Natural Language Processing (NLP) is a field of study that articulates the connection between computers and humans in natural spoken language. We know that computers rely on binary number systems with 0’s and 1’s to communicate data and process information. Computer and App developers write programs in the machine language and build a human-friendly interface for regular users to understand the features and functions of the software and utilize it to their convenience. NLP allows computers directly to process human text and voice and process it to give appropriate feedback.
What is NLP?
Natural Language Processing (NLP) is the amalgamation of computer science, Artificial Intelligence(AI), and linguistics. The primary purpose of NLP is to make a machine understand our language processing and come back with appropriate human-like responses. A great example would be AI Digital assistants like Alexa, Siri, or any chatbots. Especially with voice recognition and processing, these AI assistants utilize NLP and apply reinforced machine learning to come forward with appropriate search responses for our queries.
Ultimately, NLP allows computers to perform sentiment analysis, determine which parts of human language are significant, and understand the underlying message properly. NLP is challenging due to the nature of complicated sentence structure, grammar formats, and exceptional rules followed by every language, leading to the large amount of unstructured data the machine has to process.
NLP can be categorized based upon the nature of human data and techniques utilized to process the language.
Types of NLP
1. Optical Character Recognition (OCR)
OCR is a robust data capture solutions technology that recognizes handwritten text content and derives the information. With digitization in full bloom, all the handwritten text, either electronic or paper documents, are being migrated into the server database for better management analysis. OCR allows this task to be composed without any manual data entry work involved.
NLP enables the systems to recognize relevant concepts in the document to gather information beneficial to create a structured database for machine learning analytics.
For example, the words, numbers, and tables could be identified separately, and nouns like addresses and names can be further scrutinized to create a complete customer data set easily.
2. Text Summarization
Text summarization NLP is used by experts to assess information and trends present in research articles and journals.
Text Summarization uses extraction and abstraction techniques to achieve the results. The extraction process assesses large amounts of textual data and gathers precise and informative summaries with essential keywords, and the Abstraction tools create new summaries based on the assessment of the source text.
3. Sentiment Analysis
As the word suggests, Sentiment Analysis is the process of mining emotions from the given text. Here the NLP analyzes various unstructured data from multiple social media platforms and content assets like Facebook, Instagram, blogs, customer reviews forums, online surveys and tries to identify and extract opinions from the given text. In sentiment analysis, the data is first preprocessed to remove unnecessary prepositions and symbols. The main focus is on the adjective and the corresponding keywords; for example, if you take data from a customer review page, you can look for the right message.
Customer service – quick to respond
Packaging-Compact and secure
The features extracted are used to calculate the repetition index and conclude a polarity of sentiments if the customer feels positive or negative about the product. The NLP provides insights into whether most customers found the product pricey or which percentage of users were satisfied by the product and could use this information in future considerations while designing a new product.
4. Machine Translation
Machine Translation is a vital NLP tool that automatically converts one natural language into another, preserving the meaning of the input text and analyzing and generating the text in the output language.
Google translate is an excellent example of Machine Translation. It uses statistical models by investigating vast volumes of data in both languages, finding the compatibility between the word from the source language, and translating it into an appropriate word or sentence from the objective language to ensure it follows the linguistic structure.
5. Speech Recognition
Speech recognition and processing using NLP allow devices, such as smartphones and home assistants, interact with users with their verbal language.
AI systems like Alexa or Siri get voice input in natural language. After receiving the signal and processing it to eliminate the noise, the cleaned file is converted into Artificial language like speech recognition.NLP systems have a lexicon and a set of grammar rules coded into the system for the particular language. It utilizes the algorithms to perform statistical machine learning and apply these rules to the text and determine the most likely meaning behind the question asked by the person. This way, we can have an intelligent conversation with our digital assistants.
Deep learning and NLP
We already mentioned that everything we express, either verbally or in writing, carries enormous amounts of information. As humans, we are intelligent and understand the words, sentences, and structure based on the tone and context.
But say, you are looking to scale and analyze several millions of people’s conversations and text declarations. In that case, the data also gets very big, so NLP actively relies on Machine Learning (ML) to process large databases.
Machine learning methods utilize algorithms and weight optimization to make the best final prediction. On the other hand, deep learning relies on artificial neural networks(ANN) with many layers to process the data.
Deep learning provides NLP processes with more speed and agility to automatically learn good features or representations from raw inputs. Deep learning offers the machine to apply modeling approaches like sequence-to-sequence prediction and read the features of natural language independently with the ANN, so external extraction by data scientists is not required. The large end-to-end deep learning models easily fit the natural language problems with better regularisation and optimization methods.
Brands that use AI
The Artificial Intelligence(AI) market size worldwide was valued at USD 62.35 billion in 2020 and is bound to expand with a compound annual growth rate. Many tech companies and businesses are already invested in AI, and other sectors like healthcare are investing in R&D to use patient details to make intelligent diagnoses or book appointments automatically based upon the patient’s history using ML and AI.
The industry leader of streaming services, Netflix relies on AI and ML technology. We all know that Netflix revolutionized the streaming and refined movie and entertainment experience to the next level.
Despite all the fantastic services, Netflix faces tough competition for viewers from Amazon prime, Disney, and similar streaming sites. To remain on top of the time, Netflix mines user data using ML to serve customers and recommend movies or series that would appeal to them. To expand users worldwide and reach farther, they are compelled to support entertainment in other languages apart from English and provide them with quality subtitles in the regional languages. This is where the use of AI and NLP comes into play.
Netflix uses a novel model to produce accurate machine learning results with two steps.
