With COVID-19 transforming consumer behaviors, personalization matters more than ever before! While being an integrated part of people's everyday digital life, personalization has also become a top trend in the e-commerce industry 🌊.
Undoubtedly, you should also follow this trend to stay ahead of the game in 2022. 😉 But how? And what can be the best choice of personalization technology for your e-commerce business? 🤔
Look no further! 👀 In this article, we will first show you the power of personalization with the latest statistics, and then compare 3 personalization technologies (two traditional solutions and one innovative solution) to help you make a choice.
Let's get started ✌️
The Power of Personalization in E-Commerce
First of all, let's walk through these latest statistics and facts to understand the power of personalization:
Customers want personalization. As per Accenture’s recent study, 91% of consumers say they are more likely to shop with brands that provide offers and recommendations that are relevant to them. And 83% of consumers are willing to share their data to create a more personalized experience.
Personalization drives performance. According to McKinsey’s latest research, companies that grow faster drive 40% more of their revenue from personalization than their slower-growing counterparts. In general, 80% of companies have seen an uplift since implementing personalization.
Bad personalization destroys sales. 71% of customers feel frustrated when a shopping experience is impersonal. Worse still, 63% of consumers will stop buying from brands that use poor personalization tactics.
The next question is: How can you get personalization right?
The answer is simple. Choose the right technological solution to help you do that💡. So in the next session, we'll help you make a choice by comparing three popular personalization technologies.
Comparing 3 personalization technologies: neural networks, collaborative filtering, and ClerkCore
Neural networks and collaborative filtering are two traditional personalization technologies that have been around for decades. According to Pathmind, neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. Collaborative filtering is a technique used by recommender systems. It can recommend relevant products to customers by identifying customers' similarities based on their site interactions.
Comparatively, ClerkCore™ is a new technology and an innovative solution for e-commerce personalization. It is Clerk.io’s proprietary personalization technology with an algorithm specifically designed and tailored to the needs of modern e-commerce stores.
Here's a table that summarizes the advantages and disadvantages of neural networks and collaborative filtering:
As presented above, ClerkCore is a better personalization technology than neural networks and collaborative filtering since ClerkCore embraced all their strengths 🤝 while overcoming all their drawbacks 💪.
📚 Want to read more on the definition, applications, and advantages & disadvantages of these two traditional personalization technologies? Here’re two beginner’s guides:
It ensures diversity by automatically optimizing recommendations according to new trends and seasons (vs. collaborative filtering).
Second, ClerkCore has a better data efficiency ⚡ because:
It has no “learning periods” and no long training processes (vs. neural networks);
It supports scalability and shows no decrease in performance with an increasing volume of data (vs. collaborative filtering).
Clerk can generate more accurate predictions while optimizing data efficiency, and the reason why is that Clerk invented a more advanced AI technology with an algorithm specifically designed for the needs of modern e-commerce businesses. 🛍️The figure below illustrates how ClerkCore (Clerk's proprietary AI technology) works:
First, ClerkCore takes all relevant data as inputs, including product data, categories, order histories, customer data, and content (such as blog posts and articles).
Then, ClerkCore can discover and identify customer behavior patterns by analyzing these input data. There are three points to note here: 1) Each customer behavior represents one unique model. 2) Clerk's data analytics takes a multi-model approach that can recognize multiple browsing and buying behavior patterns of a customer. 3) Because all the models are interconnected, one model's finding (output) can be used as inputs for other models. For example, as the figure presents, the output of Model #3 can feed into Model #4 and Model #5 as inputs. Doing so allows for constant optimization, which in return makes the predictions super powerful and accurate.
Finally, based on these models, truly personalized recommendations that perfectly match products and people are generated by ClerkCore as outputs. 🏆
Now, you might wonder: How does ClerkCore identify correlations between customers, products, categories, and content? How does ClerkCore identify customer behavior patterns (models) by analyzing various data points?
ClerkCore works its magic through building an interconnected, extensive “knowledge graph” with AI-powered analytics 🔮:
In the knowledge graph:
A box with solid lines represents data about a customer, a product, a category, or a piece of content. A box with dotted lines symbolizes a “virtual note" 📑.
These boxes are connected by arrows 🔁. Each arrow has two values, namely color and length. The length indicates how closely two things relate: The shorter the arrow is, the closer the two things are. The color symbolizes a type of relationship, such as alternative options, accessories, and so forth.
Notably, virtual notes are abstract meta information that characterizes different correlations/connections, for example, brand preference, price sensitivity, trends & seasons, and so forth. 💡
The best way to explain how the knowledge graph works is by example. As the figure above displays, say Customer #3 here is browsing on the webshop, ClerkCore can instantly recognize the characteristics of his buying patterns via virtual notes- namely "cheap" and "gardening" - based on analysis of his purchase history data and real-time browsing data. As a result, ClerkCore makes a hyper-personalized product recommendation, showing Product #5 (a cheap gardening toolset) to Customer #3. 🛍️😉
This links to another advantage of ClerkCore: its omnichannel nature and broad applications. Clerk works across all channels, whether online or offline, providing data about your customers and products that helps you to provide an outstanding customer experience every time. Moreover, ClerkCore is integrated with our Search, Recommendations, Email, and Audience Segmentation tools. This all-in-one AI solution ensures a personalized experience at every touchpoint along a customer's shopping journey. 😍
Wonder how you can leverage ClerkCore, a better personalization solution than collaborative filtering and neural networks, to boost your e-commerce business 🚀? Talk to one of our talented experts today!