A new study commissioned by Nice about consumer channel preference complements Forrester’s data quite effectively and adds more data to the understanding that customer service does not need to be exceptional but just needs to be frictionless, easily and efficiently delivering answers to customer questions.
Here is some recent Forrester data from our latest Consumer Technographics® survey about US customer service trends:
45% of US online adults will abandon their online purchase if they can't find a quick answer to their question.
66% say that valuing their time is the most important thing a company can do to provide a customer with a good online customer service.
29% prefer to use online customer service rather than speak with a live person on the telephone.
Data from the Nice survey says that:
50% of respondents say that if they cannot easily achieve resolution, they will turn to the contact center.
Which supports the point that service needs to be frictionless and effortless.
40% of respondents expect agents to be informed of their experiences upon beginning the conversation and to be able to successfully resolve their issues quickly.
Which supports the point that companies need to value a customer’s time.
In addition, the Nice survey conveyed:
When asked what customers like about assisted service, 50% of respondents cited FCR as their #1 reason for consulting a live agent. 33% of respondents they derive satisfaction from dealing with knowledgeable reps with specialized training.
I love predictive analytics. I mean, who wouldn't want to develop an application that could help you make smart business decisions, sell more stuff, make customers happy, and avert disasters. Predictive analytics can do all that, but it is not easy. In fact, it can range from being impossible to hard depending on:
Causative data. The lifeblood of predictive analytics is data. Data can come from internal systems such as customer transactions or manufacturing defect data. It is often appropriate to include data from external sources such as industry market data, social networks, or statistics. Contrary to popular technology beliefs, it does not always need to be big data. It is far more important that the data contain variables that can be used to predict an effect. Having said that, the more data you have, the better chance you have of finding cause and effect. Big data no guarantee of success.
Traditional BI approaches and technologies — even when using the latest technology, best practices, and architectures — almost always have a serious side effect: a constant backlog of BI requests. Enterprises where IT addresses more than 20% of BI requirements will continue to see the snowball effect of an ever-growing BI requests backlog. Why? Because:
BI requirements change faster than an IT-centric support model can keep up. Even with by-the-book BI applications, firms still struggle to turn BI applications on a dime to meet frequently changing business requirements. Enterprises can expect a life span of at least several years out of enterprise resource planning (ERP), customer relationship management (CRM), human resources (HR), and financial applications, but a BI application can become outdated the day it is rolled out. Even within implementation times of just a few weeks, the world may have changed completely due to a sudden mergers and acquisitions (M&A) event, a new competitive threat, new management structure, or new regulatory reporting requirements.
There is no single metric against which to benchmark the performance of your customer service organization. It’s like flying a plane—you can’t do it by just looking at your altitude settings. This means that most organizations use a balanced scorecard approach, which includes a set of competing metrics that balance the cost of operations against satisfaction measures. For industries with strict policy regulations, like healthcare, insurance, or financial services, adherence to regulatory compliance is yet another metric that is added to the list.
The set of metrics that you choose also depends on your audience. Customer service managers need real-time, granular operational data. Yet your executive management team needs high-level data about key performance indicators (KPIs) that track outcomes of customer service programs.
So where should you begin when choosing metrics? It’s best to start by understanding the value proposition of your company. For example, do you compete on customer experience, where satisfaction measures are of primary importance, or do you compete on cost, where efficiency and productivity measures are most important?
Once you understand your value proposition, choose the high-level KPIs that support your company’s objectives. These metrics are the ones that you will report to executive management and include overall cost, revenue, compliance, and satisfaction scores. Next, choose the operational metrics for your organization that link to each of these KPIs and support your brand. For example, if you compete on cost, handle time and speed of answer will become your primary metrics. However, if you are focused on maximizing customer lifetime value, first contact resolution will rise to the top.
Digital disruption is real and shows no signs of slowing down – our research shows that 1 billion consumers will utilize smartphones by 2016. Digital upstart companies are disrupting long-standing business models as documented by stories in the major business news outlets such as Fortune, Forbes, NBC Universal's business channel CNBC, and The Wall Street Journal. If your industry / company is not under siege yet, it’s safe to assume it will be.
Your challenge is to disrupt while avoiding the chaos that will ensue if you fail to adapt.
"When companies adopt technology, they do old things in new ways. When companies internalize technology, they find disruptive things to do." James L. McQuivey, Forrester Vice President & Principal Analyst
In typical Microsoft fashion, they don't catch a new trend right with the first iteration but they keep at it and eventually strike the right tone and in more cases than not, get good enough. And often good enough wins. That seems the be the pattern playing out with Windows Azure, its cloud platform.
When designing application infrastructure strategy, planning for the renewal of their application landscape, or assessing their overall strategic position, banks and other types of firms in financial services typically like to know the answer to the question: “What are the others doing?”
It is time now to update the survey results: Forrester has just started surveying banks in North America, Europe, and further geographies about the current state of their application landscape, their key issues and concerns, and their plans for the future. At a high level, the survey is designed to answer the question: “What are others doing?” Phrased in a different way, it targets the question: “What are the key trends regarding the transformation of the application landscape in financial services in its multiple facets?”
To make this survey successful, Forrester needs your help. If you are working in financial services in any role that is related to financial services architecture and application delivery (including the more planning-and-strategy-oriented aspects of application delivery), please participate in Forrester’s Global Financial Services Architecture Survey 2012. Please contact Reedwan Iqbal (firstname.lastname@example.org) who will send you a link to the online survey.