How valuable is your data? According to one Amazon Prime Day promotion, highly valuable. Mark Floisand, Chief Marketing Officer, Coveo explains what the tech giant’s deal means for the AI-takes-all future of retailers.
While Amazon has been hosting Prime Day for years, this year they offered a promotion that should make marketers sit up and pay attention. Throughout Amazon’s 48-hour marketing blitz for Amazon Prime, the company is offering $10 in credit to individuals who grant permission for Amazon to access their data. According to a recent study, Amazon has over 100 million Prime users. If all of them granted access to their data, that would cost Amazon more than one billion dollars just for one element of a single promotional event, more than most companies’ entire annual marketing budgets several times over. Is the high price tag worth it?
In short, yes.
The Data Gold Mine
Retailers have traditionally thought of promotional events in terms of time brackets; a one-day event, a weeklong sale, etc. These events drive traffic throughout the duration of the promotion and boost awareness generally. However, new technology enables businesses to leverage shoppers’ promotional data on a continuous, ongoing basis throughout the entire year, turning every day into Prime Day for businesses. Prime Day data is valuable enough to spend a billion dollars to acquire only because Amazon can continue to leverage it throughout the year.
The good news for retailers is that you don’t need to be a trillion-dollar company to tap into the power of data. Most enterprises are already sitting on a massive treasure trove of information about their customers, and already have the structures in place to record customer behavior at scale. There’s just one final step.
Unification Unlocks the Door
The key to unlocking data’s true potential lies in unification. By putting data to work through AI, enterprises can realize surprising gains year-round, not just during sales events. Every time a shopper interacts with a business, he or she generates a signal. That signal data is typically stored in an information silo designated for that part of the customer’s journey, whether its ad clicks, customer service tickets, purchasing patterns and more. Unification stitches the information trapped in these silos together to yield the full picture of consumer behavior at all parts of the customer journey, at scale. This holistic view gives marketers better insights into what customers have been looking for, how they might be getting stuck, and where support staff can best help resolve the situation. This provides white glove customer treatment, without the trillion-dollar price tag.
By unifying all that data, machine learning and AI can be put to work to predict future behavior patterns based on former experience. This enables AI systems to present solutions before problems even arise, tailoring relevant content and promotional offers to individuals on a personal level. Having access to information that has been explicitly provided by customers – demographic data, color preferences, shoe size etc – and implicit information gleaned from what they interact with – like a visitor browsing through running shoes rather than cycling gear – provides a wealth of data to improve the relevance of what’s offered to them, automatically.
Relevance Yields Results
While grocery shopping data via Amazon’s acquisition of Whole Foods and clothing shopping data via Prime Day sales might seem unrelated at first blush, unification uncovers a deeper picture. Take the organic-only grocery shopper who consistently buys certified humane eggs and pesticide-free meat. By uniting disparate data silos, marketers can recognize this individual is more likely to be interested in vegan leather clothing and reusable water bottles than hunting gear and single-use plastics. With this implicit insight, AI can recommend products to customers based on their personal preferences – the more data the AI has about a customer the better it’s able to personalize customer experiences and offer maximum relevance. Increasing relevance increases conversion and decreases the time spent on ineffective marketing.
While humans can generate the same level of insight on an individual level, only AI can do it at scale. In addition, AI can also produce unexpected, data-driven insights based on patterns too broad or too complex for human comprehension. It also works around the clock and constantly improves – every customer signal adds data that helps the AI hone and tailor its offering across the entire system simultaneously.
Access to business-defining customer data is not confined to global companies with massive budgets; any enterprise with the foresight to anticipate their future business needs can invest in the technology to unify data and increase relevance. The first step is recognizing the wealth of untapped data businesses already own and exploring options to create better insights