Transcribing: The audio is converted into English words and collected as a database using Nvidia GPUs to crunch through the data.
Translation: Machine translation is applied from English to other languages where Netflix wants to distribute and provide subtitles.
They initially simplify the sentence using a corpus of words and phrases and translate that instead. Netflix’s model is called the automatic preprocessing model (APP); this framework is applied to all English-language sources. The simplified text is sent to machine translation to create subtitles in specific languages.
This way, they are able to automate the process of subtitles, which would typically take months to translate into multiple languages. And the ML applied to customer data to provide them with customized and dedicated recommendations to multiply the watch time by manifold.
Nordstrom is an outstanding fashion retailer offering a high range of clothing, shoes, and accessories for men, women, and kids. They are known for their hospitality and customer service, guiding the users with the help of a stylist and salesperson to ensure a comfortable and personalized shopping experience.
And now, due to the global pandemic restriction, all businesses shut down all the offline stores. And many of them resorted to e-commerce platforms. But, Nordstorm did not want to compromise on their customer service and wanted to make their online store personalized and comfortable as their offline shopping experience.
Nordstrom revamped its data infrastructure to enhance the customer experience and sought to create an AI-supported Nordstrom Analytical Platform (NAP) that employs real-time data collection, event streaming–centric analytics, and collects insights on everything from customers services to credit. They have also modified the platform to elevate your offline shopping experience without human interactions. They have a feature that notifies you about a particular product in your mobile cart as soon as you walk into a store; the app can track your location and pinpoint where your desired product is located within the store, allowing you to navigate without any external assistance.
With AI models leveraged at Nordstrom, users benefit from the timely delivery of information like tracking their packages. And this has redefined their online shopping experience with better selection, personalized dynamic looks, sophisticated style boards, and recommended choices.
Importance of Digital Marketing for NLP and AI and how Vajra Global can help
With the growth in e-commerce and digital marketing, knowing what customers are saying on social media is very crucial to provide an appealing product or service and authentic customer experience. A study shows that about 90% of people tend to buy from brands they follow on social media. Therefore, providing a two-way customer experience is essential, and NLP elevates the customer data monitoring and response to feedback. Social Media Marketing Services along with Online Reputation Management from Vajra helps any industry build and accelerate the social dialogue and gain momentum in social platforms. More traffic to your application and social media means an extensive database to work and test NLP and AI abilities, so the digital assistants or chatbots can be more robust and sound closer to a human.
Artificial Intelligence(AI) has transitioned from being a concept in research papers and sci-fi stories to something feasible and realistic in its early developmental stage and enterprise adoption. With the increase in computational efficiency and the availability of big data, tech giants like Google and Facebook have already implemented Artificial Intelligence and Machine Learning to create Chatbots and personal assistants in smart mobile applications that we use in our day-to-day lives.
Basics of Artificial Intelligence and Machine learning
Artificial Intelligence is an attempt to simulate the human mind and way of thinking to solve complex problems. AI is mainly aimed to have problem-solving and analytical reasoning power in machines.
Currently, AI is used as Digital Assistants, customer support using chatbots, online RPG games, and ultimately creating an intelligent humanoid robot.
Machine learning is a subdomain under artificial intelligence, which associates the ability of a machine to learn from experience without needing to be explicitly programmed to achieve a predefined conclusion.
The traditional statistical machine learning algorithms had limited capacity to process valuable information about training data. However, Deep Learning upgraded the machine to create its structured algorithms and develop its intelligent solution without a predefined algorithm.
Deep learning (DL) is a Sub-discipline of machine learning where multiple layered neural networks are constructed to deliver intricate tasks such as speech recognition, language translation, even recognising handwritten text and digitising them. Presently, the main application of DL is that they should be able to learn and extract the features automatically and interpret any given type of data set from images to video or text.
Classification of Artificial Intelligence
Artificial intelligence(AI) is a discipline under computer science associated with building smart technologies capable of performing tasks that generally require human assistance. Intelligence at the elementary level is the ability to read data, process it, and form an insightful conclusion on your own.
The AI is classified depending on how well a machine can mimic humans in terms of versatility and performance. Under this division, the system is more human-like Functionality and is considered a more evolved type of AI. There are four types under this category.
They are the most basic types of AI systems that are purely reactive and can’t use past experiences to influence current decisions. They can only focus on the real-time scenarios and react to them and come up with the best possible action.
One great example is IBM’s Deep Blue – a chess-playing computer developed with complex algorithms. It derived its playing prowess mainly from high computing power by running through all the possible moves and finding the most favourable outcome.
It was the first computer to win a chess game against a reigning world champion.
2. Limited Memory
As the word suggests, limited memory machines can store past experiences or some data for a short period. The autopilot mode in cars is the best example of how the system can observe other car’s speed and direction. That can’t be done in just one moment but instead requires identifying specific objects and monitoring them over time.
These observations are added to the autopilot mode of the cars preprogrammed with 3D representations of the street, including lane markings, traffic lights, and other essential elements, like curves and trajectories in the road. Allowing that car to make appropriate decisions like changing lanes or parks smoothly without damaging any other vehicles.
3. Theory of Mind
Theory of mind is the bridge between existing AI and future intelligent robots. Scientists and research scholars create a humanoid AI capable of human emotions and can empathise with people, beliefs and interact socially like humans.
To create these machines, scientists focus primarily on understanding memory, learning and the ability to make decisions using previous experiences and understand human intelligence as a whole.
4. Self Awareness
Self-awareness is the ultimate goal of AI development to build systems that can form representations about themselves. Once created, these machines will be super intelligent and will have their consciousness, sentiments, values and self-awareness.
