In an era where your next purchase is just a click away, the digital shopping landscape is evolving rapidly. But are you merely a part of the crowd, or does your online store stand out in this bustling marketplace?
The key lies in understanding and embracing the power of personalization in ecommerce. It's about creating a shopping experience that is tailor-made for each customer. This approach is no longer a luxury but a necessity to captivate today's discerning consumers who seek more than just transactions – they desire experiences that resonate with their individual needs and preferences.
This article will explore what exactly marketplace personalization entails, its multitude of benefits, and offer actionable strategies to transform your ecommerce platform into a personalized shopping haven. Let’s dive in!
What is ecommerce personalization?
In a nutshell, ecommerce personalization refers to tailoring the shopping experience and communications for each customer based on data collected about their preferences, behaviors, and more. This data is used to provide individual shoppers with recommendations, offers, content, and journeys unique to them.
For example, if Customer A tends to purchase athletic apparel in bright colors and neon shades, they might be shown fitness collections featuring those color palettes. Customer B prefers neutral, earthy tones and boho styles, so they would see recommendations aligned with those tastes instead.
Effective personalization makes every shopper feel special, “gotten” by a brand, and incentivized to return again and again.
Benefits of personalization in ecommerce
The numbers speak for themselves – personalization delivers major benefits across key ecommerce metrics:
- Increased conversion rates – personalized product recommendations have been found to have a 8% higher conversion rate compared to generic suggestions.
- Improved customer experience – 91% of consumers say they are more likely to shop with brands that provide personalized experiences tailored to their needs and preferences. Satisfied customers become loyal advocates.
- Higher repeat purchase rates – 49% of shoppers indicate they would likely become repeat buyers if offered personalized shopping experiences by a retailer.
- Greater average order values (AOV) – brands leveraging personalization tactics see 10% more revenue generated from these efforts than brands that don’t focus on personalization.
As you can see, personalization checks all the boxes, making it a highly valuable ecommerce strategy.
The key differences between B2B and B2C marketplace and ecommerce personalization
While both B2B and B2C ecommerce sellers can benefit from personalization, there are some differences in effective tactics and implementations between them.
For B2C marketplaces and stores, personalization often focuses on elements like:
- Customized product recommendations based on purchase history and browsing behavior,
- Personalized promotions and offers for loyalty program members,
- Email campaigns and messaging tailored to customer segments.
Whereas for B2B scenarios, common personalization approaches include:
- Account-based recommendations factoring in company size, industry, prior orders,
- Self-service portals with quick reordering, customized catalogs, and saved payment options,
- Targeted outreach and education aligned to buyer lifecycle stage.
The underlying strategy remains similar for both – catering to individuals – but the specific playbook varies across some components due to the different user journeys.
The role of AI in ecommerce personalization
Attempting to manually personalize at scale across countless customer interactions would be extremely challenging, if not impossible. This is where artificial intelligence comes into play.
AI capabilities allow brands to process massive volumes of customer and product data to identify patterns, trends, and insights that would escape human analysis. These AI-powered insights facilitate dynamic, individualized personalization like:
- Continually refreshed product recommendations based on each customer’s evolving interests,
- Chatbots providing personalized shopping assistance and suggestions,
- Email and ad content adapted in real-time to match individual preferences.
Over 74% of shoppers now actually favor chatbot interactions over human ones for resolving basic inquiries. AI augmentation enables brands to deliver personalized experiences at a level people have come to expect.
How to create personalized ecommerce experience
Before one can fully enjoy the benefits of personalization in ecommerce, there is a need to invest in creating the right infrastructure to enable the effective use of personalization. It is really worth doing it well from the beginning, because “The value of getting personalization right—or wrong—is multiplying”, as McKinsey researchers put it. Here is an overview of key steps of ecommerce personalization.
Step 1: understand your audience
As you set up to work on ecommerce personalization, you need to get clarity on who your target customers are and common characteristics. Build out detailed buyer personas documenting:
- demographics,
- psychographics,
- behaviors,
- and more.
