IoT, AI, and more: How 2018 will Change Marketing as we Know it

Today, thousands of companies all over the world are working on ways to track our digital footprints in order to learn more about every aspect of our daily lives, with the aim of providing us with more relevant information, offers and service. Due to increasing consumer expectations of having personalized experiences, people have grown more comfortable with trading their personal data in order to enjoy an experience that feels tailored to them. 2018 will be an interesting year as the sheer volume of customer data will continue growing exponentially. And thanks to the Internet of Things, companies can collect even more data about you, like when you turn the lights on and off in your home.

CUSTOMER DATA PLATFORM (CDP) BUYERS’ GUIDE 2019

Welcome to the 2019 edition of CDP Buyers’ Guide. As customer data platforms are becoming increasingly necessary for enterprise marketers, it is also becoming more complex to choose the best fit CDP platform amongst the pool of new and old vendors.

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At the same time, innovations abound in cognitive computing models that are better able to make sense of it all. As people become more proficient at teaching machines how we communicate using sophisticated data models, and as technology becomes capable of teaching itself how to build those models smarter, it’s going to dramatically enhance how marketers personalize customer experiences.

IoT everywhere

With sensors and devices now able to transmit data in real time to augment the profile data that exists for most of us already, novel highly-personalized experiences can be delivered. A friend of mine has tricked out his weekend bicycle ride, with sensors that measure just about every dimension of his and the bike’s movements, all relayed via his phone to his fitness app. After each ride, he has a full analysis of what he needs to improve next time out, based on IoT tracking of things like pedal cadence, heartbeat and hill gradient. All this data is mined for personalizing recommendations as to how to change his personal riding technique - and promote content and products that can help do just that.

Unified Interactions

Combining all the data from interactions each individual has with a brand or organization, from online digital touchpoints to “real world” data from IoT devices, holds much promise. For a snapshot at how these technologies will work together to create a personalized experience in the future, consider this example by McKinsey of a seamlessly digital shopping experience: “A young man arrives at a department store and heads for the men’s department. When he reaches the casual-wear section, he gets an alert on his smartphone. He opens the message and finds a map showing him where the jeans he has been looking at online are on display. He also learns that based on his shopping history at the store, he qualifies for an additional discount that day. After looking at the jeans on display, the shopper is asked whether he would like to try on a pair. He says yes and when he gets to the changing room, he finds a pair in his size and another in an alternate size. The jeans fit, and he walks out with the jeans—a near-field communication system has already completed the mobile payment transaction, debiting an account on the shopper’s phone. Recalling a business article he recently read, he smiles as he is reminded that when commerce is truly frictionless, shopping “feels like stealing.” While multiple technologies are subtly mentioned in this example, a foundational element of the above experience is the way technology helps the man find what he’s been searching for.

Unified Content

Because it’s such an inherent part of the digital experience, you might not even be aware that you begin most of your online experiences with some kind of search, across an index of content. Even when it’s not happening within a classic search text box, search is still the foundational underlying technology  which we’re interacting with to retrieve and receive information to achieve what we are trying to accomplish. Indeed, by 2020, Comscore predicts 50% of search will be voice searches. Whether the request for information for each consumer is typed or spoken, whether it’s in a search box, a chat bot, a web form or Alexa, the ability to return the most relevant information is key. By having a unified index of all the content resources in your organization, comprehensive, relevant information can be delivered back . 

AI-based techniques like Intelligent term Detection, make sense of the words people enter or say, to understand a request, turn it into a search query ,  and return a personalized relevant answer, from that wealth of unified content. Current machine learning approaches are making this a reality already, by picking up on specific terms - and continuously adding to a lexicon - that are uniquely relevant to a person or company. As we come to rely more on virtual assistants in our work and in our homes, this automated approach to understanding what people really mean and what they actually want, becomes central to delivering relevant responses, suggestions and recommendations.

Machine Learning

A fundamental element of machine learning is a system’s ability to continuously learn from the data it receives. Whether that learning is supervised through the use of data models, or unsupervised, where the system begins to detect patterns in the data by itself, machine learning helps marketers to attain a level of personalization at scale that was simply not possible before.  By “teaching” a system that a particular outcome is good, e.g. an online application form completion, all interactions and content associated with good outcomes can be automatically favored, over those that did not lead to a successful outcome.

Equally, by allowing machine learning to observe similarities in data patterns, small micro-segments of near-homogeneous customers can be automatically identified, and the experience dynamically changed to deliver more relevant content to them, and others that start to exhibit online behaviors like them.

Think back to the virtual assistant that I mentioned above. Machine learning models make it possible for that technology to not only deliver on a task you request, but to “intuitively” predict what you might ask next based on conclusions from crowdsourced data that were able to point to successful outcomes of similar interactions. A brand currently doing this very well is Netflix, which aims to serve each of its users personalized recommendations based on past movies or tv shows they’ve viewed. Netflix has recently gone a step further by adding variation in displayed artwork into the mix. By applying machine learning algorithms to the hundred million user accounts, they are working to display artwork options that they feel will resonate on a personal level with each user. Machine learning is enabling marketers to add this level of relevance at scale, to truthfully be able to claim that you don’t have just one product, but as many products as you have users, because each is slightly different than the next based on individual behavior.

To Wrap Up

Unifying content is key to having an asset from which to deliver the most relevant information to prospects and customers .  However, combining this with the unified interactions that modern marketers now have access to - online, offline, IOT -  and applying the power of machine learning,   gives them the ability to to drive personalization at unprecedented scale. We have within our grasp powerful technologies that are able to make unbiased decisions in real time to reach many people simultaneously, while still delivering a highly relevant personalized experience. Looking ahead, I’m incredibly optimistic that these new advances will help us to deepen connections on a business to consumer level, but more importantly on a human to human level too.