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.

 
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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 personalised 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 organising 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.

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.

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.
There’s no way you can glean useful insights from unprocessed data. Many companies choose to hire a data scientist to create organisational rules for the data warehouse.
With the right organisation, you can use specialised 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.

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.

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 many Australians 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.

 
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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 organisations 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.

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.

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.
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