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"AI-driven personalization for better e-commerce experiences."

This company uses artificial intelligence to unify customer, product, and content data. It helps businesses customize their e-commerce experiences, increasing their online revenue by anticipating customer needs and preferences.
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BlurbSTAR Case Study
Lords & Labradors & Nosto
Nosto's personalization tripled conversions and increased AOV by 37%.
3X
Increase in conversion rate after implementing the campaigns.
37%
Rise in AOV since all campaigns were implemented.
1.
Situation
Lords & Labradors, an online-only brand specializing in pet products, identified a segment of browsers hesitant to purchase high-priced items. They decided to implement Klarna as a payment solution to help spread the cost but needed to optimize how this payment option was promoted to convert browsers into buyers.
2.
Task
The task was to create more awareness and encourage conversions by promoting Klarna's payment options. They aimed to determine which messaging variations worked best to reach users not only at checkout but earlier in their browsing journey, particularly on category pages.
3.
Action
With guidance from Nosto’s Customer Success Manager, Lords & Labradors used Nosto’s Audience Insights and Content Personalization tools. They conducted A/B testing to trial different Klarna payment messages and provided a £10 incentive for payments through Klarna. They optimized these campaigns for mobile users to capture a huge portion of traffic.
4.
Result
The campaigns significantly increased Lords & Labradors’ conversion rates and average order value (AOV). They saw a more than 3X increase in conversion rates and a 37% rise in AOV. The most effective message was Klarna’s option to pay in 3 installments, and targeting mobile users proved crucial to this success.
Keywords
CONTENT PERSONALIZATION
A/B TESTING
AUDIENCE INSIGHTS
KLARNA PAYMENTS
CONVERSION RATE OPTIMIZATION
AVERAGE ORDER VALUE (AOV)
MOBILE TARGETING
The Blurbs FAQs
Nosto hasn't answered their category questions yet.
1.
What’s the average percentage of PDPs that show off an outfit across your customers?
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2.
How often do products in outfits go out of stock, and how quickly do they get swapped out?
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3.
Can you sprinkle in real-time personalization to tweak the recommendations on the fly?
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4.
How do you keep tabs on stock, and can you update outfits in real-time when something’s sold out?
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5.
Can you spot when a product is lagging behind and give it a boost by featuring it in more outfits? How do you make sure this doesn’t hurt overall revenue?
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6.
Can you make sure outfits only include products from the same warehouse or fulfillment center? What about prioritizing items from the closest warehouse to the customer?
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7.
Where does your tech plug into the retailer’s world?
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8.
How do you keep certain brands separate and make sure outfits don’t mix and match brands, teams, or seasons that shouldn’t be blended?
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9.
Are you able to use a specific brands aesthetic vs the retailers aesthetic for certain outfits?
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10.
Can you detect when a product is performing behind plan and push that into more outfits?
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