Our Site Search Trends report found that 70% of retail businesses globally said personalization would be an integral or large part of their ecommerce strategy over the next year.
Thankfully, getting started with ecommerce personalization is easier today than before, with modern personalization software and AI allowing brands to deliver personalized customer experiences at scale and drive benefits like higher conversion rates, average order values, and customer loyalty.
Key Takeaways
Ecommerce personalization software uses machine learning, customer data and behavior, and product info to deliver tailored shopping experiences across the customer journey.
The category is broad, with different solutions specializing in applying and enabling personalization across different parts of the customer experience, like search and product discovery, content, marketing, experimentation, and omnichannel orchestration.
To choose the right software, buyers should start by choosing vendors based on use cases, and then factoring in specific needs, data maturity, and goals.
With the right solution, personalization drives measurable outcomes: higher conversion rates, larger average order values, stronger customer loyalty, and a better customer experience.
What is ecommerce personalization software and how to navigate solutions
Ecommerce personalization software is software that leverages AI and machine learning to deliver tailored, personalized shopping experiences for each user across their customer journey.
These solutions typically ingest and analyze customer and product data, unify behavioral and transactional data in real time, and then use machine learning models to dynamically adjust things like search results, product recommendations, messaging, and marketing communications for each shopper.
But not all solutions offer each of these capabilities or specialize in all parts of the customer experience.
Some tools personalize search results and product recommendations, while others adapt on-site content like homepages, product listings, and CTAs based on each shopper’s behavior, preferences, and context.
And a few vendors go beyond the website into channels like email, SMS, social, push notifications, and retargeting ads, making it possible to re-engage customers with personalized offers or products aligned to their interests.
Since different solutions specialize in personalizing different parts of this journey, it’s key to align your use cases and goals with the right type of solution.
Top ecommerce personalization software, by use case
The table below outlines the main use cases of ecommerce personalization, what each one involves and enables, and which solutions are best-in-class for that use case.
Use Case
What It Enables
Top Solutions
Search & Discovery Personalization
AI-powered site search and navigation that adapts results to shopper intent and behavior.
Algolia, Coveo, Constructor, Bloomreach
Onsite Experience Personalization
Dynamic tailoring of website content, layouts, banners, and product displays in real time.
What is it: Search and discovery personalization ensures that every ecommerce shopper can quickly find products that are most relevant to their intent, preferences, and context. This is especially critical for retailers with large or complex catalogs.
Typical features include: vector and semantic search, autocomplete, dynamic filters and facets, personalized ranking, and behavioral search optimization.
Top solutions:
Algolia: Known for speed and developer-friendly APIs, Algolia combines semantic search with AI-powered ranking to personalize lightning-fast search and discovery experiences for both B2B and B2C ecommerce.
Coveo: Coveo provides enterprise-grade search and discovery with strong AI for complex, enterprise product catalogs. It excels at surfacing relevant results across commerce, content, and customer service channels.
Constructor: Built specifically for commerce, Constructor uses natural language processing and user behavior to power search, browse, and product discovery. It emphasizes business outcomes like revenue lift rather than clicks alone.
Bloomreach: Bloomreach offers advanced search, navigation, and AI-powered merchandising designed to handle large catalogs and deliver highly relevant product results.
Onsite Experience Personalization
What it is: Onsite personalization tailors the content, layout, and offers shown to visitors in real time, ensuring each shopper’s browsing journey feels relevant.
Typical features include: dynamic homepages, personalized category pages, product carousels, behavioral triggers, and on-site banners or CTAs.
Top solutions:
Algolia: The same algorithm, rules, and promotions run in Algolia search can be used on — category and collection pages, product details pages, landing pages, etc.. This allows you to create consistent user experiences throughout a customer’s journey on your site or federated sites.
Nosto: A turnkey solution for mid-market retailers, Nosto personalizes site content, product displays, and layouts without heavy engineering resources.
Dynamic Yield: A comprehensive platform offering advanced onsite personalization, including individualized product displays, banners, and tailored CTAs. Dynamic Yield integrates personalization with testing for continuous optimization.
Monetate: One of the earlier leaders in this space, Monetate provides dynamic content personalization, segmentation, and A/B testing tools, focusing on lifting conversions through tailored on-site experiences.
OptinMonster: Best known for pop-ups, exit-intent overlays, and targeted onsite promotions, OptinMonster is often used by smaller retailers looking to capture leads and reduce abandonment.
Product Recommendations & Merchandising Engines
What it is: Recommendation engines and personalized merchandising suggest the right products to the right shopper at the right time, driving higher average order values and cross-sells.
Typical features include: “Frequently bought together,” “You may also like,” upsell bundles, AI-powered recommendation carousels, and merchandising dashboards for business rules.
Top solutions:
Algolia: Beyond search, Algolia personalizes product suggestions based on user behavior and product attributes, giving retailers full control over merchandising logic.
Coveo: Coveo uses machine learning to suggest relevant products in real time, helping large retailers optimize both conversion and inventory.
Bloomreach: With strong merchandising capabilities, Bloomreach recommends products that balance personalization with business goals such as margin or inventory sell-through.
