Consumer Data Mining Through Cloud Services
In order to stay competitive and meet consumer needs, companies collect multiple pieces of information about their customers. This data is collected online through their own websites, through social media and also through other means, such as internet cookies. Over the past several years the amount of data being collected by online companies, such as Amazon, as well as the volumes of data being collected, have grown exponentially. With increased technologies, it is now a reality for online companies to collect a plethora of information on every potential customer who visits their website. In addition to their own websites, companies can collect data from sources such as Instagram, Twitter, and Facebook to see what people are saying about their company and their products.
The Benefits of Consumer Info
All of this information put together can help companies in many different ways. It can help them market products to a specific audience or carry product lines that customers are asking for. It can also help them change their marketing strategies to make them more successful, or find ways to increase customer satisfaction. Although beneficial, the task of collecting, mining, storing, and sorting this data can be burdensome in many ways. Without the help of software and computer engineering and cloud storage, it would be a lot more difficult. This task is possible, but requires updated technologies and strict processes – all known and referred to as “big data.”
What is Big Data?
Big data is defined as the capability companies have to piece together valuable bits of consumer information from large volumes of data. In fact, big data is such a large industry that the services and technology market is expected to grow from a $3.2 billion industry in 2010 to a $16.9 billion market in 2015. A scientific framework is commonly applied when mining big data.
When creating the capabilities that will handle big data, companies start with an experiment. Along with an experiment, they use a hypothesis and utilize scientific processes in order to verify their hypothesis. This data mining dimension is known as business objective mining.
Labeled data type is the second dimension in data mining. During the normal course of online business, companies will collect data from users or customers and place it in their database. The database has a certain structure to it in order to hold the transactional data. Some companies may also need to deal with data from other sources that are unstructured, such as data from social media. From this four strategies or quadrants, representing performance management, data exploration, social analytics, and decision science are formed.
To maximize performance management, you must understand how big data is used within a company utilizing pre-determined queries and multidimensional analysis. That data gathered is transaction-based and can contain many years of purchasing activity for a certain customer, as well as inventory levels, and company turnover. Managers are able to get answers in real-time to queries such as which customer segments are the most profitable. This information can be used to create long term business plans, as well as short term business decisions. The manage or analyst can pick and choose which queries they want to run, adding filters and ranking reports by specifics, such as region, in order to see the data that they need to. Performance management has the benefit of being able to interact with different aspects of business data, such as marketing, HR, sales, manufacturing data, and customer service. This can give an across the board view of how the business is performing.
As the name implies, data exploration is heavy on data in order to answer questions. With this method statistics are used to experiment and even bring up questions managers and analysts may not have thought to ask in the past. In addition, predictive modeling is used and can often predict user behavior by looking at previous purchases and preferences. Cluster groups of customers can be created based on viewing habits or past purchases. Cluster groups can then receive targeted messages, such as ones about cross or upselling. Data exploration can also predict which customers are more likely to be lost.
This quadrant represents the mining of non-transactional data. Much of this type of data is derived from social media outlets, such as a Yelp review, a Twitter update, or a Facebook conversation. Social Analytics measures awareness, engagement, and word-of-mouth reach. Awareness studies the exposure or mentions on social media content and can involve the metrics, such as number of views or followers. Engagement measures activities, such as the frequency of user-generated content. Foursquare is a good example of a company measuring engagement. Last but not least, reach measures what content is released over social platforms. Reach is measured with certain variables, like retweets, shares, and likes. This information helps managers craft their social media strategy and presence.
This method uses experiments and analysis of non-transactional data. Consumer generated product ideas and consumer product reviews are examples of information used in decision science. In this way field research is done to test a hypothesis. These decision scientists are relied upon to poll the community about their products and brands, as well as determine the fit, value, feasibility, and validity of the brand, product, company, or idea.
In today’s market, the competitive advantage belongs to the company that can effectively create and put into motion strategies for big data. This should be done using both transactional and non-transactional data. In order to stay afloat and have a competitive edge, companies should be thirsty for this knowledge and different ways they can process it.