This is a guest post by Danielle Geoffroy, Research Associate on the AD&D team who helps with our customer service and unified communications research.
Do you hear that swooshing sound of a tweet being sent in the middle of a Google Hangout? It’s faint, but strong, and it means they’re coming. Generation Y—a generation raised entirely in a technology-driven world. This new breed of consumers demands more from companies and government agencies, with particularly high expectations for friction-free customer experiences. They’re prepared with knowledge of your company, and your top competitors. In fact, they often have more information about you and your products than your own employees.
This new generation should matter to you, because by 2018, the millennials will surpass the spending power of baby boomers. Remember: there is a dollar value to every positive and negative Yelp review, tweet, and Facebook status they target at you. With so much information at consumer’s fingertips, there is some give with the take. People don’t want to retain all of the information they receive on a daily basis. Striking a balance between the knowledge of your customers, and the methods deployed by your customer support agents, will lead to an enjoyable service experience, and keep you far away from the dreaded viral video of a support request gone wrong.
There’s no other way to slice it: competition for digital audiences is brutal. Intolerance for poor performance and disengaging experiences drives customers to competitor’s sites more quickly and more permanently than any time in history. Users increasingly demand digital experiences that personalize to their immediate needs and adapt to the current context, not treat them as a market or demographic segment.
In recently published research, we found that even as expectations soar, enterprises are personalizing with methods that are too unsophisticated, too opaque, or too convoluted to meet the complexity and mutability needed to serve individuals. Persona-based segmentation is too simplistic to meet current, much less future, customer expectations. Some solutions provide predictive analytics capabilities but are limited to a few algorithms or black-box methods (e.g. neural networks) are not easily adaptable to new data or scenarios. Those that rely heavily on rules have become morasses, some customers needing to manage and maintain hundreds or thousands of rules to guide digital experiences.
I first noticed the creeping changes a few years ago. In college I majored in comparative literature and averaged about five novels read per week. Even when I entered the hustle and bustle overdrive of the working world, I still rapidly pounded through stacks of books every month. Over the past few years, while I still read more than the average American, the act of actually finishing a book became something of a notable achievement. My brain was more easily distracted, my ability to focus on and engage with complex information diminished, and my capacity to multitask as required by a modern work environment was seemingly illusory.
Of course, I wasn’t alone in experiencing these changes. This distracted mental state has become a common problem among knowledge workers and heavy users of Internet and mobile technologies. Excellent books such as Distracted: The Erosion of Attention and the Coming Dark Age and The Shallows: What the Internet Is Doing to Our Brains detailed the changes we are all undergoing and described much of the neuropsychological research that seeks to explain the mental modifications that have left us in such a state. At heart, the research shows that our tools have begun to shape our brains just as much as we fashion our tools--and not always for the better.
Such mental modifications would seem to pose some significant and idiosyncratic problems for customer service organizations. Indeed, a new generation of contact center agents has begun to vex application development and delivery professionals. The new agents seem reluctant to learn detailed product and service information that previous cohorts of agents had little problem with. These new agents prefer to learn where to find such information, but have little intention of actually memorizing product support details.
One of the developing trends in computing, relevant to both enterprise and service providers alike, is the notion of workload-specific or application-centric computing architectures. These architectures, optimized for specific workloads, promise improved efficiencies for running their targeted workloads, and by extension the services that they support. Earlier this year we covered the basics of this concept in “Optimize Scalable Workload-Specific Infrastructure for Customer Experiences”, and this week HP has announced a pair of server cartridges for their Moonshot system that exemplify this concept, as well as being representative of the next wave of ARM products that will emerge during the remainder of 2014 and into 2015 to tilt once more at the x86 windmill that currently dominates the computing landscape.
Specifically, HP has announced the ProLiant m400 Server Cartridge (m400) and the ProLiant m800 Server Cartridge (m800), both ARM-based servers packaged as cartridges for the HP Moonshot system, which can hold up to 45 of these cartridges in its approximately 4U enclosure. These servers are interesting from two perspectives – that they are both ARM-based products, one being the first tier-1 vendor offering of a 64-bit ARM CPU and that they are both being introduced with a specific workload target in mind for which they have been specifically optimized.
