Technology’s Role in Building Trust for Retail Ecommerce Brands, Pt. 3

Ecommerce consumer personalization has been dubbed a “force multiplier” that now extends to the entire customer experience. According to a recent study conducted by McKinsey & Company—Next in Personalization 2021 Report—it is a business necessity because companies that excel at demonstrating “customer intimacy”—that leads to trust and loyalty—generate faster rates of revenue growth than their peers.

Successful personalization programs yield more engaged customers that drive up the top line. In its research, McKinsey found that 71% of consumers expect companies to deliver personalized interactions. When this does not happen, 76% of consumers get frustrated. If consumers don’t like the experience, it’s easier than ever to choose something different. During the pandemic, 75% of consumers switched to a new store, product, or buying method.

Non-personalized communications for retail ecommerce brands pose a business risk, especially in a low-loyalty environment. Shoppers have a strong point of view when it comes to personalization—72% of consumers said they expect the businesses they buy from to recognize them as individuals and know their interests.

When asked to define personalization, consumers associate it with positive experiences of being made to feel “special.” McKinsey noted that consumers respond positively when brands demonstrate their investment in the relationship, not just the transaction. Jeff Bezos, founder of personalization pioneer Amazon, said, “If you do build a great experience, customers tell each other about that. Word of mouth is very powerful.”

So, what are some of the key methods or tactics for building trust and a great customer experience through ecommerce personalization? While this is an extremely in-depth and voluminous topic, most thought leaders and personalization experts in the ecommerce industry focus on the following:

01. Predicting consumer behavior,

02. Personalizing search results,

03. Establishing loyalty or membership programs, and

04. Using machine learning to make product recommendations.

01. Predicting Consumer Behavior

Predicting consumer purchase behavior is both an interesting and challenging task. According to an article that appeared in Forbes about the downstream benefits of predictive analytics, Professor Ravi Dhar of the Yale School of Management, said that it all comes down to being “able to predict what it would take to encourage a desired customer behavior.”

Another way to look at this concept of consumer behavior prediction is that it is really about describing the ecommerce customer journey that takes place during a session. This includes researching, choosing, or buying a product or service. The ultimate goal is to predict consumer behavior during this process on a retail ecommerce platform so a brand can effectively lead a consumer to a desired end goal such as a sale, extra subscription, or a booking.

To accomplish this goal, most retail ecommerce brands use some form of computational analysis or statistics to obtain meaningful patterns from their collected data that can be used to predict the future. Using predictive analytics allows online brands to gain insights.

These powerful insights can attract and engage potential customers by personalizing their ecommerce journey with relevant offers and experiences by analyzing past purchases, behavior, social media, and demographic profile. It can also improve customer retention by evaluating customer value and using proactive retention approaches to retain customers.

Predictive marketing and analytics initiatives have been delivering impressive results. A survey by Forbes found that 86% of executives overseeing predictive marketing efforts for at least two years have reported an increased return on investment (ROI). Also, when these executives were asked about the factors that helped to deliver these results, they indicated that it was a confluence of two factors: effective technology choices (63%) and organizational support (57%).

For an ecommerce platform, the way to personalize and predict its way to success comes down to discovering insights, predicting behaviors, recommending actions, automating the process, and winning customers.

02. Personalizing Search Results

Most ecommerce platforms have a search engine box, but the engine or algorithm that drives it can be drastically different from platform to platform. The goal here is to use the best technology that has the capability of powerfully personalizing search results that match what an individual really wants—consumers are very savvy and will place more trust in a platform that produces individuated search results that are better than expected and highly relevant to their needs.

The power of personalizing search results is that they are uniquely tailored to each individual consumer on an ecommerce platform. Basically, an individual profile is built for each consumer that takes into account specific attributes such as:

• Past purchase history
• Past page viewing history
• Social media activity
• Product ratings
• Brand preferences
• Demographic information
• Loyalty membership status

A goal of this type of personalization is to engender trust by delivering value to consumers as quickly as possible through smart “one-to-one” search results based on an individually developed profile that has a greater propensity to match a user’s real search intent.

This should be distinguished from the inferior search algorithm that provides “one-to-many” results. For example, people who buy X also tend to buy product Y. This method is way too generalized and may miss the mark of matching personalized search results that actually capture the user’s intent.

In a report published by Forrester about “must-have” ecommerce features, it was found that 43% of users on retail websites go directly to the search bar. Based on this, ecommerce brands should pay particular attention to personalizing site search results as it can heavily influence sales and conversions for shoppers intent on finding the “right” product.

03. Establishing Loyalty or Membership Programs

When it comes to ecommerce personalization, one very robust tactic is to establish a loyalty or membership program for customers. An ecommerce loyalty program is a customer retention tool designed to keep and engage existing customers. When deployed correctly, it can result in customers buying in higher quantities, shopping more often, or interacting with a brand more frequently.

These programs, if executed correctly, concentrate on personalization and focus on building more meaningful consumer relationships that go beyond just the transaction. They emphasize trust or establishing an emotional connection with customers so they can identify more with the brand’s core values. Personalization can be supercharged by the enhanced collection of data from some of the ecommerce brand’s most loyal customers.

An effective loyalty or membership program should always stress emotional connection, experiential rewards, and personalization. In fact, a CEO and co-founder of a popular omnichannel loyalty platform said, “Behind the scenes, an important paradigm shift is happening for loyalty programs. Of the companies that are planning to launch their loyalty program in the next two years, more than half of respondents specified that their program would be more emotional than rational.” In other words, introducing an emotional loyalty program is likely to emphasize an increased bond and level of trust with an ecommerce consumer.

To learn more about the benefits of using technology to establish consumer trust with loyalty or membership programs, see the three-part series on Capturing Customer Loyalty with Paid Membership and Reward Programs.

04. Using Machine Learning to Make Product Recommendations

“Since you bought this, you’ll also like this . . .”

Another robust way of establishing trust with ecommerce consumers is to use technology to build a product recommendation system that provides personalized offerings. The best systems make use of machine learning (ML) and artificial intelligence (AI) technology.

A product recommendation system is a tool designed to generate personalized suggestions for items that a user would likely purchase. Using machine learning techniques that absorb various data about individual users and individual products, the technology is capable of creating an advanced net of complex connections between products and people.

If a product recommendation system, driven by ML, is set up and configured correctly, it can significantly increase desired consumer actions such as conversions and sales. A study conducted by a well-known product recommendation platform found that when shoppers click on product recommendations, the chance that they will complete the sale nearly quadruples. The reason is really simple—the right product recommendation technology gives retail ecommerce brands the power to use client behavioral data to optimize their own customer service efforts while also increasing potential ROI for marketing efforts. Once trust is established, ecommerce brands can recommend even higher-margin products to increase the top line.

To get the most out of ML generated product recommendations, ecommerce retailers should consider doing the following:

• Place product recommendations above the fold
• Personalize product recommendations based on web behavior
• Use “best-selling” recommendations for new visitors
• Add personalized recommendations into emails

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Carlos Guerrero, a Senior Director at Gartner, said it the best: “The brand trust bar is higher than it’s ever been. Our research shows that 74% of customers expect more from brands—not just around product performance or durability, but in how brands treat their customers, employees, and the environment.”

A great way for retail ecommerce to leverage technology to exceed the “brand trust bar” is to focus on personalizing the overall customer journey and experience. In return, consumers will place more trust in the brand, which will increase the probability of desired actions such as conversions, sales, and subscriptions.

You can click to read part 1 and part 2 of this blog series!