Bottom Line: This year's hard reset is amplifying how vital customer relationships are and how much potential AI has to find new ways to improve them. The hard reset every company is going through today is making senior management teams re-evaluate every line item and expense, especially in marketing. Spending on Customer Experience is getting re-evaluated as are supporting AI, analytics, business intelligence (BI), and machine learning projects and spending. Marketers able to quantify their contributions to revenue gains are succeeding the most at defending their budgets. Knowing if and by how much CX initiatives and strategies are paying off has been elusive.

The next article will be about Sequence modeling with Neural Networks. We will learn how to model sequences with a focus on Recurrent Neural Networks (RNNs) and their short-term memory and Long Short Term Memory (LSTM) and their ability to keep track of information throughout many timesteps. Bio: Zied Haj-Yahia is Senior Data Scientist at Capgemini Consulting. He specializes in building predictive models utilizing both traditional statistical methods and modern machine learning techniques. He also runs some workshops for university students (ESSEC, HEC, Ecole polytechnique) interested in Data Science and its applications. He is the co-founder of Global International Trading (GIT), a central purchasing office based in Paris.

Measuring marketing's many contributions to revenue growth is becoming more accurate and real-time thanks to analytics and machine learning. Knowing what's driving more Marketing Qualified Leads (MQLs), Sales Qualified Leads (SQL), how best to optimize marketing campaigns, and improving the precision and profitability of pricing are just a few of the many areas machine learning is revolutionizing marketing. The best marketers are using machine learning to understand, anticipate and act on the problems their sales prospects are trying to solve faster and with more clarity than any competitor. Having the insight to tailor content while qualifying leads for sales to close quickly is being fueled by machine learning-based apps capable of learning what's most effective for each prospect and customer. Machine learning is taking contextual content, marketing automation including cross-channel marketing campaigns and lead scoring, personalization, and sales forecasting to a new level of accuracy and speed.

Where back in the days technology, techies, related people where seen as the weird people which every office needed for I(C)T development and maintenance, are we now looking with respect to all kinds of self-made billionaires of Silicon Valley. Compared to the pre 2000's, technology is a hot topic. Whether we talk about 3D-printing, Augmented Reality, Big Data, Social, Mobile, Analytics, Cloud, (SMAC) Internet of Things (SMACT), Wireless Power, Robotics, Computer brain interfaces, Human Augmentation, Artificial Intelligence (AI), Digital Customer Experience (DCX), all these topics are in the marketing buzz machines of big industrial leaders or even made on kitchen tables by (as Chris Anderson called them) the makers of these days. The title of this article: "Digital Transformation: The paradigm shift towards business as usual" is about the core of Digital Transformation. It will explain what Digital Transformation is, how you can use Digital Transformation to create competitive advantage, enter new market segments and deliver new value to your client, customer and end-user through an excellent customer experience.

We've had more than 150 speakers in the past three seasons. Two of them made the news this summer: Danny Bickson (E9 Season 1) one of the co-founders of Graphlab then Dato then Turi and Arjun Bansal from Nervana systems (E12 Season 3). Turi just got acquired by Apple for 300M, and Nervana got acquired for 350M by Intel. In a different direction, at the last meetup, Raymond Francis explained to us what got picked by the LA Times a month later, Curiosity now uses Machine Learning on Mars. This news is exciting on two levels: First, robots can now explore the universe better and second, it definitely brings some perspective when we talk about the dichotomy between exploration and exploitation in our discussions.

Organisations have begun to shift their analytical efforts to back office operations and away from more customer-facing processes in a search for greater competitive advantage, according to a new report. More than two thirds of companies, based on feedback from 600 operations executives surveyed as part of a report from Capgemini Consulting's digital transformation institute, revealed there is now a greater focus on operational analytics initiatives, in order to boost efficiency behind the scenes. Anne-Laure Thieullent, head of big data, Europe, for Capgemini's insights and data global practice, said: "Organisations are pivoting towards operational analytics as it can both increase the efficiency and performance of the back office as well as boost the customer experience in the front office. However, despite the focus, there are factors limiting the success of these projects; specifically siloed datasets, fragile governance models, inability to harness third party data sources, and an absence of a strong mandate from leadership teams." The four levels of maturity for operational analytics, as decided by Capgemini Consulting, are'Game Changers' - those who have both integrated analytics and realised its benefits, 'Optimisers' - those who have seen basic success but not yet scaled-up efforts, 'Strugglers' - who have adopted the analytics strategy without seeing the benefits, and'Laggards' - who are just beginning to focus on operational analytics.

'Organizations are pivoting towards operational analytics as it can both increase the efficiency and performance of the back office as well as boost the customer experience in the front office.' comments Anne-Laure Thieullent, Head of Big Data in Europe, for Capgemini's Insights & Data global practice. 'However, despite the focus, there are factors limiting the success of these projects; specifically siloed datasets, fragile governance models, inability to harness third party data sources, and an absence of a strong mandate from leadership teams.' 'Going Big: Why Organizations Need to Focus on Operations Analytics' from Capgemini Consulting's Digital Transformation Institute mapped organizations based on the extent to which their analytics initiatives were integrated with core operations processes and their success rate with initiatives, identifying four stages of operational analytics maturity: Capgemini Consulting's Digital Transformation Institute applied the four stages of operational analytics maturity to build up a geographic picture of adoption and success rates around the world. US companies are not only the most advanced with their analytics initiatives but also the most successful; 50 percent have successfully realized the desired benefits from operational analytics compared to only 23 percent of Chinese respondents, despite China ranking highly for level of implementation. A strong contributing factor of the success of US companies is their focus on setting up effective data and governance processes. The prominence of US organizations tallies with a recent resurgence in US manufacturing and will drive US manufacturing competitiveness in the coming years.