Two Ways That Customer Service Organizations Use To Build Emotional Connections

Kate Leggett

Today customers use self-service for straightforward interactions, leaving complex issues like account closure or claims disputes for a phone conversation. These questions often take longer to resolve and are opportunities to build positive customer relationships.

Customer service organizations must look out for customers' best interests and support their emotional state. Take the example of Delta Air Lines and how the airline supports customers when they receive notice about a cancelled flight. Its IVR system can tell when the caller ID field matches a mobile phone that recently received a cancellation notice via text message. It skips the standard menu in favor of one context-aware question: "Are you calling about the text message we just sent you? - saving the customer valuable time, and making him or her feel like the airline has their best interests in mind.

How are companies making better emotional connections via customer service?  First, field service is becoming more important to nurture customer relationships. These interactions are by far the most personal channel for customer engagement, and they can make or break a relationship. Modern field service technologies empower customers to control the service experience by engaging with a tech on their timetable and their terms. They can also fuel differentiated customer experiences by equipping the technician with the right customer information, parts, and knowledge to get the job done in one visit. We foresee industries outside of the traditional ones – like insurance, field health workers, contractors - adopting these technologies for their value in providing differentiated experiences.

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On An Agile Scaling Journey? Take Our Survey To Find Out How You Are Doing

Amanda LeClair

This year I’m excited to be teaming up with Diego Lo Giudice on the biannual Forrester Agile At Scale Adoption Survey. For the 2017 study, we’ve added a few more questions in areas that we see organizations struggle with. So in addition to successful Agile team practices, alignment with business stakeholders upstream and downstream with testing and operations, we are looking into more Agile at scale issues like budgeting and DevOps. Software development leaders continue to buy into Agile while eradicating traditional waterfall development. In the last Agile survey in 2015, we found that 46% of the respondents are still doing what Forrester calls Water-Agile-Fall, but not on a path to faster delivery. Leading innovator teams, which we called Agile Expert firms, have quickly turned passion projects into Agile success stories. But enterprises don’t just need to be quick and flexible on net-new projects or only at the individual team level; they need speed across the business.

As software teams mature along their Agile transformation, the biggest obstacle still is, despite some improvements, to scale up and horizontally. This means truly linking Agile initiatives, Design thinking and DevOps with business value. Our biannual Agile survey tracking the health of Agile initiatives for 2017 keeps its focus on the main challenge: Agile At Scale.

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Cognitive Search Is The AI Version Of Enterprise Search

Mike Gualtieri

Cognitive SearchWritten by Emily Miller, Senior Research Associate

Stop Wasting Time

More than half (54%) of global information workers are interrupted from their work a few times or more per month to spend time looking for or trying to get access to information, insights, and answers. The problem: Old keyword-based enterprise search engines of the past are obsolete. Cognitive search is the new generation of enterprise search that uses artificial intelligence (AI) to return results that are more relevant to the user or embedded in an application issuing the search query. Forrester defines cognitive search and knowledge discovery solutions as

A new generation of enterprise search solutions that employ AI technologies such as natural language processing and machine learning to ingest, understand, organize, and query digital content from multiple data sources.

Cognitive search solutions are different because they:

  • Scale to handle a multitude of data sources and types.Search is no longer just about unstructured text contained in documents and web pages. Cognitive search solutions can also accommodate structured data contained in databases and even nontraditional enterprise data like images, video, audio, and machine data such as from internet-of-things (IoT) devices.
  • Employ artificial intelligence technologies. The distinguishing characteristic of cognitive search solutions is that they use natural language processing (NLP) and machine learning to understand and organize data, predict the intent of the search query, improve relevancy of results, and automatically tune the relevancy of results over time.
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On An Agile Scaling Journey? Take Our Survey To Find Out How You Are Doing

Diego Lo Giudice

This year I’m excited to be teaming up with Amanda LeClair on the biannual Forrester Agile At Scale Adoption Survey. For the 2017 study, we’ve added a few more questions in areas that we see organizations struggle with. So in addition to successful Agile team practices, alignment with business stakeholders upstream and downstream with testing and operations, we are looking into more Agile at scale issues like budgeting and DevOps. Software development leaders continue to buy into Agile while eradicating traditional waterfall development. In the last Agile survey in 2015, we found that 46% of the respondents are still doing what Forrester calls Water-Agile-Fall, but not on a path to faster delivery. Leading innovator teams, which we called Agile Expert firms, have quickly turned passion projects into Agile success stories. But enterprises don’t just need to be quick and flexible on net-new projects or only at the individual team level; they need speed across the business.

As software teams mature along their Agile transformation, the biggest obstacle still is, despite some improvements, to scale up and horizontally. This means truly linking Agile initiatives, Design thinking and DevOps with business value. Our biannual Agile survey tracking the health of Agile initiatives for 2017 keeps its focus on the main challenge: Agile At Scale.

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Uber's Unicorn Ponzi Scheme

Ted Schadler

Sure, I use Uber. Sometimes it's the only option, and sometimes the price justifies riding with a guy who knows less about his city than I do, and whose car cleanliness raises concerns about fungal infections. And sometimes it's a huge win as it was recently for me after a nightmare business trip late at night, when I needed to get out to the northern burbs at 1 am. Osman in his Toyota Sienna rocked as he kindly drove and then led me bleary eyed to the door of my suburban hotel.

