Question: Stream Analytics: 7.8 Applications of Stream Analytics

7.8 Applications of Stream Analytics

Because of its power to create insight instantly, helping decision makers to be on top of events as they unfold and allowing organizations to address issues before they become problems, the use of streaming analytics is on an exponentially increasing trend. The following are some of the application areas that have already benefited from stream analytics.

e-Commerce

Companies like Amazon and eBay (among many others) are trying to make the most out of the data that they collect while a customer is on their Web site. Every page visit, every product looked at, every search conducted, and every click made is recorded and analyzed to maximize the value gained from a user’s visit. If done quickly, analysis of such a stream of data can turn browsers into buyers and buyers into shopaholics. When we visit an e-commerce Web site, even the ones where we are not a member, after a few clicks here and there we start to get very interesting product and bundle price offers. Behind the scenes, advanced analytics are crunching the real-time data coming from our clicks, and the clicks of thousands of others, to understand what it is that we are interested in (in some cases, even we do not know that) and make the most of that information by making creative offerings.

Telecommunications

The volume of data that come from call detail records (CDR) for telecommunications companies is astounding. Although this information has been used for billing purposes for quite some time now, there is a wealth of knowledge buried deep inside this Big Data that the telecommunications companies are just now realizing to tap. For instance, CDR data can be analyzed to prevent churn by identifying networks of callers, influencers, leaders, and followers within those networks and proactively acting on this information. As we all know, influencers and leaders have the effect of changing the perception of the followers within their network toward the service provider, either positively or negatively. Using social network analysis techniques, telecommunication companies are identifying the leaders and influencers and their network participants to better manage their customer base. In addition to churn analysis, such information can also be used to recruit new members and maximize the value of the existing members.

Continuous streams of data that come from CDR can be combined with social media data (sentiment analysis) to assess the effectiveness of marketing campaigns. Insight gained from these data streams can be used to rapidly react to adverse effects (which may lead to loss of customers) or boost the impact of positive effects (which may lead to maximizing purchases of existing customers and recruitment of new customers) observed in these campaigns. Furthermore, the process of gaining insight from CDR can be replicated for data networks using Internet protocol detail records. Because most telecommunications companies provide both of these service types, a holistic optimization of all offerings and marketing campaigns could lead to extraordinary market gains. Application Case 7.7 is an example of how Salesforce.com gets a better sense of its customers based upon an analysis of clickstreams.

Application Case 7.7

Salesforce Is Using Streaming Data to Enhance Customer Value

Salesforce has expanded their Marketing Cloud services to include Predictive Scores and Predictive Audience features called the Marketing Cloud Predictive Journey. This addition uses real-time streaming data to enhance the customer engagement online. First, the customers are given a Predictive Score unique to them. This score is calculated from several different factors, including how long their browsing history is, if they clicked an e-mail link, if they made a purchase, how much they spent, how long ago did they make a purchase, or if they have ever responded to an e-mail or ad campaign. Once customers have a score, they are then segmented into different groups. These groups are given different marketing objectives and plans based on the predictive behaviors assigned to them. The scores and segments are updated and changed daily and give companies a better road map to target and achieve a desired response. These marketing solutions are more accurate and create more personalized ways companies can accommodate their customer retention methods.

Question

D​‌‍‍‍‌‍‍‌‍‌‌‌‍‍‌‍‍‌‌‍​irections: Read section 7.8 "Applications of Stream Analytics" (p. 409 - 412) to identify the application areas that have benefited from stream analytics. Read three Case Studies or White Papers from Teradata that discuss big data analytics in your chosen field or industry. All three cases studies or white papers should be for the same industry. Provide full APA references with links to the three case studies or white papers in the reference/works cited section. Complete a brief s​‌‍‍‍‌‍‍‌‍‌‌‌‍‍‌‍‍‌‌‍​ummary of each case or white paper. Answer the following questions/prompts: What trends or themes have emerged from your readings of these cases/white papers? Discuss the commonalities and differences between the readings. Identify three issues within the industry that can be tackled utilizing big data in the future. Discuss these issues in detail and how big data can be used to mitigate them based on concepts from your text book Case study will be attached along with section 7.​‌‍‍‍‌‍‍‌‍‌‌‌‍‍‌‍‍‌‌‍​8