With over seven billion people on the planet, and close to half of them using the internet for shopping, whilst around 80% will still shop in store, it’s safe to say the majority of shoppers will in some way be influenced or shop through digital channels. These individuals (in-store, online or through social media) have their own personalities, likes, dislikes, needs and desires.
Retailers have strived to serve the personal needs and desires of shoppers since the jewelry store suggested putting your initials on a bracelet, or encouraged you to upload your picture to put on a birthday card. When Amazon.com figured that by letting a consumer know what other consumers also looked at or ordered additional products after finding the same item, the concept of digital personalization was born.
Today’s landscape for retail personalization has largely evolved from Amazon’s data crunching product recommendations engine. But this and almost all other forms of personalization (behavioral, contextual, technical, historic data, and collaboratively filtered) are forms of anomalously-collected user data which, when used with algorithmic technologies, represent changes explicitly or implicitly in the display of pages and products being offered to an individual on a web site. We see this kind of dynamic content personalization on most retail sites today, and through real-time behavioral ad targeting and retargeting.
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With implicit personalization, web personalization is performed based on the different categories mentioned above. It can also be learned from direct interactions with the user based on implicit data, such as items purchased or pages viewed. With explicit personalization, the web page (or underlying content management system) is changed based on the user’s engagement with the features provided by the system. Hybrid personalization combines the two approaches to try and leverage the best of both worlds.
Whilst the ROI from personalization techniques including product recommendation engines, implicit or explicit web personalization or content personalization have been widely covered, the limitations of these approaches for today’s omni-channel retailers are numerous.
Perhaps most obviously is that the identity of the shopper is not considered in the collection and utilization of anonymous data. Individuals are regarded by their segment, purchase or browser history, or their explicit stated preferences. Implying an individual’s shopper intent based on any of these elements – and predictively changing a product list or web layout – is still in many respects guessing what might be relevant to a consumer.
The notion of web personalization does not transcend to other shopping habits of the consumer and is limited to web-based interactions. By not considering an individual’s other routes to shop (Social Media, Apps or In-store) retailers are fundamentally limited in the potential value of their personalization efforts – only providing a small snapshot of a personalized experience.
The internet of things means that focusing purely on online will become more and more irrelevant. Experiences will need to be personalized across not only websites, apps and call centers, but also across these new devices. Interestingly the devices themselves also create an opportunity for brands to learn more about their consumers and enhance their personalization efforts.
The customer journey is highly complex and the experience along it has never been more important. There are huge opportunities for competitive advantage for those who can get personalization right across channels, branching out from websites and making the journey contextual at every touchpoint that a customer makes but the emphasis on personalization has to move on beyond traditional methods and start treating the customer as a unique individual across all channels and opportunities to engage.