How Do We Build Innovative Digital Creative for Mobile?

By Walter T. Geer III 
Vice President, Creative Director 
Verve




 


If you want to understand the state of mobile creative, consider what the industry was like for desktop back in 1999. We barely had a handle on anything we were doing, there were no real standards and we were mostly just building stuff — making whatever publishers wanted. Now here we are, creative professionals in 2017, still building mobile creative along these same lines. And that’s not a good thing.

Think about it. We’re developing overlays; we’re hoping someone clicks on them so we can send users off to microsites or someplace else. We’re constructing whole-screen takeovers. We’re packing our hopes for capturing user attention into intrusive special effects and context-blind dazzle. In the end, it’s all too much and it just doesn’t hit the mark.

The future of creative for mobile is not about slapping an ad over something else and treating the user’s screen like a platform — or, worse, a launch pad — for whatever technology can achieve. The future is about creative design that partners with technology to augment user activities and draw consumers close. For marketing-tech innovators, the time is now to bring new focus and new ideas to our work. Early-generation mobile ads have earned early wins for our industry, but they won’t do that job forever.

In the sections that follow, let’s look at what innovative creative can mean, in terms of data- and tech-driven design, and let’s address the kinds of teams we should be forming around our work. It will take new approaches to mobile ads if we’re going to reach the future we’re talking about, and we’ll only get there with technology on our side.

Meaningful Moments: Context + User Experience + Design

As almost anyone will tell you, data is key to mobile-marketing campaigns that succeed, ones that identify the how, where, and when of user behaviors. And while data is important, data’s impact is not the entire story. Meaning, technology and marketing-technology tend to focus on just the back-end details. At the same time, the creative guys have often been focusing on just the front-end look.

In these ways, both are losing out. Creative isn’t getting enough deep behavioral and location-based insights. The tech side is missing chances to deliver next-generation ideas … and brands are paying a ton of money for all this, by the way.

How do we avoid losing out? How do we build innovative creative for mobile? First, we return to the consumer’s point of view. Let them use their fingers. Let them use their minds. Let them make choices.

For example, we know mobile users turn to Google Maps and Apple Maps billions of times every week. And so, when bringing a map into creative, why default to a static format? If consumers expect to see a pulsating blue dot, one that shows them in data-driven real-time a location on a map relative to their position, then we need to create opportunities within that moment, with ads that enhance users’ understanding of what’s around them. If data tells us a business-consumer segment often visits a certain area after work hours, our new blue dot of the future can suggest where to go for a post-meeting meal or drink. Couple that with special offers and now you’re talking meaningful creative. Creative that dovetails with ways our user already approaches the mobile interface.

User engagement can happen in other ways, too. It can be as basic as addressing realities such as banner blindness, and then rethinking how we deploy even the most familiar units of display.

• Say you have a 320×50-pixel banner that resides at the bottom of a mobile screen, but, instead of your typical Interactive Advertising Bureau (IAB) format that might animate, imagine the creative as flipping up to 300×250 when the user scrolls up the page.
• The expanded unit — activating only when the user scrolls — now lives in this new state and position for six seconds before collapsing back down to its original size.
• This new banner format drives attention and focus not when the user is in the middle of reading or watching content, but when they’re actually moving from one part of their experience to another, when they’re between moments of focused attention. We’re intersecting with the consumer instead of denying them underlying content; that’s a major difference, going forward, between positive and negative creative for mobile.

Our mission must include a commitment to growing as creative professionals alongside the technology that delivers our designs, to grow with the experts who are figuring out new ways to put creative on mobile screens. Teach your technology counterparts the value of great design and the importance of innovative creative to the root-level choices developers make. Join a shop that values that kind of interplay, that critical kind of exchange.

Creative as a way to amplify user moments in a given situation: we need to think deeply about this as we build the future of mobile-ad design. If we’re serious about creative’s mobile future, and we’re serious about being in a leadership role as we build toward it, then it’s time to seek out technology partners and align creative with the insights data and analytics can unlock.

As Vice President and Creative Director at Verve, Walter Geer is responsible for creating compelling mobile advertising experiences that re-invent and revitalize the way brands and advertisers connect with consumers through transformative mobile technology.

A veteran of the digital advertising space, Walter holds a total of six U.S Patents for digital ad formats and has developed ad products and implemented creative strategies for a variety of publishers and leading technology and media companies including Google, Viacom and MySpace. Throughout his sixteen-year career, Walter has architected market-first usability labs, applying biometric research to the development of creative executions and minimizing risk by understanding how consumer emotions and demographics impact brand engagement.

