New technologies and advanced analytics have the potential to create an exceptional customer experience. Unfortunately, most banking organizations continue to use the same outdated, siloed processes. Understanding the customer journey is at the foundation of being able to deliver the type of experience expected by today's consumer. Disconnects occur when organizations are unable to link online and offline customer engagements, and when internal silos create communications that don't reflect a customer's needs and behaviors in real-time. The New Marketing Reality report, produced by Econsultancy in association with IBM Watson Marketing, is based on a survey of more than 1,000 marketing, digital and ecommerce professionals.

HELSINKI, Finland -- February 23, 2017 -- Comptel Corporation (OMX Helsinki: CTL1V) announced today that it's Fastermind has gained significant momentum since its launch at Nexterday North in November 2016. Fastermind provides artificial intelligence capabilities to digital service providers, enabling them to recommend, predict and automate real-time decisions, particularly supporting customer engagement automation. According to new research conducted by Comptel, today's mobile customers want to be treated as individuals by their service provider. Surveying 2,000 mobile data users in the US and the UK, the research found that while more than half of mobile data customers (55 percent) are eager to receive more proactive, personalised messages and services, fewer than one in seven (13 percent) have ever actually received this kind of communication from their carrier. Operators can respond to this demand for personalisation by using Fastermind to reach their customers at the right time, with the right messages, through automated marketing offers and service notifications.

Artificial Intelligence has been threatening to transform the Contact Centre. But, how much of this talk is hype? And, how much is likely to happen? Could this lead to the decline of contact centres as we know it? Join us for this Webinar as we debunk the myths from the reality.

Machine learning is a type of artificial intelligence that uses algorithms to iteratively learn from data and finds hidden insights in data without being explicitly programmed where to look or how to find the answer. Here at SAS, we hear questions every day about machine learning: what it is, how it compares to other technologies, and why it matters. On the more technical end of the spectrum, we also hear questions about specific machine learning algorithms and how to use them. You will get all of your answers - and more - if you take the time to read the articles listed here, from definitions and comparisons to how-to tips on data science techniques. This isn't a comprehensive list, of course, but I tried to pick some favorite articles I've seen published over the last year, and I'm trying to list them here in order from introductory to more advanced.

He graciously consented to devote a good deal of his time to this four-part interview about the video game writer's job and the special challenges and opportunities of writing Torment. The interview took place through email and has been edited for comprehension. Ziets received a Masters degree in Cognitive Psychology with an emphasis on Human-Computer Interaction in 1999. I was a member of his thesis committee.) In 2001 he took a job writing dialog for the MMO Earth & Beyond.

It's probably the most unusual question asked in a staff survey: Could a robot do your job better than you? Whether or not the 16,000 British workers at the insurance company Aviva will reply honestly remains to be seen. Those who do answer "yes" have been told that they will be retrained for another role in the company. Many other employers have introduced automation into the workplace but without any form of consultation. Staff who work in call centres, assess customers' credit ratings and calculate the price of insurance policies are most likely to have to retrain, research suggests.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Bike Sharing Systems (BSSs) are widely adopted in major cities of the world due to concerns associated with extensive private vehicle usage, namely, increased carbon emissions, traffic congestion and usage of nonrenewable resources. In a BSS, base stations are strategically placed throughout a city and each station is stocked with a pre-determined number of bikes at the beginning of the day. Customers hire the bikes from one station and return them at another station. Due to unpredictable movements of customers hiring bikes, there is either congestion (more than required) or starvation (fewer than required) of bikes at base stations. Existing data has shown that congestion/starvation is a common phenomenon that leads to a large number of unsatisfied customers resulting in a significant loss in customer demand. In order to tackle this problem, we propose an optimisation formulation to reposition bikes using vehicles while also considering the routes for vehicles and future expected demand. Furthermore, we contribute two approaches that rely on decomposability in the problem (bike repositioning and vehicle routing) and aggregation of base stations to reduce the computation time significantly. Finally, we demonstrate the utility of our approach by comparing against two benchmark approaches on two real-world data sets of bike sharing systems. These approaches are evaluated using a simulation where the movements of customers are generated from real-world data sets.

Multimodal clustering is an unsupervised technique for mining interesting patterns in $n$-adic binary relations or $n$-mode networks. Among different types of such generalized patterns one can find biclusters and formal concepts (maximal bicliques) for 2-mode case, triclusters and triconcepts for 3-mode case, closed $n$-sets for $n$-mode case, etc. Object-attribute biclustering (OA-biclustering) for mining large binary datatables (formal contexts or 2-mode networks) arose by the end of the last decade due to intractability of computation problems related to formal concepts; this type of patterns was proposed as a meaningful and scalable approximation of formal concepts. In this paper, our aim is to present recent advance in OA-biclustering and its extensions to mining multi-mode communities in SNA setting. We also discuss connection between clustering coefficients known in SNA community for 1-mode and 2-mode networks and OA-bicluster density, the main quality measure of an OA-bicluster. Our experiments with 2-, 3-, and 4-mode large real-world networks show that this type of patterns is suitable for community detection in multi-mode cases within reasonable time even though the number of corresponding $n$-cliques is still unknown due to computation difficulties. An interpretation of OA-biclusters for 1-mode networks is provided as well.

Uniform deviation bounds limit the difference between a model's expected loss and its loss on an empirical sample uniformly for all models in a learning problem. As such, they are a critical component to empirical risk minimization. In this paper, we provide a novel framework to obtain uniform deviation bounds for loss functions which are *unbounded*. In our main application, this allows us to obtain bounds for $k$-Means clustering under weak assumptions on the underlying distribution. If the fourth moment is bounded, we prove a rate of $\mathcal{O}\left(m^{-\frac12}\right)$ compared to the previously known $\mathcal{O}\left(m^{-\frac14}\right)$ rate. Furthermore, we show that the rate also depends on the kurtosis - the normalized fourth moment which measures the "tailedness" of a distribution. We further provide improved rates under progressively stronger assumptions, namely, bounded higher moments, subgaussianity and bounded support.