Personalization has become a major part of every serious eCommerce brand’s strategy. Knowing who your customers are, where they are, what they want, and how best to give it to them is something that all online retailers need to do to succeed and build brand loyalty.
In fact, 78% of customers said that they wanted a more personalized customer experience when surveyed in 2021. And according to McKinsey, personalization adds 40% to revenue compared to companies not using any personalization strategies.
Therefore, it’s apparent that today’s customer expects a digital experience that caters to their circumstances, understands their history, and gives them what they need quickly – and they expect this level of service at every stage of the customer journey.
This has never been a straightforward task, but with the proliferation of channels and touchpoints in our omnichannel world, it has become even harder to serve up consistent individual experiences.
In Monetate’s “Delivering a Personalized Customer Journey: A Step-by-Step No Nonsense Guide,” we outlined the four main stages of the customer journey – Landing, Engagement, Purchase, and Re-engagement.
In this blog post, we’re going to focus on Stage #1 – Landing.
What is the Landing Stage of the Customer Journey?
At the beginning of the customer journey, landing is important for building a strong foundation that you can build upon in the other three stages.
In the landing stage, you should be looking to answer the following question:
How do you take account of who the customer is and where they’ve been before they get to you?
When a customer lands on your eCommerce site for the first time, it’s vital that you make a great first impression. But, while this might be the customer’s first meaningful interaction with your brand, a good personalization strategy will mean that you already have a good idea about who the customer is and what they want when they first arrive on your site or one of your channels.
Things You Should Know in Advance About Visitors:
Location – You can choose different versions of landing pages to display based on geographic location.
Device – Mobile customers behave differently from desktop ones, so different messaging or layouts will pay dividends.
Traffic source – Whether your visitors have reached you through a search engine or clicking through from an ad or link will give you valuable information about their intent.
What ad campaign has brought them – Specific promotions should be highlighted to create continuity between the ad they engaged with, and their experience upon landing.
Past behavior – Has the visitor browsed your site before? Have they added to and/or abandoned cart? Have they purchased from you before or are they a completely new customer? What brands or categories have they shown an affinity towards?
1st- or 3rd-party data – Does your customer data come from your own CRM? Or has it been gathered by your personalization software provider?
What page they are currently on – Have they gone straight to a product page? Are they on your homepage?
Any or all of these contextual variables and data points can be used as rules-based targets (i.e., you can set an experience to target customers who have come from a certain location or browsed a particular brand.) And they can also be fed into a personalization engine to create more individualized experiences, where more context and data equals more accurate decisioning.
Building a Detailed Picture of Site Visitors with Data
It’s possible to build up quite a detailed picture of the visitor right from the start of the customer journey. But this can be a challenge for brands who don’t have ready access to data points.
But if all your teams, from developers to marketers, have equal access to customer data it will allow you to take some meaningful actions at the start of the customer journey that will go a long way to providing a meaningful, personalized experience from the outset.
For example: If you can see that a new customer based in France has clicked through from an Instagram ad on their mobile device during a holiday season, you’ll be able to make a much more informed decision about what welcome banners to display on the landing page, what products to promote in search, and what discounts or promotions to highlight to them.
You can also use this integrated view of the customer to test what works best, working out the most finely personalized strategies as you progress.
Incorporating Machine Learning into Personalization
Making sense of several different data points and variables and being able to derive lessons about which messaging options work best with different types of customers is something that human teams alone cannot do.
A personalization strategy that incorporates machine learning to test site variations on different customers will result in a much more personalized experience. And you and your teams will become smarter about your customers.
However, testing different banners, promotions, page versions, etc., on different customer segments will only get you so far.
Machine learning can integrate data from different sources and apply it to individual customer sessions, meaning you’re not allocating traffic to customer segments anymore, rather you’re allocating experiences to individuals.
How does this work? Let’s look at how personalization at the landing stage of the customer journey works in practice by examining how Reebok excelled during the landing stage of the customer journey.
Reebok: Revamping Landing and Product Pages with Machine Learning and Personalization
When Reebok wanted to revamp its personalization strategy, it focused on making its landing and product pages more attractive to customers.
Reebok was aware that the product pages were in effect landing pages for many site visitors. Increasingly, customers land on a product page straight away after searching for items online.
This presented a challenge: Reebok needed to display different versions of the product pages to those customers who had just landed on their site and those who had clicked through from the homepage.
As a solution, Reebok’s design teams created different versions of product pages. They tested these versions on its entire customer base, using AI to learn which combinations worked best. Reebok then empowered its machine learning personalization platform to decide on the layout of its pages.
Reebok was able to make intelligent decisions about what recommendations, branding, and reviews to display to visitors because it had been fed a wealth of Reebok’s own first-party data, in combination with a selection of out-of-the-box behavioral targets that came with the Monetate Personalization platform.
Putting a machine learning engine at the heart of Reebok’s personalization means the brand has the capability to dynamically test its marketing efforts. Reebok’s team (or the decisioning engine) could make increasingly accurate choices about what to display, reducing risk and maximizing revenue, all the while learning more and more about what makes Reebok’s customers tick.
The Role of Machine Learning in Boosting Personalization During Landing
Getting started with personalization is easy with the right partner. Monetate Personalization provides a one-stop-shop from which you can manage personalization features in one place, with no need for additional software or services.
Patented machine learning technology powers Monetate Personalization. It analyzes contextual data and previous customer behavior and understands visitor intent. This allows ML algorithms to deliver real-time, highly personalized, and relevant product and search recommendations.
Ready to learn more about Monetate Personalization for building out your customer journey?
Schedule a demo with a member of the Kibo team to see how the solution works and can fit into your plans for building out personalized customer experiences.
Also, download “Delivering a Personalized Customer Journey: A Step-by-Step No Nonsense Guide,” for a more in-depth look at building out a personalized customer journey at each of the four stages.