These machines will be more intelligent than the human mind and are generally the main plot of many sci-fi movies where robots take over the world. But till today, it’s just a hypothesis.
The other type of classification widely used by Tech companies is based upon the capabilities of an AI.
1. Weak AI or Narrow AI:
Narrow AI is a type of AI that can perform a dedicated task with intelligence. This spectrum includes all the AI that has ever been created to date. Anything from Google Assistant to Google Translate, Siri and Alexa are great examples of narrow AI. These applications nowhere possess any kind of intelligence. They only process the human language, enter it into a search engine and return to us with results.
Currently, existing AI doesn’t have the fluidity to process information and come up with solutions as we do.
2. General AI:
General AI is expected to perform tasks with intellectual knowledge and the flexibility of a human. These systems should be able to independently build multiple connections across domains and grasp the data efficiently. As of now, no such general AI system exists which could perform a task that is akin to a human
3. Super AI:
This could be dubbed as the God of AI. These machines should be able to replicate the multi-faceted intelligence of human beings and will be exceedingly better at everything. They come with high computational power, large memory, faster data processing and analysis, and precise decision-making capabilities,
No matter what type of AI at the grassroots level, all the machines need to process the data. This is where machine learning comes into play.
What is ML: Types and examples
Machine learning gathers data in any form from the database suitable for processing. The better structured the data, the easier it will be for modelling, but unstructured data gives more flexibility.
Machine learning happens in four different stages — data processing, model preparation, data training, monitoring and evaluating the model. The desired data set is fed through predefined complex algorithms to attain the best-optimised solution and other possible predictions for the given problem.
With the massive boom in the internet and social media, companies have access to a large amount of data about their customers online.
Big data is long-standing and difficult to process manually, so automated learning using Machine learning algorithms has proven to be an asset for many companies to gain valuable insights and use the predictions and form sound decisions.
ML algorithms are trained using three main methods.
1. Supervised Learning
In this method, the input data has been already labelled manually and machine-readable. The algorithm is given a small training dataset that serves as a model to provide the algorithm with an idea of the problem, solution, and future data points to be dealt with.
Eg: Predicting petrol prices for the next week with monthly crude oil and petroleum stock prices.
2. Unsupervised Learning
In unsupervised learning, unlabeled data is used without any intervention from humans manually. This algorithm is capable of adapting to dynamically changing hidden structures.
Let’s take for example that you have a picture of a dog and cat unlabeled. This algorithm memorizes the features of the two different animals and can predict the closeness if you input an image of a pomeranian. Like when we were kids, we knew the difference between a dog and a cat. They are different species and are identified based upon their distinct features without looking at every dog or cat picture in the world.
3. Reinforcement Learning
Reinforcement learning data doesn’t have any fixed data set or end goal established at the beginning but instead learning to operate using the feedback.
Alpha Go is the best example of reinforcement learning here; it plays both student and teacher. The system starts off with a deep learning artificial neural network that has no grasp of the game. It then plays games against itself. With the help of its ANN and complex search algorithm, it plays and fine tunes for updating and predicting the moves and eventually learning all the tricks and becoming the winner of the games.
Lifecycle of ML
Machine learning life cycle is a cyclic process to build an efficient and most optimised solution for the given problem.
The Cyclic process includes the following steps
1. Data gathering
The process of collecting databases from various sources, from social media analytics to direct customer files and integrating them, is very crucial. The quality and quantity of the data is used to determine the choice of algorithm and further the prediction accuracy.
2. Data preparation and preprocessing
Now that we have the raw data from various sources, we need to prepare it by what nature of data that we have to work with, find trends and discovering patterns to get a better understanding of the data leads and preprocess it to make sure the is no inconsistency or garbage data which can interfere the learning process.
3. Data analysis
This is the core of machine learning, where you choose a suitable algorithm based upon the problems and select the machine learning techniques such as regression, classification, clustering, Support vector machine etc.Then you can build the model using prepared data to evaluate the model.
4. Model training and testing
From the vast amount of data you prepared, you will take batches of data test cases and train the model using various machine learning algorithms. A training model is always required to identify the general pattern and metrics of the dataset and form segmented clusters to learn about additional features.
And once the model has been trained, we test it with input and check the prediction to find the accuracy and error percentage.
5. Deployment of the Model
This is the last stage of the ML cycle when the model has attained greater accuracy and higher speed, and it is deployed to the real-time system for the project.
And this cycle continues to add further upgradation and optimize the technology.
Importance of Digital Marketing for this industry and how Vajra can help
AI and ML can contribute to the digital marketing industry. According to a Business Insider’s report in 2019, 51% of marketers are already using AI.
With the growth rate of social media and the internet of things (IoT), there is a flood of information utilised by AI and ML industries. Digital marketing companies like Vajra bring the datalead required by these AI and ML technologies. AI and ML can utilize the content marketing platform and use the database to train their models to generate content. With more traffic to your application, you can also improve the ability of your AI chatbot to answer open questions and achieve a natural and correct response.
Vajra can provide you with lead analytics & generation, top SaaS metrics and many other services.
For more details, get in touch with us. You can also connect with us on LinkedIn / Instagram / Facebook for updates to give your business a complete digital transformation.
Top 3 Reasons to create brand awareness with User Generated Content
User-Generated Content – What is it?
If you are looking for successful ways to improve brand awareness, you are in the right place. User-generated content is defined as any form of content (content, image or a video) posted by the users online such as social media platforms.
User-generated content is extremely popular with consumers as it is created by their peers instead of a brand. It acts as social proof. According to Stackla, UGC posts that are shared to social channels see a 28% higher engagement rate compared to standard brand posts. So, when you share the content with your followers that was created by their peers, they are more than likely to pay attention. This can be done just by asking your audience to submit UGC. It will result in more engagement.
