Today’s customers expect retailers and brands to demonstrate that they know them and understand their needs. Personalized experiences that give customers confidence that the brand is using their information to help and guide them—instead of solely retargeting, hindering, or pressuring them—are truly Differentiated Experiences (DX). Such a customer experience strategy not only improve key KPIs in marketing, e-commerce, and service but also positively influence loyalty and advocacy.
Today’s customers have tremendous choices in how, when, and where they shop. Varying needs, as well as short attention spans, often result in customers exhibiting different behavior in each visit. As such, marketing and product teams in retail and e-commerce face key challenges in combining data, tools, and operations to personalize each customer’s experience. Systems that fully utilize technology to understand in-the-moment behavior at scale can make a significant impact on business KPIs and customer experience on digital channels and in store.
Typically, personalization has been powered by pre-sorting customers into specific segments and personas. However, approaches based on using just demographic or transactional data can be inadequate since today’s customers exhibit different behaviors across visits, and across channels. Successful personalization tactics must factor the dynamic nature of a customer’s in-session behavior as they are interacting with the brand. A continuous understanding of such behavior opens powerful new opportunities to personalize interactions for the customer’s immediate need.
In this interwoven world of digital and physical channels, retailers need a customer experience strategy where stores and digital channels coexist and complement each other. A recent study by NRF notes that more than half of US consumers shop online and in-store (54%) and that omnichannel customers outspend single-channel shoppers significantly (6x or more). Marketing teams are challenged to build a cohesive, real-time view of customer context that can drive seamless, cross-channel personalization.
Marketers struggle with having to manage constant data updates from different channels as well as from disparate enterprise systems (CRM, CDP, DMP, offer systems, loyalty, inventory) which are essential to deliver Differentiated Experiences. Marketers are also looking for effective and convenient ways to test and automate personalization use cases at scale — to combine historical transactions data with short-term user behavior and exogenous variables (weather, location).
Streaming analytics and machine learning models for in-session, early prediction provide new opportunities for actionable insights from clickstream data. Personalization tactics built on such high-context insights delight customers and deliver a significant boost to business KPIs. These approaches lead to innovative use cases that create urgency, scarcity, and velocity. They also have the unexpected benefit of fast deployment as data requirements are mostly limited to event parameters contained in the clickstream.
A new age of differentiated experiences is evolving from sharing real-time trending data about products, prices, offers and inventory with customers. For example, a price-sensitive customer has been browsing the footwear category but has not added shoes to her cart. Real-time data that aggregates the number of times each SKU was viewed or added-to-bag by all online shoppers over the last 10 minutes will help increase her product and price confidence through social proof.
Context decays with time, therefore, it is imperative to act on streaming data even before it can be stored. In-the-moment notifications — e.g., those pertaining to loyalty reward expiration — significantly influence buying decisions within the session or store visit. Consider real-time because high-context notifications can produce 50%-100% lift in KPIs.
Consider the following example: a time-sensitive customer has a pair of shoes in her cart on her favorite brand’s website for the past few days. She is in the mall, near a store and receives a phone notification of the availability of those shoes at that store. Along with the quantity remaining, if her size is likely to run out. Ease of access to this kind of individualized information can truly surprise and delight customers.
A use-case based approach centered on fully leveraging the value of real-time, high-context data has the added advantage that it does not involve complex integrations across multiple systems. Therefore, marketers can focus on adding use cases to their personalization goals progressively, without having to undertake long-term efforts of data aggregation, cleansing or enabling microservices and APIs. By leveraging stream analytics and machine learning, enterprises can still do real-time, multivariate decisioning that triggers the right action for every customer—and the best time to show it—based on their current journey.
ZineOne’s AI-powered Customer Engagement Hub delivers real-time personalization through its Continuous Intelligence modules that are built on Customer DNA™. This innovative technology enables enterprises to uncover in-session user behaviors and act on those with 1:1 in-the-moment experiences.