Today we’re going to put on our hard hats and headlamps and venture into the world of data mining, a set of techniques that can be very helpful in the current situation of information overload.
With today’s post, I want to explain in a simple, accessible way what these techniques involve, so that you’ll be encouraged to venture into the cave and extract the gems waiting for those who take the trouble to go looking for them.
What Is Data Mining?
Data mining is the set of techniques for automatically or semi-automatically processing and analyzing large volumes of data in order to find patterns that help us better understand how a database behaves in certain circumstances.
Data Mining techniques help us turn data into relevant information. To achieve this, statistics, computing and Artificial Intelligence techniques are applied, so putting them into practice requires a professional who specializes in this field.
Phases of Data Mining
In general terms, the data mining process has the following phases:
Selecting objectives
As in any process, the first step is setting the objectives to be achieved by applying Data Mining techniques. At this point, the company must decide, with the support of the Data Mining expert, what type of information is relevant in its case.
Based on these objectives, the model for analyzing the data will be set up, so they must be clear.
Selecting databases and preprocessing
The second step is to establish the database or databases that will serve as the raw material for extracting information.
A preliminary general analysis is performed to detect scatter plots (which show the correlation between data), histograms and anomalies, so the anomalies can be corrected and all entries normalized.
This data can be your own or purchased, but it must always be consistent with the objectives set. Otherwise, it would be like trying to get blood from a stone: you can’t expect to extract economic behavior data, for example, from a database that contains no data related to that field.
Determining the model
Once all the data is organized and the objectives are known, the Data Mining expert will create a suitable predictive model for analysis and segmentation.
Extracting information
After applying the model, the observed behavior patterns, the relationships established between the data and the conclusions they lead to are extracted.
Analyzing results
Before these conclusions can be considered valid for extrapolation and/or application, you must check that the results are logical and that their margins of error are acceptable. Otherwise, the process must be reviewed to find the source of the error, and then the whole process must be started again or restarted from a certain point, depending on the case.
Differences Between Data Mining and Big Data
A very common mistake is the confusion between Big Data and Data Mining, so we’re going to briefly set out the differences and similarities between the two.
In both cases, we’re talking about techniques for processing large volumes of data that help us draw out information relevant to our business, and both use algorithms and mathematical processes to do so.
The difference lies in the type of information we can learn after applying these techniques.
Big Data techniques will let us read a large, heterogeneous database, selecting and highlighting everything that is relevant to us, but they won’t be able to give us an in-depth analysis of that information.
In other words, with Big Data techniques we can analyze a huge amount of data (which otherwise couldn’t be processed) from different sources and apply the filters we want to find specific information, but we won’t be able to analyze the data it contains in depth.
On the other hand, Data Mining techniques require preprocessing and normalization of the databases to be studied, but they give us an overview of connections and conclusions in addition to filtering and selecting the data that match the established filters.
Big Data and Data Mining are not opposed; in fact, they can complement each other, resulting in a highly structured database from which to draw information that helps you understand your environment and anticipate trends with a very small margin of error.
Data Mining Example
Imagine you have a database that covers all the frequently updated blogs in Spain.
With Big Data techniques, you could quickly process all this information to pull out data such as how many blogs were updated in the last week, how many of those blogs cover a specific topic, their geographic origin, etc.
On the other hand, thanks to Data Mining, you could set as your objective, for example, an analysis of trends in digital marketing blogs in Spain and run a model that studies how this type of blog behaves in order to establish patterns and diagrams that would show the main topics right now, those that are gaining strength and those that are losing it.
In other words, Big Data and Data Mining are not opposed; in fact, they can complement each other, resulting in a highly structured database from which to draw information that helps you understand your environment and anticipate trends with a very small margin of error.
How to Apply Data Mining to Marketing
Is it clear to you yet what Data Mining is and what information it can offer you? Let’s move on, then, to how it’s applied in marketing.
The key point for marketing, and the biggest benefit for companies using data mining, is predicting user behavior. Staying one step ahead of the consumer and understanding their modus operandi gives you a great advantage that, if used well, can set a company far apart from its competition.
The use of these techniques has been growing over the years, especially since the boom in social media and online advertising. As you may have guessed, the main use of Data Mining today is right here: ad campaigns on digital media.