When brands share the content created by their consumers on their social media pages, it is a win-win situation for all. The consumers get their two cents because of the fame and glory of being showcased on the social media platform of a popular brand. This inspires more followers to follow suit and share their experiences as well. The brand not only gets free brand awareness marketing, but they also get concrete proof of the quality of their product. Customer happiness and satisfaction is key to word-of-mouth marketing, and UGC is the foundation of the brand awareness strategy.
The power of User Generated Content
What’s the big deal about user-generated content, and why should your brand care? Here are three key reasons UGC is a crucial brand awareness marketing strategy you can’t ignore. And why it is your best laid plan to improve brand awareness.
UGC Promotes Authenticity
Statistics show that user-generated content brings in 2.4 times more customers than the promotion created by the brand itself.
When Warby Parker Glasses promoted the picture of their customer’s post, it racked up over 15,000 likes and genuinely inspired comments such as ‘I die of cute’. Had the brand created the content themselves, it might not have screamed ‘real’. The audience would not have been moved. Authenticity not only helps increase brand awareness, it registers as a trusted brand in the eyes on the audience.
UGC Creates Trust
Be it a product, service or an experience; today’s consumers want to Google everything to make an informed decision. Research shows that 30% of millennials would never even consider the product without sufficient third-party evidence to back it up. For instance, if the restaurant’s review was not present on Zomato, it would not be present in their minds either. Such is the power of user-generated content. It’s all about the trust created at the end of the day.
Another interesting fact: 92% of prospects trust recommendations from people they know, and 70% have faith in online consumer feedback.
Now, who would not like to try out this product?
UGC Drives Purchase Decisions
Brands promote user-generated content for the ultimate goal – to influence the purchasing decisions of prospective customers. Research states that nearly 80% of consumers have confirmed that UGC plays a key role in their purchase decisions. When you increase brand awareness in the right way, you give them a good reason to buy your product as well.
Instagram stories sharing user-generated content are designed to drive purchases. Create lasting impressions that allow potential users to analyse and come to a conclusion. According to Instagram, one-third of the platform’s most-viewed Stories are from businesses.
In this example, the brand has not only posted an image by their client, but they have also engaged with them personally on the social media platform to encourage them to share their experiences as well. If this does not work, what will?
It is evident that user-generated content puts the brand on the map in more ways than one. Asking for customer reviews is not enough to convince people to open up and share their feedback. It has to be done tastefully. Give them something to smile about.
Customers need to be asked for their feedback and experience with the product and service. Brands need to take that step forward and offer something in return for their testimonial. Here is an example of the same.
This brand encourages users to share their experiences to be featured on their social media platforms. This motivates their customers, and it brings more traffic to their website.
Vajra Global as your Social Media Partner
As your strategic partner in the world of social media management, we create customized and innovative ideas to connect with your consumers, engage with them and bring forth their stories to the limelight. It is time to highlight not just what you do, but how it benefits your consumers. Let us be that bridge between you and your target audience.
Connect with us to take brand awareness strategies (B2B and B2C) to a whole new level.
For more on Digital Marketing, do follow our blogs. You can also connect with us on LinkedIn / Instagram / Facebook for updates on the latest trends and how to grow your business strategically.
Data analytics – Bring trust with precision
Data Analytics is a branch of STEM (Science, Technology, Engineering, Mathematics) that studies raw data and identifies meaningful conclusions like trends, valuable metrics, and other information to interpret the data. The setup requires database servers to hold the statistics and software applications with specified algorithms to run the data and form meaningful conclusions to make more informed decisions.
Any data can be subjected to analysis and will help you to get a better insight of the presented information, but data analysis techniques are generally used in businesses to make vital customer decisions. It is also widely utilized by scientists and students to verify or disprove the experiment’s hypothesis.
Data vs Big Data
Data is a collection of qualitative or quantitative variables that can be structured or random, theoretical or numeric, but provide us with information.
It’s pretty straightforward to conclude big data contains a large amount of data. Big data has a greater variety and volume of valuable information, with more complex data sets collected frequently from new data sources. To put it simply, we can comprise big data as the three 3V’s: Volume, Variety, and Velocity.
Classes of Analytics
Data analytics is a broad term covering all types of data analysis to transform raw data into useful information for some conclusion, but it’s not oriented to reach any immediate goals. Data analysts primarily utilize statistical modeling and predictive algorithms to create reports and visualizations that are further used to make required diagnoses and conclusions.
According to a Forbes study, at least 53% percent of companies use data analytics in their company presently. The primary aim of business analytics is to examine and analyze the data and then transform their results into trends and insights that ultimately help executives, managers, and operational employees make better deals and plan the immediate business goal more appropriately.
Business analytics examples include managing patient information and insurance database systems in the healthcare industry. Or to streamline fast-food restaurants by monitoring peak customer hours and making orders in advance and ready to go, to ensure customer satisfaction and avoid the peak hour rush.
Advanced analytics refers to sophisticated methods and tools that can help you get the most of the given raw data. Advanced analytics techniques can forecast trends, analyze customer behaviors, and set up sales objectives accordingly.
Big eCommerce giants like Amazon and Flipkart rely on advanced big data analytics. They data mine sales from their website and rely on text mining to analyze documents, emails, and other text-based content using automated machine learning techniques and predictive analysis. This ultimately leads them closer to fine-tune cart recommendations which will increase the chances of customer purchases. With many other similar websites available, personalization and knowing the customer channel better has helped them remain on top of the game.
