Monthly Archives: July 2015

Crunching Big Data and Analytics from Web2.0

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The use of data and its analyses are becoming ubiquitous practices. As a result, there has been a dramatic surge in the use of business intelligence and analytics. These developments have inevitably led to endless opportunities for marketers to leverage themselves and gain a competitive advantage by untangling big data. Relevant data could help businesses to better serve customers as they would better know what they need, want and desire. This knowledge will lead to customer satisfaction and long lasting relationships.

Businesses are increasingly collecting and analysing data from many sources for many purposes. Much of the value of data is derived from secondary uses that were not intended in the first place. Very often datasets can possess intrinsic, hidden, not-yet-unearthed value. According to a research from IBM and the Saïd Business School at the University of Oxford; nearly nine in 10 companies were using transactional data, and three-quarters were collecting log data in 2012. This study suggested that business practitioners also gathered data from events, emails and social data (eMarketer, 2012).

This data is being collected and stored in massive amounts by search engines including Google, Bing and Yahoo as well as by e-commerce conglomerates such as eBay and Amazon. For instance, Security First boosted its productivity and customer satisfaction by using content analytics to bridge social media and the claims process. Similarly, Banco Bilbao Vizcaya Argentaria has improved its online reputation with analytics that quickly responded to online feedback (IBM, 2015).

In addition, users can easily access multiple sources of digital data that is readily available through websites, social networks, blogs, as well as from mobile devices, including smart phones and tablets. Big data is being gathered from social media content and video data from Facebook, Twitter, LinkedIn and Google Plus among others. These modern digital marketing tools are helping business to engage in social conversations with consumers. Social networks have surely amplified the marketers’ messages as they support promotional efforts. Here are some of the unique pieces of data each social network is collecting:

  • “Facebook’s interest/social graph: The world’s largest online community collects more data via its API than any other social network. Facebook’s “like” button is pressed 2.7 billion times every day across the web, revealing what people care about.
  • Google+’s relevance graph: The number of “+1s” and other Google+ data are now a top factor in determining how a Web page ranks in Google search results.
  • LinkedIn’s talent graph: At least 22% of LinkedIn users have between 500-999 first-degree connections on the social network, and 19% have between 301-499.The rich professional data is helping LinkedIn build a “talent graph.”
  • Twitter’s news graph: At its peak late last year the social network was processing 143,199 tweets per second globally. This firehose of tweets provide a real-time window into the news and information that people care about. Fifty-two percent of Twitter users in the U.S. consume news on the site (more than the percent who do so on Facebook), according to Pew.
  • Pinterest’s commerce graph: More than 17% of all pinboards are categorized under “Home,” while roughly 12% fall under style or fashion, these are windows into people’s tastes and fashion trends.
  • YouTube’s entertainment graph: What music, shows, and celebrities do we like? YouTube reaches more U.S. adults aged 18 to 34 than any single cable network, according to Nielsen. YouTube knows what they like to watch.
  • Yelp’s and Foursquare’s location graphs: These apps know where we’ve been and where we’ll go. Foursquare has over 45 million users and 5 billion location check-ins” (Business Insider, 2014).

Big data is fundamentally shifting how marketers collect, analyse and utilise data to reach out to customers. Business intelligence and analytics are helping companies to get new insights into how consumers behave. It is envisaged that the IT architecture will shortly develop into an information eco-system: a network of internal and external services where information is shared among users. Big data can support business in their decision making. It could be used to communicate meaningful results and to generate insights for an effective organisational performance. New marketing decision-making ought to harness big data for increased targeting and re-targeting of individuals and online communities. On-demand, direct marketing through digital platforms has already become more personalised than ever. The challenge for marketers is to recognise the value of big data as a tool that drives consumer in-sights.

Every customer contact with a brand is a moment of truth, in real-time. Businesses who are not responding with seamless externally-facing solutions will inevitably lose their customers to rivals. This contribution posits that a strategic approach to data management could drive consumer preferences. An evolving analytics ecosystem that is also integrated with web2.0 instruments could lead to better customer service and consumer engagement.

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Evaluating big data and predictive analytics

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The use of business intelligence and marketing information systems has expanded in recent years. Through advancements in technologies, marketers can extract value from very large data sets. Very often, companies can benefit if they use and reuse the same data to extract added value from it. Sometimes, it would also make sense for these companies to acquire data that they do not own (or data that was not collected).

All individuals leave a “digital trail” of data as they move about in the virtual and physical worlds. This phenomenon is called, “data exhaust”. Initially, this term was used to describe how Amazon.com used predictive analytics in order to suggest items for its customers. Predictive analytics could quantify the likelihood that a particular person will do something — whether it is defaulting on a loan, upgrading to a higher level of cable service or seeking another job. Such data anticipates human behaviours that have not happened as yet. For instance, Fedex has predicted which customers were most likely to defect to competitors. Even, Hewlett-Packard made a good use of suitable data to identify employees that were on the brink to leave the company. The latter corporation took remedial decisions in anticipation of staff turnover.

Predictive data is usually based on large amounts of cur¬rent and past indicative information that may have been collected from multiple sources. Such data could also provide additional details of customer personas, segments and prospects. Quantitative techniques can be deployed to find valuable patterns in data, enabling companies to predict the likely behaviour of customers, employees and others. First Tennessee Bank had used predictive analytics to increase its marketing response rate by better targeting its offers to high-value customers (IBM, 2015). Through predictive analytics businesses’ could quantify how many consumers will buy their products after receiving electronic mail. They may also measure how effective their personal mailing was.

