AI Technologies and Strategies to Win with Digital Experience
Increased attention to post-pandemic strategic planning brought to light the importance of digital transformation and the need for businesses to align the customer experience they deliver with the growing demand for personalized digital interactions. Given the competition and customers’ expectations that the businesses they interact with know and understand them, using innovative technologies such as an AI-Driven personalization platform can make a difference for your business.
To help with the challenge to win the customer experience race, we teamed up with Forrester Principal Analyst James McCormick for a webinar to offer valuable information for insight-driven businesses looking to increase the value of their customer data by leveraging new technologies like Artificial Intelligence (AI) to optimize their customers’ experiences.
How do AI-Driven personalization solutions work?
The personalization magic happens by applying ML (Machine Learning) on customer data to produce insights/ actionable predictions that are contextual to the customer or interaction. Intelligence is synced with engagement to deliver 1:1 personalized interaction. What truly sets these solutions apart is the ability to do this at scale. A historical overview of enterprise businesses that have been utilizing these solutions successfully shows that being able to deliver new ways of individualized engagement and do so at scale is the powerful differentiator that helps them lead in the customer experience race.
“AI approaches are able to learn and continuously learn and adapt to that situation. So it’s the precision and accuracy and also they provide scale … scale beyond just a simple rules-based. You can apply a good algorithm to more scenarios to more engagements than a simple segmental rules-based approach.”
– James McCormick, Principal Analyst, Forrester
In the webinar, McCormick demystifies some perceptions associated with AI-driven personalization and provides insights on how the use of AI leverages data to enable individualized engagement at scale. And a bonus! A quadrant and flow chart which can help businesses looking to prioritize personalization and AI-Driven technologies assess what’s strategically right for their business. We highly recommend listening to McCormick’s detailed explanation to gain the most out of the charts which weighs-in data risk versus algorithm risk; deeper data collection (sensitivity of data) versus riskier ML models (advanced rather than rule-based).
One of the key takeaways from the webinar is that AI-based personalization offers a multi-dimensional opportunity for individualized engagement across the customer’s life-cycle. Moreover, effective personalization is highly linked to the use of AI/ML-based solutions.
As noted above, scrutiny has to be applied when choosing an AI-based solution; advanced AI algorithms can help you scale but if executed poorly things can go wrong at scale!
Indeed, the lack of skill-set and internal strategy can inhibit the personalization transformation, and herein lies the trap. Especially now, in the post-pandemic reality, businesses can no longer rely on products and services alone to stand out and must compete by providing individualized experiences or risk losing their customers.
The webinar is all about strategies to HELP you win the digital experience! Kendra Chen, ZineOne’s VP of Product Management follows McCormick and delves into how solutions like ZineOne’s platform mitigates some of the challenges and effectively opens up the door for more businesses to utilize AI-driven personalization.
Kendra is responsible for the ZineOne personalization platform roadmap and for creating use cases for effective digital engagement. Kendra and the team here at ZineOne have been helping fortune 500 companies win the experience race since 2014. A customer-obsessed company quite literally; working closely with our clients to align our solution with their needs. Kendra shares her expertise on the inner works of an intelligent platform and how it is designed to enable businesses lacking in data science skillsets to integrate AI and ML-based personalization in their digital marketing strategies and ultimately meet their business goals.
The power of layering
ZineOne’s intelligent customer engagement platform functions in layers. The data management layer flexibly brings data in, the intelligence layer is where the ML models/ algorithms driven decisions are implemented to predict and deliver better sessions, and the activation layer is where we’re pushing those experiences out to customers to help improve their journey. So, in a sense, we are helping reduce that algorithm risk mentioned above while enabling advanced decision making; delivering contextual personalized experiences rapidly, accurately, and at scale. In this process, we’re always thinking about the right mix of design and precision in data and algorithms that will make our customers most successful.
Actions speak louder than words
To demonstrate that design/algorithm mix, Kendra walks us through two use cases for a deeper dive into how AI and ML are actually used by businesses to create a personalized experience.
Time, Location, and Weather Sensitive Offers
This use case reflects the importance of aligning with what your customer is currently experiencing in their physical environment of the location they are at. This model delivers relevant and contextual experience tailored to the customer’s sentiment. The challenge is not just in knowing their location at this very moment, but also taking into account where they are spending most of their time and their typical location at a particular time of day. We are layering weather intelligence on historical and environmental data to make the decision about how it truly feels for that user. Sending the right message at the right time, be it a purchase suggestion or an incentivized offer, is a highly contextual personalized experience promoting the connectedness customers are looking for.
In-session Real-Time Offers
This use case is about accurate purchase predictions (purchase propensity) and tackles mid-funnel problems when users are already engaged with the business, like cart abandonment. By identifying early in the session whether a user is likely to purchase, the activation layer can provide a quick journey intervention, an offer or messaging that helps encourage users and change their behavior. Here we are using ML and AI to identify early in the session if a user is going to bounce and if they are unlikely to make the purchase. By the user’s 5th click our model has enough data for that intelligent layer to make informed decisions and provide a recommendation that influences the user’s journey and changes the outcome of the interaction.
These use cases work especially well for navigating the post-pandemic waters with success. Referring to Jame’s McCormick’s chart above, these are good examples of omni-feature experience optimization where the data layering enables decisions that are dynamic and contextual. With the rapid changes in consumer behavior indicative of our current reality, experiences based on past activities are no longer as effective as individualized in-the-moment predictive personalization that factors in other dimensions of data like short term behavior. Moreover, the ability to interact with the visitors while they’re still engaged with you is critical, even five seconds is too late. Finally, the use cases demonstrate the importance and value of model accuracy; effective personalization depends on custom ML models that are tailored for your business’s unique products, your customers’ characteristics, and the goal that you’re trying to achieve.
Now is the time!
AI-driven personalization engines are able to continuously learn and adapt to a situation and can be your differentiator in the experience race. As pointed out by McCormick, research shows that by 2021 insights-driven businesses will be generating $1.8 Trillion annually. So the time to take action is now and this webinar offers ample resources to get you started! AI can help you scale your personalization efforts, and better yet, modern personalization solutions like ZineOne’s intelligent platform do not require specialized skill-sets and can be utilized even by non-developer teams. AI and ML capabilities can be purchased without the need to hire data scientists and engineers to create these much needed powerful models that deliver accurate individualized experiences and can boost your digital marketing roadmap.