In Part 1 of this blog posting series, we discussed our current viewpoints on marketing attribution and conversion journey analysis in 2017. We concluded on a cliffhanger, and would like to return to our question of which attribution measurement method should we ultimately focus on. As with all difficult questions
Tag: data science
I’m a bit of a data nerd, and especially a data visualisation nerd. I love graphs and charts, but not for their own sake. What I love about them is the way that a single picture can show so many different things, and often provide new business insights into data.
Everyone has a marketing attribution problem, and all attribution measurement methods are wrong. We hear that all the time. Like many urban myths, it is founded in truth. Most organizations believe they can do better on attribution. They all understand that there are gaps, for example, missing touchpoint data, multiple
W ostatnim czasie zalała nas bezprecedensowa fala popularności tematyki Data Science/Machine Learning. Mnóstwo szumu w mediach społecznościowych, tłumy na meetup-ach i konferencjach, popularność profilowanych studiów podyplomowych – to zaledwie kilka jej przejawów.
From June 6 to 9 the first ever ‘Dutch Data Science Week’ took place in the Netherlands. The week consisted of 4 days in which 28 events took place in 6 different locations. During the week people could attend classes ranging from beginner to advanced levels, visit meetups, participate in
There is a saying in business that you can have any two out of good, fast and cheap. All three cannot be done, or at least only in an ideal world. There is therefore a strategic trade-off between the three, with a recognition that every business has a different balance
How can you tell if your marketing is working? How can you determine the cost and return of your campaigns? How can you decide what to do next? An effective way to answer these questions is to monitor a set of key performance indicators, or KPIs. KPIs are the basic
Diversity is a big topic in the press at the moment. In July, the UK’s BBC published data about pay, exposing a huge gender pay gap. But the question of diversity goes far beyond a simple pay gap. I caught up with Josefin Rosén to discuss how organizations can harness
A growing area of focus is analytics developer experience, and for good reason. As with applications, analytics teams need to respond to business needs in an agile manner, and developers play a crucial role. But how exactly does developer experience fit into the broader scheme of things? I asked Mark
Wo können Sie Ihr Können mit dem von anderen Experten vergleichen? Und in der Data Science Community über den Tellerrand blicken? Oder einfach ungezwungen programmieren - wie zu Studienzeiten - weil es Spaß macht? Zum Beispiel bei einem Hackathon. Mit Fabian Buchert, selbst Data Scientist, sprach ich über seine Erfahrungen beim
I started my training in machine learning at the University of Tennessee in the late 1980s. Of course, we didn’t call it machine learning then, and we didn’t call ourselves data scientists yet either. We used terms like statistics, analytics, data mining and data modeling. Regardless of what you call
Ensemble methods are commonly used to boost predictive accuracy by combining the predictions of multiple machine learning models. The traditional wisdom has been to combine so-called “weak” learners. However, a more modern approach is to create an ensemble of a well-chosen collection of strong yet diverse models. Building powerful ensemble models
A big part of our existence is about making choices. Preferably the best ones. Evidence-based decision-making matters more than the type and quantity of the data, and by establishing evidence-based decision-making as a deep corporate culture, you could fulfill data’s vast potential. I find the resemblance between this way of
In 1977, George Lucas released the first film in what would become the epic Stars Wars series. Back then, who could have predicted that this film would spawn the huge cultural phenomenon that is Star Wars? Films, congresses, spin-offs, toys: the list goes on, and over 40 years this year.
The development and use of self-service analytics has brought with it a new role in many organisations: the citizen data scientist. But is this genuinely a new role, or is it just a new name for a business analyst? Is this a definition thing? Business analysis is broadly defined as
In 1901, Gottlieb Daimler predicted: “The global demand for motor vehicles will not exceed one million—simply because of the lack of available chauffeurs”. Today, most people drive themselves and self-propelled cars are rapidly becoming a reality. Are we likely to see the same situation for data scientists, with more and
This resource is designed primarily for beginner to intermediate data scientists or analysts who are interested in identifying and applying machine learning algorithms to address the problems of their interest. A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is “which algorithm should
To make faster and more accurate decisions for better results is an everyday battle to management in organizations. Those who use data wisely will be the winners. Becoming a data driven organization is the key, but it often requires a change of mindset and stepping out of the old habits.
Digitalization is supposed to change everything, or perhaps even revolutionize everything, in both our private and professional lives. The way we live and work will change to match a digital society, whether we like it or not. Structures and organizational forms in companies will also change. And these changes will
The social media buzz around the Data Science Enthusiast Meetup in Istanbul on 17th Feb was hard to miss. Tuba Islam, Senior Business Solution Manager at SAS, was one of the speakers and she hosted a session around Analytics in Action. I caught up with her after the event and
Data science has perhaps become something of a victim of its own success. As big data has proliferated, and more data is available, companies have enthusiastically bought into the idea that they can use analytics to get better insights from all this data. With data storage and cleaning becoming both
I’m going to start with a question. What’s your favourite Internet of Things (IoT) device, product or service? A Fitbit, perhaps? An app on your smartphone? My next question: why do you like it? Because it’s exciting, techie and interesting? Because you like to have things before anyone else? Or
Experience design is not just like a standard advertising campaign or an online app, but rather a strategy to keep customers engaged with a brand through impactful interactions. It means that every product and service is designed to offer a delightful experience; the packaging, mobile app, web and print ads
Digital intelligence is a trending term in the space of digital marketing analytics that needs to be demystified. Let's begin by defining what a digital marketing analytics platform is: Digital marketing analytics platforms are technology applications used by customer intelligence ninjas to understand and improve consumer experiences. Prospecting, acquiring, and holding on
Citizen data scientists: WE NEED YOU! First let me ask you a question. Did you know that Miss America’s age is closely correlated with the number of murders by steam and other hot items? Or that the stork population is related to the birth rate? If your immediate reaction to
As data-driven marketers, you are now challenged by senior leaders to have a laser focus on the customer journey and optimize the path of consumer interactions with your brand. Within that journey there are three trends (or challenges) to focus on: Deeply understanding your target audience to anticipate their needs
My last post described my top general business analytics books, those that would appeal to business leaders and analysts alike. This post is a bit more specific, and covers books that will help you to learn for yourself. It is therefore mainly aimed at analysts — but I still hope
One aspect of high-quality information is consistency. We often think about consistency in terms of consistent values. A large portion of the effort expended on “data quality dimensions” essentially focuses on data value consistency. For example, when we describe accuracy, what we often mean is consistency with a defined source
One of the most frequent questions I’m asked by my students is which business analytics books to read to support their professional self-development. It is always hard to pick out the best books, especially because I like to mix classics and domain-specific references. I particularly like those that influence business
Data science may be a difficult term to define, but data scientists are definitely in great demand! Wayne Thompson, Senior Product Manager at SAS, defines data science as a broad field that entails applying domain knowledge and machine learning to extract insights from complex and often dark data. To further