Article | June 10, 2021
We discursive creatures are construed within a meaningful, bounded communicative environment, namely context(s) and not in a vacuum.
Context(s) co-occur in different scenarios, that is, in mundane talk as well as in academic discourse where the goal of natural language communication is mutual intelligibility, hence the negotiation of meaning. Discursive research focuses on the context-sensitive use of the linguistic code and its social practice in particular settings, such as medical talk, courtroom interactions, financial/economic and political discourse which may restrict its validity when ascribing to a theoretical framework and its propositions regarding its application. This is also reflected in the case of artificial intelligence approaches to context(s) such as the development of context-sensitive parsers, context-sensitive translation machines and context-sensitive information systems where the validity of an argument and its propositions is at stake.
Context is at the heart of pragmatics or even better said context is the anchor of any pragmatic theory: sociopragmatics, discourse analysis and ethnomethodological conversation analysis. Academic disciplines, such as linguistics, philosophy, anthropology, psychology and literary theory have also studied various aspects of the context phenomena. Yet, the concept of context has remained fuzzy or is generally undefined. It seems that the denotation of the word [context] has become murkier as its uses have been extended in many directions.
Context or/ and contexts? Now in order to be “felicitous” integrated into the pragmatic construct, the definition of context needs some delimitations. Depending on the frame of research, context is delimitated to the global surroundings of the phenomenon to be investigated, for instance if its surrounding is of extra-linguistic nature it is called the socio-cultural context, if it comprises features of a speech situation, it is called the linguistic context and if it refers to the cognitive material, that is a mental representation, it is called the cognitive context. Context is a transcendental notion which plays a key role in interpretation.
Language is no longer considered as decontextualized sentences. Instead language is seen as embedded in larger activities, through which they become meaningful. In a dynamic outlook on communication, the acts of speaking (which generates a form discourse, for instance, conversational discourse, lecture or speech) and interpreting build contexts and at the same time constrain the building of such contexts. In Heritage’s terminology, “the production of talk is doubly contextual” (Heritage 1984: 242). An utterance relies upon the existing context for its production and interpretation, and it is, in its own right, an event that shapes a new context for the action that will follow. A linguistic context can be decontextualized at a local level, and it can be recontextualized at a global level. There is intra-discursive recontextualization anchored to local decontextualization, and there is interdiscursive recontextualization anchored to global recontextualization. “A given context not only 'legislates' the interpretation of indexical elements; indexical elements can also mold the background of the context” (Ochs, 1990). In the case of recontextualization, in a particular scenario, it is valid to ask what do you mean or how do you mean. Making a reference to context and a reference to meaning helps to clarify when there is a controversy about the communicative status and at the same time provides a frame for the recontextualization.
A linguistic context is intrinsically linked to a social context and a subcategory of the latter, the socio-cultural context. The social context can be considered as unmarked, hence a default context, whereas a socio-cultural context can be conceived as a marked type of context in which specific variables are interpreted in a particular mode. Culture provides us, the participants, with a filter mechanism which allows us to interpret a social context in accordance with particular socio-cultural context constraints and requirements. Besides, socially constitutive qualities of context are unavoidable since each interaction updates the existing context and prepares new ground for subsequent interaction.
Now, how these aforementioned conceptualizations and views are reflected in NLP? Most of the research work has focused in the linguistic context, that is, in the word level surroundings and the lexical meaning. An approach to producing sense embeddings for the lexical meanings within a lexical knowledge base which lie in a space that is comparable to that of contextualized word vectors.
Contextualized word embeddings have been used effectively across several tasks in Natural Language Processing, as they have proved to carry useful semantic information. The task of associating a word in context with the most suitable meaning from a predefined sense inventory is better known as Word Sense Disambiguation (Navigli, 2009). Linguistically speaking, “context encompasses the total linguistic and non-linguistic background of a text” (Crystal, 1991). Notice that the nature of context(s) is clearly crucial when reconstructing the meaning of a text. Therefore, “meaning-in-context should be regarded as a probabilistic weighting, of the list of potential meanings available to the user of the language.” The so-called disambiguating role of context should be taken with a pinch of salt.
The main reason for language models such as BERT (Devlin et al., 2019), RoBERTA (Liu et al., 2019) and SBERT (Reimers, 2019) proved to be beneficial in most NLP task is that contextualized embeddings of words encode the semantics defined by their input context. In the same vein, a novel method for contextualized sense representations has recently been employed: SensEmBERT (Scarlini et al., 2020) which computes sense representations that can be applied directly to disambiguation.
