Hypertelling Is the New Storytelling: 8 Steps to Win Micro-Moments


January 30, 2020

Give me the content that I want, entertain me, but make it useful and actionable, while informing me. Whenever and wherever I want it – but also try to anticipate whatever it is I may want next. Sounds demanding, right? Consumers, as we all are, know that this is indeed our increasing expectation from marketing efforts. We exist in a world of micro-moments in which the tech supporting them demands a trend of extreme personalization.

At a recent Google summit, there was an incredible presentation on the evolution of storytelling into a new realm aptly named hypertelling. This is soothe-speak to acknowledge that the art of storytelling is now about user participation, immersion, experimentation and non-linear experiences that reflect preferences and beliefs. It anticipates exploration and change, but most importantly, it is a user-created story under the lens of your brand. 

This particular presentation led into emerging tech, and how it influences a world of VR, AR and beyond, which we will unpack further in another article. For now, let’s start with eight simple steps from a recent Google study that can lead to user personalization today and the paradigm of emerging tech down the road: 

  • Personalize advertising: Obvious. But segmentation and depth here need to anticipate micro-moments. People are loyal to their needs in a moment, more so than any specific brand. A recent use case from Burger King included a campaign targeting users stuck in traffic jams. These users were met with personalized offers to deliver food to their car as they wait; announcing “you will be stuck in traffic for 39 more minutes” followed with an offer to deliver food directly to their car while they wait. This message was then compounded with billboards on high-impact routes, and utilizing the brand’s app for a voice order against known Waze vehicle stats to execute a seamless car-side delivery. Another use case targeted airport goers whose flights were canceled or delayed with custom offer hotel stays based on their personal flight delay. Both are great examples of personalizing ad tech to anticipate user micro-moments, and in the case of Burger King, bringing online to offline seamlessly. 
  • Use predictive modeling to find LTV customers: As browser privacy regulations constrict and new initiatives like Chrome’s new Privacy Sandbox are introduced, using first party data and activating it via Google Cloud & BigQuery is more important than ever. From a personalization lens, LTV modeling can also help identify the most loyal, or impactful users and signals. User weather conditions? Location? Loyalty Program? Or, even predictive conversions based on demos? Understanding your data, and then unlocking those data streams by the most valuable users, will only increase marginal lift. 
  • Simplify reorders and checkout: In the age of one-click buying, days of multi-step checkouts are becoming cumbersome and dated. To take it further, consider Domino’s Pizza, and the advent of their Zero-Click ordering via app or voice, where users simply allow a 10-second (or less) countdown before the pizza is on the way. Ordering the same products regularly? Consider auto-population within shopping carts for your users and take one-less step away for them. In a world where the user controls the story, any checkout and re-order process must be seamless to meet their rising expectations. 
  • Personalized landing pages: This is another tactic that many marketers have likely considered. Using tools like Optimize360, creating a consistent touchpoint from front-end media to onsite UX is easier than ever. Showing pages based on users’ weather, location, or A/B testing images, creatives, any advanced modeling, etc. is an important part of giving the user the personal experience and narrative that they expect to have. 
  • Personalized recommendation engine: Reports show that Amazon gets 35%+ revenue from its recommendation engine. With Google Cloud, you can build a recommendation engine utilizing an existing Recommendation API and plugging in your own data, and custom configuration. For example, building a simple engine to promote cross-selling “if you like this, you will love this” with images, or get savvier by recommending things based on past purchases, history, browsing trends, predictive modeling, store stock and more. 
  • Personalized offers against churn: On the topic of predictive modeling for personalization, it is equally powerful to establish a model for high-risk churn. Identifying high-risk customers, or even predicting them before hitting a churn threshold can greatly increase margins. Once modeled, “can’t refuse” offers may be served dynamically by defined business signals to keep those known risk users in-market, before they have hit a churn threshold. 
  • Personalize customer service: In our previous article, Do Users Like You?, we discussed the importance of deep branding, and followed it meticulously through every step of the customer journey. This includes customer service. Every touch within a ticketing system, call center or form submit is an opportunity to evangelize customers, even if they are at risk for churn. We used the example of men’s shorts retailer Chubbies, who sent a Karate Gym membership for self defense to a distressed customer who fell victim to porch pirates. Although not every customer warrants this kind of hyper unique touch, any opportunity for differentiation and humanizing is key. 

From a tech perspective, utilizing things like LTV modeling, can help segment your customers and then route servicing appropriately with the ability to customize those touches using chatbots, personal reps, or Karate Lessons if necessary. 

  • Personalize on-site search: Finally, if you’re not using dynamic landing pages ensure that the onsite search is useful and easy. This is especially important on a retail site with thousands of SKUs. Understand your on-site analytics and what browsing patterns are performing and build your search or page flow around this to optimize UX. 

The story of the brand is evolving, and advertising tech is changing with it. A brand is a personality. However, the narrative of how users engage with that personality  is now totally controlled by them. Using deep personalization tactics and modeling will only help create the experience that we, as consumers, want to have. Plus, it will help bridge the gap as tech moves into even deeper personalization territory with AR, VR and AI along the horizon. Need help getting started? Reach out to us at sales@adswerve.com to discuss further.

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