Social measurement tools: The Fundamentals

Information Technology progress and Social media spread, as well as Mobile Social Media development, examined in this blog, contribute to t...

Information Technology progress and Social media spread, as well as Mobile Social Media development, examined in this blog, contribute to the increasing availability of a large amount of multimedia structured and unstructured content about customers and prospects (called “Big data”). Travel organizations able to gather, analyze, and interpret this information have the opportunity to enhance customers’ knowledge, and consequently, to improve service differentiation and personalization. The synchronization with various target markets allows creating a competitive advantage and increasing financial and operational performance. Therefore, a key issue turns out to be the definition of the most appropriate social media metrics able to evaluate social media performance and, if combined with other measures, to support and improve business strategies.

social measurement tools


Learning from Customers: “Big Data” and Customer Profiling Opportunities

Advancements of Web 2.0 allow companies to capture an increasing volume of data and information about customers, suppliers, and operations, produced during the transactions.

Social media sites contribute to the growth of multimedia content, and, in turn, to the exponential increase in the amount of data (called “Big data”). Blogs, social networks pages (e.g., Facebook, Twitter) record every second data, actions, images, videos, locations, etc. Almost all users’ actions on social media websites (clicking, reviewing, post on a blog, etc.) can be recorded as data (Lovett 2011) more easily and cheaply than in the past. Furthermore, as examined in earlier, IT and Internet connectivity improvements have determined a growth of the quantity of both sensors embodied in physical objects (the so-called “Internet of things”) and new sophisticated mobile devices (i.e., wearable devices) able to read them.

IT progress and Web 2.0 provide firms large quantities of customer information that then can be stored and analyzed to create value. In particular, the availability of an increasing volume of data allows organizations to improve customers’ profiles knowledge and, consequently, make decisions related to segmentation and product differentiation. The analysis of user-generated content (ratings, reviews, videos, etc.) of various market segments, combined with other information about customers
coming from transactions, allows to differentiate and personalize the service offered to target segments of each booking channel respect to competitors (Varini and Sirsi 2012).

The synchronization of companies with “social” consumers’ expectations turn out to be very useful in an environment where customers acquire an increasing power to drive the conversation with the firm, influencing its marketing and sales activities. Knowing target markets’ expectations and profiles, companies can develop supplementary services in order to personalize the tourism experience. A large part of travelers are aware of this opportunity. Maybe at first they were a little bit scared, but now they (especially those with a higher experience) expect the tourism website to reference to their past experiences to personalize the offer (PhocusWright 2013).

Obviously, the opportunity to better segment the market by learning from customer characteristics depends on the ability and the motivation of the company to gather and organize information in a unique and integrated CRM database. This is the starting point to be able to access, analyze, and use data in order to define business strategies. However, as examined in earlier post, sometimes companies have not sufficient competencies and resources (Law and Jogaratnam 2005; Law et al. 2008; Milano et al. 2011; Leung et al. 2013).3 For example, the aforementioned study conducted by Varini and Sirsi (2012) pointed out that interviewed firms do not have a unique repository of customers’ data. On the contrary, the ability to quickly analyze this huge amount of information produced by IT and more traditional systems, in order to make business decisions, represents for travel companies a way for creating a competitive advantage and increasing financial and operational
performance (McAfee and Brynjolfsson 2012).

In light of these trends and opportunities, next sections will examine the concept of “Big data” and how travel organizations can select and organize data in order to develop appropriate analytics.

The Evolution of Analytics: “Big Data”

“Big data” is relatively a new term used to define the explosion of the amount of digital data currently available. It is generally considered an evolution of analytics and refers to “datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze” (McKinsey 2011).

Some key essential features of Big data can be identified that differentiate this concept from traditional corporate databases and data warehouses (McAfee and Brynjolfsson 2012):
• volume, the amount of data is increasing exponentially and every second on the Internet we store more than what was collected 20 years ago5;

• velocity, refers to the speed of data creation. Nowadays, data on the Internet are nearly real time and offer companies the opportunity to be more flexible and react immediately to business opportunities and threats;

• variety, data take different forms, they can be structured and unstructured and they can come from diverse sources (not only internal and historical). Social media, smartphones and in general mobile devices provide a large amount of different and unstructured data about people, activities and locations (e.g., messages, images, videos, sensors reading, GPS signals).