Nowadays there are fewer inaccuracies in the measurement of big data. In addition, many applications of data can arise far from the purposes for which the data was originally intended. However, big data and predictive analytics could raise a number of concerns. Minor increases in the data accuracy of predictions can often lead substantial savings in the long term. There many companies that have saved significant financial resources by using predictive analytics. For instance, “Chickasaw Nation has used predictive and patron analytics to reduce its month-end close processes by 50%. This way it has also improved customer experience. In a similar vein, predictive tools and smart cards enabled Singapore Land Transit Authority to provide a more convenient transportation system.

Although, individuals tend to regularly repeat their habitual behaviours, pre¬dictive analytics cannot determine when and why they may decide to change their future preferences. The possibility of “one off” events must never be discounted. Many customers may be wary of giving their data due to privacy issues. The underlying question is; when does personalisation become an issue of consumer protection? In 2012, consumers learned that Target was using quantitative methods to predict which customers were pregnant. Very often, advances in technology are faster than legislation and its deployment. These issues could advance economic and privacy concerns that regulators will find themselves hard-pressed to ignore. It may appear that digital market manipulation is pushing the limits of consumer protection law.

Evidently, society has built up a body of rules that are aimed to protect personal information. Another contentious issue is figuring out the value of data and its worth in monetary terms. In the past, companies could have struggled to determine the value of their business; including patents, trade secrets and other intellectual property.

Despite its numerous pitfalls, the market is responding to the emerging demands for corporate IT solutions. Extant relational databases are capable of handling a wide variety of big data sources. Statistical analytical packages are similarly evolving and are working in conjunction with these new data platforms, data types and algorithms. Furthermore, big data is also being modified for those clients that may require cloud-based services. Cloud-based service providers offer on-demand pricing with a fast reconfiguration facility.

This short contribution suggests that in the foreseeable future many corporations would require bespoken software that is relevant for their particular line of business. Customised business intelligence software and big data systems allow organisations to load, store and query massive data sets in short time periods. Business could make good use of structured data (such as demographics) and unstructured information (including text and images) to improve their operational performance and customer service levels.

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Untangling Big Data for Digital Marketing

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The web and its online communities are expanding the use of big data. Ecommerce conglomerates including Amazon and eBay have already transformed the market through their innovative, highly scalable digital platforms and product recommender systems. Moreover, internet giants like Google and Facebook are leading the development of web analytics, cloud computing and social media networks. The emergence of user-generated content in fora, newsgroups, social media and crowd-sourcing platforms are offering endless opportunities for researchers and practitioners to “listen” to marketplace stakeholders; including customers, employees, suppliers, investors and the media.

Unlike the traditional transactional records that were conspicuous in past legacy systems, e-commerce systems continuously gather insightful data from the web. Much of the value of data is derived from secondary uses that were not intended in the first place. Every dataset can possess some intrinsic, hidden, not-yet-unearthed value. Having said that, many potential applications could skim along the edges of what might be ethical, moral or even legal.

In addition, online review sites and personal blogs often contain opinion-rich information that may be explored through textual and sentiment analysis. Arguably, consumer sentiment analysis may not be designed for automation but could be better adapted for the real-time monitoring of the marketing environment. Successful businesses strive to understand their customers’ personas so that they target them the right content with the relevant tone, imagery and value propositions.

Therefore, advertisers continuously gather consumer data and use it well to personalise every aspect of their users’ experience. They strive to take advantage of their consumers’ cognitive behaviour as they try to uncover and trigger consumer frailty at their individual level. It may appear that companies gather data on their customers in order to manipulate the market. They need to establish processes which determine when specific decisions are required. Firms use big data to delve into enormous volumes of information that they collect, generate or buy. Marketers need to realise that it’s important to analyse, decide and act expeditiously on data and analytics. It’s simply not enough to be able to monitor a continuing stream of information. Businesses should be quick in their decision making and take action.

Companies may use what they know about human psychology and consumer behaviour to set prices. Behavioural targeting is nothing new in digital marketing. When firms hold detailed information about their consumers, they may customise every aspect of their interaction with them. On the other hand, there could be instances when certain marketing practices could lead to unnecessary nuisances. Nowadays, customers are frequently bombarded with marketing endeavours including email promotions that are often picked up as spam. Therefore, one-size-fits-all messages could also have negative implications on prospective customers.

Eventually, firms could use this database to deliver promotional content to remind customers on their offerings. Consumer lists whether they are automated or in the cloud should always be used to deliver enhanced customer experiences. Customer-centric marketing is all about satisfying buyers. Customers may in turn become advocates for the business. Hence, technology has become instrumental for marketers in their ongoing interactions with people.

Evidently, without data, businesses could not keep a track record of their marketing effectiveness and performance stats. Engagement metrics; including, email-open rates, click through rates, pay per click and the like enable marketers to continually fine tune their individual customer targeting. Today, many individuals are becoming quite active on review sites, such as Yelp.com or Tripadvisor; and on social media channels; including Facebook, Twitter, Linkedin or Google Plus.These modern digital marketing tools are helping business to engage in social conversations with consumers. Social media networks are often rich in customer opinion and contain relevant behavioural information. Moreover, the social media analytics could capture fast-breaking trends on customer sentiments toward products, brands and companies.

Businesses may be interested in knowing whether there are changes in online sentiment and how these correlate with sales changes over time. Digital media is supporting many businesses to map out how customers receive promotions, messages, newsletters and even advertisements. Relevant data is also helping these businesses to keep a focus on their customer needs and wants.

This contribution suggests that there is scope for businesses to consider realigning (and personalising) their incentives toward individual consumers by using data-driven marketing. Many businesses have become proficient on the use of maintaining databases of prospects and customer lists. They gather this valuable information to communicate and build relationships. This data collection may possibly drive new revenue streams and build long-term loyalty.

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