Evolution of Analytics
Data Analytics is not entirely a fresh concept; back in the day, businesses collected customer information from reward program registrations, sales reports, advertisement returns, and other information. They manually entered the data and organized them to understand the customer trends and devise future business strategies.
Business intelligence offers a way to examine the existing data to understand trends and then decide which factor streamlines the effort. It also allows you to create reports and dashboards comprising the data. It provides descriptive information about the situation enabling the members in positions to make informed business decisions.
Business intelligence doesn’t make any prediction, it just informs about the metrics, but these insights allow the officials to work smarter and make appropriate decisions.
Big data is a recent concept that came into play because of the boom in technology and the internet with smartphones, mobile applications, social media, the cloud, and IoT(Internet of things). Big data is the essential tool that drives large IT industries and businesses of this decade.
The scope is unlimited to various sectors like travel and tourism, finance, telecommunication, virtual assistant, machine learning, and many more. With these broad sectors, the big data market is expected to have annual revenue of $274 billion US dollars in the upcoming year 2022.
Government agencies can utilize the big data collected from Aadhar cards and ration cards to segment the population and develop beneficial schemes and work opportunities based on age, demographics, and income.
Data Science initially started with statistics for businesses and has evolved to include machine learning and artificial intelligence applications. It was purely mathematical, but now data science can run algorithms for different forms of data, from infographics to text. Data scientists run algorithms on systems to extract information and insights from structured and unstructured data and apply that knowledge into actions across various application domains. They also design new algorithms and procedures for processing data in new ways and get further analysis.
Automation has become part and parcel of big data analysis, increasing the amount of information to process. Automation has come to the rescue and made the process efficient and agile.
Both the data and the procedure being complex, it’s not suitable for a data scientist to work manually and run the algorithm. Automation has not only reduced the work, but it can also generate reports efficiently and visualization and graphs. It reduces human error and precisely interprets the current data, so there is no benefit of the doubt and that teams can make accurate business decisions.
Types of data analytics based upon the results
Descriptive analytics summarizes data points that happened previously so that trends emerge and helps the team get insights. For example, has the sales improved? Or statistics of female customers buying a particular product.
It gives you a conclusion about the distribution of your customer data and helps in identifying errors and outliers.
Diagnostic analytics investigates the root of a particular problem or case. Why did this happen? So it involves comprehensive data about the specific scene and hypothesizing to find the root cause.
For example, in health care, symptoms correlate to a particular condition, and without knowing their medical history, they can’t be fully diagnosed. Or in business, like how the new advertisement affected the sales.
Predictive analyticstakes previous data and feeds it into a Learning model or algorithm that traces key trends and patterns. Then, use the data, interpolate the graphs, and predict what will happen in the immediate future.
If you take the case of predicting stock market trends and the future price, that will allow the investor to yield significant profit.
Predictive analytics gave you an idea of what will likely happen in the coming weeks. Prescriptive analytics offers you the next course of action, suggests various possible outlines, and the potential implications for each scenario.
If you are sensing a falling trend in the stock price, will you pull out from the company or sell the stocks to someone else, choose profitable solutions, and allow the investor to make sound decisions.
Big data is stored in two different forms as data lake and data warehouse. The data lake is a wide body of raw data, which doesn’t have any targeted purpose. Whereas on the other hand, the data warehouse is a structured and well-organized database and has been processed to serve a specific goal or vision.
Data lakes primarily store raw, unprocessed data, while data warehouses store processed and refined data. Data lakes are in complete disarray, so they typically require a much larger computational space. It requires a data scientist and specialized tools to understand and translate it for any specific business use.
Data warehouses are processed and have specific uses like any business or organization to collect information, so it has to be reliable and easily understood by all the employees by just looking at the charts, spreadsheets, and tables. The data warehouse is easier to decipher, but the organized architecture limits the flexibility and makes data warehouses difficult and costly to manipulate.
Organizations might use both data lakes and data warehouses to harness their big data. Let’s look into how various industries exploit the Big Data benefits and how it has reformed their business strategies.
Telecommunication and Media
Telecommunications and the multimedia sector are the early and most prominent users of data analytics. There are zettabytes of data generated every day and handled swiftly using big data technologies.
Travel and Tourism
Travel and tourism also use this technology to forecast travel requirements and facilities for a particular venue from the reviews on google and other sites. And can optimize the business through dynamic pricing and seasonal offers to attract more customers.
Finance and Banking
The banking sectors use big data analytics to determine customer behavior based on investment patterns, shopping, insurance, bonds, and loans. These obtained financial backgrounds allow them to target the right customers and enhance branch productivity; tools like TCS Optix offer contextual insights to a bank, rendered via enterprise applications and embedded analytics, and enable quicker adoption.
Big data has started to impact the healthcare sector with the help of predictive analytics and leveraging real-time data streams. Penn Medicine, a multi-specialty hospital-based in Pennsylvania, developed a dashboard that displays real-time data streams of electronic health records (EHR).
There is an inbuilt system to alert respiratory and nursing staff when interventions are needed. That can improve personalized healthcare and provide each patient with required visiting and care from the medical professionals.
Data analytics has multi-purposes across various sectors and plays a crucial role in tech companies, government offices, and finance. It is used to build features like machine translation robotics, speech recognition used in smartphones, and these analytical tools are running the digital e-commerce economy with optimization strategies.
For more details, get in touch with us. You can also connect with us on LinkedIn / Instagram / Facebook for updates to give your business a complete digital transformation.
What is ORM and why should you do it?
