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Examples of personalization in ecommerce - What is personalization?

May 7, 2021 / by LimeSpot Team

The most successful brands capture customer attention, keeping them engaged, and coming back for more. One of the most effective ways to maintain a steady stream of interactions is through ecommerce personalization.

But scaling personalization isn't always easy when your product SKUs and customer base grow into the thousands. So how can online retailers choose the right ecommerce personalization tools, implement a compelling strategy, and plan for growth? Keep reading to find out.

But first, let's start with the basics.

What is ecommerce personalization?

Ecommerce personalization is a marketing tactic that involves delivering customized experiences on ecommerce platforms through the use of dynamic content based on a user's browsing behavior, purchase history, demographics, previous interactions, and other data.

The dynamically served content can take many different forms, like tailored product recommendations and targeted promotion, to use two examples. Ecommerce personalization ensures your content and offers are as relevant and attractive to shoppers as possible.

Get our ebook: The ultimate guide to personalizing buyer experiences

Benefits of personalization in ecommerce

While there are plenty of benefits to be had from creating a more personalized shopping experience, the most important thing to recognize is that personalization is increasingly becoming a requirement, not an option, for retailers.

Before we dive into the benefits, let's talk about the risks. 71% of shoppers have expressed frustration when they have a non-personalized shopping experience. What's worse, you might just lose the customer altogether: 47% of consumers say they'll check Amazon for a product instead if the site they're on isn't providing a personalized experience.

Apart from keeping your customers (and keeping them happy), some of the other benefits of ecommerce personalization include:

While the benefits are clear-cut, the road to personalization is not. In a survey conducted by Forrester, 77% of consumers said they chose, recommended, or paid more for a brand that provides a personalized service or experience. In contrast, another Forrester survey revealed that 53% of digital experience delivery professionals said they lack the right technology to personalize those experiences.

So how can digital professionals more easily design and execute an ecommerce personalization strategy?

Designing customer-centric experiences

While a majority of ecommerce businesses understand the appeal of personalization, even those that have deployed it are still in the process of understanding how to best use it. In a way, AI-driven personalization is still a nascent part of the stack for ecommerce brands.

But as with any new marketing concept, one guiding principle holds true. It never fails (in this case) to take a page from Amazon and be 'customer obsessed'. 

At the heart of personalization is a business goal: To boost sales. But customer-centric company goals have the best chance at achieving success because personalization is not just about increasing profit. It's about helping visitors consume content at their own pace to help them achieve their goals at each stage of the buying cycle. The best personalization strikes a harmonious balance between the shopper's and brand's goals.

Truly powerful personalization goes beyond demographic information (who you are) to what you are trying to achieve in the moment. Each site visitor's interactions — a search query, browsing data, product recommendation clicks — map together to build a a more comprehensive customer journey and persona than has ever been possible before.

Every interaction a customer has with your brand creates a data point. Gather enough of these and you'll have what's known as a SVOC (single view of the customer). Understanding this makes it easier to tailor the customer's experience at every stage of their buyer journey.

The role of AI in ecommerce personalization

When referring to ecommerce personalization, it's impossible not to mention the role of Artificial Intelligence (AI) or machine learning.

Operating a personalization strategy without AI is kind of like asking one store clerk to provide one to one service with every single customer that walks in the door. In short? It's impossible. Or, it is possible, but with a few inherent issues and risks, including:

  • Time-consuming for employees to manually merchandise every product page
  • Risk of cross-selling or recommending products that are actually out of stock
  • No guarantee that whatever offer you choose for a product or collection page will actually resonate with that customer 

Think of it this way: If a customer comes to your site and only ever buys black t-shirts and jeans, if your recommended cross-sells are rainbow-printed dresses, they're going to get frustrated. 

Let's break down the way AI can improve things for ecommerce retailers.

More effective product recommendations

Because neural networks can process algorithms and data much faster than human brains, AI has made it possible for companies to collect user preferences and behavior, process that data, and use the findings to recommend personalized products to every customer in real-time.

Real-time reactions mean AI can sense user exit-intent and offer instant assistance in a way that isn't possible manually. Or, through natural language processing, AI can help prioritize items in a vast content catalog to serve the products customers are most interested in. The point is really that AI can serve users personalized content at the moment when they are most likely to consume, and even better, convert on it. Timing really is everything.

