What is Analytical Marketing?
Analytical Marketing is a subset of Data Mining and its main objective is to find patterns in marketing data and use them to predict customer behavior.
Marketing spending budgets are increasing significantly, while consumer spending on their favorite brands is decreasing. This makes companies look for new ways to cut costs while maintaining or increasing results.
It is vital to understand which campaigns are most successful so they can be scaled up and less successful ones scaled down. O Analytical Marketing helps companies identify their customers’ purchasing cycles, to better understand what drives them to buy or not, even how much is spent on a given product per capita in each country.
What is Data Mining - Data Mining?
A Data Mining is the process of analysis of large amounts of data to discover patterns, trends and associations.
It has become an important tool in business for understanding customer behavior, making strategic decisions and developing new products or services. It is a great ally of artificial intelligence (AI).
AI techniques such as Neural networks, Decision Trees, which use databases, are related to the search for the best information, based on a reading that it would be difficult for humans to perform.
Data mining can be used in many ways, depending on what you are looking for in terms of information. For example, if your company needs to develop a new product line, it would use data mining to identify potential customers interested in this type of product.
If you are trying to understand how people search for information related to your company's products online, such as e-commerce, then it could be used as well.
Although it is something else, text mining also helps a lot in the process, as the search for texts and expressions can be useful in analyzes that require a lot of content and a large volume of data (Big Data), in rich materials.
Some examples and applications of Data Mining:
Below, we'll give some specific examples of how various businesses have successfully utilized data mining techniques before providing some tips on how you too can get started with data mining.
With data mining, companies can discover when customers are most likely to purchase certain services or products, which aspects of their processes work well or need improvement, and how they should approach the design phase.
All necessary information is valuable. Currently, it is possible to combine this discovery from data and artificial intelligence, making relationships and correlations between data, providing a reading of the scenario, with statistics and applications that meet the needs of the business.
Using The technology in favor, knowledge and the set of situations are expanded, which broadens the understanding of the market and the company itself. These relationships, with statistics and technology, amidst the volume of data, bring insights and increase the variety of possibilities that were previously unseen.
In this way, Data Mining helps managers make better decisions about their commercial strategies, including knowing when to enter a certain market or what types of products would do best.
Example 1: Data mining also provides insights into how these decisions can affect other aspects of your company's operations. For example, it could provide information about whether a new product will affect sales of older products, how changes in marketing strategies affect sales, or whether a price change will have a positive or negative effect on revenue.
Example 2: There are also companies specializing in online surveillance that use data mining techniques to identify possible threats from terrorist groups or drug trafficking organizations by analyzing the behavior of potential suspects through email messages, text messages and other activities. online.
In addition to these examples, there are many others, including Health Systems, Social Network Analysis and Location Intelligence.
How can Data Mining contribute to your business?
In addition to, help make your business more profitable, data mining can help companies improve their internal processes, making them more efficient and reducing costs. The data mining process involves analyzing data from a data warehouse or databases.
The entire process can, for example, be abstracted from the user and can be applied automatically through system applications. Por exemplo, a large retailer could use data mining to find the most profitable locations to open new stores or which regions of the country provide the highest return on investment.
You can know what your customers want and how much they are willing to pay, facilitating these interactions through an e-commerce website without having to redirect potential customers from one site to another.
In summary, with Data Mining you can get to know your customers better, look for their needs and offer products or services that are most relevant to them.
How do you start a Data Mining project?
There are three steps to starting a data mining project.
The first step is to start with the business case, which should include the objective of the project and how it will help your company. It also includes some background on what you are looking for and why you are asking this question in the first place.
the second step is to determine how you will analyze the data and what tools we would use for this analysis.
And finally, there is the decision of who is involved in this process, obtaining their buy-in and ensuring their participation in all stages of the project: from the initial data collection to the final report and recommendations.
All these steps, however, depend on one thing: have access to good quality data! Therefore, before starting any of these steps, you must be sure that you have access to this data.
What are the benefits of Data Mining?
Data Mining allows you to:
Currently, there are many BI tools available to support this data mining process through a graphical interface that helps you focus on the information that interests you without requiring knowledge of formal statistics or programming language.
Types of analyzes that can be done with Data Mining
The main types of analysis that companies do are:
See in detail below:
Classification
Classification is the process by which a model is generated to differentiate between two or more classes, determining which category an item belongs to based on its characteristics.