Nosto: Nosto provides intuitive tools for personalized product recommendations, upselling, and bundling, with marketing teams able to adjust rules without coding.
Dynamic Yield: The platform offers advanced product recommendation strategies powered by machine learning, with testing frameworks to measure lift.
Lifecycle Marketing & Messaging Personalization
What it is: Lifecycle personalization extends beyond the website to email, SMS, push, and ads, keeping shoppers engaged with tailored communications that encourage repeat purchases.
Typical features include: triggered email/SMS campaigns (cart abandonment, winback), dynamic content blocks in messages, predictive send-time optimization, and personalized offers.
Top solutions:
Klaviyo: A leading choice for DTC brands, Klaviyo combines email and SMS automation with predictive analytics. Its segmentation and lifecycle flows help retailers drive retention and repeat purchases.
Insider: Insider offers cross-channel lifecycle personalization, unifying web, app, email, SMS, and messaging apps into coordinated customer journeys powered by AI.
Oracle Marketing: As part of Oracle’s enterprise suite, Oracle Marketing Cloud delivers advanced audience targeting and lifecycle campaign orchestration across email, mobile, and advertising.
Salesforce Personalization (formerly Interaction Studio): Salesforce’s personalization solution ties lifecycle personalization into its broader CRM and Marketing Cloud, giving enterprises unified data and cross-channel campaign execution.
Customer Data Platforms (CDPs) & Audience Segmentation
What it is: CDPs unify data from multiple touchpoints into persistent customer profiles, powering segmentation and predictive modeling that other tools can activate.
Typical features include: identity resolution, unified customer profiles, real-time segmentation, and predictive scores (churn, LTV, propensity to buy).
Top solutions:
Insider: In addition to lifecycle messaging, Insider provides CDP-like functionality by unifying customer data into real-time segments for activation across channels.
Oracle Marketing: Oracle’s CDP capabilities help large enterprises centralize data across commerce, service, and marketing systems.
Salesforce Personalization: Salesforce integrates CDP functionality with its CRM, enabling unified profiles and audience segmentation across the Salesforce ecosystem.
Experimentation & Optimization Platforms
What it is: Experimentation platforms let brands test different experiences, measure uplift, and refine personalization strategies for maximum impact.
Typical features include: A/B testing, multivariate testing, personalization rules, algorithm transparency, and uplift measurement.
Top solutions:
Algolia: Algolia customers can A/B test results across attributes, user segments, user behavior, and more.
Optimizely: Known as a leader in experimentation, Optimizely offers robust A/B and multivariate testing, enabling teams to measure the impact of personalization across journeys.
VWO: Offering experimentation, heatmaps, and analytics tools, VWO provides tools for optimizing onsite experiences and personalization campaigns.
Adobe Target: Adobe Target integrates testing and personalization, offering personalized content, recommendations, and both manual and AI-powered multivariate experiments within Adobe Experience Cloud.
Omnichannel & Journey Orchestration
What it is: Omnichannel personalization coordinates consistent, personalized experiences across every customer touchpoint, from web and mobile to email, ads, and in-store.
Typical features include: journey mapping, cross-channel triggers, unified orchestration, and AI-powered next best action recommendations.
Top solutions:
Insider: Offers strong omnichannel orchestration across web, app, email, SMS, and messaging apps, powered by predictive segmentation.
Bloomreach: Bloomreach Engagement combines data, messaging, and orchestration, giving retailers control over journeys across multiple channels.
Salesforce Personalization: Embedded into Salesforce’s broader ecosystem, it enables consistent personalization across sales, service, marketing, and commerce.
Oracle Marketing: Oracle supports global enterprises with orchestration across web, mobile, ads, and offline channels, tied into its data and analytics stack.
Adobe Target: As part of Adobe Experience Cloud, Adobe Target supports cross-channel personalization and integrates with Adobe Analytics for unified journey optimization.
Choosing the right ecommerce personalization platform
Choosing a personalization platform isn’t just about comparing features but about aligning technology with your business goals, team resources, data maturity, techstack, and customer experience strategy.
Use the following framework to evaluate the market and build a shortlist of vendors. A clear objective will narrow the categories of vendors that are relevant to your use cases and goals.
Step 1: Define your ecommerce personalization objectives
The first mistake many teams make is jumping straight to feature comparisons. Instead, start with the why. What is the business problem you’re trying to solve?
If your biggest challenge is helping shoppers navigate a large or complex catalog, search and discovery vendors should rise to the top of your list.
If you want to lift conversion rates on-site, focus on platforms that excel in personalized content and recommendations.
If repeat purchases and retention are your priority, lifecycle marketing and messaging tools may deliver the most immediate ROI.
For enterprises with multiple touchpoints, the goal may be to deliver a consistent, omnichannel journey—which points you toward orchestration platforms.
Step 2: Assess your current data and technology maturity
Personalization’s effectiveness is based on how much data you have and your existing techstack because platforms rely on your existing data infrastructure versus how much they bring their own intelligence.
Evaluating your own data volume and techstack is as important as evaluating the vendor. A personalization platform will underperform if you don’t have the team or data to support it.
