How to Sell More With Personalization in Ecommerce

The imperative of delivering the right experiences to the right audience at the right time has modern ecommerce businesses in a chokehold. 

With so many ready-made ecommerce personalization bundles available, it seems like any business under the sun can execute granular customer experiences with a few clicks and tweaks. So the real challenge doesn’t lie in the personalization itself, but in doing it efficiently, at a lower cost, and in a more ethical way than competitors. Let us tell you how to provide a personalized shopping experience on a dime, without going to great lengths.

Benefits of personalization in ecommerce: a match made in profits heaven

The importance of personalization in ecommerce cannot be overstated. In 2024, companies plan to ramp up their spending on personalization by an average of 29%. The primary driver behind this boom is the promise of higher profits: customers spend 37% more with brands that deliver tailored interactions. This increase in profits can be chalked up to the following gains that stem from custom-fit shopping journeys:

• Personalization drives ongoing engagement and customer loyalty

Customers gravitate towards brands that make them feel understood as individuals. According to statistics, 60% of shoppers become regular buyers after a personalized shopping experience with a brand. With numerous personal touchpoints at each stage of the customer journey and tailored loyalty programs, businesses can promote repeat purchases and establish long-term connections with their customers. 

And let’s not forget about the potential savings generated by personalization and customization in ecommerce: getting a new customer is five times more expensive than retaining an existing customer.

• Personalization supports cross-selling and upselling initiatives

By introducing shoppers to relevant products that complement their initial purchases,  ecommerce sellers can significantly increase customer lifetime value (LTV) and the average order value (AOV). Higher AOV and LTV reduce the need for businesses to constantly reel in new customers and drive higher overall sales from the current customer base.

• Personalized experiences tap into the untapped value of your VIPs

Almost every established ecommerce business can boast a small cohort of high-value customers, the golden goose of sales that demands special treatment. Personalized, next-level shopping experiences can pamper your VIPs, increasing their retention by up to 10%.  

Ecommerce personalization techniques that turn browsers into bankroll, real-world examples

Below, our team has curated a list of the most high-performing examples of personalization in ecommerce, based on the success stories of our clients and analysis of 30+ ecommerce brands. 

Product recommendations

Even if your product pages incorporate related product recommendations, basing them on seller-side data and ignoring user context can render your ecommerce personalization ineffective. Instead of looking at the popularity of items, supreme recommendation engines vacuum user-centered data, including preferences, demographics, and behavior — and blend this data with factors like time, location, and device for personalized recommendations.

Other best recommendation-related practices include:

• Using deep learning to tackle the cold-start problem; 

• Suggesting related higher-priced items;
• Analyzing visitor’s session behavior to correlate shopping activities that span multiple sessions;
• Highlighting popular products based on sales or customer reviews (e.g. you can create personalized bestseller lists);
• Incorporating guided product selection quizzes to drill down into customer preferences and needs.

The analysis of in-session activity is what yielded one of our clients, a leading prescription eyewear retailer, a 73% increase in average revenue per user. Instead of opting for a good-old collaborative filtering strategy, our team implemented an AI-augmented personalization solution that combines historical data with in-session activity to recommend the most relevant items. 

Content and display

Another personalization tactic that can double the impact of the personalized ecommerce experience is serving relevant content and media to the consumer. By fine-tuning content to individual preferences and behaviors, companies can also create more engaging experiences that boost sales. Ecommerce personalization trends in this category include:

• Personalized collections — lumping together product groups based on individual customer preferences, such as browsing history, style choices, or upcoming events, to offer tailored collections that resonate with each shopper.
• Dynamic user-generated content — displaying user-submitted photos, videos, and reviews that align with the shopper’s persona, interests, or past purchases. For example, if a user is a 40-year-old female, the reviews shown will predominantly come from middle-aged women, enhancing relevance and fostering trust through personalized social proof.
• Personalized product descriptions — adjusting the description in real-time to align it with the needs of a specific customer. 

Engagement and retargeting

Re-engaging abandoned carts and activating the buying potential of existing customers is paramount for sustainable business growth. The triple power of artificial intelligence, predictive analytics, and seamless cross-channel integration can significantly enhance your retargeting efforts and revive down-trending leads. Here’s how high-performing ecommerce brands pull it off:

• Offering well-timed price incentives, such as exit pop-ups or cart abandonment emails, to recover potential sales.
• Launching re-engagement campaigns at scale to win back past clients with special offers and tailored discounts.
• Integrating user-specific pop-ups triggered by user actions such as shopping cart amounts or adapted to the unique behavior of an online customer.
• Retargeting visitors on social media with personalized ads based on their previous interactions.

