What You Need to Know about Data Mining and Predictive Analytics

What You Need to Know about Data Mining and Predictive Analytics

 

Have you ever wondered how Netflix knew to suggest that new sci-fi comedy that’s now your go-to binge watch? How does the service keep making smash-hit original shows? It’s not because its programming team is really good at throwing darts at an idea board. Netflix seems to know you because it actually does.

Marketers are living in the world of big data. One of the greatest challenges they face isn’t getting information on consumers. Rather, it’s pulling something useful from those gigantic stores of data. Two methods of digging out useful insights are data mining and predictive analytics.

Data mining and predictive analytics are sometimes confused with each other or rolled together, but they are two distinct specialties. As you examine the big data your company collects, it’s important you understand the differences between data mining and predictive analytics, the unique benefits of each, and how using these methods together can help you provide the products and services your customers want.

What is data mining?

Much of what you do produces data. Did you use a loyalty card last time you went grocery shopping? You can bet the grocery store was eager to collect all the information it could about this specific trip and your buying habits. Your credit card company got in on the game, too. Then, after you put the groceries away and sat down to watch your new favorite sci-fi show on Netflix, the media giant was learning about you through data points.

What happens to all of this data? How do your grocery store, your credit card company, and Netflix use it to give you more personalized service? How do they use it to encourage you to buy more?

Data mining plays a key role in this process.

Investopedia has an excellent definition of data mining: It’s “a process used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data, businesses can learn more about their customers and develop more effective marketing strategies.”

In other words, data alone is pretty useless, even if you have massive amounts of it. To make any sense of the data, you need a system of organizing it, and then searching for patterns and insights. That’s exactly what data mining does, and it’s important to understand some data mining techniques and how they work.

Data mining is all about organizing and interpreting data.

If you own an online clothing retail shop, you obviously need to understand your customers as well as possible so you can offer them the clothing choices they want. When customers log on to your site, you can use cookies to track their activities. You’ll see data points that may include:

  • What time they visited your site
  • What device they used to access your site
  • Which pages they visited
  • Which items they put into their shopping cart
  • Which items they purchased together
  • Whether they compared items
  • How often they come back to your site

This is only a fraction of what you can learn about a single person. Think about what you could learn from all the visitors who land on your site each day. Once you’ve captured all that information, it’s time to process and use it.

Step One: Data Warehouse

Unsurprisingly, the first step in the data mining process is collecting all of that information and electronically storing it in a data warehouse. A warehouse can exist on a company’s private server or on the cloud.

Step Two: Organization

There’s no way you can glean useful insights from unprocessed data. Many companies choose to hire a data scientist to create organizational rules for the data warehouse.

Step Three: Insights

With the right organization, you can use specialized software to begin identifying patterns and trends in your data. For example, you may discover that women aged 30 to 35 from Massachusetts are more likely to buy Product B if they first purchase Product A. It stands to reason that if someone in that demographic purchases Product A, you should create an algorithm on your site that encourages them to buy Product B as well.

Use data mining to get to know your customer

The more you know about your customers, the better you can serve them. Effective data mining allows you to:

  • Discover patterns in massive amounts of data that would be impossible for a human alone to comb through
  • Make better purchasing and pricing decisions
  • Market more effectively and more personally to consumers

The results of data mining are easy to predict. You save on costs, increase your ROI, and impress your happy, loyal customers. Here’s one more big benefit of data mining: It is essential for effective predictive analytics.

What is predictive analytics?

Data mining gives you the insights, but what are you going to do with this information? In many ways, predictive analytics is the logical continuation of data mining. Predictive analytics is the means by which a data scientist uses information, which is usually garnered from data mining, to develop a predictive score for a customer or for a certain event to occur.

Companies often use these predictive scores to:

  • Assign a consumer a lifetime value based on how much they are predicted to spend with a company
  • Determine the best next offer to a customer based on demographic information and past actions
  • Develop marketing models for future ad spends
  • Forecast future sales numbers

One good way to understand how predictive analytics works is through an event roughly 64% of Americans have faced: applying for a mortgage. Banks, understandably, don’t want to give mortgages to risky applicants who may default. Therefore, when potential homeowners come in to request a mortgage, they have to give the bank lots of information, including:

  • Current income
  • Employment status
  • Savings-to-debt ratio
  • Credit score

The bank uses this information to predict whether the applicant would be a low or high risk for a mortgage. It also uses the information to determine how much money and what interest rate it is willing to offer the applicant. Of course, banks will never be able to predict with perfect accuracy who will pay their mortgage and who will not. The 2007–2008 housing crisis demonstrated the fallibility of bad predictive models. However, strong predictive analytics can certainly improve decision-making and overall accuracy.

Predictive analytics works off of good, clean data.

How is Netflix so good at pinpointing the right show for you, and how does it decide which new shows to greenlight for its viewers? Good predictive modeling requires three important predictive analytics tools:

Data

The first ingredient for predictive analytics is good data. According to Thomas H. Davenport in the Harvard Business Review , “Lack of good data is the most common barrier to organizations seeking to employ predictive analytics.”

