Category Archives: Media Mentions

In The News: eMarketer Wearables Forecast

I was recently interviewed by eMarketer about wearables in 2017 and how they are trending for marketers as they evaluated the future forecast of wearables.

The full report is available to eMarketer PRO subscribers.

My summarized commentary is that most of the client demand I have experienced over the past few years has been web and mobile centric.

Over the years I have focused on the intersection of wearables, and the data that’s created and how that can refine a more personalized experience. But the reality is that most wearables are simple extensions of a mobile device and that limits their value to marketers.

Most of the wearable based programs I have been a part of were focused more on the data created as well as actionable notifications but interest has shifted significantly towards conversational experiences such as chatbots and voice based systems.

The full report is available to eMarketer PRO subscribers.

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In The News: Campaign Live SXSW 2017

I was recently asked by Campaign Live about my thoughts, reactions and takeaways from SXSW Interactive 2017.

My commentary focused on the shift towards programming vs. experiences at this years event.

Additional Context to the Article Commentary:

2017 may be the year that programming both from an official and 3rd party standpoint was the focal point vs experiences. In previous years you would see major brand installations from the sponsors featuring a mix of products and technology. A lot of traditional SXSW powerhouses such as AT&T, Samsung and Chevy were noticeably absent. 

This year more experiences also featured content tracks. The feel was less amusement park and more like attending TED talks with live demonstrations thrown in. It was an odd feeling as the best word to describe SXSW Interactive this year was subdued. 

SXSW used to be the ideal event to gauge and project consumer behavior-centric tech trends. We saw consumer empowerment and amplification with the launch of Twitter in 2007. We saw the rise of location based engagement with Foursquare in 2009. We saw the rise of live streaming service Meerkat in 2015, and a slew of other disruptive tech over the years. But marketing is quickly shifting from disruptive tech to acceleration through intelligent systems. 

Now It’s less about the latest app fad, and more about how quickly the combination of data, intelligent systems and smart environments are going to fundamentally shift how we interact. This is where SXSW is at a cross-roads moving forward.

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In The News: SXSW Hope vs Reality

I was recently asked by the Drum to write an op-ed about my hope vs reality heading into SXSW Interactive 2017.

As a digitally progressive marketer, focusing both on current solutions, while keeping a close watch on the future, I am at a crossroads when it comes to identifying the value I receive from SXSW.

Each year, I have high hopes for the event. I look forward to real discussions about key topics driving digital. I want to be inspired by compelling brand experiences that showcase the latest technology, which may be a precursor to new ways to connect, empower, entertain, or all of the above.

My hopes remain high, but I am afraid of the reality, given my experience as a SXSW attendee the past few years. Instead of deep meaningful discussions, the content, especially outside of keynotes, is either too simplified or so generic it lacks any lasting impact. The other issue is that panels are selected for their title, versus their substance, and more often than not, the content is more opinion-based, rather than truth or research based.

The reality has been painful at times. I used to think about SXSW as the ideal event to gauge and project consumer behavior-centric tech trends. We saw consumer empowerment and amplification with the launch of Twitter in 2007.

We saw the rise of location based engagement with Foursquare in 2009. We saw the rise of live streaming service Meerkat in 2015, and a slew of other disruptive tech over the years.

But marketing is quickly shifting from disruptive tech to acceleration through intelligent systems. It’s less about the latest app fad, and more about how quickly the combination of data, intelligent systems and smart environments are going to fundamentally shift how we interact.

You can read the rest of the article on the Drum here.

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In The News: Ad Age Data Design & Alexa

I was recently interviewed by Ad Age discussing the efforts of my data design team and our work with Amazon and the Alexa Skills Kit.

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When I first joined the Epsilon agency team I wanted to bridge traditional brand planning, strategy and data science to uniquely assess all of our data sources and build recommendations that leverage the right data to assist planning, strategy development and data-driven insights to support strategy and creative.

Now the agency data design group is comprised of 3 core components: 1) Mapping the data landscape 2) Storytelling through data 3) Consulting & training. My goal with this team is align intelligence from the data, regardless of source, that will inform how we communicate and message with consumers as technology and behaviors evolve and most importantly drive performance.

