SAN FRANCISCO — Predictive analytics professionals of all kinds are attending Predictive Analytics World here this week to learn how to strengthen their predictive analytics deployments, capitalize on data science and better leverage big data.
The four-day event, which runs here through Thursday, delivers vendor-neutral sessions across verticals such as banking, financial services, e-commerce, entertainment, government, healthcare, manufacturing, high technology, insurance, non-profits, publishing and retail.
Launched in 2009 by Eric Siegel, executive editor of The Predictive Analytics Times, this is the first of six Predictive Analytics World conferences that will be held this year. The next one is in June in Chicago followed by four more in the fall in New York City, London, Washington, DC and Berlin.
Siegel defines predictive analytics as "business intelligence technology that produces a predictive score for each customer or other organizational element. Assigning these predictive scores is the job of a predictive model which has, in turn, been trained over your data, learning from the experience of your organization."
In theory, predictive analytics can optimize marketing campaigns and website behavior to increase customer responses, conversions and clicks, and to decrease churn. And each customer's predictive score can generate personalized, actionable business intelligence about those customers.
But are businesses fully embracing the potential of predictive analytics?
What's the promise of predictive analytics?
Matt Filios, CEO & Co-Founder, Predictful
Filios has more than 20 years of experience working with emerging technology companies in a senior leadership role. Last August, he co-founded Predictful, a provider of crowdsourced predictive analytics. Before that, he was President of Net-Results, a marketing automation vendor that competes with Marketo, Eloqua, HubSpot, Pardot and Act-On. Tweet to Matt Filios.
The true "promise" of predictive analytics is the ability to make better business decisions.
To quote The Signal and the Noise author Nate Silver, one of my statistical and predictive heroes, “Prediction is indispensable to our lives. Every time we choose a route to work, decide whether to go on a second date, or set money aside for a rainy day, we are making a forecast about how the future will proceed — and how our plans will affect the odds for a favorable outcome.”
Applying Nate’s quote to the business world, the success (or lack of success) within organizations flows largely from people (business leaders) making the right (or not as right) decisions. If we have analytics and intelligence tools at our disposal that will help us make better decisions, and in turn be more successful, then we clearly should be embracing them.
I have the word “promise” in quotations, because although predictive analytics allows us to understand historical data in ways we previously could not, it is not a 100 percent solution in terms of guaranteeing future outcomes. Nothing is, or can be.
We simply cannot without fail predict every outcome without error. But predictive analytics and other intelligence tools can sharpen the line between risk and uncertainty.
Risk is unavoidable. It is there in everything we do. You may say, “Well, so is uncertainty.” And I would agree to some level, but we as human beings are much more likely to make decisions with a certain degree of risk than we would with a certain degree of uncertainty. The more we can minimize uncertainty, the more we can tolerate risk.
Yohai Sabag, Chief Data Scientist, Optimove
Sabag is a researcher and data scientist with extensive experience applying the fields of business intelligence and advanced data analytics to practical business challenges. At Optimove, a customer retention automation platform powered by predictive micro-segmentation technology, he heads the data lab. Before that he was a teaching assistant at Ben-Gurion University of the Negev. Tweet to Yohai Sabag.
The future of predictive analytics lies in taming increasingly larger amounts of data from an exponentially growing number of sources, by producing actionable predictions that are made available in real-time.
Although data science is going mainstream, predictive analytics as a common business practice is still in the early days.
There are still vast amounts of data that aren’t mined, and large amounts of that data aren’t relational and are therefore a challenge for most predictive algorithms.
Data from social networks, tweets, posts, videos and daily internet activity are still mainly an untapped source, which potentially harbors huge predictive power. Putting this data to use through advanced and effective tools is a priority for the future of predictive analytics.
The Internet of Things heralds another trial for predictive analytics. Gartner predicts some 20 billion “things” will be in use by 2020, up from 6.4 billion this year.
Wearables, home appliances and consumer goods will all be generating actionable data in real time. Beacons will increasingly blur the boundaries between online and off-line, and quadruple the amount of customer data available to businesses.
To parse this deluge of information for both strategic and operational needs, customer marketing clouds will need to design more robust big data infrastructure and astute predictive models.
Predictive analytics are only as good as their predictions: they need to be sensitive, reliable and swift in order to provide valuable indication about every action taken.
They need to deliver impactful predictions and decisions for both the short and long run. To do this they will increasingly have to rely on machine learning. As machine learning evolves, deep neural nets and cognitive computing will enter the mix in order to enable even more far-reaching predictions and decisions.
Gil Nizri, CEO & Co-Founder, DMWay
An experienced sales and marketing professional, Nizri leads the strategic direction and the overall corporate management of DMWay, a startup specializing in expert systems for creating predictive analytics models. He was previously Corporate Vice President of Business Development for Ness Technologies, where he had responsibility for building the big data and analytics global domain and go-to-market strategies, including marketing and developing partnership relationships. Before that he led Panorama Software’s corporate directions and strategy. Tweet to Gil Nizri.
Predictive analytics models allow data scientists to use past data, current data, demographics information and other relevant data to predict future events and solve business problems. They provide a foundation for automated, smart, data-driven decision making.
Predictive analytics enables machines to trigger insightful activities (decisions) that have economic value and sound business impact — decisions that are better and more accurate than a human powered ones that are based only on personal experience and the intuition.
Machines are already making automated decisions, mainly through rule-based controlled system; for example: the ATM (automated teller machine) won’t allow us to exceed our credit.
Automated machine learning solutions provide companies with fresh and powerful market and business differentiators. They should become a feature on every facet of the organization, because the use of predictive analytics power will differentiate the leaders of industry from the rest of the players in their respective fields.
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