Data-driven retailers know that customers expect personalization, and they will readily switch brands if their expectation is not met. In a recent McKinsey study, 71 percent of consumers said they expect a personalized customer experience (CX) every time they interact with a brand, and 76 percent are frustrated when this does not happen.
Consumers make no distinction between a brand providing a personalized CX whether the customer is known to the brand or not. That is, both loyal customers and first-time visitors to the website expect a brand to make good on the promise of personalization.
Retailers cannot afford to wait until a customer is known, in other words, before initiating a differentiated experience through personalization. According to Storybloc, a staggering 60 percent of consumers will abandon a purchase due to a poor user experience on a website – experiences like providing irrelevant information or not understanding customer intent.
The ability to provide a consistent, personalized CX across an anonymous-to-known customer journey in an effort to reduce churn and increase revenue is a key reason retailers implement customer data platforms (CDPs) as a foundational component of a MarTech stack.
What is an Anonymous Customer/Prospect?
A digitally anonymous customer or prospect is a person that visits a website, contacts a call center, engages a chatbot, etc., without providing any identifying information. They are typically represented by a first-party cookie ID and device information. Their digital footprint will not have any PII and may or may not be attached to a digital history – a first-time vs. repeat visitor to the website, for example.
Once that digital footprint is created at the user’s first interaction, a brand can begin to attach signals to the anonymous ID such as page views, time on page, click-throughs, etc. Over time, the goal is to connect those signals to a known ID. Converting an anonymous customer ID to a known customer can be done many ways, such as through linking a credit card number used in a purchase to a known customer, when a customer fills out a form to receive a discount or to sign up for a loyalty program, or by matching a phone number, an address, or even the unique device ID to devices associated with a known customer (or household) at the same IP address.
What is Anonymous Personalization?
Using the example of a first-time website visitor, anonymous personalization might include showing increasingly relevant content based on how a customer (a device ID) navigates and engages with the website. A brand can build an affinity score based on pages viewed, items clicked, time on page, etc., and begin to provide a personalized experience while the online session is ongoing, such as dynamically changing out a hero image or refining product recommendations. As an example, a shopper clicking on black cotton crewneck sweaters could start to see some suggested black or gray cashmere sweaters, V-necks, or cardigans.
Even before any signals are attached to the device ID, truly anonymous personalization can include performing split a/b testing on a landing page hero image, determining which image is most successful in reaching a desired outcome (sign-ups, purchases, clicks, etc.), and then using that image for future first-time visitors while continually testing different images.
Anonymous mobile app visitors might receive a personalized experience if they’ve opted in to location sharing, which might then trigger notifications about a local store opening.
An increasingly personalized CX becomes possible for brands that collect and analyze initially anonymous signals, and takes shape as the digital footprint expands beyond the first click or interaction with an in-store kiosk, chatbot interaction, call center, etc.
Anonymous Personalization with an AI Assist
Anonymous personalization can also be achieved using AI models to segment an audience based on a desired metric or outcome. Whichever way the audience divides according to the algorithm will determine the content anonymous visitors receive. Consider a model that analyzes what landing pages a group of anonymous visitors looked at, how long they interacted with a page or what products they viewed. A first-time visitor could be placed into a certain segment based on commonalities between their own expanding digital footprint and those of previous first-time visitors, and receive content that is personalized for that segment. Dynamic models can self-optimize on the fly during a browsing session. If a model predicts a visitor is on the path toward signing up for a loyalty program, the visitor may be presented with content relevant to loyalty members.
Anonymous to Known and a Golden Record
When an anonymous record becomes known, the digital footprint and all the signals that are attached to that footprint combine to join or build a Golden Record, a single customer view that includes all there is to know about a customer. If the anonymous record belongs to an existing customer for whom there is already a Golden Record, the signals become part of that unified record.
Anonymous-to-known identity resolution and data quality are core functionality in an enterprise-grade CDP, and is a differentiating feature for Redpoint, unlike many other composable CDPs that either do not store data, or provide only basic downstream identity resolution prior to data activation.
An enterprise-grade CDP should perform data quality processes as soon as data enters the system. When advanced identity resolution using probabilistic and deterministic matching takes place continuously, the resulting Golden Record will reflect a real-time understanding of a customer. Another key feature important for relying on the accuracy of a Golden Record is the use of persistent keys, which provides retailers with a longitudinal view of a customer even amid frequent changes such as a new address, email, last name, etc. The use of persistent keys is crucial for providing consistent personalization across an anonymous-to-known customer journey.
Differentiate with AI and Dynamic Segmentation
Dynamic segmentation using AI is another key feature of an enterprise-grade CDP, providing retailers with a predictive analytics framework that allows them to engage a customer with a relevant CX across the span of an omnichannel customer journey. Whenever or wherever a customer chooses to engage, dynamic segmentation is a key feature to ensure retailers deliver a next-best action in the cadence of the journey.
A consistency of interactions as the customer proceeds along an unknown to known journey is important for building trust, providing value, and eventually driving revenue through increased loyalty and lifetime value.
Advance on Your Personalization Roadmap
With the Redpoint CDP, leading retailers have the needed tools to advance personalization build-out beginning when a customer presents as anonymous, ensuring a consistent CX that meets or exceeds customer expectations for relevance, even when presenting as unknown.
For more on how Redpoint helps retailers ignite their customer data and provide a personalized CX across an anonymous-to-known customer journey, click here.