There are three primary areas of focus for the team:

1) Proprietary data sources & methodologies e.g. Leveraging Epsilon’s structured data

2) Unstructured data sources & methodologies e.g. Finding previously invisible insights by applying machine learning & artificial intelligence to unstructured category data

3) New data sources & methodologies e.g. Uncover new types of data sets that we call affective datasets and how it will impact and reshape how we connect across the consumer journey

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Unstructured and New Data sources combined with Epsilon’s proprietary data began to accelerate our processing and analysis capabilities to uncover consumer truths with unstructured data to further fuel our agency’s strategic storytelling and data driven creative leading to an evolution of brand planning.

For the past 12 months my data design team has focused on aligning emerging artificial intelligence systems and algorithms with our structured data assets to combine all of the following elements.

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Data Design is the bridge between planning and bleeding edge tools like cognitive computing, artificial intelligence and natural language processing. Ad Age highlighted our approach with Amazon and how we leverage machine learning on amazon.com down to the product SKU level to further inform communication and engagement strategy as well as our team being one of the early adopters of the Alexa Skills Kit (ASK).

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Here is an example of data design concepts in action.

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7 Ways AI will Enhance Marketing

For the past 12 months, my Epsilon team and I have focused on multiple facets of artificial intelligence (AI) with data as the primary fuel that powers key insights. We have leveraged machine learning, natural language processing, predictive APIs, and neural networks to uncover consumer truths that previously would have taken weeks or months to uncover.

Having the opportunity to work with comprehensive, boundless proprietary data assets is incredibly exciting. In addition to fueling strategy work, it also drives emotional connections with consumers, bonding them to brands in meaningful ways. It is the future of marketing.

Now past the experimentation phase, I can say confidently that AI will be a key driver of technology growth over the next decade and will significantly impact consumer marketing. Initial predictions show the market for AI-driven products and services will jump from $36 billion in 2020 to $127 billion by 2025*. (*Source: BofA Merrill Lynch Global Research Estimates — 2017 the year ahead: artificial Intelligence; The rise of the machines.)

Most AI we work with today is categorized as Artificial Narrow Intelligence (ANI). This means that the AI is extremely adept at executing specific tasks.

Right now, there are seven subsets of artificial intelligence, outlined below. Brand marketers can better uncover insights, connect with consumers, and redefine customer experiences using this innovative technology.

Machine learning (ML)

ML uses human coded computer algorithms based on mathematical models. Probability models then make assumptions and/or predictions about similar data sets.

Currently, machine learning can be leveraged as a service to accelerate sentiment analysis and domain-specific insights. It also serves as a foundational element for identifying consumer behavior based on occasions, perceptions, and attributes to construct themes and trends from unstructured data which represents the thoughts, behaviors, and preferences of consumers taken directly from their online activities.

In 2017 and beyond, I expect more third-party providers will offer ML as a cloud service brands and agencies can leverage to transform products and services into smart objects, able to predict needs and preferences.

Machine learning solutions have allowed my team to align our proprietary structured data assets with unstructured data to combine the best of both worlds. This began to accelerate our processing and analysis capabilities to uncover consumer truths within unstructured data to further fuel our agency’s strategic storytelling.

Cognitive computing

Cognitive computing builds on machine learning using large data sets.

The goal is to create automated IT systems that can solve problems without human intervention. Marketing centric cognitive computing solutions can consist of a single, all-encompassing solution, or be comprised of multiple services that build and scale applications over time.

From a marketing application perspective, cognitive computing-based solutions range from customer experience enhancing chatbots to closed loop systems for tracking media performance.

Bank of America recently launched the Erica bot using AI, cognitive messaging, and predictive analytics to further influence consumers’ ability to create better money habits.

Cognitive computing will be key to unlocking the potential of conversational experiences. As ecosystems continue to rise, many of the 30,000 chatbots on Facebook Messenger are powered by AI services.

Facebook’s own M virtual assistant housed within Messenger will soon come out of beta testing and will incorporate cognitive suggestions based on content of a conversation users are having. The goal is to make Messenger-based interactions more convenient, enabling users to access services without leaving the conversational thread within Messenger.