Loyalty and sentiment analysis

To make customers heard and seen long after the initial purchase, ecommerce companies double down on brand loyalty initiatives while also proactively monitoring customers’ reviews to spot areas of improvement and deliver highly relevant messages. In particular, ecommerce brands can supplement their commerce personalization strategy with:

• Tailored rewards for specific customer actions or special dates, further calibrating loyalty programs and offering tiered discounts.
• Sentiment analysis that sizes up customer reviews, allows brands to scale responses and offset potentially negative experiences with tailored offerings. 

From personalization to hyper-personalization with AI

Traditionally, personalization used to rely on historical customer data along with insights on general customer segments to generate granular experiences. Although this ecommerce personalization strategy could effectively locate purchasing habits and other similarities between shoppers in a given category, it fell short of serving customized experiences to specific customers. Hyper-personalization fills this gap by tailoring companies’ marketing to individual customers. 

Why go the extra mile, you ask? Poor, less competitive personalization efforts can cost your brand 38 percent of the existing customer base. Personalization that attracts, rather than averts, should be smart, omnichannel, and almost telepathic — just like hyper-personalization is.

By leveraging data analytics, machine learning, and automation, hyper-personalization taps into the minds of online shoppers and aligns personalized efforts with anticipated customer needs. Unlike scattershot traditional customer personalization in ecommerce, AI-driven personalization exposes customers to personalized recommendations during the entire shopping journey. 

Hyper-personalized customer experiences meet a potential shopper or B2B buyer (if we’re talking about B2B ecommerce personalization) as early as the advertisement stage. By using data from various sources such as ecommerce site behavior, social media, and purchase history, machine learning algorithms help generate specific, custom advertisements that get into the customer’s heart. 

When the customer clicks on the ad, they arrive at a customized landing page, where everything — from messaging to dynamic pricing — is tailor-made to align their unique user demographics, preferences, and browsing behavior. Along with dynamic content creation, hyper-personalization hinges on recommendation engines. Powered with custom machine learning algorithms, recommendation engines suggest products, services, or content to users based on their preferences and behaviors.   

Some ecommerce companies can take it up a notch and implement an intelligent conversational interface on their websites and in mobile apps. By building on customer behavior and data, chatbots can provide assistance on par with human agents in real time.  That, paired with real-time product notifications, creates a high-touch environment customers are actively seeking today.

But keep in mind that to hit it big, hyper-personalization should be omnichannel, orchestrating a one-to-one experience to customers across all touchpoints, including online, in-store, and mobile. 

Is your personalization software up to snuff? Must-have capabilities

Not every ecommerce personalization platform can suffice the demands of modern customers. Some personalization solutions suffer from limited data handling capabilities, while others have a hard time delivering personalized shopping experiences in real time. So before investing in dedicated tools, run it by our checklist first. 

Interactions with anonymous users

According to statistics, 86% of ecommerce website visitors are anonymous. That’s why your personalization solution should know its way around tailoring interactions for both identified users and first time visitors with no fingerprints. Some personalization systems tackle this challenge by tapping into referral data and third-party insights that help create a welcoming experience for incognito shoppers.

AI-driven personalization tools pick up on users’ trails by analyzing a treasure trove of non-PII data anonymous visitors leave behind. By analyzing the subtle signals such as users’ network speed, browser extensions, time spent on site, and other clues, ML-powered solutions identify correlations, group anonymous users based on similar actions, and deliver relevant content based on inferred data points.

Automated segmentation tools

Another non-negotiable for your personalization platform is automated segmentation which allows the system to efficiently group customers based on various criteria and update customer segments in real time. To locate high-value customers, your personalization suite should also bank on automated RFM scoring that can automatically group shoppers based on their Recency, Frequency, and Monetary value. 

Support for omnichannel engagement

As we’ve mentioned earlier, personalization doesn’t work to its full potential unless it spans the entire customer journey. Your solution should provide a consistent, tailored customer experience across all devices and channels, based on integrated data and a unified personalization strategy. 

Data-driven approach

The more data, the merrier your offerings are. Your personalization tool should cast its nets wide, wielding a combination of historical and third-party data to peer into a customer’s past behavior along with analyzing additional context such as the customer’s location, weather data, and demographic information. By combining these data points, ecommerce businesses can generate highly customized experiences that strike a chord with customers.

Site layout personalization

The ability to serve dynamic content is another differentiator of top-notch personalized marketing. By incorporating AI, personalization tools churn out dynamic content at scale that automatically adjusts to individual user preferences and behaviors. For example, Shopify Plus users can virtually personalize the store for each customer with the Hypersonal tool that aligns headlines, reviews, product Q&As, and other content with the unique preferences and needs of every user — on the fly.

A blend of automation and manual controls

Full-on automation is neat and nice, but sometimes you need human oversight to refine personalization strategies according to specific requirements. Whether it’s A/B testing or product recommendations, your tool should allow for expert knowledge and human judgment, instead of monopolizing all customization capabilities.