Statistical Analysis

Not just anyone can dive into mined data and figure out whether a grocery store should increase its order of Pop-Tarts by 25% for the third quarter. Many large companies hire data scientists to carefully comb the data and pull out correlations and predictions. This is most often done using a method called regression analysis.

Educated Assumptions

Every predictive analysis is undergirded by certain assumptions, which must be monitored and updated over time as trends and opinions change. One of the reasons banks were so willing to approve mortgages so often in the early 2000s, even for applicants with low income and poor credit, was because they operated under the assumption that housing prices always go up. As soon as housing prices started to sink and overstretched customers went underwater, defaults skyrocketed. This outcome can largely be blamed on basing decisions off unsupported assumptions.

Your company benefits from predictive analytics.

It’s invaluable to know what your customers are most likely to do, what they are most likely to want, and how much they’ll likely spend to get it. With the right information, predictive analytics can dramatically improve your marketing success by helping you to find the right audience at the right time at the right place with the right message.

Your recent Netflix binge of that recommended sci-fi show is proof that predictive analytics works.

How should you use data mining and predictive analytics?

Both data mining and predictive analytics deal with discovering secrets within big data, but don’t confuse these two different methodologies. The best way to understand how they differ is to remember that data mining uses software to search for patterns, while predictive analytics uses those patterns to make predictions and direct decisions.

In this way, data mining often functions as a stepping stone to effective predictive analytics. While data mining is passive and provides insights, predictive analytics is active and offers clear recommendations for action.

As a marketer, you need both as you navigate the world of big data. Yes, that avalanche of information can seem intimidating, but rather than running away, embrace it. Tools like data mining and predictive analytics can give you priceless insights into consumers, as well as into greater trends in your industry.

With the help of data mining and predictive analytics, you can save money, increase your ROI, and potentially convince your customers you’re a bit psychic — just like Netflix.

Check out our upcoming Machine Learning Bootcamp- Predictive Analytics on the 28th of November in Berlin.

Secure your spot NOW.  

 

About the Author:

Jessica Bennett  is a writer, editor, and novelist. Her clients span a number of industries, and she’s written blog posts, product descriptions, articles, white papers, and press releases— all in the name of inbound marketing. She’s proud to be Inbound Certified, but her VP of Morale, Avalon, doesn’t quite get what all the fuss is about. But he’s a rabbit, so you can’t really blame him.
The original post was from Salesforce. You can find it here.

How to land the sexiest job- Data Scientist in 2018?

We have conducted some research online that we found out the major obstacles and challenges to work as data scientist or to switch a career to be the data scientist?

#Don’t know where to start or where to learn?

#No Network to find a job or company you like?

#No past experience working as the data scientist? Even you know how to write the algorithm?

Here come the 8 Essential Tips to follow

1. Set your goal first

Looking for new challenging jobs? Just enjoy algorithm or mathematics? Want to start your own startup? Have a special problem, want to look for the solution? Or just to have a better job with more money?

2. Find someone who went through the path that you are interested in, then ask him/her to be a mentor?

It is like running a startup, it takes a lot of energy, time, dedication, motivation, discipline, sometimes even amount of money. You can learn all the best deep learning courses from those famous professors and experts all over the world, however, when it comes to soft skill, you need a mentor to help you, if he/she has even extensive networks, that is even better.

3. Use online education and make a plan to learn and study

Needless to say Andrew Ng’s deep learning courses at Coursera, partnered with NVIDIA Deep Learning Institute.

Andrew Ng is one of the best instructor and teacher I have ever heard and seen, he is perfect articulated, patient, and full of passion to teach as well. I personally took a couple of his classes online at Standford. (the course was really simple enough for anyone who is determined to start!

Btw, I sucked at Linear Algebra during high school 🙁

If you like more entertaining ones, the Youtube star: Siraj Raval also has Youtube channel and Udemy courses online to follow and learn, his goal is to use machine learning to build anything you can be dreamed of. After enough time for studying and online learning, it is always great to apply to the topic you are especially interested.

Don’t forget the traditional way, reading a book: The Bible of the deep learning which was written by Ian Goodfellow and Yoshua Bengio and Aaron Courville.

Certainly, if you like more personal touch, we offer Deep Learning Bootcampin January in Berlin with the topic of NLP, time series forecasting, and computer vision. Our instructors are from IBM and the famous research institute Max Planck Institute. Romeo Kienzler, he also has Youtube channel and is one of the most successful Deep Learning instructor in Europe.

4. Join some online communities and also offline to help you to learn and engage also receiving some feedback to improve

Online: GithubCodeacademyKaggleKD Nuggets

Offline: MeetupEventbrite

5. Choose a Tool / Language and stick to it

This is one of the most common questions for beginners. We can have one more article to go deeper into that in the future. For now, choose a language that you are the most familiar with or the simplest for you. If you are completely new, then choose in between the data handling capabilities, advancements in tool, career chance, deep learning support. From my personal experience, I was consulting a machine learning startup with one Python scientist, one R scientist, every time when I had problem with data, I asked Python scientist to write me a quick code to fix my problem while R could not really deal in a such an easy way. Python becomes love of my life for now.