What happened recently has rocked the world of marketing in many ways. Cristiano Ronaldo’s removal of two Coca-Cola bottles at a Euro 2020 news conference coincided with a $4 billion drop in the market value of the American drink giant.
This is the backstory for the uninitiated.
During a press conference at the Euros, Ronaldo was seen shifting two Coke bottles and grabbing a bottle of water while saying ‘Agua,’ which means water in Spanish. Almost immediately after his gesture, Coca-Cola’s share price dipped by 1.6%, with its market value going from $242 billion to $238 billion. (Source: ESPN)
Coca-Cola’s investment in marketing.
Coca-Cola spends about $4 billion on marketing annually. The superstar’s gesture cost the company the equivalent of its marketing budget for the whole year.
The Domino’s effect
The very next day, Paul Pogba was seen removing a bottle of Heineken kept in front of him as well.
On a side note
Cristiano Ronaldo and his team Portugal are out of the UEFA Euro 2020 soccer championship. Still, the debate continues whether (or not) the football icon was solely responsible for The Coca Cola Company losing four billion dollars in market cap on the New York stock exchange (NYSE).
A post-truth world.
In 2016, the Oxford English Dictionary’s Word of the Year was “post-truth.” Post-truth is defined as “relating to or denoting circumstances in which objective facts are less influential in shaping public opinion than appeals to emotion and personal belief” (Source: Forbes)
So, what does this mean?
It is a fact that there is a torrent of opinion and feedback made by prominent people, and this impacts public preferences and decision-making to a large extent. This affects both individuals as well as businesses. This incident has only highlighted the importance of what is being said and done about you as a brand, product, or service in the real world and online.
Having said that, what is the damage control that brands have to do to ensure that such repercussions do not happen? Accepted that Ronaldo is a heavy-weight figure and anything he does is likely to have colossal consequences, and this kind of damage is not likely to happen in the near future, if at all. But it is most definitely a wake-up call for brands to keep an eye on the reactions it brings out in people and the impact of that reaction on the rest of the world.
Introducing Online Reputation Management
To put it in simple terms, Online Reputation Management blends traditional & digital marketing with public relations. It involves understanding the search engine results and how it can get impacted by your brand’s online presence and reputation when negative exposure takes place. Damage control is the critical essence of online reputation management.
Let’s demonstrate the above exercise with how Coca-Cola handled the situation.
Coca-Cola’s market value plummeted by £2.8 billion after Ronaldo’s antics, but the company has since released a statement insisting that “everyone is entitled to their drink preferences.” A wise and mature reaction, so to speak.
Examples of online reputation management
Whether you like it or not, it will happen at some point, and there is nothing you can do about it proactively. A hiccup in service. Badly packaged food. Rude delivery person. Product not working. Murphy’s Law has a place everywhere, and while you do your best to make sure that nothing can go wrong, you must be prepared with a plan B. What you should do when things do go wrong. Because, if it can, it will. What next?
Please find below a couple of real-life examples of how brands have put it together to bring damage control in the form of a timely Online Management Strategy.
Joe Dough Sandwich Shop
Sometimes, humor plays a critical role in online reputation management. Pay attention to the content one. You are in no way underestimating the issue, but you are handling it in a different out-of-the-box manner. This could be risky, and you need to handle it carefully to make sure that the situation is not blown out of proportion.
Situation: One bad Yelp Review
Their handling the situation:
Wine Country Tour Shuttle
Sometimes, it takes more than a sorry to handle an angry customer. Then it becomes more about soothing them by solving the problem with care and sensitivity. It also highlights the competency and attention you bring to the situation and not pooh-pooh it.
How they handled it
Negative reviews are part and parcel of a brand’s life. Responding calmly instead of reacting strongly is critical. The transparency played in the brand’s reply to the situation will stay in the customers’ minds for a long time.
The bigger the brand, the harder the fall. Bringing articulate content marketing, social media management, and ORM into the picture is not an easy task. Online reputation management is a niche field of internet marketing that is increasingly critical with the majority of your current and prospective customers online.
As your online management team, we bring you a comprehensive safeguard mechanism to your brand’s reputation on the internet by keeping an eye on the information related not just to your brand but to your industry as well. By building consistent branding on all your social media platforms along with traditional public relations strategies, we ensure reputation monitoring from the point of view of employees, customers, stakeholders, and audiences.
Connect with us to explore Online Reputation Management strategies for your brand.
For more on Digital Marketing, do follow our blogs. You can also connect with us on LinkedIn / Instagram / Facebook for updates on the latest trends and how to grow your business strategically.
Cloud Computing – Expanding Possibilities
In businesses, rapid growth in e-commerce and digital marketing has increased the demand for computational resources. Substantial databases can be turned into meaningful information by utilizing features like machine learning and artificial intelligence. But handling Big Data can take quite a toll on the network architecture and the server. This is where cloud computing services come into play.
The coffee house chain Starbucks collaborated with Microsoft cloud services and digitally transformed their app experiences. Starbucks wanted to automate their coffee makers so numerous recipes can be made easily with just the flick of a button. Cloud services along with IOT proposed a guardian module to maintain and identify the issues with their machines and automatically add the new customer recipes. The reinforced learning of app data allowed them to customize the drinks and give the user an authentic experience. And all the new recipes could be stored in the cloud and sent directly to the coffee machines automatically. This personalization positively impacted direct marketing and sales.
Life before and after Cloud Services
Before Cloud services were embraced in organizations, the primary server ran all communications and file systems. So if this server were corrupted, all the data would be lost. And the server infrastructure has to be regularly maintained and updated in terms of hardware and software to ensure smooth functioning. Individual workers generally processed all the server information on their office desktop.