Speech recognition and natural language processing (NLP)

NLP refers to intelligent systems able to understand written and spoken language just like humans, along with reasoning and context, eventually producing speech and writing. NLP plays an essential role in the creation of conversational experiences.

Voice-based experiences, such as Alexa’s voice services (AVS), will become pervasive over the next few years. It is projected that by 2020, 30 percent of web browsing sessions will happen without a screen.* (*Source: Gartner analysts at Symposium/ITxpo 2016.)

The core of the AVS experience is a combination of automated speech recognition, natural language processing, and a cloud-based AI that comprise a voice-based user experience.

As with most artificial intelligence entities, learning new skills is how personalized and contextual experiences will be created. With Alexa, it is possible to “teach” new conversational elements and interactions through developing skills.

Here is an example from Domino’s pizza that allows consumers to order pizza directly through Alexa voice services.

Alexa skill development is one of the quickest ways for brands to connect with the rapidly growing audience that calls upon Alexa to empower their daily lives.

Fitbit is another brand leveraging Alexa-based skills to extend brand engagement. Traditionally Fitbit users depend on an app to visualize their data. With the Fitbit Alexa skill users can get a quick update on the stats that matter the most without the need of a screen.

Deep learning

Deep learning builds on machine learning using neural networks. Neural networks are statistical models directly inspired by, and partially modeled on, biological neural networks such as the human brain. The use of neural networks is what differentiates deep learning from cognitive computing.

Deep learning is currently redefining Google’s approach to search, and search engine optimization (SEO) will never be the same. Previously, Google search results were based on algorithms defined by a strict set of rules and SEO was based on regression models that looked at past behavior to adjust a given strategy.

With the introduction of RankBrain, Google’s machine learning technology, in 2016, search algorithms are now enhanced with artificial intelligence. Google is now processing roughly 15 percent of daily queries by mixing the core algorithms based on each search type.

The system is adept at analyzing words and phrases that make up a search query. It also decides what additional words and phrases carry similar meaning.

Expect the percentage of search queries handled by AI to significantly increase. Marketers will need to rethink site architecture, content, and the signals being sent via backlinks as the systems continue to learn on a query-by-query basis.

Predictive application programming interfaces (APIs)

A predictive API uses AI to provide access to predictive models, or expose access to an ability to learn and create new models.

Fortune 500 company USAA is analyzing thousands of factors to match broad patterns of customer behavior through its intelligent virtual assistant Nina.

As we shift from consumers using technology to technology enhancing consumers, predictive APIs will play a key role in providing recommendations, enhancing customer service, and providing real-time analytics without in-house data scientists. This is key to unlocking new forms of value exchanges with consumers in a hyperconnected world.

Image and object recognition

Image recognition finds patterns in visually represented data, pictures, and objects. Facebook and Google are two organizations focused on AI research and solutions in this area.

As image recognition is extended into video and live broadcasts, it will redefine contextual relevance, categorization, and automation of content distribution.

Combined with the advancement of cameras, image recognition and machine learning are transforming the way we process data, including much more than just attitudes and behaviors.

Brand marketers can now leverage images, facial expressions, body gestures, and data collected from IOT-enabled devices to understand the triggers behind behavior and build experiences that anticipate their customer’s needs. This requires brand marketers to transform their data strategy to expand beyond first- and third-party data to also incorporate unstructured datasets that capture affect and unconscious data inputs.

Snap’s pending patent on object recognition is potentially game changing. A recent patent application shows its desire to built object recognition into snaps that can enhance recommended and sponsored filters most likely powered by an AI-based system. This showcases how any object can be aligned with creating immediate context with a consumer and brand.

Olay launched an AI-powered Skin Advisor that ingested user generated photos and provided recommendations for suitable products.

Dynamic experience generation

AI-based systems not only have the ability to parse through large data sets and offer predictive solutions, but also can drive the creation of dynamic experiences. AI will become a powerful tool for creating vs. analysis.

Many startups are leveraing AI APIs to create intelligent solutions. The Grid (https://thegrid.io) is leveraging AI to automate web design with Molly. Molly analyzes design decisions and creates new web experiences.

Eventually, AI will be a key driver of creating augmented reality experiences. Dynamic experience generation through AI will recreate physics, recognizing gestures and movements that can generate new consumer experiences.

