Tag Archives: Analytics

Targeted Segmentation Through Mobile Marketing

mobile

The mobile is an effective channel to reach out to many users. The mobile devices, including smart phones and tablets could increase the productivities and efficiencies of organisations. For the time being, the mobile applications (apps) are an “in demand” area for research and development. Gartner (2015) anticipated that mobile analytics was going to be one of the latest technologies that could disrupt business intelligence. In fact, the market for advertising on mobile is still escalating at a fast pace. Moreover, there are niche areas for professional growth, as more and more individuals are increasingly creating new applications for many purposes on 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 (Camilleri, 2016). Location-targeted mobile advertising involves the provision of ad messages to mobile data subscribers. This digital technology allows marketers to deliver ads and coupons that are customised to individual consumers’ tastes, geographic location and the time of day. Given the ubiquity of mobile devices, location-targeted mobile advertising are increasingly offering 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. Very often, when users leave the sites they visited, the products or services they viewed will be shown to them again in advertisements, across different websites. Hence, many companies are using browsing session data combined with the consumers’ purchase history to deliver “suitable” items that consumers like. There are also tourism businesses who are personalising their offerings as they collect, classify and use large data volumes on the consumers’ behaviours. As more consumers carry smartphones with them, they may be easily targeted with compelling offers that instantaneously pop-up on their mobile devices.

For instance, consumers are continuously using social networks which are indicating their geo location, as they use mobile apps. This same data can be used to identify where people tend to gather — this information that could be very useful. This information is valuable to brands as they seek to improve their consumer engagement and marketing efforts. It may appear that businesses are using mobile devices and networks to capture important consumer data. For instance, smart phones and tablets that are wifi-enabled interact with networks and convey information to network providers and ISPs. This year, more businesses 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 the retailers’ items possessing the NFC tags. The latest iPhone, Android and Microsoft smartphones have already included these NFC ca­pabilities. This development has recently led to the use of mobile wallets. The growth of such data-driven, digital technologies is surely adding value to the customer-centric marketing. Therefore, analytics can enable businesses to provide a deeper personalisation of content and offers to specific customers.

The geo-based marketing message or offer is delivered at the right time, and at the right place. The brands that hold customer data can gain a competitive edge over their rivals. Of course, firms will need more than transaction history and loyalty schemes to be effective at this. They may require both socio-demographic and geo-data that new mobile technologies are capable of gathering.

For instance, many mobile service companies are partnering with local cinemas, in response to the location-targeted mobile advertising; as cinema-goers often inquire about movie information, and they may book tickets and select their seats through their mobile app. The consumers who are physically situated within a given geographic proximity of the participating cinemas could be receiving location-targeted mobile ads. The cinemas’ ads will inform prospects what movies they are playing and could explain how to purchase tickets through their phone. The consumers may also call the cinemas’ hotlines to get more information from a customer service representative. Besides location-targeted advertising, the mobile companies can also promote movie ticket sales via mobile ads that arte targeted to individuals, according to their behaviour (not by location). Therefore, the companies may direct mobile-ad messages to those consumers who had previously responded to previous mobile ads (and to others who had already purchased movie tickets, in the past months). Moreover, the cinema companies could also promote movies via Facebook Messenger Ads if they logged in the companies’ website, via their Facebook account. The mobile users might receive instant message ads via pop-up windows whenever they log into the corporate site of their service provider.

It is envisaged that such data points will only increase as the multi-billion dollar advertising monopolies are built on big data and analytics that can help businesses personalise immersive ads to target individual customers. The use of credit card transactions is also complementing geo-targeting and Google Maps, with ads; as the physical purchases are increasingly demanding personalisation, fulfillment and convenience. Consumers and employees alike are willing to give up their data for value. Therefore, the businesses need to reassure their customers through concise disclosures on how they will use personal data. They should clarify the purpose of maintaining consumer data, as they should provide simple user controls to opt in and out of different levels of data sharing. This way, they could establish a trust-worthy relationship with customers and prospects.

Companies are already personalising their mobile shopping experience based on the user situation and history. Tomorrow’s tourism businesses are expected to customise their user experiences of applications and web interfaces, according to the specific needs of each segment. Big data and analytics capabilities are increasingly allowing businesses to fully leverage their rich data from a range of new digital touchpoints and to turn this into high impact interactions. Those businesses that are able to reorient their marketing and product-development efforts around digital customer segments and behaviours will be in a position to tap into the hyper-growth that mobile, social media and the wearables market are currently experiencing.