Advantages of Applying Data Mining in Business
Lower campaign costs are just the first of the benefits that applying this type of strategy can bring you.
Trend forecasting
Being prepared for what’s coming before it arrives is the best way to ensure your success. Evaluating the behavior of your target audience, your environment and your competition will help you see where to head and where to focus your efforts.
Improved ROI
As a direct consequence of the above, the return on investment will increase, since it won’t be diverted to sectors with little likelihood of profit and the risk will be reduced.
Understanding user behavior
Analyzing your consumers’ behavior will not only help you predict their future, but also understand how they currently behave, information that can be very useful for generating cross-sales.
Identifying drop-off points
Likewise, studying our users will help us identify drop-off points and find a solution to retain them and complete the conversion.
Fraud and crisis prevention
They say prevention is better than cure and, for a company, this can be tricky, but thanks to Data Mining techniques, this detection is simplified because of the view it gives us of our business and its environment, as we’ve seen in the previous points.
Improved CRM
Happy customer, loyal customer. The information data mining gives you about your users will also help you build their loyalty by making them feel special, heard and understood.
At this point, it’s very important to have a team and tools that help you reach people and convey your message and image. Social media, email marketing, advertising, public relations activities… when all of it is segmented according to the behavioral conclusions of the model you ran, each person gets the right message and connects with it instantly.
Improved decision-making
Finally, and as a summary of all the previous points, using Data Mining will help you make more informed decisions, on a solid footing, about the present and future of your company in every respect.
Analyzing and understanding your environment, your users and your business is a key that’s in your hands.
If you want to take a small step into this world, I suggest you start by dipping a toe in with your email marketing database.
Thanks to the different tools and data they give you on opens, rates and heat maps, you’ll be able to get a surface-level introduction to the world of data mining. It will help you better understand how it works so you lose your fear of it and put it into practice at the level it’s designed for.
Disadvantages of Applying Data Mining
- Requires investment and specialized staff: It isn’t an automatic technique: it needs trained professionals and suitable tools.
- Depends entirely on data quality: If the data is incomplete, messy or poorly defined, the results won’t be reliable.
- Can lead to errors if misinterpreted: Models show probabilities and patterns, not absolute certainties; human judgment is still essential.
- Has legal and ethical implications: Data use must comply with data protection regulations and be transparent so as not to damage the company’s reputation.
- Doesn’t replace business strategy: It supports decision-making, but it doesn’t replace the company’s vision, experience and objectives.
Data Mining Success Stories
Time to look at examples of real-world Data Mining applications.
Obama and the presidential campaign
The success of Obama’s campaign when he ran for President of the United States was based on the analysis of massive data on the population and the competition.
His entire marketing campaign used this study as a reference in order to reach the most receptive audience through the right channels and even with the right person.
Every step of the campaign was taken with confidence thanks to the application of Data Mining.
Amazon and customer service
Rather than talk about Amazon’s remarketing and email marketing campaigns, which of course have this type of strategy behind them, I want to focus on the use of this information for customer service.
Amazon is one of the companies that stands out most for its good management in this area, and the key to its success is Data Mining. Thanks to its knowledge of users and its tracking of their behavior, Amazon gets ahead of possible crises and secures customer loyalty.
Cross-referencing each department’s data on the same user allows the company’s employees to see a user’s behavior history and quickly resolve any incident.
Starbucks and choosing locations.
Have you ever wondered why there are two Starbucks close together? The answer is simple: because they can.
The chain uses Data Mining to determine the best location for opening its stores, based on demographic and traffic data, among other things.
Phishing, Ethics and Data Protection Laws
To finish, I’d like to stop at a very important and very timely topic due to the update to European data protection law.
While it’s true that data analysis helps us understand how our users behave, we must always keep in mind that we have to be responsible with how we use the information they give us and, if it isn’t our own, with where it came from.
The law update means companies will have to state more clearly how they will use the data, and this, in my view, is a good thing if we know how to handle it. Transparency is a strength that few companies have and know how to take advantage of.
Be transparent, be clear and explain to your users the benefit they’ll also get from the way you’ll handle the data they give you.
A good marketing information system is key to making the most of this data, and it usually relies on marketing data integration (MDI) processes that unify information from different sources.