But can Uber stop losing money already? According to the Wall Street Journal, Uber lost $800 million in its latest quarter,on revenues of $1.7 billion. And it lost $1.2 billion on gross revenues of $5.4 billion in the first half of its fiscal year, after growing only 8% year-over-year. That's ridiculous. Why can't the company manage its way to profitability?

I would argue it can't because it doesn't have to yet. I think Uber benefits from the Unicorn Ponzi Scheme. I'll explain, but first a few words about my townsman, Chuck. Charles Ponzi's house in Lexington where I live is a shining example that ponzi schemes can work . . . for a while. As you can see from the picture, the Ponzi house is gorgeous, perfectly sited, and of course, famous. But Charles' runup in valuation ended in ruin.

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Five Factors That Make Deep Learning Different - Go Deep Baby!

Mike Gualtieri

At the highest conceptual level, deep learning is no different from supervised machine learning. Data scientists start with a labeled data set to train a model using an algorithm and, hopefully, end up with a model that is accurate enough at predicting the labels of new data that is run through the model. For example, developers can use Caffe, a popular deep-learning library, to train a model using thousands or millions of labeled images. Once they train the model, developers can use it within applications to probabilistically identify objects in a new image.  Conceptually like machine learning, yes, but deep learning is different because:

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AI Is Not An Exception – Technology Has Always Taken Jobs

Mike Gualtieri

Yes, AI will take jobs away from many workers - our relatives, friends, and neighbors. So too have all technologies created throughout human history. We invent things to make things easier and the impossible possible. The invention of the wheel made transport easier. Gutenberg’s printing press put lots of monk’s out of business. The chainsaw saw a reduction in the number of sawyers (lumberjacks). Modern medicine created a sharp decrease in snake oil charlatans. The Wang word processor annihilated typing pools. The list goes on. Technology changes how and who performs work, but it also enables new work that no one ever imagined. AI is but another technology in a long list of technologies dating back to the blunt club.

The culprit is gray matter

It is human intelligence. There is nothing that can stop it. But, it is that same gray matter that finds a way – a way for humanity to flourish – at least statistically. If life is precious, then the last hundred years have seen a dramatic increase in life expectancy. According to the National Institute On Aging, the most dramatic and rapid gains have occurred in East Asia, where life expectancy at birth increased from less than 45 years in 1950 to more than 74 years today.

AI will short-term replace workers just as all technology has, but longer term it will raise wages as human workers become exponentially more productive because their efforts are augmented by intelligent machines – non-human servants.

We can go back or we can go forward. Let’s go forward.

Are You On An Agile+DevOps Journey? Don’t Miss Out On Continuous Testing Services!

Diego Lo Giudice

It happens often in conversations with clients that I realize they have disjointed initiatives going on to support their digital transformation. The most dangerous parallel initiatives are those where, on one side, they are changing their development teams to become more Agile, but a separate initiative in the same enterprise exists where their Operations folks are running a development and operations (DevOps) transformation. The first thing I recommend to those clients is to unify or tightly connect those programs with an underlining common lean strategy. But I don’t want to dig in here about Agile+DevOps and how overused and abused the term “DevOps” is. I will just recommend to you some reports we’ve published explaining how “Agile” and “DevOps” are two sides of the same coin (see, for example, “Faster Software Delivery Will Accelerate Digital Transformation”).  The Modern Application Delivery playbook I’ve co-authored for years is all about what it means to adopt Agile+DevOps. Check that out too.

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Not too late to catch Digital Transformation Forum, 2017

Stephen Powers

The moment of truth for your digital re-invention has arrived. Digital technology has rendered your legacy systems obsolete, and has liberated your customers to adopt - and abandon - your offerings at a moment’s notice. You already know it’s time to change. You need to transform your firm to meet your customers’ expectations and ensure flexibility for the future. For hungry companies, the idea of "digital transformation" is an opportunity to expose new business opportunities, evolve operations, and grow.

Next week in Chicago, on May 9-10, Digital Transformation Forum 2017 will help you lay out the next steps in your digital strategy. It will feature sessions where leaders from companies such as Allstate, Bloomingdale’s, Gap, GE Oil & Gas, Expedia, Nespresso, Visa, and AIG will tell stories of how they helped their firms digitally transform and what they learned. In addition, Forrester analysts will present sessions on how you can:

  • See the big picture. Martin Gill will frame Digital Transformation as an enterprise-wide initiative – and one that can’t wait.
  • Engage your customers on any platform. Julie Ask will show how amorphous channels will house your firms’ digital customer interactions in the future and help you plan to add value and win customers through better experiences.
  • Mature your AI strategy from novelty to strategic advantage. Rob Koplowitz will introduce Forrester’s framework for developing the next generation of human/machine interactions.
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Forrester Methodology To Select Business Intelligence Implementation Service Providers

Boris Evelson

Business Intelligence (BI) pros continue to look for outside professional services. Forty-nine percent of decision makers say their firms are already engaging and/or expanding their engagements with outside data and analytic service providers, and another 22% plan to do so in the next 12 months. There are two main reasons for this sustained trend:

  • The breadth and depth of BI deployments cannot be internally replicated at scale. Delivering widely adopted and effective BI solutions is not easy. It requires rigor in methodology, discipline in execution, the right resources, and the application of numerous best practices. No internal enterprise tech organization can claim this wealth of expertise and experience; this only comes after delivering thousands of successful and unsuccessful BI projects — which we believe is solely the realm of management consultants and systems integrators. These partners have collectively accumulated such experience over many years and thousands of clients and projects.
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