References:

Camilleri, M. A. (2016). Using big data for customer centric marketing. Using Big Data for Customer-Centric Marketing. In Evans, C. (Ed.) Handbook of Research on Open Data Innovations in Business and Government, IGI Global, Hershey, PA, USA. https://www.um.edu.mt/library/oar/bitstream/handle/123456789/10682/Using%20Big%20Data%20for%20Customer-centric%20Marketing.pdf?sequence=3&isAllowed=y

Gartner (2015) Gartner Says Power Shift in Business Intelligence and Analytics Will Fuel Disruption. http://www.gartner.com/newsroom/id/2970917

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Responsible Investing: Making a Positive Impact

flower

Impact investing is one of the fastest growing and promising areas of innovative development finance (Thornley, Wood, Grace & Sullivant, 2011; Freireich & Fulton, 2009). This form of socially-responsible investment (SRI) also has its roots in the venture capital community where investors unlock a substantial volume of private and public capital into companies, organisations and funds – with the intention to generate social and environmental impact alongside a financial return.

The stakeholders or actors in the impact investing industry can be divided into four broad categories: asset owners who actually own capital; asset managers who deploy capital; demand-side actors who receive and utilise the capital; and service providers who help make this market work.

Impact investments can be made in both emerging and developed markets, and target a range of returns from below market to market rate; depending on the investors’ strategic goals. Bugg-Levine and Emerson (2011) argued that impact investing aligns the businesses’ investments and purchase decisions with their values. Defining exactly what is (and what is not) an impact investment has become increasingly important as it appears that the term has taken off among academia and practitioners.

The impact investments are usually characterised by market organisations that are driven by a core group of proponents including foundations, high-net worth individuals, family offices, investment banks and development finance institutions. Responsible entities are mobilising capital for ‘investments that are intended to create social impact beyond financial returns’ (Jackson, 2013; Freireich & Fulton 2009). Specific examples of impact investments may include; micro-finance, community development finance, sustainable agriculture, renewable energy, conservation, micro-finance and affordable and accessible basic services, including; housing, healthcare, education and clean technology among others.

Micro-finance institutions in developing countries and affordable housing schemes in developed countries have been the favorite vehicles for these responsible investments, though impact investors are also beginning to diversify across a wider range of sectors (see Saltuk, Bouri, & Leung 2011; Harji & Jackson 2012). Nevertheless, micro-finance has represented an estimated 50% of European impact investing assets (EUROSIF, 2014). This form of investing has grown to an estimated €20 billion market in Europe alone (EUROSIF, 2014). The Netherlands and Switzerland were key markets for this investment strategy, as they represented an estimated two thirds of these assets. These markets were followed by Italy, the United Kingdom and Germany.

Generally, the investors’ intent is to ensure that they achieve positive impacts in society. Therefore, they would in turn expect tangible evidence of positive outcomes (and impacts) of their capital. Arguably, the evaluation capacity of impact investing could increase opportunities for dialogue and exchange. Therefore, practitioners are encouraged to collaborate, exchange perspectives and tools to strengthen their practices in ways that could advance impact investing. The process behind on-going encounters and growing partnerships could surely be facilitated through conferences, workshops, online communities and pilot projects. Moreover, audit and assurance ought to be continuously improved as institutions and investors need to be equipped with the best knowledge about evaluation methods. Hence, it is imperative that University and college courses are designed, tested and refined to improve the quality of education as well as  professional training and development in evaluating responsible investments.

For evaluation to be conducted with ever more precision and utility, it must be informed by mobilising research and analytics. Some impact investing funds and intermediaries are already using detailed research and analysis on investment portfolios and target sectors. At the industry-wide level, the work of the Global Impact Investing Network (GIIN) and IRIS (a catalogue of generally accepted Environmental, Social and Governance – ESG performance metrics) is generating large datasets as well as a series of case studies on collaborative impact investments. Similarly, the Global Impact Investing Rating System (GIIRS) also issues quarterly analytics reports on companies and their respective funds in industry metrics (Camilleri, 2015).