Below, Mark Zuckerberg discusses the future of AR/VR at Facebook’s F8 conference.

The various subsets of artificial intelligence will continue to be interconnected, redefining how we approach connecting with consumers. AI makes it possible to know the consumer better than ever before. If approached correctly, with the right mix of AI subsets leveraged, companies will see their business grow.

This is a repost of my recent iMedia cover story.

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In The News: iMedia 7 Ways AI Enhances Marketing Cover Story

This morning my new article 7 ways artificial intelligence will enhance marketing was the cover story for iMedia Connection.

The article reviews seven subsets of artificial intelligence from machine learning, cognitive computing, natural language processing, deep learning, predictive API’s, object recognition and dynamic content generation and how brand marketers can better uncover insights, connect with consumers, and redefine customer experiences using this innovative technology.

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In The News: Entrepreneur.com & AI

I recently sat down with Jeffrey Hayzlett of C-suite TV for the first episode of season 7 for Executive Perspectives live.

He recently wrote a piece for Entrepreneur.com outlining 5 business trends that will take off in 2017. Jeffrey referenced our conversation regarding automation of conversational experiences through artificial intelligence.

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The infusion of voice-based technology into consumer products, and the ways in which brands are shifting from social media to social messaging strategies were the subject I addressed with Epsilon Chief Digital Officer Tom Edwards, during a recent interview. Edwards told me how “disruption is the new normal” and how chatbots are the next thing chief marketing officers will have to deal with as technologies keep evolving.

For more insight from the discussion here is a link to the full interview.

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In The News: Marketing Dive & 2017 Trends

I was recently asked by Marketing Dive about how digital marketing will evolve in 2017.

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One of the key territories I discussed for this piece was the role artificial intelligence, machine learning and cognitive experiences will play in the near future.

From leveraging machine learning to accelerate sentiment analysis and domain-specific insights to cognitive computing solutions that automate experiences without human intervention to the rise of voice-based user experiences that will continue to expand in 2017 to deep learning that will fundamentally change how brands approach SEO to predictive API’s that will expose access to predictive models to further create seamless experiences for consumers, cognitive and intelligent systems will play a key role in how we approach marketing in 2017,” said Tom Edwards, Chief Digital Officer at the agency within Epsilon.

When asked about social media marketing in 2017:

Marketers will need to shift their strategy from one of personification of the brand to a seamless experience that is about simplifying and predicting needs while also empowering consumers to create their own stories,” said Epsilon’s Edwards.

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C-Suite TV Executive Perspectives

I recently had the pleasure of sitting down with Jeffrey Hayzlett of C-Suite TV to kick off season 7 of Executive Perspectives. We discussed digital disruption, conversational experiences, artificial intelligence and best practices for leveraging data to connect with consumers.

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C-Suite TV Discussion – Disruption, UX, The Future

This week I had the pleasure of joining the C-Suite TV team at their San Francisco event and was interviewed by Jeffrey Hayzlett. It was a fun discussion as he asked me about the shift from social media to social messaging, strategies to make the shift, voice based experiences, disruption, galactic cannibalism, trends and the future of connecting with consumers.

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Below is a recap of my key talking points for each question.

(C-Suite TV – JH) As we’re on the verge of a transformational moment in marketing with the shift from social media to social messaging, how are marketers making this shift?

(Tom Edwards – TE) Over the past 5-10 years we as marketers have focused primarily on the open web + social media. Earlier this year social messaging passed social media in terms of monthly active users. Consumers are ready for conversational experiences. Part of the reason for the appeal is that it is seen as safe, comfortable and intimate.

I spent most of this year researching, writing and educating our brand partners about what this shift can mean for their business. We conducted proprietary research on what consumers want from conversational experiences that led to an ebook on the topic.

Social Shift Toward Messaging

As we dug into consumer expectations around conversational experiences, our research found that they want experiences that are convenient and support local experiences, there is openness to pay within social messaging and an expectation that it will connect physical and digital elements such as in store coupons and discounts, there is also a willingness to interact with intelligent systems.  Research also shows that 60% of millennials would prefer talking to a chatbot vs. talking to a human when it comes to resolving questions about online shopping.

