In 2002, the City of Baltimore, led by Director Lisa N Allen, was the first city in the country to launch 311 as an intake center for all service request and requests for general information from citizens and visitors. Over the years, many cities and counties have modeled Baltimore’s 311 and have implemented 311 call centers internationally.
On March 11, 2019 local governments of all sizes are using this date – 3/11 – to showcase and to celebrate how they are using 311 and centralized contact centers to provide a coordinated and seamless approach to service delivery.
For many communities, contact centers have become the face of government to the public. With the implementation of 311 systems, digital and civic media, and apps which allow the public to make service requests and enables direct interact with government officials, local governments are setting new standards for customer/community participation.
I encourage city, county, state, and federal governments that are using 311 and centralized contact centers to use March 11 (3/11) as an opportunity to promote your efforts to provide for responsive service to the public.
In the words of Philadelphia’s Benjamin Franklin, “Well done is better than well said.” The idea of customer service is often reserved to describe interaction with stores, restaurants, and other organizations in the private sector. Rarely do we hear people say “Wow, I had a great experience dealing with the staff at any government agency!” Fortunately, those outside of government might be surprised at how seriously excellence in service delivery is taken in the public sector.
Let’s take a look at national Customer Service Week which was created by 1992 by the President of the United States, citing the value of service excellence in a free market economy.
The President’s proclamation said:
A business will do a better job of providing high-quality goods and services by listening to its employees and by empowering them with opportunities to make a difference. Customer service professionals work in the front lines where a firm meets its customers; where supply meets demand. With responsive policies and procedures and with simple courtesy, customer service professionals can go a long way toward ensuring customer satisfaction and eliciting the next round of orders and purchases. The Congress, by Senate Joint Resolution 166, has designated the week of October 4 through October 10, 1992, as “National Customer Service Week” and requested the President to issue a proclamation in observance of this week.
NOW, THEREFORE, I, GEORGE BUSH, President of the United States of America, do hereby proclaim the week of October 4 through October 10, 1992, and the first week of October in subsequent years, as National Customer Service Week. I invite all Americans to observe this week with appropriate programs and activities.
IN WITNESS WHEREOF, I have hereunto set my hand this eighth day of October, in the year of our Lord nineteen hundred and ninety-two, and of the Independence of the United States of America the two hundred and seventeenth.
George H. Bush
Across the country, including the federal government agencies, there is a movement to improve the delivery of information and service to those in need.
During the first week of October 2016, they are making Benjamin Franklin proud by not just talking about customer service but actually doing something (many things actually) to make sure our customers are properly “served.”
I wanted to reblog this post by Tony Consentino, Ventana Research VP and Research Director, because it was very insightful and thought provoking. In summary, when using or talking about big data, one should think of terms “What, So what, Now what & Then what”.
The idea of not focusing on innovation is heretical in today’s business culture and media. Yet a recent article in The New Yorker suggests that today’s society and organizations focus too much on innovation and technology. The same may be true for technology in business organizations. Our research provides evidence for my claim.
My analysis on our benchmark research into information optimization shows that organizations perform better in technology and information than in the people and process dimensions. They face a flood of information that continues to increase in volume and frequency and must use technology to manage and analyze it in the hope of improving their decision-making and competitiveness. It is understandable that many see this as foremost an IT issue. But proficiency in use of technology and even statistical knowledge are not the only capabilities needed to optimize an organization’s use of information and analytics. They also need a framework that complements the usual analytical modeling to ensure that analytics are used correctly and deliver the desired results. Without a process for getting to the right question, users can go off in the wrong direction, producing results that cannot solve the problem.
In terms of business analytics strategy, getting to the right question is a matter of defining goals and terms; when this is done properly, the “noise” of differing meanings is reduced and people can work together efficiently. As we all know, many terms, especially new ones, mean different things to different people, and this can be an impediment to teamwork and achieving of business goals. Our research into big data analytics shows a significant gap in understanding here: Fewer than half of organizations have internal agreement on what big data analytics is. This lack of agreement is a barrier to building a strong analytic process. The best practice is to take time to discover what people really want to know; describing something in detail ensures that everyone is on the same page. Strategic listening is a critical skill, and done right it enables analysts to identify, craft and focus the questions that the organization needs answered through the analytic process.
