There are currently four main strategies companies use to leverage big data to their advantage: performance management, decision science, social analytics, and data exploration. Performance management is where all things start. The Big Data Course in Bangalore has gotten one of the most looked for after exercises for any goal-oriented programming proficient.By understanding the meaning of big data in company databases using pre-determined queries, managers can ask questions such as where the most profitable market segments are. It can be extremely complex and require a lot of resources; however, things are beginning to get easier. Most business intelligence tools today provide a dashboard capability. The user, often the manager or analyst, can choose which queries to run, and can filter and rank the report output by certain dimensions (e.g., region) as well as drill down/up on the data. Multiple types of reports and graphs make it easy for managers to look at trends. With functional and “easy”-to-use dashboards, companies are starting to be able to do more with less; but we have yet to see a solution that offers a clean design with simple functionality, that offers even higher insights then what currently exists.
Data exploration is the second strategy that is currently in play by businesses. This strategy makes heavy use of statistics to experiment and get answers to questions that managers might not have thought of previously. This approach leverages predictive modeling techniques to predict user behavior based on their previous transactions and preferences. Cluster analysis can be used to segment customers into groups based on similar attributes that may not have been originally planned. Once these groups are discovered, managers can perform targeted actions such as customizing marketing messages, upgrading service, and cross/up-selling to each unique group. Another popular use case is to predict what group of users may “drop out.” Armed with this information, managers can proactively devise strategies to retain this user segment and lower the churn rate.
The well-known retailer Target used big data mining techniques to predict the buying habits of clusters of customers that were going through a major life event. Target was able to identify roughly 25 products, such as unscented lotion and vitamin supplements, that when analyzed together, helped determine a “pregnancy prediction” score. Target then sent promotions focused on baby-related products to women based on their pregnancy prediction score. This resulted in the sales of Target’s Baby and Mother products sharply increased soon after the launch their new advertising campaigns.
The next strategy companies’ use is leveraging social media sites such as Facebook, Twitter, Yelp, or Instagram. Social analytics measure the vast amount of non-transactional data that exists today. Much of this data exists on social media platforms, such as conversations and reviews on Facebook, Twitter, and Yelp. Social analytics measure three broad categories: awareness, engagement, and word-of-mouth or reach. Awareness looks at the exposure or mentions of social content and often involves metrics such as the number of video views and the number of followers or community members. Engagement measures the level of activity and interaction among platform members, such as the frequency of user-generated content. Finally, reach measures the extent to which content is disseminated to other users across social platforms. Reach can be measured with variables such as the number of retweets on Twitter and shared likes on Facebook.
Social analyzers need a clear understanding of what they are measuring. For example, a viral video that has been viewed 10 million times is a good indicator of high awareness, but it is not necessarily a good measure of engagement and interaction. Furthermore, social metrics consist of intermediate, non-financial measures. To determine a business impact, analysts often need to collect web traffic and business metrics, in addition to social metrics, and then look for correlations. In the case of viral videos, analysts need to determine if, after viewing the YouTube videos, there is traffic to the company web site followed by eventual product purchases.
The final strategy companies’ use has been given the name “Decision Science”. It generally involves experiments and analysis of non-transactional data, such as consumer-generated product ideas and product reviews, to improve the decision-making process. Unlike social analyzers who focus on social analytics to measure known objectives, decision scientists explore social big data as a way to conduct “field research” and to test hypotheses. Crowd sourcing, including idea generation and polling, enables companies to pose questions to the community about its products and brands. Decision scientists, in conjunction with community feedback, determine the value, validity, feasibility and fit of these ideas and eventually report on if/how they plan to put these ideas in action. For example, the My Starbucks Idea program enables consumers to share, vote, and submit ideas regarding Starbuck’s products, customer experience, and community involvement. Over 100,000 ideas have been collected to date. Starbucks has an “Ideas in Action” section to discuss where ideas sit in the review process.
Many of the techniques used by decision scientists involve listening tools that perform text and sentiment analysis. By leveraging these tools, companies can measure specific topics of interest around its products, as well as who is saying what about these topics. For example, before a new product is launched, marketers can measure how consumers feel about price, the impact that demographics may have on sentiment, and how price sentiment changes over time. Managers can then adjust prices based on these tests. Many companies are providing big data tutorial to make things easier and simple.