For the most part, those responsible businesses often convert positive impact-investment outcomes into tangible benefits for the poor and the marginalised people (Garriga & Melé, 2004). Such outcomes may include increased greater food security, improved housing, higher incomes, better access to affordable services (e.g. water, energy, health, education, finance), environmental protection, and the like (Jackson, 2013).

Interestingly, high sustainability companies significantly outperform their counterparts over the long-term, both in terms of stock market and accounting performance (Eccles, Ioannou & Serafeim, 2012). This out-performance is stronger in sectors where the customers are individual consumers, rather than companies (Eccles et al., 2012).

It may be complicated and time-consuming to quantify how enterprises create various forms of humanitarian and environmental value, yet some approaches and analytical tools can help to address today’s societal challenges, including the return on impact investments in social and sustainability projects.

References

Bugg-Levine, A., & Emerson, J. (2011). Impact investing: Transforming how we make money while making a difference. innovations, 6(3), 9-18.
Camilleri, M. A. (2015). Environmental, social and governance disclosures in Europe. Sustainability Accounting, Management and Policy Journal, 6(2), 224-242.

Eccles, R. G., Ioannou, I., & Serafeim, G. (2012). The impact of a corporate culture of sustainability on corporate behavior and performance (No. W17950). National Bureau of Economic Research.

EUROSIF (2014). Press Release: 6th Sustainable and Responsible Investment Study 2014. Europe-based national Sustainable Investment Forums. http://www.eurosif.org/wp-content/uploads/2014/09/Press-Release-European-SRI-Study-2014-English-version.pdf (Accessed 14 May 2016).

Freireich, J., & Fulton, K. (2009). Investing for social and environmental impact: A design for catalyzing an emerging industry. Monitor Institute, January.

Garriga, E., & Melé, D. (2004). Corporate social responsibility theories: Mapping the territory. Journal of business ethics, 53(1-2), 51-71.

Harji, K., & Jackson, E. T. (2012). Accelerating impact: Achievements, challenges and what’s next in building the impact investing industry. New York, NY: The Rockefeller Foundation.

Jackson, E. T. (2013). Interrogating the theory of change: evaluating impact investing where it matters most. Journal of Sustainable Finance & Investment, 3(2), 95-110.

Saltuk, Y., Bouri, A., & Leung, G. (2011). Insight into the impact investment market: An in-depth analysis of investor perspectives and over 2,200 transactions. New York, NY: J.P. Morgan.

Thornley, B., Wood, D., Grace, K., & Sullivant, S. (2011). Impact Investing a Framework for Policy Design and Analysis. InSight at Pacific Community Ventures & The Initiative for Responsible Investment at Harvard University.

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Crunching Big Data and Analytics from Web2.0

social media

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

bigdata

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

customers
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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Crunching Big Data for Operations Management

Big data

For decades businesses have been using data in some way or another to improve their operations. For instance, an IT software could support small enterprises in their customer-facing processes. Alternatively, large corporations may possess complex systems that monitor and detect any changes in consumer sentiment towards brands.

Recently, many industry leaders, including McKinsey, IBM and SAS among others have released relevant studies on big data. It transpires that they are using similar terminology to describe big data as a “situation where the volume, velocity and variety of data exceed an organisation’s ability to use that data for accurate and timely decision-making” (SAS). These providers of business intelligence solutions have developed technical approaches to storing and managing enormous volumes of new data.

The handling and untangling of such data requires advanced and unique storage, management, analysis and visualisation technologies. The terms of “big data” and “analytics” are increasingly being used to describe data sets and analytical techniques in applications ranging from sensor to social media. Usually, big data analytics are dependent on extensive storage capacity and quick processing power requiring a flexible grid that can be reconfigured for different needs. For instance, streaming analytics process big data in real time during events to improve their outcome.

Insightful data could easily be retrieved from the Web, social media content and video data among other content. Notwithstanding, such data could be presented in different forms; ranging from recorded vocal content (e.g. call centre voice data) or it can even be genomic and proteomic data that is derived from biological research and medicine.
Big data is often used to describe the latest advances in technologies and architectures. Nowadays, big data and marketing information systems predict customer purchase decisions. This data could indicate which products or services customers buy, where and what they eat, where and when they go on vacation, how much they buy, and the like.