6. Focus on real cases and applications not just learning theory

Think about some scenario you want to work on, music recommendation, Game of throne’s ending? Parking problem? Predict stock market? (Check out our DeepLearning Bootcamp on Eventbrite)

7. Don’t forget machine learning and deep learning it comes down to your mathematics capacity.

Warning: do not just take deep learning and NLP courses, but forget math is the real foundation. Take also some linear algebra courses also or at least linear algebra.

8. Finally, you can start the network but always remember, play smart.

Don’t waste too much energy and time on it, especially a lot of them come with free alcohols…Check the topic before you attend also attendees, are they your target audiences or not really? Or sign up some recruiting event, there will be tons of recruiters and HR managers from companies. (We are launching our First Recruiting Day focused on the data team recruitment in March 2018)

P.S. M.I.E is currently re-branding to Beyond Machine, our mission to connect and boost AI ecosystem through connecting and training. We will launch series events in 2018, following with Deep Learning Bootcamp, Data Analytics Workshop, Recruitment Day, Self-Driving Workshop, Machine Learning Week. Furthermore, we will launch also outside of Berlin and bring more corporation and partnership exchange in and outside of Germany. If you are interested in any kind of sponsorship, partnership, just simply drop us a line.

 

Build it, Train it, Test it!

 

XAIN- Leif-Nissen

5 Problems that we tried to solve at traditional conference

You should abandon traditional conferences but come to our open-air AI Summit

  1. It is time to break rules to only go to traditional conferences and host in hotel rooms. Why? Who wants to sit in the closed environment for the whole day and listen to a lot of keynote talks?
  2. Those conferences have talks last from 30 minutes to 1 hour. Do you know human being has only 20 minutes attention, as an organizer. why do you bother to ask speakers to prepare at least 30 minutes to talk?
  3. There are numerous tech/startup festivals, they are fun, relaxed with endless alcohol and food. We know it is amazing, but what do you bring home from the “FUN” festivals?
  4. Finally, those conferences/shows cost you a fortune.

Agenda is announced on the website

Final Chance to RSVP!

7 reasons you should join us — the world first OPEN AIR AI summit in Berlin

 

WHY SHOULD YOU COME?

  1. The statistic shows the size of the global market for artificial intelligence for enterprise applications, from 2016 to 2025. In 2016, the enterprise AI market is estimated to be worth around 360 million U.S. dollars worldwide.

2. We are the world FIRST OPEN AIR MACHINE INTELLIGENCE SUMMIT, we had two last evening summits in rooftop bars in Berlin and Paris and this summer will be held in a Biergarten. We know boring conference rooms kill creativity.

AI market estimation from till 2015

3. We can awesome speakers confirmed to talk on the stage.

  • Reiner Kraft, VP@Zalando
  • Alex Housley, CEO@Seldon
  • Romeo Kienzler, Chief Data Scientist@IBM
  • Claudio Weck, Head of Data Science@MHPLab: A Porsche Company
  • Johannes Schaback, Co-founder@Visual Meta
  • Ulrike Franke: Drone&warfare scholoar@Oxford

4. We have mentoring session hosted by TechstarTechstar SAPRockstar and other exciting accelerators/incubators.

5. We also offer workshops session hosted by IBM Watson Chief Data Scientist, Romeo Kienzler, he is also instructor at Coursera.

The description of his workshop at M.I.E Summit Berlin.

6. We are INTERNATIONAL, we are the first media&community from Berlin expanding outside of Berlin and had speakers from London, Paris, NYC, Amsterdam. Zurich…

More info, please visit our website.

7. Early bird tickets are going to be out soon! Hurry up before the end of May.

MIE Summit

The founding story behind Beyond Machine (rebranded from M.I.E)

Beyond Machine (rebranded from M.I.E) was spawned from Lele and Irene constant frustration during the founding of their AI startups. In the end of 2015, they both left their jobs at rising mobile ad tech and product companies. Lele first started SoCrowd and pivoted to Deckard after 3 months. Irene wanted to tackle the challenges of visual recognition.

It quickly became apparent that there was a need for a more developed community an outlet for media around Machine Intelligence. After running a fruitful and inspiring Evening Summit, they decided to take Beyond Machine. to the next level, founding a media company.focus on the training, re-education, and networking in the field of AI and innovative technology. Irene decided to leave FindEssence, the first company she co-founded and push forward the growth of Beyond Machine.

 

Beyond Machine’s mission: To connect people in AI industry globally, bringing profound and engaging content and to start a conversation about job substitution issues.

The blurb of M.I.E Summit 2017:

M.I.E. Summit Berlin 2017 is the World’s first open-space machine intelligence summit, which will be held on the 20th of June 2017.

This event will give you the opportunity to learn, discuss and network with your peers in the MI field. Backdropped in one of Berlin’s most vibrant and artistic locations, break free from traditional conference rooms and share a drink in a typical Berliner Biergarten.

The M.I.E Summit Berlin 2017 will provide you with two in-depth event tracks (keynotes, workshops, and panels) as well as over 25 leading speakers and unparalleled networking opportunities.

 

Agenda is announced on the website