Personalisation in Ecommerce

Ecommerce personalisation is the practice of dynamically tailoring online shopping experiences — product recommendations, search rankings, content, pricing, and communications — to individual shoppers or segments based on their observed behaviour, stated preferences, or inferred attributes. According to McKinsey research (cited by Algolia, 2024), companies that grow faster drive 40% more of their revenue from personalisation than slower-growing counterparts.

What it covers

Personalisation in ecommerce spans four major application areas:

Recommendation Engine|Recommendation engines — algorithmic surfaces that present "you may also like", "frequently bought together", "recently viewed", and cross-sell/upsell widgets. Amazon's recommendation engine is widely cited as driving approximately 35% of the company's annual sales (cited across multiple sources; original Amazon disclosure unconfirmed).

Search personalisation — re-ranking search results based on individual browsing history, category affinity, and purchase behaviour. Algolia (2024) reports that customer Decathlon Singapore achieved a 36% increase in click-through rate and a 50% increase in conversion rate after implementing personalised omnichannel search (as-of 2024).

Segment-based content — homepage banners, category landing pages, and email campaigns targeted to behavioural or demographic segments. Emarsys (2025) identifies AI hyper-personalisation, cross-channel data unification, and enhanced mobile personalisation as the leading implementation trends for 2025.

Real-time personalisation — adapting experiences within a session based on signals such as referral source, device, geolocation, and click behaviour, rather than relying solely on historical data.

Benchmarks

All figures stamped as-of their publication date. CVR lift figures vary widely by source credibility — see Contradictions section.

McKinsey estimates personalisation can lift retail revenues by 10–15% (cited via multiple secondary sources; McKinsey primary report undated) (as-of 2024)
Named vendor case studies (Algolia, 2024, high confidence):
- Huckberry: +9.4% website revenue from AI-driven personalisation
- Decathlon Singapore: +36% CTR, +50% CVR from personalised omnichannel search
- Staples Canada: double-digit CVR increase from AI personalisation
- Your Surprise: +9% CVR increase alongside significant reduction in manual work
83% of consumers are more likely to purchase from a brand that suggests products they recently browsed (Wunderkind 2024) (as-of 2024)
31% of consumers say they are more likely to remain loyal to a brand due to personalised shopping experiences (Emarsys/SAP 2025) (as-of 2025)

Adoption and maturity

In a 2024 B2C ecommerce survey of 1,100 respondents (Algolia), only 56% of retailers provide personalised shopping profiles and just 46% offer recommendations based on browsed or purchased items (as-of 2024)
67% of retailers believe they excel at personalising their website, but only 46% of consumers agree (Sailthru/Marigold 2022) (as-of 2022)

Deloitte Retail Distribution Outlook (2025) found 44% of retail executives want to enhance omnichannel experiences in 2025 (as-of 2025)
82% of retailers identify maintaining real-time customer data as their biggest personalisation challenge (Mastercard 2023 Retail Touchpoints Report) (as-of 2023)

Fashion ecommerce

45% of fashion executives identify AI-driven marketing personalisation as a major value driver for 2025 (McKinsey State of Fashion 2025 webinar) (as-of 2025)
Algolia (2024) notes fashion retailers can deliver style-specific personalisation — for example, surfacing products matching a customer's known preferences for shoe colour and size — using purchase and browse history

Data strategy and implementation

The shift toward privacy-compliant personalisation has elevated two data types (Algolia, 2024):

Zero-Party Data — information voluntarily supplied by shoppers (quiz responses, preference toggles, style profiles). Consent is explicit.
First-party data — observed browse, cart, and purchase behaviour collected on an opt-in basis. No third-party dependencies.

Algolia's 2023 Ecommerce Site Search Trends report (via their 2024 blog post) found that more than half of retailers developing personalisation in-house recognised they could not evolve it fast enough to keep pace with market expectations.

Emarsys (2025) identifies five implementation trends for 2025: AI hyper-personalisation, integrated voice and visual search, seamless cross-channel data unification, advanced analytics and predictive modelling, and enhanced mobile commerce personalisation.

Market size

Two market research firms report irreconcilable figures for the ecommerce personalisation software market. Market.us (cited by Contentful, 2025) values the market at $263 million in 2023, growing to $2.4 billion by 2033 at a CAGR of 24.8%. Global Growth Insights values the same market at $2.87 billion in 2025 alone — implying it was already larger than Market.us's entire 10-year target by the time that target was set. The most likely explanation is a significant difference in scope definition (e.g., personalisation software only vs. broader AI-in-ecommerce tooling). Neither source's methodology is published openly. [https://www.contentful.com/blog/ecommerce-personalization-statistics/] VS [https://www.globalgrowthinsights.com/market-reports/e-commerce-personalization-software-market-102550]

Precedence Research (cited by Emarsys, 2025) values the broader AI-in-ecommerce market at $9.01 billion in 2025, projected to exceed $64.03 billion by 2034 at a CAGR of 24.34% (as-of 2025).

Contradictions

Adoption rate estimates vary dramatically. One aggregator source claims 92% of companies now use AI-driven personalisation (ecommerce.folio3.com, undated, no primary source). Algolia's 2024 B2C survey of 1,100 respondents finds only 46% of retailers even offer recommendation-based personalisation. The 92% figure almost certainly reflects an extremely broad definition (e.g. any form of automated targeting, including email segmentation). The Algolia B2C survey figure is more credible as it is a named, recent, primary study. [No direct URL for the 92% primary source] VS [https://www.algolia.com/blog/ecommerce/ecommerce-personalization-benefits-challenges-and-how-to-implement]

CVR lift figures from unverified aggregator sources (e.g. "369% higher AOV for recommendation-engaged sessions", "760% more email revenue from segmented campaigns") are presented without named primary sources and should not be treated as equivalent to named vendor case studies (Algolia: 9–50% CVR/CTR improvements across named accounts). The aggregator figures may derive from cherry-picked vendor case data or methodologically unverified studies. [growth-engines.com aggregator] VS [algolia.com named case studies, 2024]

Key terms

Term	Meaning
Recommendation engine	Algorithm surfacing products to individual users based on behaviour, similarity, or purchase history
Zero-party data	Data explicitly volunteered by the customer (e.g. quiz results, style preferences)
First-party data	Behavioural data collected by the retailer on opt-in (browse, cart, purchase events)
Hyper-personalisation	Real-time, individual-level personalisation using AI and streaming data, beyond segment-level targeting
Personalisation maturity	A framework for assessing how advanced a retailer's personalisation capabilities are, from manual segmentation (L1) to AI-driven real-time (L3+)
Cold-start problem	The challenge of personalising for new or anonymous shoppers with no historical data