Analytics and reports, as traditionally intended, are only one part of Big data analysis process. Therefore, the analysis of both internal structured and external unstructured available information should be jointly considered to fully exploit the opportunities of Big data (PhocusWright 2014). Internal structured information refers mainly to accounting, transaction, and customers’ data, while unstructured external data are information provided by social media (e.g., social networks, blogs,
virtual communities, and travel review websites).

Social media and mobile devices offer a large amount of information about both customers and prospects (e.g., personal user information, geo-location data, social graphs, user-generated content such as reviews, rankings, posts, tweets, machine logging data, and sensor-generated data). Differently from customers, prospects are connected with the company on social media but they are not yet customers (i.e., Like on Facebook). Data coming from these online relationships can be very useful to learn about potential customers’ profiles in order to attract them with personalized offers and promotions. For example, by analyzing Facebook users’ nationalities, a hotel could realize the existence of a new market segment and could start studying its potentialities of development.

Some travel companies have recently started to exploit Big data opportunities. For example, some hotels create very detailed customer databases composed by structured and unstructured data to be able to anticipate needs, especially of loyal customers (e.g., luxury hotels). Moreover, softwares like ReviewPro and TrustYou crunch unstructured data from thousands of travelers and can be used by hotels to analyze their online reputation.

Therefore, measuring and proceeding Big data can be very helpful for travel companies to make better predictions and, in turn, better decisions. The central question is how to identify the most appropriate methods to adopt. Next section will try to give some insights about Big data analysis process.

Big Data Analytics

The starting central issue in the development of Big data analytics is understanding how to collect and organize data to be able to translate them into business advantage. Traditional tools and technology seem to be no longer enough (PhocusWright 2014). The large volume of data available implies for travel companies some challenges for both storage and process activities. Fortunately, advances in IT satisfy this need and help companies to measure Big data, thanks to new technologic
instruments able to analyze semi-or unstructured data. They provide access and process of information located in multiple separate computing devices as if it is on a single device. Main tools that allow this measurement are NoSQL, Hadoop, and MapReduce, along with Semantic web.

NoSQL “Not Only SQL” represents a shift from “structured query language” SQL, the most common language for accessing databases. It allows to process data of various type and size, splitting large databases across multiple computers to enable real-time parallel searching (Mayer-Schönberger and Cukier 2013). NoSQL systems “are distributed, non-relational databases designed for large-scale data
storage and for massively-parallel data processing across a large number of commodity servers” (Moniruzzaman and Hossain 2013). They have been increasingly employed by main Internet companies like Google, Amazon, and Facebook in order to collect and to process real-time a large volume of unstructured data.

Hadoop by Apache is an open-source framework that manages high volume of data and enables utilization in computing format. Both firms and social media use this system to store, analyze, and process information in real time. MapReduce is a set of programming libraries that works with Hadoop to analyze and map unstructured data to key values.

The combination of Web 2.0 and semantic web generates the so-called “social semantic web.” A new class of applications that can “leverage the semantic relations that exist between certain kinds of web-accessible data to automatically locate and fuse information, perform basic reasoning and pivot and transform representations to meet a wide variety of user needs” (Mika and Greaves 2012).

But “it is not so much about the volume of data that is stored, but rather the ability to use stored data in a meaningful way” (Lovett 2011). Big data measurement allows firms to extract value from data in order to improve the service provided and enhance internal operations. Hereafter, some examples concerning how Big data analysis can support firms and create value will be examined.

Provide real-time information. Big data real-time processing allows companies to provide more detailed and personalized information to consumers. For example, transportation companies are able to monitor the real-time position and possible delay of trains and airplanes. This permits to give updated information to customers on the corporate websites or on specific mobile applications improving Social Media Customer Care (SMCC). By means of online devices, users can verify in
each moment where the train is, possible delay, and can receive notices and SMS directly from the airlines or the airport about air schedule and gates changes. Moreover, all data are recorded and then can be used to improve internal operations organization, market analysis, predictions, and consequently to refine future corporate decision making.

Provide recommendations. Various firms and social media analyze Big data, in particular users’ buying patterns, in order to provide recommendations to consumers and prospects. An example is the function of Linkedin “people you may know” similar to that of Amazon, Booking, Expedia, TripAdvisor, etc.: “People who have viewed this item have also viewed…”. Furthermore, the access to social media personal information of customers offers also opportunities to further refine the recommendation process. Some airlines, for example, give customers the possibility to choose where to seat during the online check-in procedure consulting the Facebook profiles of other passengers who have already checked-in online.

