Personalization, Customer Experience, and AI

Aug 26, 2019


Article ImageAccording to “2019 Trends in Personalization,” a report by Evergage and Researchscape International, a personalized experience results in an overall better customer experience (CX). This sixth edition of the study surveyed 314 marketing professionals across major industries to understand the uses, challenges, and benefits of personalization better. Eighty-eight percent of marketers choose personalization primarily to deliver a better CX, per the results of the survey. Other benefits include increases in customer loyalty, ROI, and lead generation.

Loyal fans appreciate the tailored customer journey created by brands that cater to their individual preferences—especially as the likes of Facebook and Netflix set the standard for customer expectations across all industries. “We believe that consumers are fairly conditioned by some best-in-breed service providers like Amazon and Spotify, on such personalized experiences, so they have come to expect it more and more,” says Andy Zimmerman, Evergage’s CMO.

The challenges of personalization have long been too much for some brands to overcome. But technological advancements have helped make personalization a more attainable goal. “The interesting takeaway is that companies are also challenged to deliver those experiences and a lot feel like there is room for improvement with their personalization efforts,” says Zimmerman. But technology (especially advances in AI and machine learning; ML) is rapidly changing what’s possible in personalization. Significantly, he adds, “We are also seeing ML become popular—26% last year said they were using it, but this year, it is up to 40%. That’s one of the biggest changes we saw year per year among any datapoint.”

  

Benefits of Machine Learning

Evergage’s client Zumiez (a specialty retailer of apparel, footwear, accessories, and gear for skateboarding, snowboarding, and surf lifestyles) uses ML to create personalized brand experiences, gauging shoppers’ intent and responding in real time. Algorithms take into account the individual’s past session activity and purchase history to customize the shopping experience at every level—from the homepage to on-site search and from product detail pages (PDP) to checkout.

Zumiez has placed individualized ML-powered recommendations in several areas (such as its Recently Viewed and More to Check Out sections) and directly in the search results so that shoppers can quickly discover relevant new products. This has translated to high conversions in which shoppers who click a personalized product recommendation converted to a purchase 2.7 times more often than those who did not; they also spent 2% more per order. Additionally, Zumiez experienced increased engagement as shoppers who viewed and clicked on a personalized product recommendation on a PDP had a session duration nearly four times longer than those who had not clicked on a recommendation.

Carhartt, another Evergage client, is a family-owned retailer known for work clothes (such as boots and overall for farmers, hunters, etc). The company implements ML algorithms to recommend complementary items that “complete the look” for a customer’s individual preferences and has generated a 5% lift in clickthroughs to its PDPs. When shoppers leave items in their carts, Carhartt sends a triggered email containing the item left behind. ML algorithms are also used to select other relevant products customers might like, and this campaign has increased conversion rates seven times over the previous triggered email solution.

“AI helps improve personalization by automating individual personalization of product/services across multiple channels,” says Morgan Lathaen, a marketing and brand coordinator with the print and marketing logistics company thumbprint. “For email marketing and advertising, AI enables us to study our customers’ behavior and manage the data to create customized emails and ads tailored to each individual subscriber. It allows us to figure out what a customer would like to receive and when in an automated fashion. This is how we manage our social media engagement as well. We use AI to customize our social media content to deliver exactly what our audience wants, on the channels we are most likely to reach them.”

 

Challenges in Implementation

The study made it clear that marketing is moving toward a one-to-one approach versus the long-standing one-to-many segment-based or rules-based strategy companies have used in the past. With the use of AI and ML, this is a quick and scalable scenario. Predictive analytics and algorithms help provide tailored recommendations to customers in real time based on past user behavior. However, not many organizations fare very well in terms of current personalization efforts. Only 32% of respondents believed that marketers are getting personalization right, and a mere 18% expressed high confidence in the strategy their organization employed. Fewer still considered their company’s strategy to be advanced, with only 5% grading themselves an A on their current efforts.

Miami-based brand strategist and neuromarketer Lexi Montgomery adds, “Technology can provide an incredible amount of assistance in streamlining and automating time-consuming processes. However, it’s not an improvement if the business owners are operating within limiting beliefs and blind spots. The degree of progress accomplished directly correlates with the amount of understanding of the problem being solved.”

According to Montgomery, since AI algorithms are gathering information based on users’ actions online, there is no way to determine the why behind a specific decision or the motivation for a certain action. “AI is reactive, collecting data and creating new opportunities based on the data previously collected. Consumers are conditioned to only see things through a filter, based on algorithmic, historical data. This poses a great challenge when it comes to obtaining ‘accurate’ data on human nature.” 

 

Looking Ahead

For the first time, the survey asked respondents if privacy concerns might curtail personalization, and only 18% said yes. The majority of marketers (80%) were moderately, slightly, or not concerned about the impact of the European Union’s General Data Protection Regulation (GDPR) on their efforts. Of promise, though, is the finding that among digital marketers not currently implementing any ML or other algorithmic personalization, 42% plan on it in the next year; of those currently using it, 48% expect that budget spend to increase.

“Anyone from any industry could likely be using personalization and benefit. Retail is an aggressive adopter of ML strategies because they have large product catalogs and value product recommendations,” says Zimmerman. Most companies, he says, have a catalog of products or services that potential customers need help navigating, “and personalization can save them a lot of time and effort.”    


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