Surveys and customer interviews can provide additional context around preferences and pain points. Personas should capture channel usage habits – whether shoppers begin journeys onsite via search and category landing pages or discover you through ads and social platforms initially. Document different persona behavior patterns. This foundational information serves as the springboard for downstream personalization efforts by ensuring they cater to true user needs.
Step 2: ensure gathered data is properly integrated, structured and made accessible to ecommerce personalization tools
Centralize customer data from across channels like:
- email,
- chat transcripts,
- help desk tickets,
- and brick-and-mortar purchases
into a unified commerce platform or customer data platform to avoid data silos. Invest in quality data management, cleansing files to improve accuracy and completeness.
Continually pipe in new data, including zero-party and first-party data from:
- account profiles,
- surveys,
- IoT sensors,
- and other martech systems using pipelines and connectors.
To leverage personalization in ecommerce, structure information using customer-centric data models factoring in previous schemas. Implement identity resolution to tie activity to individuals. Finally, ensure marketplace personalization tools and algorithms – whether part of your core stack or separate plugins – can actually access cleansed datasets in order to drive insights.
Step 3: map out the buyer’s journey
The third step of your path to your marketplace personalization consists of cataloging all key phases and the different touchpoints customers typically engage with when initially discovering your brand, evaluating products, purchasing items, engaging post-order, and everything in between.
Highlight pain points reported during the journey – for example, high cart abandonment rates on mobile checkout pages. Capture emotional elements as well, like initial excitement upon checking email for shipment notifications. Also outline key micro-conversions desired within each journey stage, like newsletter sign-ups or requesting swatch samples. With buyer journey models in place, identify prime opportunities to provide personalized value – say, product suggestions based on recent views during the evaluation phase.
Step 4: create segments for user profiling and targeting
Leverage behavioral signals, attribute data, and machine learning models to divide customers into audience groups for more targeted personalization messaging. Maintain flexibility to assign users to multiple segments simultaneously, not just a single broad bucket. Refresh group assignment continually to avoid stagnant, outdated profiles. Validate segments regularly ensure meaningful differentiating attributes with business relevance. Keep an eye out for emerging niche clusters that could represent new personalization opportunities.
Step 5: decide where and what kind of ecommerce personalization should occur
Catalog existing site pages, campaigns, and offline channels already offering varying degrees of personalization. Determine key high impact yet underutilized touchpoints for enhancement based on your audience makeup and conversions metrics goals. Identify invasive or mediocre efforts providing limited differentiation that are better omitted given tradeoffs. For each priority touchpoint, brainstorm creative tactics and compelling content types tailored to user segment needs for deployment. Solicit customer input during planning phases to home in on resonating concepts.
Step 6: ensure omnichannel ecommerce personalization
Continually coordinate unified personalization across web, mobile, email, on-site devices, brick-and-mortar, and additional emerging channels to deliver a true omnichannel experience. Eliminate channel-specific barriers in data availability and leverage capabilities hindering this objective through initiatives like digital transformation. Treat each channel as a jumping off point, not a dead end, and guide users naturally between environments depending on their personalized journey. Unified commerce technology stacks naturally enable omnichannel consistency more seamlessly.
11 best marketplace and ecommerce personalization tactics
Taking inspiration from leading retailers excelling at personalization, below are 11 straightforward yet highly effective tactics for ecommerce merchants to consider rolling out.
Dynamic content
The list of ecommerce personalization examples opens dynamic content that involves tailoring website content to individual users based on their:
- user account status,
- recent browsing history,
- current promotion eligibility,
- and more.
Marketplace personalization includes messaging around payment, shipping, support contact cards and account profile reviews. Progressive web apps (PWAs) help facilitate fast dynamic updates. Focus dynamic efforts on high-value pages, not lower funnel ones.
For example, a clothing retailer may display different homepage banners based on a customer's past purchase history and browsing behavior.
Product recommendations
Product recommendations use data and algorithms to suggest relevant products to customers based on their past behavior and preferences. This can include "You may also like" or "Frequently bought together" sections on product pages, as well as personalized email recommendations.
To succeed in e-commerce personalization in product recommendations, treat every view, click, cart add, order and return as a signal into an ever-updating shopper taste profile. Cue complementary recommendations from similar items added to carts recently or wishlisted. And vary recommendation content itself, using different titles, descriptive snippets and product images tailored to resonating themes.
