Digital personalization, which started with Facebook newsfeeds and Netflix movie recommendations, has gone from a differentiator to an expectation. But, when personalization goes too far, customers get “creeped out” and the offending company risks lost trust. This privacy risk is outsized for companies focused on advancements in personalization, such as machine learning, to inform product development. Particularly, big tech – the firms with the most data and talent, at the bleeding edge of artificial intelligence.
Why personalization matters
Personalization is no longer a nice-to-have in the world of marketing, customers demand it. According to Salesforce, 52% of customers will go elsewhere if email content isn’t personalized.  Infosys found that 86% of customers say personalization influences what they purchase, with 25% saying it significantly influences their purchase decision.  Good news for companies: there is a return on this customer experience investment. According to Harvard Business Review in partnership with McKinsey, “personalization can deliver five to eight times the ROI on marketing spend and can lift sales by 10% or more.”  From Gartner, “we expect that by 2018, organizations that have fully invested in all types of online personalization will outsell companies that have not by more than 30%.”  In order to remain competitive and relevant to customers, firms need to invest in personalization. Those that do will be rewarded.
Big tech on the bleeding edge
Facebook, Google, and Amazon (among others) are all engaged in developing ever-increasing levels of personalization. Recently, this is driven by advancements in machine learning – algorithms that leverage vast amounts of customer data to predict what is most relevant to you. Machine learning enables these companies to deploy personalization at scale, delivering a unique individualized experience to each of their millions of users. This personalization appears in many forms. How can Facebook have a unique newsfeed for each of their 2 billion users? Or in Gmail, how do the suggested responses sound more like you now than they did before? How are Amazon’s emails with product recommendations hyper-relevant to you? The answer to all of these is machine learning and this personalization is just the tip of the iceberg. As machine learning advances, big tech is finding new ways to leverage personalization to create new products and experiences. Facebook’s ad targeting is so accurate some customers are uneasy when the exact product they viewed a couple hours ago appears in Instagram. Customers even suspect Facebook is leveraging phone microphones to listen, and target ads based on what it hears.  Real or not, this perception alone is enough to damage a brand. Google’s camera “Google Clips” leverages machine learning to recognize people and take spontaneous photos. While some may find this convenient and interesting, others “first instinct was: Holy s*** this is creepy.”  Amazon’s new store, Amazon Go, leverages computer vision and machine learning to track shoppers in the store and determine what they purchase. This “Just Walk Out” technology eliminates the need for a cashier, but some shoppers can’t get over the “Big Brother aspect of their shopping trip.” 
Innovative or creepy? A fine line
It can be argued that each of these products are innovations that create new benefits to users or customers. But, is the benefit enough to outweigh privacy concerns? How does a firm decide if a personalized product is appropriate or not? These are difficult questions to answer, but there are a few strategies than can help mitigate this risk. First, operators should ask themselves a simple question: is the benefit of this new product enough to justify the resulting loss in privacy? This is difficult to measure, but it can be done through surveys, focus groups or user testing and is worth the effort to avoid a PR disaster. At a minimum, this simple heuristic can ensure operators are thinking through the privacy risk of their product. Additionally, firms need to ensure boundaries are in place, so product teams don’t launch a product with reputation risk. If teams have these privacy conversations in silos, mistakes will happen. Organizations need to develop clear rules for teams to operate under, so they can continue to innovate while keeping customers’ best interests in mind. These strategies are by no means an all-encompassing solution. All firms engaging in personalization will need to test and iterate strategies to protect consumer privacy.
Personalization is evolving and there are no easy answers. We’re left with important questions to think about and debate. As machine learning continues to advance, how will big tech know where to draw the line? Are the proper internal controls in place to prevent inadvertent “creepiness”? What other strategies might be employed to mitigate privacy risk without stifling innovation? If operators aren’t already thinking through these, they should be.
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