Retailers all over the world are using data and AI in order to maximize sales and meet consumer experience expectations. The likes of predictive analytics, personalized shopping and omnichannel integration are allowing retailers to build more individualized and effective buying journeys.
However, as these tactics become ever more refined and rely on increasingly detailed insights, the need to assuage customer concerns about data privacy and ensure AI use is ethical is paramount. This blog explores how ethical trend prediction can work in practice, and how to implement ethical, compliant AI frameworks.
Analytics and AI systems thrive on the data that is fed into them. The greater the quality and quantity of the data involved, the better the level of insights that can be generated to inform business decision-making and take first-mover advantage. But in a retail landscape, that requires collecting and processing substantial volumes of highly personal data, often in real time as consumers are browsing websites and apps, or are going through the checkout process.
This has led to increasing concerns among the public about how their data is collected, used and stored: Norton has found that 78% of consumers are concerned about data privacy, while 57% of people feel that AI poses a ‘significant threat’ to their privacy.
For retailers, dealing with these concerns is becoming more of an issue as younger consumers make up a gradually increasing proportion of the global economy. The younger the shopper, the more aware they tend to be about their data and privacy: IAPP found that 40% of consumers aged between 18 and 34 have exercised data subject access rights to find out what retailers know about them, compared to only 15% of those aged 55 to 64.
While consumers want personalization, it’s clear that they don’t want it without their data being handled responsibly. Retailers who aren’t able to do so are at serious risk of:
If customers don’t feel confident that their data will be treated properly, they will be less likely to shop with a brand in the future.
Connected to the previous point, if word gets around that a retailer’s approach to data is poor (rightly or wrongly), it can damage a brand’s standing for months or even years.
Breaches of data protection regulations like GDPR can lead to major financial and legal penalties being applied.
All of the above can have either direct or indirect impacts on the bottom line, and harm a retailer’s chances of success in what is a highly competitive industry.
All good retailers understand the importance of responsible data usage in the current climate - but what does that look like in practice?
At Ciklum, we’ve worked with countless organizations who have aimed to leverage analytics and AI insights in ethical ways. Through that extensive experience, we have learned that three common philosophies guide the best way forward:
As the use of AI and data has increased exponentially, so has the scope of regulations around the world designed to keep that use responsible and ethical.
For example, the European Union’s General Data Protection Regulation (GDPR) demands that retailers gain consent for data collection, are transparent around its use, and give customers the right to access, correct or delete that data. And in the United States, California’s Consumer Privacy Act (CCPA) has encouraged similar data privacy legislation in at least 20 states.
Maintaining compliance with all these regulations may seem like a necessary burden, but it’s entirely possible to turn it into a driver of competitive advantage. Baking data transparency into customer touchpoints and buying journeys - often known as ‘privacy by design’ - can foster consumer trust as they can see clear evidence of a strong commitment to ethical data use. Taken to its fullest extent, this transparency can even be leveraged in marketing to differentiate a brand from its competitors.
Ethical decision-making frameworks are just one part of making a responsible approach to data a business success.
When rolling AI and analytical tools out in practice, it makes sense to start with test cases at a small scale and prove business value before expanding deployment. Partnering this with privacy by design principles, regular auditing and continuous evaluation can ensure the balance between ethical business use and using AI to good effect.
Given the constant evolution of both regulations and AI technology, it’s also important to build and implement the right systems to adapt to future changes.
Get in touch with the Ciklum team to find out more about these systems, and read our eBook on using AI to inform retail product design.