Tag Archives: big data

The future of marketing is mobile…


An IBM (2012) technology trends survey indicated that mobile devices could increase the productivities and efficiencies of organisations. This study showed that mobile software was the second most “in demand” area for research and development. In addition, Gartner BI Hype Cycle (2012) also anticipated that mobile analytics was one of the latest technologies that may potentially disrupt the business intelligence market. At the same time, the market for mobile advertising is escalating at a very fast pace. Interestingly, eMarketer (2012) had predicted that mobile advertising shall experience a surge from an estimated $2.6 billion in 2012 to more than $10.8 billion in 2016. Evidently, there are niche areas for professional growth, particularly in this specialised field; as more and more individuals are increasingly creating new applications for mobile operating systems.

Recent advances in mobile communication and geo-positioning technologies have presented marketers with a new way how to target consumers based on their location. Location-targeted mobile advertising involves the provision of ad messages to cellular subscribers based on their geographic locations. This digital technology allows marketers to deliver ads and coupons that are customised to individual consumers’ tastes, geographic location and time of day. Given the ubiquity of mobile devices, location-targeted mobile advertising seems to offer tremendous marketing benefits.

In addition, many businesses are commonly utilising applications, including browser cookies that track consumers through their mobile devices as they move out and about. Once these users leave these sites, the products or services that they had viewed online will be shown to them again in advertisements, across different websites. Hence, businesses are using browsing session data combined with the consumers’ purchase history to deliver “suitable” items that consumers like. Therefore, savvy brands are becoming increasingly proficient in personalising their offerings as they collect, classify and use large data volumes on their consumers’ behaviours. As more consumers carry smartphones with them, they are (or may be) receiving compelling offers that instantaneously pop up on their mobile devices.

For instance, consumers are continuously using social networks and indicating their geo location as they use mobile apps. This same data can be used to identify where people tend to gather — information that could be useful in predicting real estate prices et cetera. This information is valuable to brands as they seek to improve their consumer engagement and marketing efforts. Businesses are using mobile devices and networks to capture important consumer data. Smart phones and tablets that are wifi-enabled interact with networks and convey information to network providers and ISPs. This year, more brands shall be using mobile devices and networks as a sort of sensor data – to acquire relevant information on their consumers’ digital behaviours and physical movements. These businesses have become increasingly interactive through the proliferation of near-field communication (NFC). Basically, embedded chips in the customers’ mobile phones are exchanging data with retailers’ items possessing the NFC tags. It is envisaged that mobile wallet transactions using NFC technologies are expected to reach $110 billion, by the year 2017. The latest Android and Microsoft smartphones have already include these NFC capabilities. Moreover, a recent patent application by Apple has revealed its plans to include NFC capabilities in their next products. This will inevitably lead to an increase in the use of mobile wallets (GSMA, 2015). Undoubtedly, the growth of such data-driven, digital technologies is adding value to customer-centric marketing. Therefore, analytics can enable businesses to provide a deeper personalisation of content and offers to specific customers.

Apparently, there are promising revenue streams in the mobile app market. Both Apple and Android are offering paid or free ad-supported apps in many categories. There are also companies that have developed apps for business intelligence. For example, enterprise / industry-specific apps, e-commerce apps and social apps. Evidently, the lightweight programming models of the current web services (e.g., HTML, XML, CSS, Ajax, Flash, J2E) as well as the maturing mobile development platforms such as Android and iOS have also contributed to the rapid proliferation of mobile applications (Chen et al., 2012). Moreover, researchers are increasingly exploring mobile sensing apps that are location-aware and activity-sensitive.

Possible future research avenues include mobile social innovation for m-learning; (Sharples, Taylor and Vavoula, 2010; Motiwalla, 2007), mobile social networking and crowd-sourcing (Lane et al., 2010), mobile visualisation (Corchado and Herrero, 2011), personalisation and behavioural modelling for mobile apps in gamification (Ha et al., 2007), mobile advertising and social media marketing (Bart et al., 2014; Yang et al., 2013). Google’s (2015) current projects include gesture and touch interaction; activity-based and context-aware computing; recommendation of social and activity streams; analytics of social media engagements, and end-user programming (Dai, Rzeszotarski, Paritosh and Chi, 2015;  Fowler, Partridge, Chelba, Bi, Ouyang and Zhai, 2015; Zhong, Weber, Burkhardt, Weaver and Bigham, 2015; Brzozowski, Adams and Chi, 2015).