This method comes from the field of statistics and machine learning, where algorithms are trying to identify patterns in data that allow us to distinguish one type of behavior or answer our question from other behavior or similar answers.
The end result is a set of logical conditions called IF/THEN rules. For example, if customer X buys product Y, then customer is likely to buy product Z.
Although it seems very simple, there are a few considerations you should keep in mind: What is being modeled? Who will use the model and how will it be used? These are questions that can give us a better answer about whether or not this technique is suitable for our problem.
grouping
Clustering is a fundamental part of data mining and is especially useful for Segment large datasets into groups of similar objects.
Émile Borel first introduced the concept of "clusters" in his work on probability theory and then introduced them as a mathematical tool for understanding the phenomena of aggregation of large groups. But this wasn't until 1960, when John Stuart Foster first applied the idea to marketing with a paper on consumer clusters.
Even today, many businesses continue to use this technique as a way to divide their customer or prospect base into smaller, more manageable groups or segments that can be more effectively targeted with specific messages and offers.
Association Rules
Association Rules is another type of analysis that can be done using data mining techniques.
Involves analyzing transactional data sets (when transactions such as purchases are recorded) and the search for correlations between different events. The goal of learning association rules is to discover interesting associations between variables that can be mined to make predictions.
For example, an association rule might tell us that if a customer buys butter, it is very likely that he will also buy bread.
Forecasting
The forecast allows predict future trends, project demand or anticipate the effects of certain strategies before acting on them, in search of accurate information.
These models use data from the past and current conditions to generate predictions. This technique is especially useful for planning activities such as production planning, product launches, transportation routes, etc.
But it can also be used to project consumer behavior based on demographics, lifestyle or life cycle.
Simulation model
The simulation model is all about the predicting the probability of an event given certain variables defined by the analyst through mathematical equations.
This model uses statistical data to simulate real-life scenarios and test different factors (environmental, political, economic...) that can influence the outcome of an event.
The main objective is to create KPIs for your company based on information extracted from reports created by analytical marketing software. This way, your decisions are always backed by numbers so you know which areas need improvement and where resources should be allocated.
After all, it is much easier to make a decision when you have the correct data to support it. Additionally, your employees will be much more empowered because they have all the necessary tools at their fingertips to make key business decisions.
When does using this strategy make sense for your company and when does it not make sense?
A business intelligence ou Business intelligence contributes to the company's strategy, complementing other strategies that are already being used by the company. And it's just as important to have a balance of your own strengths or weaknesses compared to the strengths and weaknesses of your competitors.
The use of business intelligence It can be part of different strategic activities within an organization because it provides an effective way of analyzing information so that correct decision-making processes can take place.
Na Bytebio we use it with great intensity, searching for useful information from raw sample data that can be used in machine learning.
When using this strategy MAKES sense for your company:
1- It is necessary to provide solutions and/or products that best meet customer needs;
2- Reduce costs and improve efficiency (improve resource utilization and control costs using data mining techniques);
3- Well-informed decisions can lead to better adapting to the changing competitive environment;
4- Decision making can be improved when using reliable data that confirm or refute hypotheses;
5- You need to make your marketing efforts more effective.
When using this strategy does NOT make sense for your company:
1- You don't have a culture that learns from data;
2- Leaders and decision makers don’t want to be open to data-driven suggestions;
3- The idea is to avoid making decisions with data because this could lead you away from what you already know or believe about the market or your customers. People tend to prefer information that confirms their existing beliefs, which makes it difficult to make good decisions when using business intelligence. This can happen if there is no leadership prepared to accept business intelligence as a tool, considering it as an opportunity rather than a threat.
4- It's difficult for people who only use analytics marketing once in a while.
5- Your product is not much different from other products on the market. In this case, it can be difficult to measure what makes your product different as there are no suitable tools. Or if you have enough resources available each year, then leverage existing tools to create a unique image for your product in the minds of consumers;
6- At the moment, you would like to use analytics marketing but at the same time you don't have enough resources. In this case, you need to carefully choose the actions that you can prioritize.
Why should you consider using Analytical Marketing as a strategy for your company?
O analytical marketing It is based on data mining to discover hidden relationships between variables. Data Mining is used to find meaningful patterns in this information.
