How To Orchestrate Personalization In The Experience Economy

How To Orchestrate Personalization In The Experience Economy

Business-to-consumer (B2C) brands are facing unprecedented competition from peers as well as adjacent industries in the budding experience economy. Consumers no longer just buy based on product or price point. They are in the market to buy experiences.

Additionally, there’s a growing demand from consumers that the brands they interact with know and understand them, in real time. Gartner points out that 67% of consumers feel it’s very important for brands to automatically adjust content based on customer context and 42% get annoyed when content isn’t personalized.

And, with the linear customer journey now a thing of the past, not to mention that every consumer follows a unique path to purchase, it’s plain to see that a shift to 1-to-1 outreach is imperative. Personalized experiences help create emotional bonds between a consumer and a brand, which, in turn, drives engagement, loyalty, repeat purchases, and long-term financial returns.

Of course, consumers have to be willing to share at least some of their personal information with you for that to happen. Many are. According to eConsultancy, 57% of consumers are willing to do so as long as it’s beneficial to them and is used responsibly.

Although many businesses do have a personalization strategy in place, many lack the right focus and are using personalization as another selling instrument—rather than for delivering the best customer experience.

Let us look at a few approaches that companies can take to upgrade their personalization strategies and improve customer experience.

1. Allow Consumers To Participate In The Personalization Process

Ask customers what they want to be recommended, rather than bombarding their screens with upsell or cross-sell suggestions. Maintaining a feedback loop for personalization with your customers can go a long way in increasing retention and conversion rates.

For example, Amazon lets users manage their personalized recommendations by giving them the option to remove products or categories they don’t want to receive recommendations for. Similarly, Stitch Fix, an online personal styling service, ensures better personalization of its products over time by collecting qualitative feedback on each delivery and updating customer preferences accordingly.

2. Remove Silos Between Departments And Partners

Organizations must ensure that data collected by their business units is shared incessantly and none is wasted. They must also become progressively open to let partners leverage their data to weave a seamless online-offline experience for consumers.

Consider a scenario where a customer visits an automotive brand’s website. They show interest in a vehicle and fill out a preference form. The details get stored in a QR code that can be shared with a representative at any local dealer shop. As soon as the customer shares the QR code with the local dealer, all of that person’s details and preferences become available to the dealer. In one click, the customer enjoys a personalized experience without having to repeat preferences.

3. Augment Algorithmic Personalization With Human Decision-Making

For certain consumer segments such as high-end and luxury, the human touch can be the key to winning the experience battle. Unfortunately, according to a PwC survey, 64% of U.S. consumers and 59% of all consumers feel companies have lost that touch.

Regardless of scale or innovation, brands can arrive at genuine personalization when they combine a human touch with their algorithmic insights. For example, Trunk Club, a personalized/mid- to high-end men’s and women’s clothing service based in the U.S., is bringing back the customization of a personal stylist with the convenience of online shopping. Each Trunk Club customer works with a stylist who chooses their clothing, which is shipped to their home; the customer can purchase the clothes outright or send them back to Trunk Club.

4. Intertwine Personalization With Artificial Intelligence And Machine Learning

For marketers genuinely looking to achieve “individualization” at scale, artificial intelligence (AI) and machine learning (ML) will be their keys to success. Gartner projects that by 2020, smart personalization engines used to recognize customer intent will enable digital businesses to increase their profits by up to 15%.

To personalize at scale, large volumes of data must be processed in a reliable and repeatable basis using ML models to decide the next best action for users. The quality and granularity of data used by ML models becomes extremely crucial for predicting meaningful, 1-to-1 recommendation for customers.

For example, Netflix collects feedback on every visit to its service and retrains its algorithms with those signals to improve the accuracy of its predictions of what a subscriber is most likely to watch. Netflix tracks not just macro-events, like video completion, but also micro-events, such as scrolling behavior and video pause/rewind behavior, which makes data more insightful. Its extremely granular data, algorithms, and computation systems continue to feed into each other to produce fresh recommendations to users with a product that brings them joy.

According to research conducted by Adobe in partnership with eConsultancy, only one in 10 organizations see themselves as “very advanced” in respect to customer experience in 2019—just a two-percentage-point improvement since 2015. Meanwhile, consumer expectations around personalization continue to rise. Marketers need to decide whether they want to take advantage of these expectations and lead the personalization run or become laggards and lose relevance in the eyes of consumers.