Using Data Science and Data Mining to leverage your business strategy
Technological advances has enabled the production, collection and storage of massive databases in all fields. According to Cisco’s report, this year there will be 10.4 zettabytes (1.000.000.000.000.000.000.000 bytes) of data available on the web.
The act of handling this enormous amount of data is called Data Mining. In today’s post, we will show the different ways to apply data mining to your company’s strategic planning. After all, what company doesn’t deal with data?
Before anything: what is Data Mining?
Data Mining is a term linked to computing and it means, quite literally, the act of mining data.
- Aggregating and organizing data;
- Finding relevant patterns, associations, changes and anomalies.
In the business plan, the term refers to the development of insights and opportunities in the digital age, where plenty of data can help decision-making in marketing.
Data mining techniques help in decision-making through extraction and pattern recognition, to predict and understand consumer behavior in large databases – an extremely difficult task to be done manually.
Data Mining and Data Science
Many Data Scientists start off creating codes and algorithms and later discover that those weren’t quite what the client wanted, be that an internal or external client. For such occasions there is the CRISP-DM (Cross Industry Standard Process for Data Mining), a process model enabling a holistic view of a project’s life cycle.
The CRISP-DM methodology gathers the best practices so that these Data Mining tools can be used in as productive a way as possible.
CRISP-DM can be applied when analyzing commercial, financial, human resources, industrial production, services rendered and other data. 42% of companies that perform Data Mining use CRISP-DM to mine their data.
Check out how it works in 6 steps.
Step 1: Understanding the needs of the business
Obviously, it’s crucial to have knowledge of the problem that needs to be solved within the business, even though this problem may change throughout the process.
This might seem obvious but, in many cases, this demand comes in an ambiguous form. This is the creative analysis step, where the human aspect is crucial to a sound understanding of the real problem to be solved.
Step 2: Understanding the Company Data
The available data is the raw material on which the solution will be built.
Often, not even the companies are aware of the potential their data holds. This inquiry is done to identify the qualities and limitations of the available data, even checking if there is enough data to mine.
In this case, one can assess whether there is another source of data available and if it’s possible to acquire it.
Step 3: Preparing the Data
Many companies underestimate the effort needed during this step to prepare the data so they are adequate for mining.
The analytical tools usually require that the data be in specific formats and data conversion is one of the most common tasks during data preparation.
On several occasions, the data also needs to be cleaned and enriched with other data sources so they can be correctly analyzed.
Step 4: Modeling
A model is an attempt to understand or represent the reality from a certain perspective, usually technical or scientific in nature. It’s an artificial construction, where non-relevant details are removed or ignored.
The modeling stage is where Data Mining techniques are applied to the data themselves. It’s crucial for the entire team to have good knowledge, even of the types of techniques and tools available.
It’s common to revisit data preparation during this stage.
Step 5: Evaluation
The evaluation phase is where the results are analyzed and checked to see if they are valid and trustworthy, thus deciding if there is justification for undertaking new investments.
It’s crucial to trust that the models and patterns extracted from the data represent everyday situations of the business and are not idiosyncratic anomalies. At the end of the day, the main objective of the analysis phase is to ensure that the generated models meet the objectives of the business initially outlined.
Step 6: Development
The knowledge gathered through modeling is organized and presented in a way that enables the client to apply it.
The possible results are new applications, changes in company processes or even new product launches.
The 5Ps Model (People, Product, Promotion, Price, Place)
Do you remember the 5 p’s of Marketing?
The five possibilities of data mining also applies to them (Public, Product, Promotion, Price and Place), which we grouped according to the type of knowledge extracted.
This model enables better development of marketing strategies in the information age: we call the combination of these techniques market mining.
The source of the data is plural: from surveys and polls to data from social networks and the like. Even though data visualization permeates through all other categories, it stands out on its own, since it is directly responsible for a range of insights.
Let’s see the 5’s for market mining:
Identifying and understanding the public it is crucial for the efficiency of contemporary marketing to identify and understand specific customer groups due to the high competition in the markets. From segmentation according to behavior patterns to identification of different types of personas.
Due to the large amount of data available currently, and due to a few computer limitations, it is still more feasible to characterize client profiles, than carrying out strictly individual analyses.
It is better to identify common interests and behaviors and group them into profiles.
Potential and limitations: the granularity of these activities can even be similar to individual marketing, but the performance requires greater efforts in terms of data quality and computational system.
How is your brand perceived?
Several models can be used to analyze products. An extremely useful application is the so-called Sentiment Analysis: constant monitoring of the content published on social networks and the web in general, on a particular product, marketing strategy and the likes.
This analysis helps marketing strategies that focus on the product’s and company’s reputation by measuring the sentiment of the target audience, regulating campaign assertiveness. Another classic application is product ontology. The concept involves combining products that have some sort of similarity.
Potential and limitations: as there is no central categorization, different stores can categorize their products in random ways, making it hard to make any association with competing products. One can also associate products to identify opportunities for combined sales.
Promotion: creating successful campaigns
One way to increase sales and customer retention involves promotions aimed at a specific audience. In this context, it is important to understand the economic and social impact of your campaigns so that you are able to align strategies.
Analysis focused on promotional campaigns can benefit from data collected from surveys, online polls and, in some cases, social media analysis.
Another common example is the shopping cart, which focuses on the development of intelligent product recommendation systems. This practice is already widely used by e-commerce companies like Amazon.
Price: assessing the Relationship Between the Economic Scenario and Demand
The idea behind the price element involves supporting marketing managers in engaging different pricing strategies, supporting the reaction to the strategies of different competitors. The focus is to estimate the optimal price for products according to forecasts of economic scenarios and demand.
Place: mapping space strategically
This element is becoming increasingly important due to the increased use of GPS devices such as smartphones. Stores can deliver real-time offers based on the geolocation of their customers.
This enables opportunity marketing, where companies launch quick offers for consumers who are close to their stores.
It is also possible to predict the customer’s location, considering aspects from social media, and offer, in advance, products and services that depend on the customer’s location, such as trips and events.
Try Getting to Know your Customers
In the age of information and competition between companies, it is undeniable that there is power in knowing how to extract the information from data in order to enhance marketing decisions.
The data mining activity is very specialized.
It is necessary to have several professionals: a team prepared to seize opportunities and go beyond the numbers and graphs. The information collected through this activity makes it possible to deduce how to direct efforts to win over the public and meet their real needs.
There is an open road to a major evolution on the ability to predict customer needs.