From a marketing perspective there has been a significant amount of experimentation trying to create the ideal experience. With Apple, Facebook, LINE, Kik, Skype and more providing tools and services that will allow others through 3d party SDKs & API’s to create an ecosystem. Their hope is to become the central portal in order to empower consumers and drive commerce. Facebook doesn’t own the hardware or the operating system, so they are invested in keeping people in the messenger experience.

Some experiences are trying to further personify the brand, others are about creating utility or a sense of intimacy with the brand. The goal is to create a real-time experience that is centralized in one conversational thread.

The key will be creating experiences that are not disruptive but are actually attentive to the current and future needs of the consumer. The ideal experiences will be built around the premise of simplification + prediction. It’s not about a deeper personal connection like a friend, but to be able to anticipate, predict and enhance a consumers experience.

This is where we see the idea of CONNECTION + COGNITION coming together.

(JH) What processes and strategies do you need in place to make this shift effective?

(TE) I recommend an approach that is based on five core factors of Simplification, Data Design, Prediction, Ambient Design & Physical to Digital.

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1 – (SIMPLIFICATION) The key is to reduce complexity in consumers lives and create experiences that are ownable by the brand’s domain. Mine customer data for most commonly asked questions and expand from there with use cases focused on enhancing and simplifying experiences.

2 – (DATA DESIGN) Have a strategy not just to capture data but how to use it. Define the role of unstructured data in refining the experience. Consider what new data points are being integrated to inform future prediction. How are you making the data actionable? On my team we now have a data design team that sits between traditional brand planning + digital strategy. This is the intersection of Big Data + Design Thinking. They own the tools, assets and data sources and understand how to craft a data driven narrative.

3 – (PREDICTION) Anticipate consumer needs is key for the future of conversational experiences. Messenger experiences are not designed to be like Google search, at least not yet. Google is working towards the ideal intersection between search & retrieval vs. predictive. But again a combination of data, predictive analytics built on working data is the entry point towards truly predictive experiences. (cognitive will accelerate this)

4 – (AMBIENT DESIGN) The future of computing is tied to ambient experiences, or how your environment interacts with you. It is critical to approach designing conversational and voice based UX differently.

5 – (PHYSICAL TO DIGITAL) One of the other elements is the rise of conversational commerce. There is a concerted effort to closely align physical & digital shopping experiences as a means to enhance the customer experience. Our research shows there is an expectation from consumers to have local experiences connect to digital through conversational experiences.

(JH) Let’s talk about some newer technologies, how does voice based technology play into this shift to a conversational user experience?

(TE) I am a strong believer in the fact that voice based experiences and artificial intelligence systems will become pervasive in our everyday lives. The core of the experience is a combination of automated speech recognition, natural language processing and a cloud based AI that comprise a voice based user experience.

I am very intrigued by the possibility of the ability to create context through voice services such as Amazon Alexa Voice Services & the recently launched Google Home. Voice based experiences will play a key role during this time as our interactions with connected systems and the rise of micro services as a primary mechanism to navigate a hyper connected world will become the new normal.

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I strongly believe that we will begin to see a convergence over the next few years where elements that enable connection such as social messaging and voice based conversational user experiences combined with cognitive computing (AI) and immersive experiences such as holographic computing will become interconnected and will redefine how we approach connecting with consumers.

We will begin to see services such as Alexa Voice Services quickly proliferate throughout 3rd party devices from in home IOT systems to connected vehicles and “skills” will become a key component for how we navigate beyond screens. Estimates already show over 28 billion connected devices by 2019.

(JH) We hear you say that “disruption is the new normal” what do you mean by that?

(TE) Digital disruption has been at the center of major consumer shifts over the past 10 years. Disruption is now the new normal. The Premise is change is constant and experimentation is critical and how you integrate trends into your existing business is key.

The acceleration of technology has led to the rapid empowerment of the consumer. What organizations have to consider is that with each iteration of technology and consumer empowerment new types of interactions will lead to the need to rethink the business models of today.

