Collaborating Authors

Predicting customer lifecycle outcomes with machine learning


In our last article, Lifecycle mapping: uncovering rich, predictive data sources, we discussed the importance of mapping out your customer lifecycle to better understand where your most predictive customer data is hiding. Now, we'll pose some questions to help identify your predictive customer attributes and lifecycle events, pinpoint where that data is located, and recognize patterns to predict outcomes for future prospects, leads, and customers. Data discovery is the second stage in the customer lifecycle optimization (CLO) process. The primary task of this stage is to expand on your lifecycle map to identify authoritative data sources that establish progress. At each stage, prospects, leads, and customers will complete certain events that will individually or collectively trigger a transition in or out of that stage.

Top 20 Data Science Conferences in 2020 That You Can't Miss


They are going to be briefing majorly into key topics like data creation, management, and value creation lifecycle. They will also deliver unconventional intelligence and analysis of the key data issues challenging companies as 5G begins to roll-out and the Internet of Things continues to rise. It will bring together pioneers in all of these areas and will furnish best-in-class wisdom to those striving to understand the multiple legal and business issues that go into fabricating a world-class data management and exploitation strategy.

DSC Webinar Series: The Analytics Lifecycle Revolution: Evolution or Extinction


With so many digital initiatives competing for resources, breaking through legacy analytic slowness and modernizing the analytics lifecycle is vital. The key is turning data assets into insight driven actions as quickly and collaboratively as possible― and without reinventing the wheel. Levon Johnson, Manager of Employee Data and Analytics at Alaska Airlines, will further illustrate how he single-handedly elevated employee analytics from next-to-nothing (gut-driven decisions) to company-standard (data-driven/intensive). Join us for this latest DSC Webinar and find: Resolution to the analytic barriers of time, effort, and pain Deeper accuracy and efficiency through reuse and collaboration Real examples of quick-win projects and meaningful reports Break down data barriers keeping analytic teams from getting the insights that matter. It's a new age for how companies innovate with analytics, and it's time the analytics process caught up.

Tales of the scary data lifecycle: Cambridge Analytica and Emerdata


When salacious tales of Cambridge Analytica's activities emerged in 2018, we thought the company was finished. The many on-air discussions of illegal activities, law enforcement warrants, legal action threats from partners, and questionable ethics had appeared to doom them. We then saw multiple suspensions and resignations, and on May 2, 2018 Cambridge Analytica announced it would file for bankruptcy. For a brief moment, it seemed like the world was a better place: The "good team" had won, the evil doers were vanquished, and the world was moving on. The victory celebration was short-lived.

The Lifecycle of Data


In this initial phase, you'll develop clear goals and a plan of how to achieve those goals. You'll want to identify where your data is coming from, and what story you want your data to tell. If you plan on hypothesis testing your data, this is the stage where you'll develop a clear hypothesis and decide which hypothesis tests you'll use (for an overview, see: hypothesis tests in one picture). One way to think about this phase is that you're focusing on the business requirements, rather than the data itself. Data can be collected in this stage, but you won't be working with the data.