Bart, Y., Stephen, A. T., & Sarvary, M. (2014). Which products are best suited to mobile advertising? A field study of mobile display advertising effects on consumer attitudes and intentions. Journal of Marketing Research, 51(3), 270-285.

Brzozowski, M. J., Adams, P., & Chi, E. H. (2015, April). Google+ Communities as Plazas and Topic Boards. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 3779-3788). ACM. Retrieved May 22nd, 2015, from http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43453.pdf

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 36(4), 1165-1188.

Corchado, E., & Herrero, Á. (2011). Neural visualization of network traffic data for intrusion detection. Applied Soft Computing, 11(2), 2042-2056.

Dai, P., Rzeszotarski, J. M., Paritosh, P., & Chi, E. H. (2015). And Now for Something Completely Different: Improving Crowdsourcing Workflows with Micro-Diversions. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (pp. 628-638). ACM. Retrieved May 17th, 2015, from http://dl.acm.org/citation.cfm?id=2675260

eMarketer (2012). eMarketer in the News: June 1, 2012 Retrieved January 28th, 2015, from http://www.emarketer.com/newsroom/index.php/emarketer-news-june-1-2012/

Fowler, A., Partridge, K., Chelba, C., Bi, X., Ouyang, T., & Zhai, S. (2015, April). Effects of Language Modeling and its Personalization on Touchscreen Typing Performance. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 649-658). ACM. Retrieved May15th, 2015, from http://cslu.ohsu.edu/~fowlera/Fowler_CHI2015.pdf

Gartner (2012). Big Data Drives Rapid Changes in Infrastructure and $232 Billion in IT Spending Through 2016. Retrieved January 20th, 2015, from https://www.gartner.com/doc/2195915/big-data-drives-rapid-changes

Google (2015). Human-Computer Interaction and Visualization Research at Google. Retrieved May 20th, 2015, from http://research.google.com/pubs/Human-ComputerInteractionandVisualization.html

Ha, I., Yoon, Y., & Choi, M. (2007). Determinants of adoption of mobile games under mobile broadband wireless access environment. Information & Management, 44(3), 276-286.

IBM (2012) Tech Trends Report. Fast track to the future. Retrieved May15th, 2015, from http://www-01.ibm.com/common/ssi/cgi-bin/ssialias?subtype=XB&infotype=PM&appname=CHQE_XI_XI_USEN&htmlfid=XIE12346USEN&attachment=XIE12346USEN.PDF#loaded

Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. T. (2010). A survey of mobile phone sensing. Communications Magazine, IEEE, 48(9), 140-150.

Motiwalla, L. F. (2007). Mobile learning: A framework and evaluation. Computers & Education, 49(3), 581-596.

Sharples, M., Taylor, J., & Vavoula, G. (2010). A theory of learning for the mobile age. In Medienbildung in neuen Kulturräumen (pp. 87-99). VS Verlag für Sozialwissenschaften.

Yang, B., Kim, Y., & Yoo, C. (2013). The integrated mobile advertising model: The effects of technology-and emotion-based evaluations. Journal of Business Research, 66(9), 1345-1352.

Zhong, Y., Weber, A., Burkhardt, C., Weaver, P., & Bigham, J. P. (2015). Enhancing Android accessibility for users with hand tremor by reducing fine pointing and steady tapping. In Proceedings of the 12th Web for All Conference (p. 29). ACM. Retrieved Ma7 20th, 2015, from http://dl.acm.org/citation.cfm?id=2747277


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Using Big Data for Customer-Centric Marketing

Big data

The latest advances in information and communications technologies have brought significant improvements for the processing and storage of digital information. Nowadays, users can easily access multiple sources of data that is readily available through websites, social media networks as well as from mobile devices, including smart phones and tablets. These developments have inevitably led to endless opportunities for marketers to leverage themselves by using big data analytics.