For example, Amazon's product recommendation engine analyzes customer data to suggest products that are likely to be of interest to each individual customer.
Omnichannel shopping
To leverage personalized omnichannel shopping, present consistent experiences across channels by unifying backend data, enabling seamless and secure customer identity linkage as they transition between web, mobile, offline and IoT touchpoints.
Eliminate logged-out experiences lacking personalization depth unless strategically advantageous – for example, allowing easier gift purchases. You should also think of allowing partial journey savings and context carryover whenever possible.
For example, a customer may start browsing products on a retailer's website and later receive a personalized email with product recommendations based on their browsing history.
Data-driven marketing campaigns
Data-driven marketing campaigns build on customer data to create targeted and personalized marketing messages. You will need to segment customers using an orchestration of attributes, behaviors, predictive models and sentiment signals across the first-party data ecosystem to use this tactics of ecommerce personalization.
However, avoid simplistic rules-based grouping alone. Instead, enable integrated targeting across execution channels tied to unified segment definitions. Evaluate each segment and the broader grouping strategy routinely to ensure alignments with overarching business objectives around Customer Lifetime Value (CLV) and churn. And inspire engagement through tailored content – for example, encouraging reactivation among dormant high-spenders by highlighting recently added items aligned to their purchase history interests.
Dynamic pricing
Another tactic used by experienced marketplace personalization experts is dynamic pricing. In short, it involves adjusting product prices in real time based on factors such as demand, competition, and customer behavior. Well implemented, it allows you:
- To offer customized promotions and tactical discounts for targeted buyer groups based on willingness-to-pay and differentiated user value backed by statistical models factoring in elasticity curves.
- To test pricing variations systematically via controlled rollout,
- To monitor larger environments post-launch to catch undesirable spillover effects.
Personalized emails
Marketing emails themselves can become more relevant and compelling through personalized elements like:
- Fully customized product suggestions based on recent views and purchases,
- Personalized incentives tailored to user value tiers,
- Individualized subject lines,
- Timing based on past open history and pre-populated preference center values.
Some pro tips for pro marketplace mailing personalization include allowing end users instant opt-in or opt-out, You can also benefit from AI-based experimentation with copy tones aligned to personas and personal identifiers in greetings. Allow end users
On-site search improvement
Personalization of on-site search involves enhancing the search experience on a retailer's website to deliver more relevant and accurate search results. This can include semantic search, auto-complete suggestions, and advanced filtering options. You can:
- provide auto-complete prompts tailored to individual search strings typed,
- recommend suggested search results and refinements reflecting personal query history, typical site navigation patterns and buying category affinities.
This tactic requires reweighting search algorithms towards personalized dimensions. However, you may need to consider enabling an easy log-out option and secure one-click account reauthentication during high-intent journeys when logged out.
Personalized checkout
To make checkout experience as smooth as possible, pre-populate form fields wherever possible, leveraging prior successful purchase inputs. This simple marketplace personalization method includes e.g. presenting preferred shipping addresses and payment methods ahead of alternatives based on frequency, recency and purchase volume.
For example, a customer may receive a personalized checkout page with recommended products, special offers, and customized messaging based on their past interactions and purchase history.
Cross-selling
When a customer adds a camera to their cart, they can benefit from personalized cross-selling suggestions for related camera accessories and equipment. This strategy of marketplace personalization contributes to 35% of Amazon’s purchases.
Personalized cross-selling entails suggesting products that complement or relate to the customer's existing or previous purchases. Key strategies for effective ecommerce personalization in cross-selling include:
- Prompt impulse purchases at checkout with product recommendations complementing existing cart contents based on order history or algorithms determining items unlikely to be abandoned if delivery is delayed by a few days.
- Limit distraction for extremely focused shoppers while facilitating discovery.
- Capture infrequent buying category affinities too like jewelry for predominantly toy shoppers for broadened scope over time.
Dynamic ads
Dynamic ads use customer data to create precisely targeted and relevant advertising campaigns. This can include personalized product recommendations, special offers, and customized messaging.