Optimization flexibility and scalability

Customer behavior and markets are never static — and your personalization tools should go hand in hand with shifting buying patterns and market gyrations. By combining agility, continuous testing, and seamless integration capabilities, your tool can keep your personalization strategies up to date, no matter what. The ability of AI to learn from data helps with that, too.

Along the same line, the tool should be open to innovation facelifts without limiting you in adopting new technologies or industry-best personalization techniques, such as propensity models or predictive next-best-action algorithms.

System scalability

As your ecommerce business grows, your data processing needs follow suit, exposing your personalization tool to higher data volumes and user numbers. Scalability by design ensures that your system doesn’t crumble under the growing workloads and can deliver personalization in real time, no matter the number of customer interactions.

Microtargeting capability

Microtargeting is one way companies can send out targeted advertisements to specific individuals or small groups. Unlike broader targeting, microtargeting segments your audience based on highly specific criteria, including real-time data, to reach prospects with the highest conversion potential. Microtargeting and one-on-one interactions call for personalization tools that can dig into even the most subtle customer data.

Personalization challenges that can dilute your value

Over 60 percent of companies still struggle to get their one-to-one marketing initiatives right. Here are the common hurdles underperformers might face.

Scalability issues

A good personalization tactic is scalable in a healthy way: it can grow alongside your sales without eating into your time or resources. But not all personalization systems are developed with scalability in mind, some of them provide increased capacity as a pricey add-on. 

Building your system upon cloud platforms enables you to automatically ramp up or down your processing resources to handle fluctuating resources and provision additional resources with no investment in hardware. Microservices architecture is another antidote against stiff and potentially disruptive scalability. As each microservice can be scaled independently, your platform can rise to the increasing needs without compromising performance.

Modular components run entirely on the server side can also pave the way for localization at-scale — and that’s exactly what we did for an established premium jewelry seller. By the time of our collaboration, the company had already mastered hyper-personalization. However, they decided to up their game by introducing tailored navigation options and personalized homepages. By integrating modular architecture, our team helped the client deliver dynamic content across multiple regions and brands from a single campaign.

Responsible personalization

Over-personalization — the kind of experience that borders on creepiness due to its reliance on excessive personal data — can not only hinder your marketing efforts but also damage your brand image. That’s why companies should take a thoughtful and sensitive approach to data management, making sure that personal data controls stay in the hands of customers and stating explicitly what type of data is being collected and for what purpose. 

Data privacy and compliance can become a point of differentiation when it comes to personal data collection. Those brands that adhere to a privacy-by-design approach, supplemented with consent management tools, and comply with commonly accepted standards such as GDPR and CCPA are more likely to see their personalization initiatives pay off.

Also, data minimization practices and regular compliance audits, as well as anonymization and pseudonymization, can shift customer perception towards higher acceptance of personalization and demonstrate that you treat their personal data as carefully as they do themselves.

Technical complexity

Hyper-personalization is a tough initiative to pull off because of how technically demanding it is. Limited integration capabilities of your personalization platform, flimsy algorithms, the lack of low-latency processing, and other tech hurdles can refrain ecommerce sites from providing an effective personalized ecommerce experience. To head off this obstacle, make sure your system’s architecture can handle computationally intensive workloads, support real-time processing, and is powered by advanced algorithms that can accurately predict customer preferences.

Data concerns

Hyper-personalization hinges on a granular view of the entire customer life cycle to cater to individual needs. To establish this elevated view, personalization systems demand access to a whole lot of data, including customer segments and microsegments as well as behavioral, transactional, and engagement trends. 

And this is where it can get tricky: integrating these data bits into a single puzzle requires companies to break down data barriers. Otherwise, they’ll end up with inaccurate, incomplete, or outdated data that will lead to personalization gone wrong, potentially alienating your customers. With an AI-powered, unified customer data platform, companies can set up a centralized customer database that has all the right types of data in a ready-to-use state.

Balancing ecommerce personalization and ethics: what data to capture?

While 71% of consumers expect highly customized experiences, there is a parallel demand for privacy, with 30.9% concerned about misuse of their personal data. As an ethical business, where do you draw the line between offering tailored services and respecting privacy?

Customer-provided data (zero-party data)

One of the best practices that demonstrate your company’s commitment to sensible use of personal data is to gather information directly from your customers with their consent. Any insights that customers intentionally and proactively share with a company can classify as customer-provided. This type of data is a basic building block for any engagement efforts, whether it’s providing tiered discounts or sending special offers relevant to the customer’s needs. 

Organizations can extract customer-provided data from surveys, account creation, first-hand feedback, and reviews. Consider explicitly asking users to share their data upon website entry in exchange for personalized offers or offering rewards like bonuses or promo codes to encourage users to provide personal information such as gender, age, and preferences.