Social graph analysis. Social media provide not only information about single users but also about social graph, that is the existing connections among people and the influence they have each other. The opportunity to analyze and combine this large amount of digital data allows some types of companies to improve customer service. For example, Facebook in March 2013 launched “Facebook Search Graph” an improved and new search method for users, for the time being available in the U.S.
It combines unstructured internal data acquired from its users and external data into a search engine providing user-specific search results. “Facebook Search Graph” is based, as the previous research function, on relationships and connections among users, but the search method has changed focus: from keywords to semantics. In practice, it is designed to match phrases and not keywords. For example, a user can search “Photos of my friends in New York” and Facebook will display all the photos the user’s friends took in New York and that were shared with him or her. Users can also make researches that go beyond the friends’ network. For example, they can look for “people who live in a certain city” and Facebook will search in the connections among friends and “friends of friends”. Since the launch of this new function, privacy concern arose. The mechanism is the same of ever: by properly managing privacy options, users can determine what friends and other people can see when searching on Facebook.

Sentiment analysis. The analysis of unstructured data coming from social media and online conversations can help travel companies to determine the “sentiment” toward a product, service, destination, company, etc. Results of sentiment analysis can be wealthy information that can help firms manage possible complaints, improve the service, as well as monitor brand online reputation. They represent a way for firms to tune in with the market.

Marketing insights. These kinds of analyses allow firms to monitor the results of specific marketing actions. For instance, an advertising campaign can be controlled in order to understand its effectiveness. Facebook advertising monitoring (provided by Facebook Insights) presented in earlier post represents an example.

Other possible analyses could regard churn management. Data mining can help firms to calculate the churn rate9 and to develop churn predictions (Hung et al. 2006). Moreover, Big data are available also for competitors and this allow companies to monitor the companies of a predetermined competitive set.

It is Not Only About Technology, It is About People

The previous section highlighted the opportunity for firms to collect and analyze a large amount of data about customers and prospects, provided by advanced technology tools. However, in order to transform data into meaningful recommendations and to make successful business decisions, data have to be selected and interpreted (Lovett 2011). Among various raw data extracted from social media, the company should consider only valuable information, respect to its business objectives. On the contrary, data overload could generate a sense of frustration and discourage the management to undertake measurement operations.

We can identify some actions a firm can realize to successfully analyze Big data:
• register Big data;
• select data really important for the organization;
• interpret data in order to understand results (success vs failure);
• assure a proper communication within the organization;
• learn from data to develop recommendations for future strategies.

We immediately notice that most of the aforementioned actions imply a human intervention directed to filter the large quantity of data available, in light of the importance for the organization. Therefore, the staff in charge of data analysis plays a key role in determining the most appropriate metrics able to support the firm in decision making. Data analytics is not a mere operational function that can be
carried out by a very restrained group of people, maybe of one single department or even outsourced. Given the importance of selection and interpretation of raw data, firms should preferably commit this function to a group of employees that represents main corporate departments with their different perspectives. Moreover, the results of measurement should then be shared with the rest of the staff within the firm. Internal communication by means of a clear and synthetic reporting that summarizes successfully and unsuccessfully actions and identifies future directions can involve employees in the organization’s life.

As examined previously, we notice a restrained propensity of travel companies to adopt IT, to analyze data, and to develop metrics, sometimes due to a lack of knowledge and/or to insufficient resources. Moreover, research points out that many tourism organizations adopt a social media approach based on single social media projects that are not always linked to the whole business strategy.

In light of the observations stated in the first part of this section, travel companies should grasp the opportunity offered by social media to gather and analyze unstructured users’ information in order to develop appropriate metrics. The combination of these social media metrics with other internal measures is a critical factor to improve business strategies. Therefore, a first issue travel companies
should consider is the opportunity to develop a measurement process characterized by proper technological equipment able to register the large amount of data available. Second, the appropriate group of data analysts should be identified in order to select and interpret data considering various objectives for each department. Finally, metrics produced by the analysis should be summarized in reports able to describe results of business actions, ongoing trends, and possible future actions to be undertaken for each department.

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