AI is gaining in popularity, and for good reason. When paired with a CDP, both can provide customers with even more accurate product and service recommendations, targeted ads, personalized content, and dynamic websites.
Thanks to the recent rise in popularity of generative Artificial Intelligence (AI), like ChatGPT and Bard, it can seem like AI is a new phenomena in the workforce.
The reality is that AI is an established technology that has recently taken a quantum leap forward. Now, businesses are sitting on the cusp of a new era – AI enables them to deliver uniquely personalized experiences with the potential to catapult customer satisfaction to unprecedented heights.
AI-driven personalization uses AI and machine learning (ML) to analyze customers' data, understand their preferences and needs, and tailor the experience to an individual’s specific information for better customer engagement.
AI, paired with a customer data platform (CDP), can provide consumers with even more accurate product and service recommendations, targeted ads, personalized content (like messaging and emails), and dynamic websites. This helps users to see specific content matched to their interests, browsing habits, and purchase history.
We are living in an exciting time where data, AI, and personalization are coming together to redefine the consumer experience, and that’s pretty cool.
We’ve (hopefully) all heard the phrase, garbage in, garbage out to explain how the quality of the output is determined by the quality of the input. This phrase holds true for AI.
Because AI-powered personalization is only as good as the customer data it was trained on, the quality of your data determines the quality of your AI-driven personalization.
The “intelligence” in AI is based on a data set. If your data is siloed, inconsistent, stale, or incomplete, even the most innovative AI or ML applications can have a negligible impact.
Companies are increasingly adopting AI and ML to power their personalization efforts. AI-powered personalization is essential for standing out in a competitive market. It’s also key to increasing customer retention and conversion.
Ninety-two percent of businesses today are using AI-driven personalization to drive growth, according to a recent Twilio Segment report. And 62% of business leaders cite improved customer retention as a benefit of personalization efforts.
Because AI-powered algorithms can analyze vast amounts of data to understand customer preferences, behavior, and past interactions, businesses will be able to deliver highly tailored information to customers, ensuring they receive the most appropriate content, products, or services. Those not leveraging AI are at risk of falling behind.
Power AI with a quality customer data infrastructure
The path to smart customer engagement starts with high-quality, first-party data. A trusted data infrastructure that is built “data up” with unified, real-time, and consented data is critical to any AI strategy. Twilio Segment’s leading CDP helps to collect, clean, and activate data so businesses can leverage AI applications to their fullest potential.
With such powerful tools at their disposal, businesses can automate personalization at scale and deliver tailored customer experiences. However, businesses must ensure their AI/ML models are trained on high-quality data and aligned with customers’ preferences.
Build intelligent customer engagement into every channel
With the power of AI-driven data analytics, marketers can effortlessly incorporate intelligent customer engagement strategies across various channels. By leveraging real-time customer touchpoints from web interactions, mobile apps, targeted ads, email marketing, and social media platforms, they can construct dynamic audiences and deliver highly effective campaigns without relying on data teams.
Innovative companies are sure to introduce new AI capabilities in the months and years ahead, but businesses can start laying the groundwork for their AI strategy today with a trusted data infrastructure that relies on first-party customer data – the cornerstone of a CDP.
Ali Miller is Vice President of Product Management, Ads, at Instacart. She previously held the role of Senior Director of Product Management, and prior to that, spent 11 years with Google. We spoke with her about how AI and personalization go together by dynamically recommending products that will resonate with customers. Listen to the full interview.
Winning the digital shelf
When your business revolves around helping customers shop for groceries and personal care products online, winning the digital shelf is crucial. Unlike physical store shelves, the digital shelf is infinite with endless opportunities to level the playing field for emerging brands by displaying products customers may want but haven’t heard about.
Instacart provides a more diverse product mix for consumers by leveraging data based on their preferences and past buying patterns. They use AI capabilities to find the best match based on the intent the consumer is showing through various discovery pages on their shopping journey.
Unlocking consumer insights with AI
Each of Instacart’s ad stacks is powered by predictive AI and machine learning, which helps drive the right ad for the right user at the right time.
Looking to the future, the company is excited about what generative AI may be able to do. Generative AI models learn the patterns and structure of their input training data, and then generate new data with similar characteristics.
It may be able to identify products that are better linked to each other. For example, it can tell Instacart which concepts to consider and identify products that go together. Take hiking snacks, with large language models (LLM), the team could extract a common theme across an array of different snacks and then recommend them to more users.
When you multiply that discovery by thousands and thousands – if not millions – of possibilities, the number of additional products that may be sold is astounding. And this all comes from a new way of seeing data using AI.
Ali shared, “There's an entirely new threshold of innovation and testing and creation that we can dig into. It’s fun to see how excited everybody is about this. There's a lot we don't know about what's going to be possible. We need to figure out the right ways to actually engage with this new set of possibilities, but it’s definitely an endless set of possibilities.”
Leveraging customer data and AI
Some of Instacart’s greatest revenue returns have come from using AI in their ad campaigns. Key metrics they track include likelihood to engage, likelihood to click, and relevance to the query. Using that data allows Instacart to lead with their optimized bidding product, ensuring they drive maximum ROI for the advertiser – table stakes for a solid advertising strategy.
The company is also using predictive AI with customer data to analyze relationships between products. For example, if a customer is browsing cuts of red meat, the app may offer a personalized recommendation for red wine to pair with it. Or perhaps it's a snack food recommended, such as chips and guacamole or chips and salsa.