Prior to Verve, Walter was Vice President of Product Strategy at PointRoll where he was responsible for the company’s innovation, design strategy and mobile and display product teams.

A New Model For Enterprise Resource Planning (It's About Time!)

By Joseph Bradley 
President, Business Ventures 
Uptake
 






Today, if you are not creating value through digital business, you are falling behind.
 
But capturing that value demands three core capabilities. Your business must be hyper relevant (with a customized and contextual customer experience); frictionless (that is, fast and efficient); and expanding its community (accessing talent and resources through crowd sourcing and an expanded ecosystem).
 
What do these capabilities have in common? They all create value through insight—those nuggets of actionable intelligence locked up in countless terabytes of data.
 
I believe that insight has become the core currency of the 21st century. But in my opinion, too many organizations continue to manage it badly. The main culprit? Outmoded Enterprise Resource Planning (ERP) strategies.
 
ERP was created to help companies deal with their expanding troves of data, which is a noble enough idea—except that it’s tapped out.
 
ERP was all about getting the data in the system. It assumed that the data was correct and the world would forever drive value through “query.” This assumption worked great as long as the data was all in the same context. Today, however, wildly varying data flows are the norm—contextual data, machine data, video, mobile, qualitative, quantitative, all of which is raw and enormous.
 
Querying and cleaning all that data becomes a nightmare. At least, that is, with current ERP systems. And digital transformation—to meet the new challenges—becomes a drawn-out process, demanding outside consultants and straining company cultures and bottom lines.
 
But what if you could find a solution that handles the complexities while charging only for the results you want to drive with data?
 
To me, data management is like building a home. Current ERP systems represent only the tools and resources behind the house (wood, steel, tile, etc.), and the consultant ecosystems represent the carpenters. However, you don’t want tools or carpenters, you want a house.
 
I’m fortunate to be part of the solution to the current ERP problem—that is, selling the house, not the tools and the workers. To do that, the solutions-provider segment must transform into an Outcome-as-a-Service model. That means selling the results from data integrity and insight.
 
To do this, Outcome-as-a-Service providers must first tackle the three T’s of ERP challenges:
 
Technology – With the advances of just the last few years in computing power, Artificial Intelligence, the Internet of Things, analytics, robotics, cloud, and so on, we are now ingesting vast amounts of data—and from an ever-expanding number of sources. Many ERP systems were built to handle data from a limited amount of sources. Today, all of that data from myriad sources needs to be cleaned and verified to ensure data integrity, which must extend to the full journey of your data, with visibility at every stage—including how it was ingested and any processing to put it into a usable format. Just as you have an NOC (network operating center), you also need a DOC–data operating center.
 
Tradition – ERP was built around a traditional model in which the solution was designed simply to ingest the data. It then relied on a series of consultants and dev shops to create solutions, without ownership of the ultimate goal. In other words, this system provides no connection between paid activities and results. So, over the years, the system fed itself despite the fact that it was not the most efficient or economical one. It also did not always result in effective solutions even after vast investments in time and money. Even worse, the business itself assumed all of the risk.
 
Time – In the business world, time has gone from ticking clocks to warp drive. Top companies are driven to extinction faster than ever. And digitized startups scale to vast proportions in months not years. Today, a market leader can be disrupted in practically the blink of an eye, by competitors that arise seemingly from nowhere. A bookseller like Amazon can become a cloud giant. A computer maker like Apple can disrupt music, phones, and video. And a car company like Tesla, can set its sights on the utilities industry.
 
When I think about these three challenges, I’m amazed that the current ERP model hasn’t been disrupted sooner. We’re overdue for a new model that better positions companies for innovation, allowing startups to contribute their entrepreneurial speed and agility on solving complex and relevant industrial problems. This leads to digital transformation and true IoT value, with a minimum of pain and expense.
 
As Einstein said, the definition of insanity is doing the same thing over and over, while expecting a different outcome. He could have been speaking about ERP.

Joseph Bradley is President of Business Ventures at Uptake, a predictive analytics software company that captures new value for Fortune 1000 companies by connecting and analyzing massive amounts of untapped data. Joseph oversees Uptake’s global sales, marketing and strategy functions, including revenue generation, client engagement, business vertical creation, messaging, thought leadership, and overall customer success.

Previously, Joseph was Vice President of Cisco’s Internet of Everything (IoE) Practice, where he led a team of technology and business consultants counseling CXOs and government leaders on realizing IoE value and digital business transformation. Joseph built the foundation for the IoE Practice by directing the groundbreaking research and production of Cisco’s influential thought leadership, “Embracing the Internet of Everything To Capture Your Share of $14.4 Trillion” and “Internet of Everything Value Index: How Much Value Are Private-Sector Firms Capturing from IoE in 2013?” 

