaging content to consumers to an environment where analytics, profile information, and segmentation data can be used in real-time to create customer-centric, generative content that converts buyers.

In retail advertising as one example, AI allows advertisers to present advertising content with surgical precision in ways that we could only dream of five years ago.

Truly Data Driven

Leveraging AI and ML is becoming increasingly crucial for brands to maintain relevance in a digital-first world, to remain competitive, and to create compelling customer interactions. Businesses can increase top-line revenue and reduce costs by collecting the correct data, compiling a “single view of the customer,” and creating real-time interactions.

However, it’s important to note that simply investing in these technologies is not enough. The key is using data, a company’s most valuable resource, to impact core KPIs at the enterprise level directly. As AI and ML adoption continues to rise, companies implementing these strategies will be well-positioned to remain agile and stay ahead of the competition.

Originally published on ReadWrite.