This Forum will help you identify brand new software opportunities and run with them. It will hit on the must-have competencies that will empower application development and delivery leaders to execute on their company’s engagement strategies. This includes accelerating development processes, creating digital experiences, reaching mobile customers, and exploiting analytics and big data. Forrester analysts will deliver forward-thinking content while industry specialists – from companies such as McDonald’s, Mastercard, and GE Capital - will provide insight into some real and revolutionary new business approaches that are relevant to you right now.
Recent news of a a computer program that passed the Turing Test is a great achievement for artificial intelligence (AI). Pulling down the barrier between human and machine has been a decades long holy grail pursuit. Right now, it is a novelty. In the near future, the implications are immense.
Which brings us to why should you care.
Earlier this week the House majority leader, Eric Cantor, suffered an enormous defeat in Virginia's Republican primary by Tea Party candidate David Brat. No one predicted this - the polls were wrong, by a long shot. Frank Luntz, a Republican pollster and communication advisor, offered up his opinion on what was missing in a New York Times Op-Ed piece - lack of face-to-face discussions and interviews with voters. He asserts that while data collection was limited to discrete survey questions, what it lacked was context. Information such as voter mood, perceptions, motives, and overall mind set were missing. Even if you collected quantitative data across a variety of sources, you don't get to these prescient indicators.
The new wave of AI (the next 2 - 5 years) makes capturing this insight possible and at scale. Marketing organizations are already using such capabilities to test advertising messages and positioning in focus group settings. But, if you took this a step further and allowed pollsters to ingest full discussions in person or through transcripts in research interviews, street polls, social media, news discussions and interviews, and other sources where citizen points of view manifest directly and indirectly to voting, that rich content translates into more accurate and insightful information.
To jump on this R feeding frenzy most leading BI vendors claim that they “integrate with R”, but what does that claim really mean? Our take on this – not all BI/R integration is created equal. When evaluating BI platforms for R integration, Forrester recommends considering the following integration capabilities:
I’ve been experimenting for the past year or so with several proactive assistant apps to guide my day — they remind me to get on conference calls with clients, offer to text participants if I'm running late to an in-person lunch, and keep me in touch with friends and colleagues. Some of these apps also integrate Salesforce, Yammer, and BaseCamp for job-specific context and assistance.
Among the most popular apps, Google Now personalizes recommendations and assistance by applying predictive analytics to data stored in email, contacts, calendar, social, docs, and other types of online services users opt in. Other examples include Tipbit applying predictive analytics to make a more intelligent inbox, and EasilyDo using the notification system to recommend ways to automate common everyday tasks. Expect Labs is tackling this space from the other end of the spectrum, offering an intelligent assistance engine for enterprises to plug into and add proactive features to their own apps.
Here’s what we think:
• Vendors will experience burnouts and early customer frustration, much like in voice recognition. In the music industry, it’s said that an artist is only as good as her last hit. We saw that analogy apply to voice recognition when users got frustrated at Siri as soon as she failed once on them. Expect a similar dynamic with all types of predictive apps.
We attended the recent Glimpse Conference 2013, where members of New York's tech scene came together at Bloomberg headquarters to talk about social discovery, predictive analytics, and customer engagement.
Our key takeaway from the event: small, real-time data coming from very personal apps like email, calendar, social, and other online services will fuel next-level predictive apps and services. Specifically:
• Better insight doesn’t require more data; it needs the right data. Amassing large databases of customer profiles, purchase history, and web browser activity only goes so far, and is costing companies millions, if not billions of dollars every year. Mikael Berner from EasilyDo sees a new opportunity in better utilizing data scattered across personal email indices, calendars, social networks, and file and content repositories that directly indicate customers’ plans, interests, and motivations.
• Email, calendar, and location data is a goldmine for predictive analytics. Expedia or TripAdvisor can track web activities to recall a user searched for hotels last November and is likely to travel again this year, but a flight confirmation sitting in email or vacation time logged in calendar is a much stronger indicator of travel plans.