CHIEF FINANCIAL OFFICER
The term artificial intelligence (AI) was coined by John McCarthy, an assistant professor at Dartmouth College in 1955, and formalized as a field of study one year later in July/August of 1956. At that time, organizations had yet to see the potential, let alone realize how AI was about to radically and permanently change everything. Today, over half a century later, the extraordinary potential AI presented is recognized. AI has seeped into every aspect of society – from the personal to the professional. However, many Posts have yet to fully unleash AI to improve the customer’s experience, reduce costs, or increase sales.
Posts are ideally suited to reap the rewards AI offers. They deliver millions of letters and packages, daily, plus they have also got a network that simultaneously carries out millions of transactions per day. As a result, Posts are already collecting a considerable amount of data. The problem is that prior to AI, they had very limited ways of visualizing, let alone analyzing this wealth of information. AI changes that irrevocably. With AI, Posts can extract the necessary findings from their various branches to see what is it that works in the busiest branch, why one branch is selling more than the others, and combine this data to produce the blueprint for optimum performance. However, what many Posts do not know is how to actualize a coherent and strategic AI solution in their organization. Essentially, achieving this goal boils down to two choices:
Some Posts will look to build the capability in-house. If this is the route they chose, they must bear in mind the following:
They will need to hire data scientists who are in short supply and high demand.
Apart from the collection and cleaning of their data, they will need to build their machine learning tools. Then they must develop the algorithms that work for their Post.
Once they have these, they will have to test and iterate to ensure they are getting the data they require. These tests and adaptations can take months, even years, depending on how smoothly things go.
Developing the right solution is time-consuming and can distract postal operators from their primary function- running the networks, delivering mail, and keeping customers happy.
In this scenario, a third-party vendor is responsible for the collection of the data from all the Posts’ locations- the mobile units, self-service kiosks, PUDO applications, retail partner systems, and customer engagement surveys. All this data is then fed into one solution, which in turn produces a host of invaluable business insights for the Post.
What can the data show? For example, it can tell a Post that if they add one employee to their Main Street location on a Thursday between 11:00 am and 2:00 pm, they can improve their volume of daily transactions by 20%. Or, by adding a self-service kiosk to their South Street location, they might increase revenue by 30% because currently, they are losing customers during these busy times.
Data analytics is in the DNA of Escher’s Riposte technology. Our focus is to create a platform that is purpose-built for Posts. Escher’s Riposte leverages AI to enable postal operators to benefit from the efficiencies and opportunities that this technology offers. Another advantage of Escher’s platform is that Riposte is built with an open architecture. So should a Post need to add other applications to help the business evolve, they can easily do so. We also offer multiple deployment options – on-premise, in the cloud, or a hybrid. Escher’s platforms are also infrastructure agnostic, so our solutions will work with your current technology framework rather than making it obsolete. Riposte has built-in redundancies for power outages or extreme weather, and we take every measure to ensure the security of your data and your customer’s data. In short, Riposte meets the criteria- it’s secure, it’s stable, it’s scalable.