Better audience insights & segmentation

Traditionally, audience segmentation has fallen along the lines of semi-arbitrary categories like age, gender, and other external demographic parameters. Now, AI can recognize behavior patterns humans might miss and identify buyer segments based on less obvious similarities. It can even help identify new buyer segments that have never previously existed, and can uncover market trends. Using AI for segmentation brings data patterns to the forefront of customer segmentation and helps remove human biases.

Next-level email personalization

The shopping experience hardly ends when someone hits checkout. Email is a critical communication path to keep customers engaged, which means personalization should play a role here too. Tapping into customer purchasing as well as browsing behavior can help create AI-driven emails that ensure customers are not only motivated to click through, but motivated to open every email to see what personalized recommendations and content you have for them.  

According to stats recently published by Forbes:

  • Marketers witness a massive 760% increase in email revenue from personalized and segmented email campaigns.
  • Personalized email calls to action (CTAs) convert up to 202% better than static ones. 
  • 55% of consumers believe that targeted promotional messages create a more enjoyable shopping experience. 

Improved ROI

According to ClickZ, 70% of retailers that used advanced AI-driven personalization achieved an ROI increase of 200% or more. When AI is integrated with as many touchpoints as possible, ROI increases yet again to 300%.

Ultimately, as advances in machine learning continue, so do the possibilities for personalization.

 

Ecommerce personalization tactics

We've covered what AI personalization is about and why it's critical. Now let's get into specific personalization tactics rooted in marketing and user experience (UX) best practices.

Targeting & profiling

By using data to profile new visitors, you can use AI to process the information and match their behavior and intent to your product catalog inventory in real-time. 

For example, if a retailer knows that shoppers will soon begin searching for Mother's Day gifts, the company can create a landing page that curates a selection of chosen items for which they think buyers will be looking.

AI can then step in, ideally aligning with both the customers' preferences AND the store's goals. For example, the store might want to prioritize certain collections that have high conversion rates or bigger margins. At the same time, the AI should take into consideration what price point a customer is comfortable with (among other factors) when choosing which products to serve up.

1:1 Personalization

When you have rich data about your customers, you can use 1:1 personalization to show highly personalized products to a single individual. Because of the depth of this personalization, on the surface, 1:1 personalization poses several challenges. To do it properly requires high-speed data aggregation and analysis, machine learning optimization, and an infrastructure that supports cross-channel deployment.

Brands use AI and 1:1 personalization to deliver better recommendation and search results to their customers over time. For example, a shoe retailer can use a customer's past behavior and browsing history to conclude that a customer only searches for flat shoes. When the customer arrives on the website to search for boots, machine learning can serve them flat boots.

If the customer has browsed through shoes from the brand Stuart Weitzman, as another example, machine learning can even suggest a Dynamic Category, "Stuart Weitzman," or perhaps, "Boots by Stuart Weitzman," to display shoes matching or similar to those categories.

Adjust navigation to visitors' interests

Another popular example of personalization is changing a user's homepage navigation based on their interests or profile. If, for example, we know someone has browsed through women's clothing, we can automatically redirect the shopper to an experience tailored to the women's clothing section, whether by re-sorting the navigation order, or spotlighting women's collections in navigation through the use of featured collections.

Send personalized emails based on user behavior

Even though email has been widely available since the 1980s, email marketing still remains one of the best channels through which you can reach your customers. Using personalization and marketing automation, you can trigger customized emails to customers based on their interests, marketing to them even after leaving your site.

Amazon is known for doing this type of personalized email particularly well, sending personalized newsletters with deals on items through which customers have previously browsed.

Show category-specific discount coupons

You can increase the efficacy of your discount promotional codes by using personalization to target specific offers, testing product-specific messaging as users browse correlating product categories.

Sort recent products by interest level

Another popular AI application is using it to sort a collection’s products based on a buyer’s interest. Retailers can order products based on the user's browsing history so that each buyer will see a different product sort order, placing more relevant products higher up to increase the likelihood of conversion.

Suggest complementary products

If you are starting out with personalization, an easy tactic to implement is showing related or similar products to your shopper.

Clothing retailers, in particular, see boosts in sales when using personalization to show clothing items that can be styled together. But the practice can go well beyond clothing. For example, you could sell refillable gels for a tooth whitening kit, or a tripod that works well with a particular camera.

Pitch an upsell at checkout and after purchase

Another tactic Amazon successfully employs can be replicated by a range of ecommerce brands. You may have noticed that they offer recommended products both during the checkout process and immediately after.

This strategy is particularly effective when bundling an offer with a free shipping cart minimum threshold. By showing shoppers items AI knows they are interested in at the time of checkout when their shopping cart minimum dollar amount has not yet been met, customers can easily be persuaded to add the additional item to their cart before completing their order.

