With 80% of customers more likely to buy from brands that provide a tailored experience, many brands rate personalization as their Number 1 priority. Yet they report struggling to get measurable value out of their personalization efforts due to the complexity of analyzing and taking action on a huge number of variables. Evolv AI automates the analysis and delivery of a personalized customer experience (CX), unlocking previously inaccessible gains by targeting the right customer segment with the right experience.
When visitors land on a website, what keeps them there? 72% of customers say they’ll only engage with a brand if content is personalized. How is this even possible when so little is known about a first time visitor? What will create that sense of belonging and give them a level of confidence that they’ve landed on the right website to find what they’re looking for?
The trend towards personalization is driven by customer fatigue:
- 71% of visitors are frustrated by impersonal shopping experiences
- 42% of visitors are frustrated by impersonal content.
Visitors don’t have time to waste searching several pages to find what they’re looking for. Rather they want the quickest and easiest shopping experience which means having relevant, value-filled content served up to them.
Is personalization a privacy risk for customers?
There is a perception that personalization requires using personal data which is often seen as an intrusion and gives rise to privacy concerns. Customers seem happy to share some essential or basic information, but are nervous when companies appear to know too much about them as this can feel invasive. While the impending demise of third-party cookies has been met with dismay by many marketers, customers welcome any steps to improve privacy. The good news is that with Evolv AI, third-party cookies aren’t needed for personalization. We utilize far better ways to improve customer experience (CX). It’s about knowing which website data to use and how to use it.
For personalization to deliver on customer expectations, move them further along the customer journey, and get them to buy, it needs to be part of a broader CX strategy. This means considering each potential point of friction and optimizing every part of the digital CX to delight and connect better with customers. This is what will help to build long-term relationships that can increase the average order value (AOV) and the customer lifetime value (CLV).
AI-driven optimization is the starting point for personalization as it enables brands to automatically experiment with hundreds of ideas and variables in thousands of combinations. This provides brands with meaningful insights that indicate what customers are responding to. From there it becomes possible to personalize CX even more by focusing on specific elements. The key is being able to experiment with real visitors and customers and having the ability to serve up new experiences in real-time as the platform learns what works and what does not.
Two key features of AI-driven optimization that improve CX and personalization
Evolv AI has developed Targeted Optimization and Auto Targeting to enhance the benefits of AI-driven optimization and help brands serve up more personalized digital experiences.
Targeted Optimization is a feature that allows companies to specify a particular variable to be shown to an audience segment, without requiring any personally identifying information.. For example: a travel site may have several different digital ads, each with unique wording, but pointing to the same landing page. Targeted Optimization can be used to create unique experiences on the landing page to align with each traffic source and ad. The objective is to discover what particular version aligns best with the expectations set up by each traffic source.
If one ad highlights all-inclusive deals, for example, then the content served up on the landing page will align with that. This type of post-click optimization helps personalize the digital experience by adding context to the information displayed in the next step of the customer journey.
Another example of targeted optimization is Behavioral Targeting. This looks at mouse movements, click patterns, and time on site to engage with a visitor at just the right time in their journey. It can be used to identify when a visitor is likely experiencing a level of frustration and is about to leave a site, abandoning a shopping cart in the process. By having a pop-up offering assistance via chat or email just at the right time, cart abandonment can be prevented and a sale made instead.
Auto-Targeting is a feature that leverages AI to combine experimentation and personalization so that they are integrated flows in the same system. This means that experimentation results are analyzed and then used to automate personalization decisions. Continuous AI-driven optimization generates an enormous amount of data and auto-targeting is able to leverage that data to improve personalization capabilities. It creates a seamless experience by managing content for both experimental and targeted content and integrating with the experimentation framework.
A key benefit is that auto-targeting enables personalization for smaller target segments. Continuous optimization builds a persistent knowledge base, accumulating enough information to provide a tailored experience for individual customer segments. This takes personalization beyond the next best offer and basic demographic or geographic targeting. It is something no traditional approach can ever do since they cannot accumulate enough data.
With personalization being seen as a core component to improving CX, having technology that can generate the right data and then act on it is critical. AI-driven optimization can advance personalization and CX optimization capabilities. This also delivers scale which provides the ability to align with customer expectations and continually improve CX.