But, the introduction of cloud computing services gave them the freedom to access the data as long as they had access to computers and the internet.
Cloud Computing – What is it?
Cloud computing is the Internet-based service that provides storage, databases, networking, system resources, and processing power without direct active management by the user.
When the database is extensive, cloud computing allows for reduced queuing time in servers and efficient processing. It also benefits with remote data access to the employees, which is essential for all work-from-home projects, especially in the pandemic. According to the2019 Survey, 94% of software experts confirmed their organizations used cloud computing services.
Benefits of Cloud Computing
Reduces expenses on hardware maintenance
Allows efficient collaboration in projects despite remote working conditions
Ensures automatic data updates, accommodating up/downscaling data storage, and
Provides a secure backup
Despite all the benefits as an emerging technology cloud computing can be expensive to migrate immediately. With more third party companies investing to host these services, the cost will reduce immensely in the coming years. There is also a risk of vendor locking, where organizations might find themselves locked into a certain cloud provider sinceit is very difficult to shift the database to a new platform in cloud computing. But there are multi-cloud and hybrid strategies which ensure data accessibility even when the service provider goes out of the market. If you are worried about threats regarding data security there are private cloud deploying services which would protect sensitive information.
Any business can migrate towards cloud computing services depending upon their need for data security and accessibility. They can deploy the cloud from any of the four cloud computing models.
1. Public Cloud
The word public itself is self-explanatory. The shared services are available to the general public for free or can be operated as a pay-per-use model and provided by third-party services. The most commonly deployed public cloud models are Amazon Elastic Compute Cloud, Microsoft Azure, Google App Engine, IBM Cloud, etc.
In the Public cloud, the third party is responsible for managing and maintaining the data centers, establishing connectivity, deploying new products, releasing product updates, configuring and assembling servers. This makes it quick and easy for the cloud user to deploy and scale the cloud according to their requirements.
But public clouds could be more vulnerable to data theft since the resources are shared publicly. Not only that, the performance of the cloud depends upon an individual’s computer configuration and internet connection. Since a third-party organization hosts all the services, there is not much room to customize the cloud to suit your business perfectly.
Private cloud provides computing services to a particular organization and selected authorized users. Ubuntu, HP Data Centers, and Microsoft are the most common examples.
Private cloud ensures a high level of data security and privacy by internal hosting so that sensitive client data and other confidential information are not accessible to any other party.
Private cloud is only accessible within the organization so that data resources can be scaled only within the capacity of internal computational and infrastructure resources, and the company needs to hire technically skilled IT engineers to manage and maintain the cloud.
A community cloud is a collaborative systems service to be accessed by a group of several organizations and share the information among them.
Community cloud is the place for joint projects and applications; this distributed infrastructure allows various departments or mutual businesses to work together.
Any government organization in the country that collaborates with each other and handles similar audit and privacy requirements can use the community cloud. They can commission their project together and update it on the cloud for easy synchronizing and tracking.
Community cloud is not ideal for every organization since the database size and requirement for each employee can vary, but the cloud only offers a fixed amount of data storage, and bandwidth is shared among all community members.
4. Hybrid Cloud
A hybrid cloud is the balance between public and private cloud services. This cloud service is dedicated to creating a unified and automated computing environment where critical activities with sensitive information are securely performed on a private cloud, and non-critical activities are carried out on a public cloud.
With dual infrastructure, the hybrid cloud is secure and ensures conventional risk management. But hybrid cloud services are costly and challenging to deploy and maintain.
After choosing what type of cloud to deploy, companies need to select the infrastructure suitable for their applications and computations.
Cloud Computing Infrastructure
There are three major types of cloud computing Infrastructure a company can use.
Software as a Service (SaaS)
SaaS is an on-demand software that caters to the particular application required by the customer where the service is hosted using the cloud.
The most common example is an application like Gmail that runs online directly in the browser, where the service is hosted by the Google Cloud server.
Infrastructure as a Service (IaaS)
The IaaS cloud provider manages features like storage, server, and networking resources and delivers them to subscriber organizations through Virtual machines via the internet.
Amazon Web Services (AWS) and Google Cloud Platform (GCP) are such examples. A business can also opt to build its private cloud and implement automated data handling.
Platform as a Service (PaaS)
The PaaS service supplies a Virtual environment for developing, testing, and managing software applications. Google App Engine (GAE) is a cloud services platformthat provides Web app developers and enterprises with access to Google’s scalable hosting.
The cloud services market trends show us the market size was valued at $264.80 billion in 2019 and is projected to reach $927.51 billion by 2027. AWS leads the cloud computing service leader for Infrastructure as a service (IaaS), and subsequently, Microsoft Azure is expanding to provide hybrid deployment services, and at third position Google Cloud is holding with $11 Billion annual run rate as the fastest-growing, building out industry approach and sales scale.
The practicality and feasibility of cloud computing are primary reasons for the shift in the paradigm of global adaptation of cloud computing services. Its promising ability to cut infrastructure costs led to an increase in the adaptation of cloud computing services in major software companies. Still, financial companies are yet to utilize these cloud computing services due to the potential risk of security protocol breaches due to an insecure interface or account hacking.
In the future, with upgraded security protocols, financial institutions can store data, provide customer services, and carry out complex transactions faster and with trust via the internet.
How can vajra help:
As a SaaS company, how you present your services to your target audience with B2B marketing strategies is very critical to your business growth. The core message as well as your unique selling proposition should be delivered to the right target audience at the right place at the right time. As a 360-degree digital marketing agency, we bring you a comprehensive and thought-through approach to project your brand to your prospective customers.