There are tons of upselling and cross-selling techniques you'll want to master to increase AOV and sales - some of which are easier to set up than others. There's a reason why so many retailers do upsells though: They work!

Preemptively handle exit-intent

As alluded to earlier, AI can prevent visitors from abandoning a site by deploying personalized offers before leaving the site. Often taking the form of exit-intent popups, this tactic is also easy to deploy for personalization rookies.

Use geo-location tagging

Another easy personalization win is to serve custom content based on a visitor's location. Geo-location tagging makes it easy to segment your shoppers and offer location-specific promotions. Combining AI and geo-location tagging also allows you to give shoppers real-time store inventory and shipping availability for brands with brick and mortar locations or multiple warehouses.

Geo-location tagging also allows retailers to offer landing page content based on weather patterns. For example, clothing retailers will offer warm-weather outfits styled together on sunny days or vice versa. The same principle can be applied to year-round seasonal considerations, suggesting appropriate clothing for regional climates. 

Integrate user-generated content across your funnel

In the U.S., 54% of consumers say they learn about new products from friends and personal acquaintances. In light of the sway behind peer-based recommendations, it's simple to see why the integration of user-generated content (UGC) can increase conversions. While many companies show UGC on product pages, most businesses stop short of integrating it across the entire demand funnel, missing out on easy wins.

The most basic form of applying UGC is spotlighting customer 'star reviews' and ratings with your recommended products. A customer will have more confidence to click through and learn about a cross-sell product when they see it's got hundreds of reviews or a star rating of 4+. 

Timed social retargeting

Social media retargeting isn't a new concept, but the use of AI allows for more advanced granulated retargeting. Because a site visitor's value declines as their time away from the site increases, timing retargeted product recommendations correctly is vital. AI allows you to specify the retargeting period better to serve ads when shoppers are most likely to buy.

AI can also save ad spend by matching retargeting efforts to the shopper's declining value, so you aren't wasting dollars on shoppers who are unlikely to convert.

Give a continuous shopping experience 

Continuous shopping is a personalization tactic recommended by Shopify Plus that employs a simple algorithm to add immense value to the shopping experience. The concept revolves around keeping a shopper's experience consistent — or continuous — across different shopping sessions and even across devices. By remembering visitor browsing history, preferences, and shopping carts across previous sessions, you can offer a seamless shopping experience that makes it easier for customers to browse and buy products on their own time.

Get our ebook: The ultimate guide to personalizing buyer experiences

How LimeSpot improves personalization

LimeSpot is an ecommerce personalization software that delivers real-time personalization at scale across web, mobile, and email.

Website personalization

Provide unique website experiences — tailored static images, copy, offers, and recommendations — that reduce drop-off rates, foster customer loyalty, and ultimately increase sales. Advanced A/B testing lets you create, run, and monitor results from multiple experiences, and unlimited segmentation prioritizes 1:1 personalization. 

Email personalization

Populate emails with personalized and dynamic content to show relevant products, boost click-through rates, and drive sales. Deliver 1:1 personalized campaigns and newsletters to drive customers back to your site, and then easily measure campaign and recommendation effectiveness.

Google Shopping Ads automation

LimeSpot Ads enriches Google Shopping Ads for more impressions, higher ROAS, and better rankings with less effort. LimeSpot also layers on additional product attributes such as gender, descriptions, and product details for richer search listings, while behavioral data and more granular audience segmentation allow for better targeting. By improving what data gets passed to Google, LimeSpot Shopping Ads can improve discoverability and performance by 10x.

Start personalizing today

By defining both a long-term personalization strategy and optimization process, you can begin offering ecommerce personalization to increase engagement, improve your customer shopping experience, and boost overall sales. 

Although some personalization tactics require more complex cross-channel deployment, there are several easy personalization wins to execute upfront to reap quick results while simultaneously designing more sophisticated plans.

Get Started!

With the right ecommerce personalization software, you can serve visitors personalized dynamic content at the right time to turn shoppers into buyers.

Need help getting off the ground? Request a demo to get started on your personalization journey.

Topics: Product Recommendation, Personalization

LimeSpot Team

Written by LimeSpot Team

LimeSpot is an AI driven personalization platform proven to lower acquisition cost, improve customer experience and increase merchant revenue. Thousand of ecommerce brands worldwide trust LimeSpot to create optimal customer experiences. "Request a demo" or "contact us" to learn more.