Still, there is a long way to go regarding context(s) research. The linguistic context is just one of the necessary conditions for sentence embeddedness in “a” context. For interpretation to take place, well-formed sentences and well-formed constructions, that is, linguistic strings which must be grammatical but may be constrained by cognitive sentence-processability and pragmatic relevance, particular linguistic-context and social-context configurations, which make their production and interpretation meaningful, will be needed.
Article | June 9, 2021
In recent years, we have seen more industries adopt data analytics as they realize how important it is. Even the hotel industry is not left behind in this.
This is because the hospitality industry is data-rich. And the key to maintaining a competitive advantage has come down to ‘how hotels manage and analyze this data’.
With the changes taking place in the hospitality industry, data analysis can help you gain meaningful insights that can redefine the way hotels conduct business.
Article | June 21, 2021
The marketing industry keeps changing every year. Businesses and enterprises have the task of keeping up with the changes in marketing trends as they evolve. As consumer demands and behavior changed, brands had to move from traditional marketing channels like print and electronic to digital channels like social media, Google Ads, YouTube, and more. Businesses have begun to consider marketing analytics a crucial component of marketing as they are the primary reason for success.
In uncertain times, marketing analytics tools calculate and evaluate the market status and enhances better planning for enterprises.
As Covid-19 hit the world, organizations that used traditional marketing analytics tools and relied on historical data realized that many of these models became irrelevant. The pandemic rendered a lot of data useless.
With machine learning (ML) and artificial intelligence (AI) in marketers’ arsenal, marketing analytics is turning virtual with a shift in the marketing landscape in 2021. They are also pivoting from relying on just AI technologies but rather combining big data with it.
AI and machine learning help advertisers and marketers to improve their target audience and re-strategize their campaigns through advanced marketing attributes, which in turn increases customer retention and customer loyalty.
While technology is making targeting and measuring possible, marketers have had to reassure their commitment to consumer privacy and data regulations and governance in their initiatives. They are also relying on third-party data.
These data and analytics trends will help organizations deal with radical changes and uncertainties, with opportunities they bring with them over the next few years.
To know why businesses are gravitating towards these trends in marketing analytics, let us look at why it is so important.
Importance of Marketing Analytics
As businesses extended into new marketing categories, new technologies were implemented to support them. This new technology was usually deployed in isolation, which resulted in assorted and disconnected data sets.
Usually, marketers based their decisions on data from individual channels like website metrics, not considering other marketers channels. Website and social media metrics alone are not enough. In contrast, marketing analytics tools look at all marketing done across channels over a period of time that is vital for sound decision-making and effective program execution.
Marketing analytics helps understand how well a campaign is working to achieve business goals or key performance indicators.
Marketing analytics allows you to answer questions like:
• How are your marketing initiatives/ campaigns working? What can be done to improve them?
• How do your marketing campaigns compare with others? What are they spending their time and money on? What marketing analytics software are they using that helps them?
• What should be your next step? How should you allocate the marketing budget according to your current spending?
Now that the advantages of marketing analytics are clear, let us get into the details of the trends in marketing analytics of 2021:
Rise of real-time marketing data analytics
Reciprocation to any action is the biggest trend right now in digital marketing, especially post Covid. Brands and businesses strive to respond to customer queries and provide them with solutions. Running queries in a low-latency customer data platform have allowed marketers to filter the view by the audience and identify underachieving sectors. Once this data is collected, businesses and brands can then readjust their customer targeting and messaging to optimize their performance.
To achieve this on a larger scale, organizations need to invest in marketing analytics software and platforms to balance data loads with processing for business intelligence and analytics. The platform needs to allow different types of jobs to run parallel by adding resources to groups as required. This gives data scientists more flexibility and access to response data at any given time.
Real-time analytics will also aid marketers in identifying underlying threats and problems in their strategies. Marketers will have to conduct a SWOT analysis and continuously optimize their campaigns to suit them better.
Data security, regulatory compliance, and protecting consumer privacy
Protecting market data from a rise in cybercrimes and breaches are crucial problems to be addressed in 2021. This year has seen a surge in data breaches that have damaged businesses and their infrastructures to different levels. As a result, marketers have increased their investments in encryption, access control, network monitoring, and other security measures.
To help comply with the General Data Protection Regulation (GDPR) of the European Union, the California Consumer Privacy Act (CCPA), and other regulatory bodies, organizations have made the shift to platforms where all consumer data is in one place. Advanced encryptions and stateless computing have made it possible to securely store and share governed data that can be kept in a single location. Interacting with a single copy of the same data will help compliance officers tasked with identifying and deleting every piece of information related to a particular customer much easier and the possibility of overseeing something gets canceled.