Giant retailers such as Tesco or Sainsbury every single day receive long-range weather forecasts to work 8-10 days ahead. Evidently, the weather affects the shopping behaviour of customers. For example, hot and cold weather can lead to the sales of certain products. It may appear that weather forecasting dictates store placement, ordering and supply (and demand) logistics for supermarket chains. Other retailers like Walmart and Kohl’s also use big data to tailor product selections and determine the timing of price markdowns.

Shipping companies, like U.P.S. are mining data on truck delivery times and traffic patterns in order to fine-tune their routing. This way the business will become more efficient and incur less operational costs. Therefore, big data extracts value by capturing, discovering and analysing very large volumes of data in an economic and expeditious way. This has inevitably led to a significant reduction in the cost of keeping data.

Big data can also be linked with production applications and timely operational processes that enable continuous improvements. Credit card companies are a good illustration of this dynamic as direct marketing groups at credit card companies create models to select the most likely customer prospects from a large data warehouse. Previously, the process of data extraction, preparation and analysis took weeks to prepare and organise. Eventually, these companies realised that there was a quicker way to carry out the same task. In fact, they created a “ready-to-market” database and system that allowed their marketers to analyse, select and issue offers in a single day. Therefore, this case indicates that businesses became much more effective (and efficient) in their processes through iterations and monitoring of websites and call-centre activities. They could also make personalised offers to customers in milliseconds as they kept tracking responses over time.

Organisations are increasingly realising the utility of data that could bring value through continuous improvements in their operations. This contribution indicated that relevant data needs to be captured, filtered and analysed. Big data is already swamping traditional networks, storage arrays and relational database platforms. The increased pervasiveness of digital and mobile activity, particularly from e-commerce and social media is leading to the dissemination of meaningful data – that is being created each and every second. Successful, online businesses can gain a competitive advantage if they are capable of gathering and crunching data.

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The future of marketing is mobile…

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).

 

References:

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.

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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

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Using Content Marketing Metrics for Academic Impact

Academic contributions start from concepts and ideas. When their content is relevant and of a high quality they can be published in renowned, peer-reviewed journals. Researchers are increasingly using online full text databases from institutional repositories or online open access journals to disseminate their findings. The web has surely helped to enhance fruitful collaborative relationships among academia. The internet has brought increased engagement among peers, over email or video. In addition, they may share their knowledge with colleagues as they present their papers in seminars and conferences. After publication, their contributions may be cited by other scholars.

The researchers’ visibility does not only rely on the number of publications. Both academic researchers and their institutions are continuously being rated and classified. Their citations may result from highly reputable journals or well-linked homepages providing scientific content. Publications are usually ranked through metrics that will assess individual researchers and their organisational performance. Bibliometrics and citations may be considered as part of the academic reward system. Highly cited authors are usually endorsed by their peers for their significant contribution to knowledge by society. As a matter of fact, citations are at the core of scientometric methods as they have been used to measure the visibility and impact of scholarly work (Moed, 2006; Borgman, 2000). This contribution explores extant literature that explain how the visibility of individual researchers’content may be related to their academic clout. Therefore, it examines the communication structures and processes of scholarly communications (Kousha and Thelwall, 2007; Borgmann and Furner 2002). It presents relevant theoretical underpinnings on bibliometric studies and considers different methods that can analyse the individual researchers’ or their academic publications’ impact (Wilson, 1999; Tague-Sutcliffe, 1992).

 

Citation Analysis
The symbolic role of citation in representing the content of a document is an extensive dimension of information retrieval. Citation analysis expands the scope of information seeking by retrieving publications that have been cited in previous works. This methodology offers enormous possibilities for tracing trends and developments in different research areas. Citation analysis has become the de-facto standard in the evaluation of research. In fact, previous publications can be simply evaluated on the number of citations and the relatively good availability of citation data for such purposes (Knoth and Herrmannova, 2014). However, citations are merely one of the attributes of publications. By themselves, they do not provide adequate and sufficient evidence of impact, quality and research contribution. This may be due to a wide range of characteristics they exhibit; including the semantics of the citation (Knoth and Herrmannova, 2014), the motives for citing (Nicolaisen, 2007), the variations in sentiment (Athar, 2014), the context of the citation (He, Pei, Kifer, Mitra and Giles, 2010), the popularity of topics, the size of research communities (Brumback, 2009; Seglen, 1997), the time delay for citations to show up (Priem and Hemminger, 2010), the skewness of their distribution (Seglen, 1992), the difference in the types of research papers (Seglen, 1997) and finally the ability to game / manipulate citations (Arnold and Fowler, 2010).

