Achieving Hyper-Relevance Through AI

Customer satisfaction is passé. So is customer delight. Both have been eclipsed by what Amazon’s Jeff Bezos calls “customer obsession.” At Amazon, every effort is centered on providing the best possible experience for the customer—delivering on their needs and being worthy of their trust. The idea aligns with our previous writings on Loyalty 3.0: the entire approach to loyalty needs to be customer centric, which requires not only thinking about loyalty differently, but doing it differently. Brands must first show their loyalty to customers before expecting a customer’s loyalty in return.  


And rightly so. Today customers are bombarded with numerous choices; they will flock to a brand that not only pays attention to them, but also gets hyper-personalized in its approach to act accordingly. Brands need to enable this hyper-personalization by: 

  • Being exceptionally diligent in capturing, safeguarding and using customer data  

  • Applying context to ensure relevance in each touch with the customer 

  • Using sophisticated (but now readily available) technologies such as Artificial Intelligence (AI) and Machine Learning to optimize each customer interaction throughout their journey 

Trading privacy for hyper-personalization: 

It’s easy to identify the fundamental need to capture customer data, but the reality is that it does present risks to the brand. Yet despite the numerous consumer data breaches brands encountered over the past 12 months, and the increased sensitivity around sharing data that followed, only 19% of consumers say that they would completely stop shopping at a retailer after a breach (KPMG study). Additionally, our proprietary research shows that 40% of travel and retail program members are still willing to share their data with brands in exchange for receiving relevant discounts and offers. 

Consumers have very specific expectations of their preferred brands and are willing to take the first step towards a profitable data-value exchange. The equation is simple: I’ll give you my data if you use it to deliver contextual relevance.  

How does this contextual relevance come to life?  

It comes to life at an InterContinental Hotel in China when a customer says, “I am going to sleep” and voice control technology alerts their smart room to automatically dim the lights and close the curtains.  

Or when a Facebook Messenger chatbot keeps a Wimbledon fan in the know by giving him access to information about his favorite players. 

Clearly, loyalty leaders have embraced technology to showcase their customer obsession. AI, empowered by brands like Baidu and IBM Watson (respectively, in examples above), is rapidly becoming so deeply embedded in all customer experiences that we may soon be shopping at Walmart with a machine-learning powered, self-driving shopping cart. 


The Personalization Gap: 

However, there is still a gap even after consumers explicitly state their expectations of brands. According to the Bond Brand Loyalty Report of 2017, only 25% of loyalty program members are satisfied with the level of personalization they receive.  And according to a recent Accenture study, 41%  of U.S. consumers have moved away from their preferred brands due to a lack of personalization and trust.  

Why is customer trust eroding?   

  • Brands are still macro-segmenting—or worse, mass marketing—instead of targeting 1:1  

  • Customer experiences across channels (app, website, phone, in-store) are siloed, not unified 

  • While contextual relevance is the end game, today very few brands truly deliver 


The Personalization Opportunity: 

There is still time for other loyalty leaders to close this gap and use personalization to get there. According to McKinsey, hyper-personalization can lead to five to eight times the ROI on marketing spend. And, based on the cases mentioned above, AI technology is the right catalyst to achieve this growth.  

So, let’s consider just a few ways AI technology can help deliver on personalization: 

1. Use contextually relevant variables such as location and calendar as decision enablers:  

The personalized recommendation engines within Netflix and Amazon have mastered leveraging customers’ historic data (“based on your past purchase, we recommend this”). But what if a brand could anticipate the customer’s unexpressed need (or better, create that need) by identifying contextual areas of opportunity?  

The idea is simple: being present in the right place at the right time. At rDialogue, we like to call this IoX —Internet of Experience—and AI can equip this experience using predictive/anticipatory analytics.  

Marriott, a loyalty leader in the hospitality industry, is one brand that combines AI with customer context to deliver powerful, relevant messaging and a personal connection to their guests. Their “Local Perks” initiative uses beacon technology inside a hotel property to send guests geo-targeted mobile messages/offers throughout their stay.  

Starbucks leverages contextual relevance through their customers’ calendars. Their “happy hour” messages to customers are now accompanied by an add-to-calendar call-to-action button. The calendar alerts serve as timely reminders, but more importantly can create the need for an afternoon Starbucks fix. What if Starbucks asked (and received) permission to read customer calendars and tailored the happy hour to its customers’ calendar availability? 

2. Track customer decision journey and plug-in personalized incentives: 

AI has made it possible for brands to tap into a customer’s behavioral data throughout the customer journey and decipher patterns from millions of data points. And, when assessed correctly, brands can go one step further by using that knowledge to target customers through personalized incentives. Mammoth Resorts, a Four Seasons property, did exactly that; with IBM Watson they delivered customized deals to potential vacationers based on their specific behaviors (e.g. promotional messaging delivered the day a customer’s ski pass is scanned, or a post-visit email with discounted rates on a return trip). By deploying these tactics, their email click-through rates increased from 8 percent to 34 percent year-over-year.  

With technologies like AI and machine learning becoming more prevalent, it’s time for marketers to stop talking about loyalty differently and to start doing loyalty differently. With customer expectations shifting so much in the past decade, a one-size-fits-all approach no longer works.  Loyalty leaders know this and are using hyper-personalization to drive this evolution toward “customer obsession”—and they’re seeing the financial return on their efforts. If brands continue to make strides toward using customer data to provide personalization and thus, relevance, customers will continue to trust brands with their loyalty in return.

To learn more about personalization, how it can be used to deliver relevance to consumers, what concerns consumers may have about data, and how personalization factors into Loyalty 3.0, check out our new white paper.