Technological advances have enabled the production, collection and storage of massive databases in all fields. According to Cisco’s report made in April this year, by 2019, 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 – without using any numbers or graphs – 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. It involves:
– 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.
Market Mining: Combining Data Mining and Marketing
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
The last P is the place element. 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.