Understanding the Role of the Hacker, the Hipster and the Hustler on the Road to Innovation: Key Take-Aways from the 11th Annual New Product Innovation & Development: A Frost & Sullivan Executive MindXchange

By Rajiv Kumar
Senior Partner and Global Vice President
Frost & Sullivan





 

Among the wealth of innovation strategies and best practices shared at the event, we present some of the most useful and critical ideas from key sessions. But don’t take our word for it, read on for the event take-aways that the participants applauded and are most likely to bring back to their teams.
 
From ASK THE EXPERTS! Panel Discussion: Teaching an Old Dog New Tricks: Best Practices for Engaging Every Employee in Entrepreneurship and Innovation 
 
The event’s most memorable terminology: the hacker, the hipster and the hustler are the three types of people who can help companies to innovate more efficiently. Their roles:

• The Hacker is the content or product maker
• The Hipster is the customer experience designer
• The Hustler is the sales team that makes the deals

If your innovation team is lacking a certain skill, ask the functional leaders to augment with one of their team members. The key is to view innovation as a talent development opportunity that makes a conversation about resourcing easier.

To attract talent from various functions, hold information sessions and emphasize the benefits of participation in various innovation initiatives; career change or advancement as graduates bring newly learned skills back to their functional teams; knowledge and skills necessary to start a new business; and networking with local entrepreneurs.

Have top ideas go through an extensive three-month incubation process as part of the corporate accelerator. It is not uncommon for engineers who come out of the program to discover their true calling is to be on the business side and explore career opportunities in product management, for example.

From CHANGE AGENT: Using Artificial Intelligence to Drive Dynamic Product Innovation

 
Artificial Intelligence is driving product innovation – and the biggest areas that will be impacted are robotics, cyber-intelligence and hyper-personalization.

Analytics will continue to play a critical role in our lives and businesses in the future.

There are four kinds of data science analytics:

1. Descriptive Analytics – What happened and why.
2. Diagnostic Analytics – Why it happened.  Analyzes correlations and relationships.
3. Predictive Analytics – What will happen next and  when.  Ask: Can I develop predictive models, statistical real time scoring using data to forecast future trends?
4. Prescriptive Analytics – What actions should be taken and when? What if” questions to decide what to do.

If your organization doesn’t already have a Data Science Manager or Engineer, it probably will in the future.  What are you going to do with all that data you’re collecting?

Other key take-aways:

• In order to achieve some of the growth that we’re going to see in AI, we’re going to see a massive amount of capital that’s going to drive innovation
• AI is going to lead toward transformative innovation – not in the abstract, but in the everyday. It will transform your internal business, and it’s going to happen globally 
• We are going to ultimately have a strong relationship, even an emotional relationship, with our devices. How many of us, if we leave our cell phone somewhere, feel a bit lost without it?

From SUCCESS STORY: Why Intelligence, Speed and Leverage are the Keys to Unlocking IoT Value 

 
The top 5 implications of ongoing and accelerating digital disruption are:

1. Real time information is too late - predicting the near future is critical. We need to get beyond real time and move toward predictive models.
2. If it doesn't work on mobile, it doesn't work. Just stop.
3. Context is King - no two digital scenarios are the same. Just because you know my name doesn’t mean you know me. The context of the interaction is crucial.
4. Innovation is more than ideation. Invention + Execution = Innovation.
5. Insightful intelligence is the currency of the 21st century (Key question: How do I extract value from this data?)

How do you make data actionable and valuable? It involves intelligence, speed and leverage:

Intelligence: There are a lot of players, providing a limitless amount of tools for data services. Intelligence must equal shared risk; in other words, they need to own the outcome – intelligence needs to be delivered as a service
Speed: An average company with all the predictive analytics in the world takes 12-18 months to produce a data science model. A lot can happen in 12-18 months, especially now. Best in class performance now can put a data science model in place in 2-3 days
Leverage: Shift your thinking; don’t view your business as what you do, but how you do it. Businesses are made of core capabilities; you want to be able to draw on multiple verticals to be able to solve your problems

Critical Insights to Bring Back to the Team

In addition to the timely insights above, we asked the 11th Annual New Product Innovation & Development: A Frost & Sullivan Executive MIndXchange event participants to share their most valuable strategy or insight from the event—the take-away they would be most likely to share with their team. Their responses included:

• The need to create and invest in a separate innovation team
• Innovation = invention + execution
• Culture eats strategy for breakfast
• Different stages of innovation call for different metrics
• Activity-based metrics are okay early on, while outcome based metrics are needed later in the innovation cycle
• Innovation can be viewed as a value, not as an objective

From creating an internal organizational culture that supports innovation to leveraging and integrating technologies such as Artificial Intelligence and robotics to help drive innovation, a thorough understanding of these important trends and insights is necessary to drive not only innovation, but more importantly, transformational growth.