For example, a retailer may use customer purchase history to create a targeted ad campaign promoting related products or special offers on social media or other advertising platforms.
Nevertheless, using this marketplace personalization strategy needs attention, and shifting rotational creatives intelligently in response to short-term engagement trends. You should also remember to vary paid ad content and creative – including affiliated catalogs and retailer sites ultimately linked to – depending on recent anonymous browsing, demographic data and clearer transaction data signals if available.
User-generated content
Reviews, ratings, and social media posts are the most important medium to spotlight authentic customer images featuring products in context. You can use their power and curate visual testimonials and video reviews from social channels and site interactions to reinforce credibility.
Think of employing these steps to implement marketplace personalization effectively using user-generated content:
- Analyze customer networks and enable direct peer-to-peer connections between frequent buyers with high similarity.
- Foster brand community participation, providing tools facilitating content creation and engagement beyond basic reactions.
- Develop programs incentivizing top advocates.
Real-life examples of marketplace personalization
Let’s explore some real-world case studies of top marketplaces deploying personalization to phenomenal results:
Amazon
With hundreds of millions of active customer accounts, mastering personalization is an imperative for Amazon to cut through the noise and match people with relevant products.
As unquestionable marketplace personalization leader, Amazon leverages numerous tactics like:
- personalized recommendations throughout the site,
- email and ads calibrated to user browsing, and
- chatbots able to have conversational exchanges tailored to individuals.
Amazon's search history functionality in particular is impressive because it continually refines and tailors the shopping experience to each customer’s needs and past searches. This means faster, more accurate search results, personalized product recommendations, and an overall customized shopping experience, making it easier for customers to discover and purchase items matching their interests.
While location personalization is not a key focus currently, Amazon's semantic search stands out from competitors. It adeptly identifies synonyms and related terms relevant to search queries.
Etsy
For an online handmade and vintage marketplace like Etsy, personalized localization makes perfect sense to connect artisan sellers with nearby buyers. Etsy takes customer location into account and displays search results and recommendations that are most relevant to each user’s geographic area. This helps shoppers discover unique, locally-created products and directly benefits individual sellers by putting their wares in front of neighborhood customers.
Etsy also provides a personalized search history experience that evolves product recommendations based on preferences, assisting customers to uncover handcrafted items aligned to their tastes. The continually improving, individualized results help nurture loyal followers for Etsy sellers and facilitate recurring purchases.
Furthermore, Etsy's semantic search capabilities outmatch typical keyword-based experiences. By analyzing factors like listing titles, descriptions, tags, and buyer behavior patterns, it can interpret the actual intent behind search terms like “handmade jewelry” and return the most on-target products.
Alibaba
As a marketplace behemoth serving over 900 million customers, Alibaba leverages personalization to better cater to users across its brand portfolio. The company consolidated real-time audience data streams nationwide into a unified processing platform. This powers granular, consistent segmentation across Alibaba’s diverse properties including Tmall and Taobao.
Wherever users shop – category sites like AliHealth for healthcare products or cross-border importer Tmall Global – the experiences reflect integrated personalization.
While Alibaba has invested significantly in location-based personalization, customizing search results and recommendations by factors like customer climate and geography, search history functionality is still developing compared to peers. There remain opportunities to refine algorithms towards true one-to-one relevance versus keyword-driven ratings. As the vendor continues enhancing its stack, consumers can expect even more tailored interactions catering to their individual needs.
eBay
As a marketplace pioneer (since 1995) now serving over 130 million active buyers globally, eBay has focused intently on personalization to reduce search friction and match consumers with suitable items. Customized product feeds and real-time promotions leverage shopper data including past purchases and browsing across eBay’s app and site to showcase relevant products. eBay also applies AI to list titles and images to infer specific item conditions, categories, and attributes, enabling smarter connections despite incomplete user inputs.
While eBay may not lead in all areas, its semantic search capabilities stand out for adeptly honing in on shopper intent with minimal initial input. By extrapolating effectively to find the right products, eBay delivers customers a more streamlined, personalized journey.
