Navigating the Future of Personalized Marketing with AI
In a world where seventy-one percent of consumers expect companies to deliver personalized interactions. And seventy-six percent get frustrated when this doesn’t happen. Personalization is no longer a luxury; it’s an essential.
As consumers' demands for personalized messaging continue to rise, brands are facing a challenge to deliver outstanding customer experiences that surpass traditional marketing methods. To tackle this problem, Movable Ink introduced its Da Vinci AI platform, which decodes real-time user intent and tailors email communications to meet the ever-changing expectations of consumers.
I recently spoke to Anjali Yakkundi, the Vice President of Product Marketing at Movable Ink, who began by discussing the limitations of moments-based marketing and how AI has transformed their approach to customer engagement.
AI allows us to move away from moments-based marketing, which was all about segmentation, putting people into different audience buckets dependent on specific triggers, such as cart abandonment.
But that typically doesn’t give you enough information to start a meaningful conversation with a customer. The data is way too limited. We need to develop long-term conversations with our customers based on ALL their interactions with the brand. The AI allows us to better understand the customer’s needs to provide an experience more akin to a personal shopper.
How does Da Vinci determine the optimal time, subject line, and content to engage each customer?
Da Vinci optimizes customer engagement through content decisioning, which maps customer behavior, product catalogs, and creative performance, integrating promotion and editorial content.
We utilize AI for send time optimization, predicting when customers are most likely to open emails, generating relevant subject lines, and adjusting email frequency based on an individual’s preference. Instead of just focusing on one aspect, we aim for a holistic conversation with the customer, ensuring every message is personalized and timely.
So, is it about brands being honest with customers, saying, ‘Okay, hands up, we don't always send you the right things, but we still want to be useful.’?
Email marketing today is primarily guided by a content calendar, often influenced by internal dynamics like inventory or merchandisers' priorities, for example. This brand-centric approach can overshadow the individual needs of your customers. We aim to change that.
While major promotions like Black Friday will always exist, we’re advocating for a shift where most content is driven by the customer's preferences, not just the brand's agenda. The challenge is for the AI to get to a stage where it understands you enough and those quirks we all have to start being helpful.
The AI allows us to understand individual customer facets and use them to start meaningful conversations with them. Different things engage customers: their different motivations, tastes, preferences, and what they like and dislike when they shop.
The AI maps each customer's traits to produce customer facets. These are then mapped onto the product catalog, and then we engage the communication and have the conversation with the customer to match their interests and needs.
How does Da Vinci understand your needs at any specific time to ensure they match your intent?
Da Vinci uses two main AI approaches to understand the customer's journey. Firstly, it leverages an ensemble approach, combining multiple algorithms in unison to build a comprehensive understanding of customer needs.
Secondly, we incorporate hyperbolic geometry, which reflects the non-linear approach to most customer journeys by mapping customer behavior, product catalogs, and content efficacy in a three-dimensional space.
Instead of leading someone directly from one product category to another or hitting them with similar products to one they’ve recently purchased, we introduce them to the products relevant to their journey, then gradually advancing across the brand catalog and product categories.
How does AI help marketers be more effective at their jobs?
AI doesn't replace marketers but empowers them to be more efficient and strategic. AI can handle tasks beyond human capacity, eliminating tedious work like managing campaign calendars. Instead of focusing on small details, like last-minute copy changes, marketers can use Da Vinci to strategize the role of email within their broader planning and create more engaging campaigns and content.
AI excels at detecting subtle shifts in vast data sets, which humans are likely to overlook. However, it's essential to maintain some human control, especially in scenarios like retail, where brand placement matters.
For instance, luxury brands might have guidelines about not being placed next to certain other brands. While AI is a valuable tool, marketers should always have oversight and the ability to impose brand-specific rules. The goal is a collaborative approach between AI and human expertise.
How does Da Vinci correlate short-term metrics, such as open and clickthrough rates, with long-term customer engagement metrics? Is this integration built into the platform?
The AI is trained on short-term data like today's opens and clicks while aiming to introduce new products that enhance conversion, average order value, and incremental revenue lift. We believe in achieving a balance; it's essential to consider both immediate results and long-term gains.
Another critical component we focus on is content performance. Marketers often operate on intuition or internal feedback when it comes to content. With Da Vinci, we provide insights into which content resonates best with different types of customers. This helps our clients develop creative editorial content that truly resonates.
What sort of ROI can Da Vinci deliver?
We conduct daily A/B tests comparing traditional campaigns to those powered by Da Vinci. Our findings consistently show Da Vinci outperforms the business-as-usual approach.
On average, users of Da Vinci experience a 19% increase in conversion rates and a 23% boost in revenue compared to standard methods.