By knowing how consumers are interacting across different categories and product pairings, Instacart can better deliver the right ad to the consumer at the right time.
A recipe for trust
Any good marketer in the e-commerce industry knows the importance of cross-sells and upsells, but with AI it’s easier than ever to extract new insights and recommend products at the right time. Creating a recipe for trust with consumers is important, and Instacart knows it’s a balancing act between building connections and driving results.
Kristin Maa is Senior Vice President of Growth at Saks Fifth Avenue, and is responsible for growth and retention marketing as well as online category growth. We sat down with her to discuss how the luxury retail brand uses AI-powered personalization to stay connected with its customers. Listen to the full interview.
Expanding into more digital channels
Customers who share their data digitally also expect that information to be used to improve their in-store experiences. To meet customer expectations for omnichannel personalization, Saks is structuring in-store conversations around that digital data.
Today, the expectation for brands is that they’ll engage in open dialogue with customers. There are many ways that can happen. Saks has been moving more into creator-driven content. They see increased interest in communities built around livestream content, where customers can ask questions and have conversations.
Whether those conversations include designers or buyers, being able to engage directly with a brand is a powerful way to build customer engagement. Customers want to be heard regardless of the platform, so Saks is dabbling to varying degrees in different digital spaces, trying to figure out the best way for customers to communicate with them, and understand how they want to interact.
Creating personalized digital experiences
At Saks, personalization is more than an accessory. When it comes to strengthening customer relationships, it’s the pièce de résistance. Considering the enormous number of categories and items on their website, it's mission critical for them to use personalization to guide customers to products that reflect their personal preferences.
With over 150,000 styles available, finding the right combination for customers is a lot easier with AI working behind the scenes. It pulls customer information and displays products that match the information known about that shopper, and it does it quickly.
Saks has been designing personalized initiatives into their marketing campaigns to ensure the messages sent to customers are data-driven. Currently, over 90% of the messages have a personalization element compared to 10% from a few years ago. They’ve also ramped up their email marketing content, figuring out how to incorporate dynamic content so it’s not the same product script being used again and again.
Customer DNA
Saks uses personalization to make customers feel that the content they receive is curated just for them. They’ve collected a lot of data over the years through physical stores, an e-commerce site, and a loyal customer base. All that data is considered by their analytics team as their “customer DNA.” Essentially, a customer’s DNA is their collection of brand and category preferences. It also lets Saks know how the customer likes to engage with the brand i.e., are they an app shopper or a desktop shopper?
Saks supplements all their first-party data with AI predictions. This allows them to “model a customer” who behaves like a specific type of shopper. For instance, when a customer visits the website and is introduced to a particular brand or category that triggers a strong match based on their profile, there’s a high likelihood they’ll be interested in those products, so Saks builds a unique experience based on this information.
Kristin finds that to be an added value. As she describes in one hypothetical, “We noticed that you really love these three brands. There's this other brand you probably haven't heard of and you would love them as well, so why don't you give that a try?" Saks then adds that data to the DNA of what they think the customer would like, assuming that if they try it, they’ll like it. Layering on top of that, the AI tool then identifies the customer’s current actions. The process has been so successful that sometimes the store runs out of inventory on products because so many customers have been exposed to the item.
Finding actionable insights with customer data
The marketing team is pushing themselves to find more ways to infer what customers are looking for without customers needing to tell them. They’re continuing to test and roll out new strategies that expand on that idea. But they want to be smart with their data. As Kristin shared, “Everybody can collect data and anybody can collect a lot of data, there's no shortage of that. It's being able to take it and activate it and make it meaningful.”
One example of how they’re using data innovatively is by arming in-store associates with information about what customers are shopping for online. A lot of customers have stylists they work with in-store who sometimes order things on their behalf. Having customer data that is clean, reliable, and structured in a way that's ingestible is key for stylists to delight their customers with items they know they’ll absolutely love.
Discovering new use cases with AI
Saks plans to use AI more and more as new use cases become available. And while they see opportunities such as writing product descriptions as valuable, they really hope to leverage it to further personalize the customer experience and cultivate customer relationships. They see a world where sales reps will know more about each customer and be able to find out what the customer cares about and what they need to make the shopping experience more exceptional.
A powerful AI future will be here before you know it: with capabilities like predictions, generative, and workflow automations that will make AI consumable for business users across teams. These are just some of the intuitive capabilities that will soon be available with Twilio’s CustomerAI.
With all that information based on intelligent insights from YOUR customer data, the sky’s the limit in terms of delivering the best outcomes for your customers and deepening your relationships along the way using AI and a customer data platform.
To fuel efficient growth, you need to have a complete understanding of who your customers are and how they want to interact with your business. This is especially important because in this new world of AI, it’s about getting all of your data ready for AI use cases. This will be your competitive advantage moving forward.
But it takes time to get there. Afterall, AI is only as good as the customer data it was trained on. Don’t fall victim to GIGO (garbage in, garbage out) with your AI strategy. The path to intelligent customer engagement starts with high-quality, first-party data.
You can get access to more customer stories in our ebook, Personalization in the Age of AI: How 4 Brands are Shaping the Future.
Our annual look at how attitudes, preferences, and experiences with personalization have evolved over the past year.