 

The Coming Age of Sentient ToolsWhen Tools Think, Socialize, and Are Aware

  

By Brian David Johnson
Futurist in Residence
Arizona State University - Center for Science and the Imagination
Fellow
Frost & Sullivan






Sentient tools are the next stage of intelligent, aware, and social machines 
 
Significant advances in technology and shifts in economies and culture are bringing about a new age of intelligent tools that are aware, can make sense of their surroundings, and are socially cognizant of the people who are using them. Sentient tools are the next step in the development of computational systems, Smart Cities and environments, autonomous systems, artificial intelligence (AI), Big Data and data mining, and an interconnected system in the Internet of Things (IoT). These tools are “what comes next” and emerge from a base of computational, sensing, and communications technologies that have been advancing over the last 50 years.
 
The “awareness” of these sentient tools is not comparable to a human level of consciousness. They are not meant to mimic, mirror, or replace human interaction. These tools are designed for specific physical and virtual tasks that could be vastly complex but are not meant to replace humans. Conversely, they are meant to work alongside the human labor force.
 
The rise of sentient tools will have a significant impact on the global work force and education, leaving practically no industry unaffected.

Sentient Tools: An Overview

Sentient tools represent the next stage of intelligent, aware, and social machines that are designed specifically to interact with people. To better understand this new classification of machines, it helps to first dissect the term’s meaning.

Sentience is defined as the ability to perceive the world and to derive feeling or meaning from those experiences. For a machine or tool, being able to derive meaning infers that the tool is capable of some level of perception, processing, and thinking. In this case, sentience involves both the ability to sense the world around the tool and to process, make meaning, and communicate with that world. To effectively interact with its environment, however, the sentient tool must be socially aware of the people working with it. It must understand a person as an individual so that it can communicate effectively.

The definition of a tool is simple. People have been using tools for millions of years. A tool is anything used as a means of accomplishing a task or purpose, typically a device held in the hand, used to carry out a particular function. The definition of tools has been expanded to include both physical and virtual tools, but one defining element is that they are used by an operator.

By this definition, a sentient tool is a tool that can think and is aware both of its surroundings and of the person that is using it. The tool is, therefore, socially aware: It understands its environment, can make sense of it, and can communicate appropriately with the person using it.

This is an essential component of the sentient tool concept. The tool may have some autonomy for thought processing and movement, but it is designed to accomplish a specific task and to work with humans.

The 4 Components of a Sentient Tool:

1. Situational Awareness: Sensing the outside world via local and networked sensors as well as through data and expertise sharing
2. Intelligence: Processing, understanding, learning, making sense of the world
3. Social Awareness: Understanding who it is working with
4. Communication: The ability to communicate with the human (multimodal interactions, e.g., voice, visuals, audio, haptic)

Sentient tools may have specific abilities that are greater than human abilities (e.g., mathematical computation, physical strength), but they are not designed to replace human labor completely. They are ultimately just tools designed for a specific task to be completed in the company and under the control of humans.

The rise of sentient tools will be enabled by a variety of technologies including artificial intelligence, Internet of Things, Smart Cities, cloud intelligence, robotics, intelligent mobility, and autonomous vehicles.

Brian David Johnson is a Futurist in Residence at The Center for Science and the Imagination at Arizona State University where he engages in research, outreach and radical collaborations to reinvent our relationship with the future. He is also a Futurist and Fellow at Frost & Sullivan. Previously, Johnson was the Chief Futurist at Intel Corporation.

Inspection Technology Strides Power Digital Innovation

By Mark Rosenberg
Vice President, Digital Inspections
GE Oil and Gas






Where to begin in the Digital Business Transformation journey can be overwhelming, and, sometimes paralyzing.  Fortunately, the cost, capability, and performance of several technologies have evolved such that the process of “inspections” now represents a very accessible on-ramp and accelerator of Digital Business Transformation. The key to a successful Digital Business Transformation is to focus your strategy on digitizing functions and processes that drive outcomes important to the business.  Re-thinking inspections and leveraging a digital inspection model can now offer significant benefit to any business looking to increase revenue, minimize risk, and reduce costs.
 
