Ecommerce Personalization: How to Make Each Customer Feel Like a VIP
In 2018, Kraft-Heinz launched a venture capital fund called Evolv Ventures to fund the next unicorn startup that would become “the Netflix of food.”
Silicon Valley startup founders like to describe their businesses as “the Uber of [insert industry name]” or “the Amazon of…” because these marquee brands all stand for one thing: a smooth, personalized user experience delivered by sophisticated data mining techniques.
There’s a running joke in the Twitterverse that Spotify’s algorithm knows people’s music tastes better than they do.
The case for personalization is clear: people expect brands to recognize them. In fact, 80% of customers are more likely to make a purchase when brands offer personalized experiences. Personalized email campaigns deliver six times higher transaction rates.
Meanwhile, a lack of personalization hampers sales, with 66% of customers saying that encountering content that isn’t personalized would discourage them from buying.
Ecommerce personalization lets you treat each customer like a VIP. Personalization platforms use AI and machine learning-driven algorithms to track onsite behavior and customer data points in real-time to deliver a unique experience to each site visitor. Retailers can personalize everything from onsite search to product recommendations, ad retargeting, push notifications and even dynamic pricing, if applicable.
Brands must collect, store and analyze customer data to provide personalized experiences, which makes them responsible for informing customers how they collect this data, how long they store it and what they do with it. That’s why 83% of consumers are willing to share their data to enable a personalized experience.
We created this guide to show you how to gather customer data and implement personalization tactics into your ecommerce website.
The Benefits of Ecommerce Personalization
Personalization can mean the difference between customers choosing your brand or shopping elsewhere. The most important aspect of personalization is that it reduces friction: customers can more easily find what they’re looking for if your personalization algorithm infers their search intent.
Here are some other benefits of ecommerce personalization:
Personalization increases the likelihood of a sale because you are helping customers cut through the clutter and find exactly what they’re looking for. A personalized homepage has a lower bounce rate because the content is curated according to the shopper’s IP address, ensuring the language, currency and shipping costs match their location.
For customers unsure of what they’re looking for or are simply window shopping, you can engender purchase intent by serving product recommendations aligned with their browsing habits. Personalized recommendations are designed to lead customers from awareness to repeat purchases.
You can also set up triggers for cart abandonment to win back customers. Time-limited, personalized offers create a sense of urgency.
Increased customer loyalty.
The best loyalty programs offer personalized rewards rather than generic points redemption. For example, customers interested in a specific product line should receive rewards within that product category.
Customers should also be rewarded for higher levels of loyalty (eg: X number of repeat purchases within 60 days or an order value of a certain amount). Referral programs reward customers commensurate with the number of successful referrals made and help boost retention and customer engagement.
Enhanced customer experience.
Curated product recommendations, promotions and offers help customers narrow down their choices. Nearly 40% of consumers have left a website because they were overwhelmed by excessive options.
If you offer a wide variety of products catering to different demographics, you must provide a website experience that lets customers filter content and see curated recommendations.
Competition between ecommerce brands is fierce, with many of them differentiating on price. By offering a personalized shopping experience, you can serve your customers with targeted offers when they have purchase intent.
A better understanding of your customers.
First-party data (data collected by the organization) provides a wealth of insights into the types of products customers favor, their purchasing habits and their motivations for buying. You can use these insights to improve your personalization strategies and determine which product lines to enhance or discontinue.
How to Start With Ecommerce Personalization
Personalization starts with data collection. You must build a complete picture of your website visitors at each step of the customer journey using a combination of CRM data and website analytics.
Here are some important data points to track:
First-time site visitors.
Time on site.
Items added to cart.
Exit page (the last page a visitor views before they leave your website).
Average order value.
The time interval between purchases.
Customer lifetime value.
Interactions via email or social media.
While the goal of personalization is to individualize the experience for an audience of one, you’ll need to create customer cohorts and personalization strategies for each one. From there, you can personalize the experience further for high-value customers.
