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.
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:
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:
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.”
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.
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.
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.
Every product manager talks about data-driven product management but what is the real explanation of it?
Product decisions are used to be based on product managers/owners and C-Level executives’ desires and instincts. What is the main driver of these instincts; customer feedbacks, competitive market intelligence or digital analytics results? The answer should be all of them. In this article, I will try to explain crucial data sources and which metrics should be considered in these data sources to make right product decisions.
Successful products belong to customers more than product managers. But it doesn’t mean product managers should only rely on customers’ needs and demands. Sometimes even customers don’t know what they want and need. Product managers must validate their customer feedbacks with different data sources.
As stated in the chart, Product Management should consider the data from Market Research, CMI (Competitor Market Intelligence), Internal Feedbacks, Digital Analytics and Customers’ Feedbacks to make data-driven decisions and create a value-added information. Sounds easy but assessing the value of the information and tying them to product roadmap needs an effort. Let’s deep dive into them one by one, and I will try to explain how I am using this model in my product management efforts.
Data Product Management Bootcamp for Decision Makers
The workshop will help product managers level up their product leadership skills. Expect to work through collaborative exercises alongside other smart, creative product leaders who want to enhance their skills at both identifying the right products and features and building the support to do so.
What are the responsibilities of Data PM:
Build new products by organizing teams of ML researchers and engineers who build, optimize, scale, and integrate new research results.
Work closely with analytics-focused data scientists to track user behavior
Bring to machine learning (ML) focused data scientists and test data-driven modeling solutions.
Enable data engineers to build the right infrastructure and deliver robust data pipelines.
What you will learn:
Key analytic technologies and techniques, e.g. predictive modeling and clustering, and how these can play a role in managerial decision making
How to effectively manage the analytical processes and use the results of these processes as the basis for making informed, evidence-based decisions
How companies can use analytics as the basis for creating value
You will learn the tools and techniques to become a data-driven or “evidence-based” manager.
Which companies have already applied?
Google, Airbnb, and Skyscanner have all applied machine learning into every step of their user experience.
If you’re excited to tackle problems like these on top product teams, join us and pre-register. Data PM is the next for your career and company move. Some companies is starting to hire Data PM. The trainer is experienced Data PM in several companies and have sold out 2 startups in SF and an experienced trainer and implemented data-driven product management successfully. In Silicon Valley, New York, London already started to train their employees, Beyond Machine will be the first one which offers the data-driven product management training.
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.
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.