It has always been true that inspections are critical across industries to ensure optimal production, quality outcomes, safety, environmental protection, and compliance with regulations. While traditional inspections have been highly manual, technology innovations such as cloud computing, installed sensors, and new data collection methods are enabling a transformation in the inspection community and across industries.  Digital Inspections have gone from simply monitoring asset conditions to prevent losses and manage operations to driving new and better outcomes by increasing organizational productivity and asset life. 
 
The world of inspections has changed and continues to change greatly to the benefit of those organizations that embrace what the new technology can offer and adapt their operations with the implementation of these new technologies.  Inspections have been paper-based and viewed as costly to minimize.  Adding to this, many types of inspections are fraught with human safety risks, such as those associated with descending into vessels and other hazardous confined spaces.  The complications continue in the capture of the data.  Today, results are captured in the field, manually transferred, and stored locally.  The time to access the captured data is often delayed by days to months for use and cannot be leveraged efficiently for organizational learning and operational improvement.
 
With the acceptance of change come  significant improvements to the world of inspection: centralized, cloud-based technology, sensor optimization, and proliferation of new data collection methods and vehicles.
 
Cloud: Cloud technology has enabled data availability, integration, storage without limits, and computation capacity. Inspection data is no longer a representation of a single point in time, but rather becomes an informative and transformational business tool.  Cloud technologies enable easier access, real-time updates, and more effective data storing, integrating and sharing across the business network. Today, less than 10% of the data in oil and gas companies is effectively used (IDC). The cloud unlocks data so that more informed decisions can be made across the enterprise.
 
Installed Sensors: Over the past several years, sensors have become notably more powerful and cost effective, enabling a broader scale and depth of information to  be continuously available.  Sitting on-premise and connecting directly to the cloud, installed sensors can provide real-time visibility across your assets through a simplified, central view of inspection data including thermal and thickness data. Instead of periodic, time-based inspections that leave windows in which a failure can occur, organizations can continuously “inspect” and detect actual problems and predict potential areas of concern.  This enables more proactive asset management that can keep operations both productive and safe.
 
New data collection methods, such as crawlers and aerial drones, enable inspections of assets that were previously hard to reach, a safety risk, or very expensive to monitor.  These tools allow for assets to be inspected and analyzed at a lower cost, more frequently, and more safely with better quality inspection modalities than previously used.
 
The combination of the above three trends also has the potential to generate new inspection methods and outcomes that were previously impossible. When a robot carries sensors or cameras to conduct inspection of a vessel from the inside, the inspection results, such as images or videos, can be mapped onto a cloud-based 3D digital model with precise coordinates of flaw location. The monitoring techniques can then be applied from outside of the vessel and consistently provide data to the same 3D model on the exact spot where flaws occur. The combination of cloud, sensors, and new forms of robotic data collection creates the foundation for brand new ways to conduct inspections and asset management. Paired with advanced analytics and learning systems, a “Digital Twin” -- or digital representation of a physical asset-- can then be used to trend actual versus optimal performance. This enables production and maintenance staff to identify potential operational deviations, expedite root cause analysis, and drive predictive maintenance. 
 
By embracing change and enabling technology, you can take a simple task such as the inspecting of a storage tank and quickly derive financial, health, safety, and environmental benefits:

• Minimize Risk: Using a drone, instead of a human, reduces risk to the health and safety of your workforce or service crew, decreases the time to access and utilize data, and lowers overall costs of the inspection. 
• Enhance Quality: Utilizing digital technology to ensure the quality and consistency of the collection and analysis of inspection readings, for corrosion as an example, drives more consistent, precise, and usable data than those collected by a human, who would typically perform the work with a flashlight, pencil, and paper.
• Improved Uptime: Reducing the non-productive time associated with an inspection shutdown by nearly 70% enables higher productivity of a tank (techna.no). Furthermore, technologies are rapidly evolving that will enable vessels to be inspected with zero downtime and to enable machine learning algorithms to detect previously unidentified anomalies in the data and avert costly disasters. 

Mark Rosenberg, Vice President of GE Oil & Gas Digital’s Product Management Center of Excellence & Digital Inspections Platform, joined the company in late 2016 with a 20-year track record in the enterprise software applications marketspace as a product management leader, product division general manager, functional architect, and consultant.

During his product management career prior to GE, Mark spent 11 years creating, growing, and setting the direction for the PeopleSoft Asset Lifecycle Management solution suite and 15 years working in a similar capacity with the PeopleSoft Projects solution suite at Oracle. He served as the leading voice representing a portfolio of 16 products to customer, partner, and analyst communities; launched and scaled multiple new ERP and analytical applications; and drove product positioning across a dozen industry verticals, including Oil & Gas.