Let your customers “opt in” to personalization.
As consumers grow increasingly leery of how companies store and use their data, retailers have responded by enabling customers to opt into personalization by volunteering personal data and building their own “living profile.”
For example, when subscribing to a newsletter, customers might be prompted to select their gender and age group in exchange for personalized offers. This profile combines data explicitly shared by the customer together with data from other sources, such as your CRM tool or social media analytics.
Allowing customers to self-identify their needs builds trust. It also makes it easier for organizations to be transparent about how they use customer data and comply with consumer data privacy laws including Europe’s GDPR and the California Consumer Privacy Act (CCPA).
Ecommerce Personalization Examples
Using marketing automation, personalizing your ecommerce site enables you to create a differentiated journey for each customer across multiple touchpoints.
Serving dynamic content to traffic segments.
Companies with a large customer base and product catalog must create segments to target different demographics. For example, if you cater to both middle-aged men in the U.S. and children in Japan, you must provide a unique shopping experience for each user based on their intent.
Audience segmentation is the most important part of your ecommerce personalization strategy. Most Conversion Rate Optimization (CRO) tools and ecommerce platforms offer options to segment your traffic.
Here are some suggested ways to segment your traffic:
Identify users by country, region, state and locality so you can offer personalized search results and product listings. Examples include:
Showing relevant languages, currencies and shipping details based on the customer’s location.
Display relevant products according to weather conditions in that city, particularly if you sell apparel.
Recognize customers according to their operating system, device (web vs. mobile) and channel they came from.
Mobile vs. PC: Average order value on mobile tends to be lower than when users shop from a desktop computer. Retailers like Home Depot capitalize on this using ‘price steering’ — meaning shoppers are shown fewer and higher-priced products when shopping on mobile.
Traffic source: Segment visitors via the channels they arrived from (email marketing, special offers, Pay Per Click (PPC), social, organic search and referral URL).
Operating system used: Online retailers can use dynamic pricing based on the OS users are on.
Age: Tailor messaging, visuals and product recommendations to the user’s age group.
Gender: Gender is a major segmentation point, particularly regarding apparel and jewelry.
Job title/Income: This can be used to estimate the types of products and experiences users are looking for. For example, you can determine whether someone is seeking a good deal or high quality.
New customers: Help new customers get situated, especially if they are unfamiliar with your brand or website interface. They may need introductory messages or specific experiences to educate them on your product categories, different subscription tiers, free trials, etc.
Returning customers: Display the products they viewed, wishlisted or added to cart during their last session so they can pick up where they left off. Entice them with new products added to the site since their last visit.
Newsletter subscribers: Offer exclusive coupon codes for subscribers as an incentive.
Cart abandoners: Program site pop-ups based on exit intent to remind customers to complete their purchase, send follow-up emails and display retargeted ads.
Pricing can be tailored to the user’s demographic information, purchase history, browsing history and general buyer profile (eg: deal-seeker vs. quality-seeker). Pricing may be contingent on other factors such as time of day, competitor pricing and inventory levels.
Only one in five ecommerce companies in North America and Europe used dynamic pricing in 2021, while 17% planned to start doing so.
Pricing algorithms manage pricing based on competitive price points, historical sales data and market demand. Slow-moving products (identified by last time of sale, number of visitors to the product page and percentage of conversion rates) can be priced more aggressively to increase turnover.
On-site targeting: dynamic content block.
A dynamic content block is a customizable widget on a landing page or email that displays different types of content depending on the visitor segment.
For example, the content block might display related products, last viewed products, last viewed categories or last order details.
Dynamic content block
Dynamic content blocks display related products, last viewed products, last viewed categories or last order details. They are also great places to announce exclusive sales events for VIP customers, discount codes for loyal customers and CTAs to complete questionnaires and surveys.
