Artificial Intelligence technologies are empowering businesses around the globe with their ability to process data sets which would be incomprehensible to humans, and intelligently produce actionable findings with ever-increasing acuity. But while AI can digest data and create unique findings and insights for maximising business efficiency, when it comes to creating art and designs, AI cannot reach the levels of originality of a human.
London-based fashion designer, Sophia Webster has never had such trouble in creating unique and original designs, and has transformed her innovative shoe and handbag designs into a thriving global business. The brand did not need any help creatively – but did turn to AI to gain a cutting edge in a competitive e-commerce environment.
Worth over £586bn in the UK alone annually, the e-commerce environment is one that continues to evolve rapidly. Standing still and using the same marketing strategy every month is not a viable option for retailers. Ahead of a new product launch, Sophia Webster was faced with this familiar challenge: how do you increase sales by targeting new customers, as well as promote the new range to loyal customers, in a cost-effective manner?
For Sophia Webster, the solution to this challenge came through AI and machine learning. Machine learning delves deep into data that is otherwise invisible to the marketer’s naked eye, revealing new customer trends or segments that are finely matched to a retailer’s proposition. Even the most subtle targeting can be matched to audiences with a high degree of accuracy. Marketers accept the importance of data in building their customer base, and machine learning can help uncover trusted, actionable insights.
Working with its partner agency, Croud, Sophia Webster took the decision to experiment with machine learning in its digital strategy to generate improved acquisitions rates of its most relevant products. In this instance, that meant testing out Smart Shopping Campaigns, which combined Sophia Webster’s existing product feed with Google’s machine learning capability, creating and distributing a range of ads across several ad networks.
Leveraging this technology enabled the brand to match potential customer intent, derived from factors including search history and demographics, with the most relevant ads from the retailer.
These strategies significantly shortened the path to purchase for Sophia Webster’s customers, pushing shopping interactions towards direct conversions. The difference was dramatic. Cost-per-click (CPC) fell by more than half (57%) while return on investment grew more than four-fold (408%). Critically, through adapting to machine learning and automation-based campaign management which captured more upper-funnel traffic, the retailer drove nine times more conversions – a 900% increase – than before launching the Smart Shopping tool.
We are in an interesting position in marketing where brands have so much data it can be difficult to know what to do with it – for many marketers the more you have, the less you actually know. Machine learning is emerging as a trusted ally for brands as it can digest massive data sets and extrapolate insights that most humans simply could not have seen. For Sophia Webster, AI enabled the brand to scale its growth globally, reaching new audiences and all the while reducing its overall cost-per-click (CPC) and improving its return on investment.
The ecommerce environment is as lucrative as it is competitive – but for marketers looking to gain a cutting edge, it’s a case of working smarter, not harder. The brands who are able to establish an advantage will be those who can leverage tools that can best make sense of the masses of available data and produce crystallised insights. For Sophia Webster, AI may still be some way off being able to design an appealing handbag, but as a tool for creating an effective digital advertising strategy, machine learning is proving to be far more than just an accessory.
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GUEST COMMENT Shopping (AI)ssistant: how machine learning helped Sophia Webster find the perfect fit
by James Pearson
Artificial Intelligence technologies are empowering businesses around the globe with their ability to process data sets which would be incomprehensible to humans, and intelligently produce actionable findings with ever-increasing acuity. But while AI can digest data and create unique findings and insights for maximising business efficiency, when it comes to creating art and designs, AI cannot reach the levels of originality of a human.
London-based fashion designer, Sophia Webster has never had such trouble in creating unique and original designs, and has transformed her innovative shoe and handbag designs into a thriving global business. The brand did not need any help creatively – but did turn to AI to gain a cutting edge in a competitive e-commerce environment.
Worth over £586bn in the UK alone annually, the e-commerce environment is one that continues to evolve rapidly. Standing still and using the same marketing strategy every month is not a viable option for retailers. Ahead of a new product launch, Sophia Webster was faced with this familiar challenge: how do you increase sales by targeting new customers, as well as promote the new range to loyal customers, in a cost-effective manner?
For Sophia Webster, the solution to this challenge came through AI and machine learning. Machine learning delves deep into data that is otherwise invisible to the marketer’s naked eye, revealing new customer trends or segments that are finely matched to a retailer’s proposition. Even the most subtle targeting can be matched to audiences with a high degree of accuracy. Marketers accept the importance of data in building their customer base, and machine learning can help uncover trusted, actionable insights.
Working with its partner agency, Croud, Sophia Webster took the decision to experiment with machine learning in its digital strategy to generate improved acquisitions rates of its most relevant products. In this instance, that meant testing out Smart Shopping Campaigns, which combined Sophia Webster’s existing product feed with Google’s machine learning capability, creating and distributing a range of ads across several ad networks.
Leveraging this technology enabled the brand to match potential customer intent, derived from factors including search history and demographics, with the most relevant ads from the retailer.
These strategies significantly shortened the path to purchase for Sophia Webster’s customers, pushing shopping interactions towards direct conversions. The difference was dramatic. Cost-per-click (CPC) fell by more than half (57%) while return on investment grew more than four-fold (408%). Critically, through adapting to machine learning and automation-based campaign management which captured more upper-funnel traffic, the retailer drove nine times more conversions – a 900% increase – than before launching the Smart Shopping tool.
We are in an interesting position in marketing where brands have so much data it can be difficult to know what to do with it – for many marketers the more you have, the less you actually know. Machine learning is emerging as a trusted ally for brands as it can digest massive data sets and extrapolate insights that most humans simply could not have seen. For Sophia Webster, AI enabled the brand to scale its growth globally, reaching new audiences and all the while reducing its overall cost-per-click (CPC) and improving its return on investment.
The ecommerce environment is as lucrative as it is competitive – but for marketers looking to gain a cutting edge, it’s a case of working smarter, not harder. The brands who are able to establish an advantage will be those who can leverage tools that can best make sense of the masses of available data and produce crystallised insights. For Sophia Webster, AI may still be some way off being able to design an appealing handbag, but as a tool for creating an effective digital advertising strategy, machine learning is proving to be far more than just an accessory.
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