Rakuten
Japanese retail giant Rakuten takes a different approach to personalization by recognizing customers across its 70+ affiliated ecommerce brands and services using its proprietary Rakuten Unified ID system. With appropriate permissions, this unified profile shares data internally to power personalized messaging wherever users interact with Rakuten offerings. This identity-based personalization helps promote umbrella brand affinity and engagement across categories like online retail, travel bookings, and mobile phone service.
In addition, Rakuten’s newest Personalized Rewards product combines robust data science with the group’s expansive loyalty network. Instead of relying solely on external signals like cookies, Rakuten can directly identify known customers visiting partner sites using first-party CRM data. Upon recognizing high-value targets, the automated platform delivers tailored cashback incentives to motivate initial or repeat purchases aligned to customer value and brand profitability needs.
Allegro
Leading Polish online marketplace Allegro emphasizes personalization in areas like localized delivery and payment options as well as notifications alerting users to upcoming order deadlines and auctions ending soon on listings they’ve previously watched.
These event-triggered messages reduce missing out and facilitate frictionless follow-through on purchases.
Top ecommerce and marketplace personalization platforms
While tactical implementations will differ by organizations, advanced ecommerce personalization software can dramatically ease execution. Here are some top-rated platforms worth exploring:
Salesforce Commerce Cloud
Salesforce Commerce Cloud seamlessly integrates with other Salesforce solutions for unified data. This cloud-based software solution provides a platform for businesses to create personalized and efficient ecommerce experiences. It powers coordination across B2B and B2C systems enabling both branded manufacturer storefronts and multi-tenant marketplaces on a single stack. Salesforce offers a range of features to help businesses personalize their marketplaces, including:
- AI-powered product recommendations,
- Personalized search results, and
- Targeted promotions
Bloomreach
Bloomreach is an open SaaS solution focused specifically around personalized discovery and shopping experiences for consumers. Real-time data ingestion and analysis automatically surfaces the most relevant content across channels.
This AI-powered ecommerce personalization platform offers a range of features to help businesses create personalized and relevant experiences for their customers. The platform's data engine unifies real-time customer and product data, allowing businesses to understand what customers really want and personalize the ecommerce experience accordingly. Bloomreach's powerful personalization tools display products at the right time to boost conversions, making it a valuable platform for businesses looking to enhance their ecommerce personalization strategies.
Optimizely
Optimizely’s platform accelerates creation and optimization of digital experiences through its AI engine. Optimizely is an experimentation platform that allows businesses to test and optimize their websites and mobile apps to deliver personalized customer experiences. The platform offers a range of features to help businesses create and launch personalized campaigns through channels such as web, email marketing, mobile app engagement, and digital advertising. Optimizely's ability to integrate with analytics software and software that manages interaction with customers allows businesses to access data on the experiments performed and their results to improve personalization based on the available data. Optimizely recently further enhanced its capabilities via the acquisition of Welcome, an optimization solution tailored for marketplaces.
Vue.ai
Vue.ai’s catalog offerings make AI-level personalization accessible for enterprises without data science expertise. The software focuses specifically on ecommerce applications, allowing for elements like configurable shopper segmentation, campaign management, and customizable widgets to encourage engagement.
Vue.ai's AI-powered personalization engine leverages advanced machine learning algorithms to analyze customer behavior, preferences, and purchase history, enabling businesses to deliver personalized product recommendations, content, and marketing messages to their customers.
The platform delivers advanced customer segmentation and targeting features, enabling companies to provide tailored experiences to distinct customer groups. Moreover, Vue.ai facilitates effortless integration with top ecommerce platforms, allowing businesses to utilize their current technology infrastructure while gaining from Vue.ai's sophisticated personalization features.
RST Software can help you choose and integrate a personalization engine that fits your needs
While leveraging an advanced platform goes a long way, working with an experienced implementation partner ensures your stack is properly configured and personalized experiences are designed for success from the start.
RST Software helps leading retailers architect and launch data-driven commerce technologies tailored for their objectives around customer lifetime value, loyalty program expansion, headless storefronts, progressive web apps (PWAs), and more.
We can guide your journey at any stage – from selecting platforms through capturing data to measuring business impact. Simply contact us.