You can also create banners or image carousels with localized updates and announcements. For example, if a sale or specific event is happening in someone’s city (eg: a music festival), you can display relevant content accordingly.
A website overlay is a graphical content box that appears in the middle of a page, obscuring the background content. Overlays are typically used to increase conversions by asking the visitor to enter an email address in exchange for a coupon, discount or free trial. Use overlays with care; timing is everything.
Here are the most opportune times to display an overlay:
Greeting first-time visitors: Offer a quick website tour, exclusive first-time buyer discount, or a prompt to sign up for your newsletter.
Visitors who are about to bounce: Use exit-intent triggers (such as moving the mouse up to the browser toolbar) to pop up an overlay presenting an offer.
Customers who are having difficulty: When a visitor seems to be struggling to complete a task — perhaps they’re stuck at checkout or idling on a landing page — offer assistance via live chat or a form where the customer can enter an email or phone number to receive further assistance.
Header, footer and sliders
If site visitors aren’t responding to modal pop-ups (most people just click the ‘X’ button to close them without looking at them), consider using header and footer banners. For example, you can add countdown timers for sales events, social proof (such as star ratings) and other CTAs in the header or footer of your website.
Trigger pop-up offers in line with each visitor’s in-session behavior. For example, you can offer first-time visitors exclusive deals in exchange for their email addresses. When someone adds items to their cart, you can offer tiered discounts or free shipping to drive up the average order value.
You can personalize pop-ups based on cart value, time on site, number of products viewed and other factors. Timing and action-based triggers are important to make this work.
Dynamic on-site personalized product recommendations.
“Continuous shopping” recommendations allow returning visitors to pick up where they left off. Inspired by Netflix’s “Continue watching” feature, this approach remembers visitors’ selected items and preferences from previous sessions and customizes the homepage accordingly.
You can also create personalized bestseller lists (eg: ‘Top 10 products in your area’).
Segmenting and extrapolating your top VIP customers.
VIP customers have the highest lifetime value, frequency of purchase or average order value. For retailers with fast-moving inventory, their most high-value customers are those who purchase items at full price (some customers will only buy during a sale). Use your CRM data to identify these gems.
According to the Pareto Principle, 80% of your sales are represented by 20% of your customers. Reward them accordingly. Include a time restriction when defining a VIP segment in your CRM system, otherwise, you may capture inactive customers. You can reward them with exclusive online events and previews, loyalty programs, subscription plans or appreciation gifts.
Product recommendations on checkout pages.
Recommendations at checkout must be configured to drive up average order value. Rather than showing customers variations of the item already in their cart, up-selling and cross-selling techniques will induce them to add more items.
For example, adding a ‘You May Also Like’ widget lets you show cheaper or more expensive alternatives to suit the shopper’s budget. You can also show similar products or accessories to go with the selected product, such as headphones that are compatible with the tablet in the shopper’s cart.
Integrate RFM scoring into your shopping cart abandonment strategy.
Ecommerce stores lose $18 billion in sales revenue annually because of abandoned carts. Personalized reminder emails and ad retargeting are effective ways at winning cart abandoners back. However, since you won’t be able to recover every lost sale, segmenting your customers helps you identify the most important ones.
RFM scoring is the practice of using CRM data to understand the behavior of potential customers and segment them accordingly. RFM analysis refers to recency, frequency and monetary value.
RFM analysis measures the following:
When was their last purchase?
How often did they purchase in the past?
The amount spent on each transaction.
Marketers use the RFM model to score each customer by their most recent purchase by date, their purchase frequency and their cumulative order value over a specified period of time.
BigCommerce’s Marketing Insights Reports delves deeper into the Recency metric by providing customer lifetime value (Monetary Value) reports by marketing channel over the last one day, 90 days and 180 days (Recency). This report helps provide clarity on marketing channels (AdWords, email, Facebook, etc.) that drive the highest lifetime loyalty and repeat purchase rates.