The apparatus is customer-facing and includes survey tools, feedback widgets, and chatbots. CRM systems like Salesforce, HubSpot, or Zoho CRM then help companies manage, prioritize, and analyze customer communications and feedback.

First-party data

Compared with zero-party data, first-party data is considered to be more accurate, reliable, and insightful as it’s action-based, coming directly from customer interactions with your ecommerce site. First-party data includes past purchase history, search queries, category browsing habits, average spend amount, time of past purchases, and any demographics gathered through interactions. Geospatial data also falls into this category. 

Speaking about location data, it can turn out to be a goldmine of insights for ecommerce companies. One of our clients whose business focuses on appliance merchandising increased its direct-to-consumer revenue by 18% through localized promotions and loyalty programs. Our team helped the company implement a custom AI-based microsegmentation solution that enabled at-scale localized personalized experiences in 12 markets — by getting a deep dive into purchase behaviors and geospatial data. 

As for tech enablers, geolocation services like Google Maps API and MaxMind GeoIP help companies obtain location data, while web analytics tools like Google Analytics and Adobe Analytics can get into the nitty-gritty of user behavior, including purchase paths and actions on the site. Purchase history, order details, and transactional data is snuggled up safely on ecommerce platforms like Shopify, WooCommerce, and Magento.

Keep in mind that this type of data alone doesn’t suffice when it comes to hyper-personalization, but companies can cash in on this data to identify high-value customer segments. 

Data from sources other than your own collection efforts

Online sellers can also join forces with partner systems as a part of a collaborative effort or business agreement. These include CRM providers, marketing automation platforms, data analytics platforms, and other partner systems whose data is highly relevant to the seller’s specific needs. To enrich their customer profiles and fill in data gaps, sellers might have to pay licensing fees or secure data sharing agreements.

As for the data itself, it can span demographic information, purchase, history, website behavior, and CRM data. To get hold of this data, ecommerce companies might require data integration tools like Zapier or Mulesoft to plug into data from partner systems. Partner-provided APIs and collaborative CRMs with shared access capabilities are other ways to access the databases of partner systems.

While companies seek to deliver hyper-personalized experiences, establishing clear data privacy policies comes to the fore. Publicly committing to avoid data collection from third-party services or through questionable means can build trust with customers. Similarly, ethical targeted advertising — focusing on socially responsible campaigns — ensures that businesses respect both the need for personalization and the imperative of data protection.

Personalize or perish: ecommerce personalization best practices in 2024

Mediocre personalization won’t cut it in 2024. Let’s go over the essentials for building a truly exceptional personalization engine.

Know where your customers are and meet them there

Serving customers in their channel of choice is the rule of thumb in the world of personalization, which mandates companies to establish a consistent customer experience across all channels, including online and offline touch points. 

In 2024, ecommerce businesses are in for a more granular approach, transitioning to highly personalized channel prioritization. 

Modern brands opt for channels with the highest value, prioritizing effectiveness over scale. To replicate this approach, you need an experimental mindset and continuous testing to determine the optimum channel for each message and customer.

Set ambitious goals, take measured actions

In something as incremental as personalization, perfection isn’t essential, but continuous improvement is. So think big, acknowledge the complexity of this endeavor, and take small steps towards it, relying on the culture of experimentation. While the path to personalization varies by brand, we recommend getting the following essentials in place to succeed in it:

1. Develop a strategy — define and prioritize high-impact use cases where personalization can enhance the customer experience.
2. Audit internal capabilities — analyze the skill sets and tech infrastructure you need to support personalization at scale.
3. Define how you collect, store, and link customer data —  make sure you have a dedicated, centralized destination, such as a customer data platform (CDP), to orchestrate customer data. Prioritize first-party data ownership and management, focus on customer identity resolution and privacy-compliant data integration.
4. Build, test, and scale your analytics and modeling capabilities — once your data is in order, leverage robust analytics to determine what to share, when, and where. 

Marry your CMS with a personalization engine

Integrating your personalization engine with a CMS through data feeds, webhooks, API calls, or elsehow allows you to serve hyper-relevant content at scale and ensure cross-channel content consistency. By connecting the two systems, you can create dynamic content variations that trigger targeting conditions based on specific criteria. Advanced algorithms can then refine these variations over time, making sure each user receives content personalized to a tee. 

Personalization value is up for grabs….for those who get it right

A truly exceptional shopping experience feels effortless, like finding the perfect pair of jeans the moment you land on a website. To provide something of a comparable experience and not bust their budgets, ecommerce brands require a triple power of data, AI technology, and personalized content — weaved into a broader strategy and a roadmap.  

For companies, it’s hard to be all things at once, that’s why collaborating with an experienced tech partner can enable ecommerce businesses to accelerate the roadmap to personalization and shore up both fundamental and advanced capabilities needed for hyper-personalization at-scale.