Protecting consumer privacy is imperative for marketers. They offer consumers the control to opt out, eradicate their data once they have left the platform, and remove information like location, access control to personally identifiable information like email addresses and billing details separated from other marketing data.
Predictive analytics’ analyzes collected data and predicts future outcomes through ML and AI. It maps out a lookalike audience and identifies which strata are most likely to become a high-value customer and which customer strata has the highest likelihood of churn. It also gauges people’s interests based on their browsing history. With better ML models, predictions have become better overtime, leading to increased customer retention and a drop in churn.
According to the research by Zion Market Research, by 2022, the global market for predictive analytics is set to hit $11 billion.
Investment in first-party data
Cookies-enabled website tracking led marketers to know who was visiting their website and re-calibrate their ads to these people throughout the web.
However, in 2020, Google announced cookies would be phased out of Chrome within two years while they had already removed them from Safari and Firefox.
Now that adding low-friction tracking to web pages will be tough, marketers will have to gather more limited data. This will then be then integrated with first-party data sets to get a rounded view of the customer. Although a big win for consumer privacy activists, it is difficult for advertisers and agencies to find it more difficult to retarget ads and build audiences in their data management platforms.
In a digital world without cookies, marketers now understand how customer data is collected, introspect on their marketing models, and evaluate their marketing strategy.
Emergence of contextual customer experience
These trends in marketing analytics have become more contextually conscious since the denunciation of cookies. Since marketers are losing their data sets and behavioral data, they have an added motivation to invest in insights.
This means that marketers have to target messaging based on known and inferred customer characteristics like their age, location, income, brand affinity, and where these customers are in their buying journey. For example, marketers should tailor messaging in ads to make up consumers based on the frequency of their visits to the store.
Effective contextual targeting hinges upon marketers using a single platform for their data and creates a holistic customer profile.
Reliance on third-party data
Even though there has been a drop in third-party data collection, marketers will continue to invest in third-party data which have a complete understanding of their customers that augments the first-party data they have.
Historically, third-party data has been difficult to source and maintain for marketers. There are new platforms that counter improvement of data like long time to value, cost of maintaining third-party data pipelines, and data governance problems.
U.S. marketers have spent upwards of $11.9 billion on third-party audience data in 2019, up 6.1% from 2018, and this reported growth curve is going to be even steeper in 2021, according to a study by Interactive Advertising Bureau and Winterberry Group.
Marketing analytics enables more successful marketing as it shows off direct results of the marketing efforts and investments.
These new marketing data analytics trends have made their definite mark and are set to make this year interesting with data and AI-based applications mixed with the changing landscape of marketing channels. Digital marketing will be in demand more than ever as people are purchasing more online.
Frequently Asked Questions
Why is marketing analytics so important?
Marketing analytics has two main purposes; to gauge how well your marketing efforts perform and measure the effectiveness of marketing activity.
What is the use of marketing analytics?
Marketing analytics help us understand how everything plays off of each other and decide how to invest, whether to re-prioritize or keep going with the current methods.
Which industries use marketing analytics?
Commercial organizations use it to analyze data from different sources, use analytics to determine the success of a marketing campaign, and target customers specifically.
What are the types of marketing analytics tools?
Some marketing analytics’ tools are Google Analytics, HubSpot Marketing Hub, Semrush, Looker, Optimizely, etc.
"name": "Why is marketing analytics so important?",
"text": "Marketing analytics has two main purposes; to gauge how well your marketing efforts perform and measure the effectiveness of marketing activity."
"name": "What is the use of marketing analytics?",
"text": "Marketing analytics help us understand how everything plays off of each other and decide how to invest, whether to re-prioritize or keep going with the current methods."
"name": "Which industries use marketing analytics?",
"text": "Commercial organizations use it to analyze data from different sources, use analytics to determine the success of a marketing campaign, and target customers specifically."
"name": "What are the types of marketing analytics tools?",
"text": "Some marketing analytics’ tools are Google Analytics, HubSpot Marketing Hub, Semrush, Looker, Optimizely, etc."
Article | March 23, 2020
Big data is a modern phenomenon transforming businesses of today. Organisations hold vast swathes of data, from historic and current orders to detailed insights about supply chain operations. This information, combined with external data such as market intelligence and even weather patterns, can provide businesses with a foundation on which to base their planning and decision-making. Business intelligence and analytical solutions pull valuable insights from huge datasets. From workforce optimisation to cost management, access to big data and the tools that manage and evaluate it allows firms to streamline key parts of their business. Adopters of modern solutions are seeing vast improvements in all areas of the company.