Impact Factors (IFs)
Scholarly impact is measure of frequency in which an “average article” has been cited over a defined time period in a journal (Glanzel and Moed, 2002). Journal citations reports are published in June, every year by Thomson-Reuters’ Institute of Scientific Information (ISI). These reports also feature data for ranking the Immediacy Index of articles, which measure the number of times an article appeared in academic citations (Harter, 1996). Publishers of core scientific journals consider IF indicators in their evaluations of prospective contributions. In Despite there are severe limitations in the IF’s methodology, it is still the most common instrument that ranks international journals in any given field of study. Yet, impact factors have often been subject to ongoing criticism by researchers for their methodological and procedural imperfections. Commentators often debate about how IFs should be used. Whilst a higher impact factor may indicate journals that are considered to be more prestigious, it does not necessarily reflect the quality or impact of an individual article or researcher. This may be attributable to the large number of journals, the volume of research contributions, and also the rapidly changing nature of certain research fields and the increasing representation of researchers. Hence, other metrics have been developed to provide alternative measures to impact factors.

h-index
The h-index attempts to calculate the citation impact of the academic publications of researchers. Therefore, this index measures the scholars productivity by taking into account their most cited papers and the number of citations that they received in other publications. This index can also be applied to measure the impact and productivity of a scholarly journal, as well as a group of scientists, such as a department or university or country (Jones, Huggett and Kamalski, 2011). The (Hirsch) h-index was originally developed in 2005 to estimate the importance, significance and the broad impact of an academic’s researcher’s cumulative research contributions. Initially, the h-index was designed to overcome the limitations of other measures of quality and productivity of researchers. It consists of a single number that reports on an author’s academic contributions that have at least the equivalent number of citations. For instance, an h-index of 3 would indicate that the author has published at least three papers that have been cited three times or more. Therefore, the most productive researcher may possibly obtaining a high h-index. Moreover, the best papers in terms of quality will be mostly cited. Interestingly, this issue is driving more researchers to publish in open access journals.

 

Webometrics
The science of webometrics (also cybermetrics) is still in an experimental phase. Björneborn and Ingwersen (2004) indicated that webometrics involves an assessment of different types of hyperlinks. They argued that relevant links may help to improve the impact of academic publications. Therefore, webometrics refer to the quantitative analysis of activity on the world wide web, such as downloads (Davidson, Newton, Ferguson, Daly, Elliott, Homer, Duffield and Jackson, 2014). Webometrics recognise that the internet is a repository for a massive number of documents. It disseminates knowledge to wide audiences. The webometric ranking involves the measurement of volume, visibility, and the impact of web pages. Webometrics emphasise on scientific output including peer-reviewed papers, conference presentations, preprints, monographs, theses, and reports. However, these kind of electronic metrics also analyse other academic material (including courseware, seminar documentation, digital libraries, databases, multimedia, personal pages and blogs among others). Moreover, webometrics consider online information on the educational institution, its departments, research groups, supporting services, and the level of students attending courses.

Web 2.0 and Social Media
Internet users are increasingly creating and publishing their content online. Never before has it been so easy for academics to engage with their peers on both current affairs and scientific findings. The influence of social media has changed the academic publishing scenario. As a matter of fact, recently there has been an increased recognition for measures of scholarly impact to be drawn from Web 2.0 data (Priem and Hemminger, 2010).

The web has not only revolutionised how data is gathered, stored and shared but also provided a mechanism of measuring access to information. Moreover, academics are also using personal web sites and blogs to enhance the visibility of their publications. This medium improves their content marketing in addition to traditional bibliometrics. Social media networks are providing blogging platforms that allows users to communicate to anyone with online access. For instance, Twitter is rapidly becoming used for work related purposes, particularly scholarly communication, as a method of sharing and disseminating information which is central to the work of an academic (Java, Song, Finin and Tseng B, 2007). Recently, there has been rapid growth in the uptake of Twitter by academics to network, share ideas and common interests, and promote their scientific findings (Davidson et al., 2014).