To develop an effective process and create an adaptive mindset, organizations should instill a Bayesian sensibility. Bayesian analysis, also called posterior probability analysis, starts with assuming an end probability and works backward to determine prior probabilities. In a practical sense, it’s about updating a hypothesis when given new information; it’s about taking all available information and finding where it converges. This is a flexible approach in which beliefs are updated as new information is presented; it values both data and intuition. This mindset also instills strategic listening into the team and into the organization.
For business analytics, the more you know about the category you’re dealing with, the easier it is to separate what is valuable information and hypothesis from what is not. Category knowledge allows you to look at the data from a different perspective and add complex existing knowledge. This in and of itself is a Bayesian approach, and it allows the analyst to iteratively take the investigation in the right direction. This is not to say that intuition should be the analytic starting point. Data is the starting point, but a hypothesis is needed to make sense of the data. Physicist Enrico Fermi pointed out that measurement is the reduction of uncertainty. Analysts should start with a hypothesis and try to disprove it rather than to prove it. From there, iteration is needed to come as close to the truth as possible. Starting with a gut feel and trying to prove it is the wrong approach. The results are rarely surprising and the analysis is likely to add nothing new. Let the data guide the analysis rather than allowing predetermined beliefs to guide the analysis. Technological innovations in exploratory analytics and machine learning support this idea and encourage a data-driven approach.
Bayesian analysis has had a great impact not only on statistics and market insights in recent years, but it has impacted how we view important historical events as well. It is consistent with modern thinking in the fields of technology and machine learning, as well as behavioral economics. For those interested in how the Bayesian philosophy is taking hold in many different disciplines, I recommend a book entitled The Theory That Would Not Die by Sharon Bertsch McGrayne.
A good analytic process, however, needs more than a sensibility for how to derive and think about questions; it needs a tangible method to address the questions and derive business value from the answers. The method I propose can be framed in four steps: what, so what, now what and then what. Moving beyond the “what” (i.e., measurement and data) to the “so what” (i.e., insights) should be a goal of any analysis, yet many organizations are still turning out analysis that does nothing more than state the facts. Maybe 54 percent of people in a study prefer white houses, but why does anyone care?Analysis must move beyond mere findings to answer critical business questions and provide informed insights, implications and ideally full recommendations. That said, if organizations cannot get the instrumentation and the data right, findings and recommendations are subject to scrutiny.
The analytics professional should make sure that the findings, implications and recommendations of the analysis are heard by strategic and operational decision-makers. This is the “now what” step and includes business planning and implementation decisions that are driven by the analytic insights. If those insights do not lead to decision-making or action, the analytic effort has no value. There are a number of things that the analyst can do to make the information heard. A compelling story line that incorporates storytelling techniques, animation and dynamic presentation is a good start. Depending on the size of the initiative, professional videography, implementation of learning systems and change management tools also may be used.
The “then what” represents a closed-loop process in which insights and new data are fed back into the organization’s operational systems. This can be from the perspective of institutional knowledge and learning in the usual human sense which is an imperative in organizations. Our benchmark research into big data and business analytics shows a need for this: Skills and training are substantial obstacles to using big data (for 79%) and analytics (77%) in organizations. This process is similar to machine learning. That is, as new information is brought into the organization, the organization as a whole learns and adapts to current business conditions. This is the goal of the closed-loop analytic process.
Our business technology innovation research finds analytics in the top three priorities in three out of four (74%) organizations; collaboration is a top-three priority in 59 percent. Both analytics and collaboration have a process orientation that uses technology as an enabler of the process. The sooner organizations implement a process framework, the sooner they can achieve success in their analytic efforts. To implement a successful framework such as the one described above, organizations must realize that innovation is not the top priority; rather they need the ability to use innovation to support an adaptable analytic process. The benefits will be wide-ranging, including better understanding of objectives, more targeted analysis, analytical depth and analytical initiatives that have a real impact on decision-making.