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Use CaseMarketing

Personalise product recommendations for existing customers

One of the fastest ways to build revenue is to increase average shopping cart size and to cross-sell to exisiting customers with whom you have a relationship, data and a brand affinity. Also called recommendation engine or recommendation system, these systems leverage customer data to ensure that existing customers purchase more.

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Revenue

Revenue - Customer retention

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Revenue

Revenue - Customer engagement

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Revenue

Revenue - Churn risk reduction

Description

Personalise product recommendations for existing customers

Risk

High Risk

Linked Case Studies

Atolla Skin Lab

Atolla identifies skin health issues and recommends skin care products using machine learning

Nike offers foot measurement for accurate individual shoe size guide using computer vision

Pinterest enhances image search by not only searching for matching items but also related items such as suggesting recipes for grocery items doubling the click-through rates

Godiva boosts website wide customer conversion rate by 24% by offering personalised recommendations to online visitors using machine learning

Stitch Fix assists personal stylist staff in choosing matching clothing items for customers by filtering products to closely match customer preference using recommendation algorithms

Gousto, a British meal kit retailer, grows customer base by 700% by using machine learning to forecast demand and personalise recommendations

Jaeger-LeCoultre

Jaeger-LeCoultre offers personalised recommendations that match items in cart or those bought previously, using chatbot service

The North Face offers customers a personalised, engaging virtual shopping experience using chatbot and machine learning

Seymourpowell proposes personalized skincare service which recommends suitable products based on skin type, preferences, location etc.

Amazon makes personalised product recommendations to customers with machine learning

Toronto-Dominion Bank

Toronto-Dominion(TD) Bank plans to offer personalised recommendations for its customers using deep learning

Trulia achieves double-digit increase in consumer engagement through a more personalized, predictive experience using machine vision

The John Lewis Partnership

John Lewis improves shopping experience by keeping track of customer purchase patterns to predict inventory and tailor offers for individual customers with machine learning

Proven offers personalised skincare products with the use of machine learning

Global Retailer

One of the world’s biggest retailers recommends groceries to online shoppers

''Rooms to go'' uses Machine Learning to offer tailored add-on options to customers

StitchFix identifies best matching personal stylists for each customer using machine learning to better serve its 2.2m customers

TGI Fridays creates a customised beverage recommendation system on the restaurant''s mobile app

The North Face improves omnichannel experience increasing customer satisfaction and loyalty with natural language processing

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Source: appliedai.com · Editor: original-sdg