Conclusions and Implications

There are various sources of bibliometric data, each possess their own strengths and limitations. Evidently, there is no single bibliometric measure that is perfect. Multiple approaches to evaluation are highly recommended. Moreover, bibliometric approaches should not be the only measures upon which academic and scholarly performance ought to be evaluated. Sometimes, it may appear that bibliometrics can reduce the publications’ impact to a quantitative, numerical score. Many commentators have argued that when viewed in isolation these metrics may not necessarily be representative of a researcher’s performance or capacity. In taking this view, one would consider bibliometric measures as only one aspect of performance upon which research can be judged. Nonetheless, this chapter indicated that bibliometrics still have their high utility in academia. It is very likely that metrics will to continue to be in use because they represent a relatively simple and accurate data source. For the time being, bibliometrics are an essential aspect of measuring academic clout and organisational performance. A number of systematic ways of assessment have been identified in this regard; including citation analysis, impact factor, h-index and webometrics among others. Notwithstanding, the changes in academic behaviours and their use of content marketing on internet have challenged traditional metrics. Evidently, the measurement of impact beyond citation metrics is an increasing focus among researchers, with social media networks representing the most contemporary way of establishing performance and impact. In conclusion, this contribution suggests that these bibliometrics as well as recognition by peers can help to boost the researchers’, research groups’ and universities’ productivity and their quality of research.

References
Arnold, D. N., & Fowler, K. K. (2011). Nefarious numbers. Notices of the AMS, 58(3), 434-437.

Athar, A. (2014). Sentiment analysis of scientific citations. University of Cambridge, Computer Laboratory, Technical Report, (UCAM-CL-TR-856).

Borgman, C. L. (2000). Digital libraries and the continuum of scholarly communication. Journal of documentation, 56(4), 412-430.

Borgman, C. L., & Furner, J. (2002). Scholarly communication and bibliometrics.

Bornmann, L., & Daniel, H. D. (2005). Does the h-index for ranking of scientists really work?. Scientometrics, 65(3), 391-392.

Bornmann, L., & Daniel, H. D. (2007). What do we know about the h index?. Journal of the American Society for Information Science and technology, 58(9), 1381-1385.

Björneborn, L., & Ingwersen, P. (2004). Toward a basic framework for webometrics. Journal of the American Society for Information Science and Technology, 55(14), 1216-1227.

Glänzel, W., & Moed, H. F. (2002). Journal impact measures in bibliometric research. Scientometrics, 53(2), 171-193.

Harter, S. (1996). Historical roots of contemporary issues involving self-concept.

He, Q., Pei, J., Kifer, D., Mitra, P., & Giles, L. (2010, April). Context-aware citation recommendation. In Proceedings of the 19th international conference on World wide web (pp. 421-430). ACM.

Java, A., Song, X., Finin, T., & Tseng, B. (2007, August). Why we twitter: understanding microblogging usage and communities. In Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis (pp. 56-65). ACM. http://scholar.google.com/scholar?q=http://dx.doi.org/10.1145/1348549.1348556

Knoth, P., & Herrmannova, D. (2014). Towards Semantometrics: A New Semantic Similarity Based Measure for Assessing a Research Publication’s Contribution. D-Lib Magazine, 20(11), 8.

Kousha, K., & Thelwall, M. (2007). Google Scholar citations and Google Web/URL citations: A multi‐discipline exploratory analysis. Journal of the American Society for Information Science and Technology, 58(7), 1055-1065.

Moed, H. F. (2006). Citation analysis in research evaluation (Vol. 9). Springer Science & Business Media.

Nicolaisen, J. (2007). Citation analysis. Annual review of information science and technology, 41(1), 609-641.

Priem, J., & Hemminger, B. H. (2010). Scientometrics 2.0: New metrics of scholarly impact on the social Web. First Monday, 15(7).

Seglen, P. O. (1992). The skewness of science. Journal of the American Society for Information Science, 43(9), 628-638.

Seglen, P. O. (1997). Why the impact factor of journals should not be used for evaluating research. Bmj, 314(7079), 497.

Tague-Sutcliffe, J. (1992). An introduction to informetrics. Information processing & management, 28(1), 1-3.

Wilson, C. S. (1999). Informetrics. Annual Review of Information Science and Technology (ARIST), 34, 107-247.

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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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