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This has a significant impact on the C-suite as the pressure on CMO’s to be creative thinkers, intelligent around data, domains and disciplines as well as mitigation of risk, pressure to innovate, find and retain talent and try to be as agile as possible. Combined with the pace of new interaction models there is a lack of strategy to deal with the shifts in a meaningful way as the focus is on short term stability.

This is why it’s important to build a plan with a foundational approach to data and understand what domains the brand can own and where in the new interaction types there are opportunities to redefine business models. This is why I have chosen Connection, Cognition and Immersion as the pillars of how brands can map to the new interaction types of the near future.

(JH) I heard you say we’re on the verge of galactic cannibalism can you explain what this means for marketers and how can marketers stay ahead of the game?

(TE) I have spoken a lot recently about how disruption is the new normal. I recently heard someone compare the last five years as a “supernova” of disruption in terms of the intensity and velocity of change.

With the rise of artificial intelligence, conversational & ambient experiences, connected systems and mixed reality on the horizon we are moving well beyond a supernova and are now on the verge of galactic cannibalism.

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Galactic cannibalism is when one galaxy collides with another and there is a subsequent absorption of parts of one into the other. From a consumer marketing standpoint how we consume and interact via digital channels is about to be absorbed and redefined through new advancements in connection, cognition & immersion.

The key point to surviving and thriving is to have a comprehensive data strategy as data assets will serve as the fuel of this shift. Regardless of which galaxies collide a thorough understanding of data, content, experiences and outcomes is a marketing foundation for the future.

Also, it is important to understand how data will evolve. Currently the focus is on 1st part & 3rd party data. But in the emerging world think of the data created by connected systems as well as new forms of real time sentiment data, such as your eyes in a VR experience or facial recognition in a retail setting. These will require a comprehensive data design effort to craft content, experiences and drive outcomes as a marketing foundation for the future.

Ultimately we will have to acknowledge that the relationship between consumers and technology will fundamentally change from consumers operating technology to technology operating for consumers through data.

(JH) How do you apply the trends of today to the business models of the future?

(TE) The first step is to be aware of what is happening. Analysts such as Gartner and Forrester are evaluating and publishing their rankings of where technology is going. One of my favorites is the Gartner Hype Cycle.

One of my responsibilities with Epsilon is I lead the innovation practice for the agency business. We have designed an approach that is consumer centric, data driven, iterative and allows our brand partners to scale emerging technologies and integrate trends into tangible solutions that drive business outcomes. The practice is comprised of four distinct elements that span research, workshops, experimentation and transformation.

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Regarding research & trends, we leverage Epsilon’s proprietary data and analytics, first and third party research, emerging companies and established partner networks to research, curate and educate on the latest trends and how it can apply to our clients business.

Our approach is as follows:

Our team identifies a new tech/emerging tech…

1. Track Product/Technology Announcement

2. Measure Velocity of coverage & discussion

3. Conduct Initial analysis & POV outlining potential value/impact

4. Explore outcome impacts & role of tech in consumer journey

5. Map vertical specific use cases

6. Educate internal teams & external clients

7. Identify early vendor partners and alpha/beta opportunities

8. Conduct Project based experiments

9. Capture & package project based success

10. Build business value case for horizon consideration

Once you have identified your trends its helpful to begin to filter across key macro trend territories, in this case I am exploring trends that reach across

Connection, Cognition & Immersion

(JH) What’s really resonating with consumers right now? What should marketers be paying attention too?

(TE) Anonymous personalization through dynamic content, targeted video content, Personalized, connecting the consumer experience across digital to physical & 1:1 messaging that is authentic, provides value and is contextually relevant is key.

Human attention is now a scarce commodity. Attention is a resource – and we only have so much to give. The key to experience design is built around data, content & channels or experiences.

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I like to start with data, it can be 1st party or secondary data sources, but I look for attitudinal, behavioral in addition to standard demographic. Transactional data can also be a key element and consistency of message is key.

(JH) What is the future of connecting with consumers?

(TE) I strongly believe that we will begin to see a convergence over the next few years where elements that enable connection such as social messaging and voice based conversational user experiences combined with cognitive computing (AI) and immersive experiences such as holographic computing will become interconnected and will redefine how we approach connecting with consumers.

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The key will be to create data designed experiences that empower consumers.

Here is a link to the full video interview kicking off season 7.

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