Big data has expanded in recent years. As a matter of fact, digital data has dwarfed analogue content and continues to grow at an exponential rate. This data is being collected and stored in massive amounts by search engines and eCommerce conglomerates. In addition, more information is being gathered through social media networks. In fact, 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 customers. Hence, predictive analytics anticipates human behaviours that have not happened yet. Evidently, it is based on large amounts of current and past indicative data that has been collected from multiple sources. Yet, at the moment, such analytics cannot determine when and why individuals may change their preferences for certain brands. Another new addition to big data is called preventative analytics. This latter one is aimed at reducing the likelihood of contingent situations, risk and uncertainty. It may be particularly relevant in the fields of healthcare, public services and law enforcement.

Data is the new currency for connecting people, ideas and products. Today, digital information is being gathered in innovative, new ways that have dramatically changed and improved consumers’ experience. For instance, online businesses are commonly utilising browser cookies to track websites that are visited by internet users. Once individual users leave these sites, some of the products or services they had viewed; will be shown to them again and again in native advertisements, across different websites. Therefore, businesses are using browsing session data, combined with the consumers’ purchase history to deliver “suitable” items for consumers. Many brands are becoming quite proficient in personalising their offerings – as they collect, classify and use large data volumes on consumers’ behaviours.

This year, more brands shall be using mobile devices and networks to acquire sensory data. As more customers are increasingly carrying smartphones with them, they are (or may be) getting used to receiving compelling offers that instantaneously pop up on their mobile devices. This type of geo-based marketing message is delivered at the right time and the right place. Of course, firms will need more than transaction history and loyalty schemes to be effective at this. They will inevitably require socio-demographic and geo-data that other businesses are not capturing. Moreover, anonymous cookieless data-capture methods are connecting consumer data with matching geo-location-based data. It may appear that these methods are empowering marketers to hyper-target consumers with real-time mobile ad campaigns before, during and after in-store activity. Geo-location capabilities are not only enabling advertisers to capitalise on leads, in real time; but they can also offer valuable insights on shopping habits and consumer behaviours. This information is valuable to brands as they seek to acquire relevant information on their consumers’ digital behaviours and physical movements.

Notwithstanding, businesses have become even more interactive through the proliferation of near-field communication (NFC). Basically, NFCs are embedded chips situated inside smart devices. These chips exchange data with retailers’ items possessing NFC tags. It is envisaged that mobile wallet transactions using this NFC technology are expected to reach $110 billion by 2017 (CNBC, 2013). The latest Android and Microsoft smartphones already include these NFC capabilities. Indeed, these technological developments can enable businesses to provide a deeper personalisation of content as well as bespoken offers to individuals. Consumers use apps that may involuntarily indicate their geo-location to third parties. As a result, data collection has greatly benefited from geo-data services like satellites, near-field communication and global positioning systems. These systems track users’ movements that measure traffic and other real-time phenomena. Arguably, the emergence of such data-driven, digital technologies are adding value to customer-centric marketing endeavours. Unsurprisingly, sensor analytics, geo-location and social data-capture were some of the big trends that were recently announced during the 2015 Consumer Electronics Show.

Big data is fundamentally shifting how marketers collect, analyse and utilise data to reach out to customers. It is helping companies to get new insights into how consumers behave. The challenge for marketers is not to become dependent on big data and analytics to drive business strategies, but rather to recognise its value as a tool for customer satisfaction. Therefore, big data should inform, not consume marketing efforts. Perhaps, new marketing decision-making ought to harness big data for increased targeting and re-targeting of individuals and online communities. Lately, on-demand, real-time marketing has become more personalised. 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 risk losing their loyal customers to rivals.

This contribution suggests that a strategic approach to data management can generate leads and conversions. It also maintains that an evolving digital ecosystem will lead to superior levels of customer service, engagement and repeat business.

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