If you already have a unified customer view through a CRM or CDP, you’ll want a platform that can plug into that ecosystem seamlessly.
If you lack that foundation, consider vendors that can act as both personalization engine and data layer (e.g., Insider, Salesforce Personalization).
Think about technical resources: Do you have developers who can manage APIs and tuning (e.g., Algolia, Constructor), do you need a marketing-friendly interface with templates and drag-and-drop controls (e.g., Nosto, Klaviyo), or do you need a platform suited for both developers and non-technical merchandisers (Algolia)?
Step 3: Prioritize core capabilities versus advanced nice-to-have’s
Not every business needs advanced AI or omnichannel orchestration on day one. Divide your requirements into:
Table stakes: Recommendations, personalized search ranking, dynamic segmentation, triggered lifecycle messaging. These are the minimum bar.
Advanced differentiators: Generative content like shopping guides, dynamic pricing, conversational commerce, cross-channel orchestration, real-time personalization. These are capabilities that create future advantage but may require more investment and change management.
Understanding needs versus nice-to-have’s keeps you from overbuying. Many teams pay for advanced features they never implement, while neglecting the basics that actually move the needle.
Step 4: Evaluate vendor fit by category
Once you know your objectives and requirements, map vendors into the categories that matter for you, and avoid comparing across categories.
For example, a personalized marketing tool like Klaviyo isn’t competing with a personalized search platform like Algolia. They solve different problems and personalize different parts of the ecommerce shopper experience.
Step 5: Score vendors against business criteria
Use a weighted scorecard so you’re forced to make trade-offs explicit instead of being swayed by flashy demos or vendor hype. Build the scorecard around dimensions like:
Capabilities: Does it deliver the features you actually need?
Ease of use: Can your marketers manage campaigns without constant developer support?
Integrations: Does it fit into your commerce, CRM, and data stack?
Scalability: Can it handle your traffic, catalog size, and growth trajectory?
Support & services: What training, onboarding, and success resources are included?
Cost & ROI: Is pricing usage-based, enterprise-only, or tiered — and how does that map to your budget and expected revenue lift? What’s the total cost of ownership? Are all necessary features included or are they costly add-ons?
Step 6: Confidently make the decision
At this stage, you should have a shortlist of 2 to 3 vendors, plus comparative scoring against your criteria.
Now weigh factors beyond the product, like vendor roadmap, cultural fit, reference customers, and long-term partnership potential.
The best choice is the one that balances today’s needs with tomorrow’s growth, while fitting your team’s capacity to actually execute.
Get started with personalization
Personalization has become a key driver of customer experience transformation, and a cornerstone of modern ecommerce. But the path to getting it right depends on more than just picking a tool off the shelf. The market is broad, with vendors specializing in different areas, and no single platform is the perfect fit for every business.
The key is to start with your goals, map them to the right use cases, and evaluate vendors based on how well their strengths align with your needs, resources, and level of data maturity.
If your use cases happen to be AI-powered personalized search and product recommendations, and your goals are increasing revenue while improving the customer experience, Algolia may be an ecommerce personalization solution to shortlist.
Frequently asked questions
How do I know if my business is ready for a personalization solution?
You’re ready if you have enough site traffic or customer data to benefit from personalization, and if you’re hitting limits with built-in tools or manual rules. Even smaller retailers can see ROI, but enterprises with larger catalogs and more touchpoints typically gain the most from advanced solutions.
Does catalog size affect which personalization platform I should choose?
Yes. Platforms built for smaller or mid-sized catalogs (hundreds to a few thousand SKUs) often prioritize ease of use and faster time to value. For large or complex catalogs (tens of thousands to millions of SKUs), you’ll need a platform with advanced indexing, AI-driven discovery, and strong merchandising controls like Algolia, Coveo, or Bloomreach.
How do I measure ROI from personalization?
Track metrics that tie directly to revenue: conversion rate lift, average order value (AOV), repeat purchase rate, and cart abandonment reduction. Strong platforms provide A/B testing and reporting tools so you can attribute uplift directly to personalization efforts.
Do personalization platforms work differently for B2C vs. B2B?
Yes. In B2C ecommerce, personalization usually focuses on driving conversions and order value through product discovery, recommendations, and lifecycle marketing at high volume. In B2B commerce, personalization emphasizes account-based experiences, contract or tier-specific pricing, complex product catalogs, and longer purchase cycles that involve multiple stakeholders.
Can a single vendor serve both B2C and B2B?
Some can, but usually with different strengths. For example, Algolia and Constructor can support both B2C and B2B discovery, while Salesforce and Oracle can cover complex B2B orchestration. That said, many mid-market B2C-first tools (like Nosto or Klaviyo) may not fit well in B2B contexts where catalog complexity, pricing models, and buying cycles are very different.
How should I build a business case for personalization?
Start with pain points: lost revenue from poor product discovery, low conversion rates, or weak retention. Quantify the opportunity (e.g., a 10% lift in AOV or a 5% drop in cart abandonment). Then align vendor capabilities with these goals, highlight ROI benchmarks from case studies, and frame personalization as a growth driver rather than a cost center.
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