The use of analytical techniques to gather and process data can help you understand your customers better, your wants and needs, helping you focus on strategic decisions that maximize your business opportunities.
But why should I use analytical marketing as a strategy for my company? Here are some reasons:
1- Allows your business to sell more
Increasing customer lifetime value (CLV) by analyzing data about past transactions, browsing history, or other available information about your customers allows you to determine the value of each customer.
For example, knowing that someone spends $100 per month on your e-commerce site tells you something about that person, their interests and needs. This information is useful for recommending the right products to your customer, increasing sales and average order value (AOV).
By tracking reward points, you will also be able to determine which products are most likely to appeal to different customers. To take this a step further, you could even consider implementing a personalized advertising strategy based on past transactions.
2- Allows better targeting of offers
Analyzing consumption patterns can help you determine which products should be offered in certain stores or regions based on weather conditions, for example.
Take advantage of promotions and other strategies at the right time, increasing your revenue by 5% or 10%. With data mining you can also identify the best combination to sell your product or service.
3- Helps in reducing expenses
Reduce risky marketing strategies, saving money in the process, by only promoting items that will sell and not testing ads with a "spray and pray".
You will also be able to reduce return rates on products that do not meet customer expectations and allocate resources (time and money) more efficiently, leading to increased revenue overall.
4- Improve the customer experience
You can learn more about your customers and what they want. By digging into the data, you'll better understand what motivates people to buy your product or service and improve their overall experience.
Using marketing analytics also allows you to measure the effectiveness of each promotional strategy and increase revenue over time.
5- Improve customer retention
Data lets you know what specific strategies work best with different types of customers (based on location, purchasing power and other individual characteristics).
This way, you can observe trends to retain current customers while targeting new audiences, thus increasing sales and minimizing costs.
With this information you can also review content on your website that may affect user experience (CTR), encouraging visitors to come back soon!
6- Improves the SEO from your website
By using data mining correctly, you will also learn more about what people are looking for when they search online.
You can then use this information as part of your strategy of SEO and discover new keywords that will increase your site's visibility in search engines.
Although these tools generate great information about keywords and key phrases, not everyone knows where to find the most important data mining tools on the web.
7- Using data to develop new products and services
Data helps you learn more about your target audience analyzing the different needs they have, which, in turn, allows you to improve your product or service offering.
You can also identify emerging opportunities in the market and determine what your competitors are doing.
9- Improvement in service quality
When trying to adapt your marketing strategy to the different needs of specific markets, you will need tools capable of delivering great results from your data mining efforts.
O data-driven marketing provides a wealth of information about the user experience, meaning you can make the necessary adjustments to ensure positive results.
Conclusion
Data mining is a powerful analytical tool that can help your company make more assertive decisions, based on content stored in a database.
Having access to the right data and interpreting it correctly will take time, but we've outlined some tips for getting started with this strategy. We also want you to know what types of analytics are possible and when it might be appropriate for you to use analytics as a marketing strategy.
If any part of this sounds like something you could benefit from, contact us today! Our team at Bytebio specializes in analyzing customer behavior across digital channels so we can provide insights into how they think about your products or services before purchasing them.
What questions do you have? How has data mining helped improve your business operations? Let us know, we will be happy to help you!
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Data-Driven Marketing is a growing trend that shapes the way marketers work. Now it is fully integrated into companies’ strategies, related to Digital Marketing.
Data-driven marketing is no longer an option, but a necessity for companies looking to become agile and efficient in today's market.
The main idea is to make the best use of the data you have already collected. This way, you can better understand your target audience, improve the customer experience, and drive growth.
Data-driven marketing applies to the use of online advertising channels in order to collect user data through specific tools that allow you to track users' actions on websites or applications.
It allows Marketing professionals to be proactive and anticipate market trends, optimizing decision making.
Data is a powerful tool for decision making. But how do you know which data to use?
What should your company measure and analyze to make decisions with an informed approach, beyond just going through the motions of digital transformation or big data analysis projects?
“Data-driven culture” goes far beyond technical skills; It requires cultural changes within each department where those working on behalf establish goals as well as metrics that can also track progress.
Data-Driven Marketing is all about using insights to optimize digital campaigns and improve the customer experience.
This type of marketing approach requires a lot of organization, which is why project managers are so important in data-driven organizations for data analysis.
See our page on Data-Driven Marketing and find out a little more.
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