We create successful partnerships that deliver digital marketing requirements like marketing automation, content writing, social media profile creation, search engine optimization, lead analytics & generation, top SaaS metrics and many other services. We assist you in developing your business by helping you identify the business objectives using accurate and exhaustive data analytics, lead customer channels, and digitally reconstruct your business.
For more details, get in touch with us. You can also connect with us on LinkedIn / Instagram / Facebook for updates to give your business a complete digital transformation.
Real Estate Content Ideas that Help Claim your Position Online
Listings with sharp photos sell at or above list price 44% of the time direct-mail response rates more than doubled in 2016.
It’s time for you to rev up your real estate business through engaging content and build an everlasting impact on your audience. A well-presented story of your brand, housing facilities, and location would add a feather to your cap for your real estate marketing goals. Content has the power to captivate buyers through emotion, empowerment, inspiration, and motivation. Harness the power of real estate content creation through diverse content marketing strategies and claim your position at the top of the search results among competitors.
Building a good first impression on your audience can be quite daunting; real estate content ideas that can help prospects connect with your brand on a whole other level. Also, with emerging users on social media every day, content plays a vital role in building brand awareness and showcasing properties with the right information. Customers keenly gauge your profile through the content you churn out and make decisions accordingly. Stand apart from the crowd with unique content and bring your business on top with words that capture your audience. Here are some real estate content ideas that can amplify your business and fatten your wallet.
Use Captivating Power Words in Property Listings
Property listings are the first thing your target audience looks at when they encounter your brand. It is one of the most important things that drive the decision-making process for homebuyers. The question is, “what words would capture the attention of my prospects and make them want to explore for my property?” Well, the answer is simple, it’s the right adjectives. One of the best content ideas for real estate property listings is the clever use of adjectives. Also, the key is to make sure that the information you provide is well researched in detail and articulated properly.
Start a property description with a USP feature that your audience would love. Use words such as landscaped and luxurious rather than cosmetic or nice. Stay away from using too many punctuations or long sentences, as readers often tend to lose interest and move ahead if they see a long paragraph in your listing. Also, another key aspect of real estate content creation is to avoid starting with basic information and focus on important features of the property.
Persuasive Eye-catchy Content on your Website
What is the first thing that your buyer lands on once he sees your property listing – online advertisement, or social media post? It’s your website. In order for your prospect to gain trust in your brand, website content plays a crucial role in establishing your integrity in the mind of your customer. One of the best real estate website content ideas is to capture and convey the core message on your website along with the style of presentation. Highlighting the key aspects such as project location, market comparison, amenities, and USP features of the property are essential.
Persuasive eye-catchy content is the key to real estate content creation. Pay close attention while writing content to highlight the location and key features of the property you are selling. Use high-quality images to showcase the house or workspace and add relevant information with infographics and maps to assist homebuyers through their journey. Some key takeaways for content ideas for real estate are:
Align the content with the tone of your brand by understanding your target audience, identify business goals, and use an appropriate tone that captures the audience’s attention.
Do in-depth research and include information that is rich in facts and streamlines your writing to the market needs.
Segment your content with the right consistency by dividing it into paragraphs and crisp lines.
Create a stellar headline and optimize your content for SEO with relevant keywords that can boost your website ranking.
Keep a check on the grammar and spelling by proofreading your content and make sure to post consistently.
Crisp One-liners in Banner Content and Carousel ads for Social Media
Long format content and big paragraphs never work on social media as users’ attention span is very limited. To make that killer impression on your prospects, you need a show-stopping one-liner that drives your audience to click on the CTA and direct them to your desired webpage. A concise, catchy, and creative one-liner is the perfect way to rope in your prospect and drive them to your website. Also, don’t forget to add infographics with relevant information. One key aspect of real estate content marketing is to provide updated information on the latest trends, popular locations to purchase properties, and their proximity to schools, colleges, or workplaces.
The key to effective real estate content creation is to avoid complex sentences and hard-to-understand vocabulary. Never over-promote your content as it could annoy readers. Most importantly, avoid keyword stuffing as it could impact your website ranking. Usingduplicate content, ov duplicate content, overusing keywords or copied content from other sites would result in your website getting blacklisted. Also, using black hat tools and techniques would result in your website getting severely penalized or worse taken down.
It is time for you to build that stellar impression on your visitors and claim your position in the search results through simple yet convincing real estate content ideas. At Vajra Global, we understand that creating the right content for your business amidst running a tight business could be quite challenging. We are here to help you land on the right audience and fulfill your business goals through captivating, mesmerizing, and engaging content. We do not stop with just content but also offer SEO, website design, and a wide range of digital marketing and SaaS services. Get in touch with us and position yourself as a budding real estate company through our services.
For more on Digital Marketing, do follow our blogs. You can also connect with us on LinkedIn/Instagram/Facebook for updates on the latest trends and how to grow your business strategically.
The ultimate guide for healthcare content marketing
Today, with health at the epicenter of everything around us, it is natural for us to be concerned about a sudden skin allergy or abnormal hair loss. In situations like these, we often turn to Google or other search engines to find the remedies or seek answers.
Statistics from Google state that approximately 70,000 people worldwide prefer Google to research health-related terms every minute. So, as a healthcare provider, it is crucial to focus on creating accurate, trustworthy and relevant content. In addition, as an expert in this field, you would want to ensure that you have the solutions for the pain points, reflecting in your content strategy.
The million-dollar question is, “how can you use content to attract or retain customers in this competitive world?” The answer would be through practical, out-of-the-box strategies. For example, personalized content marketing strategies for the healthcare industry help you connect and engage with your visitors and convert them into long-term customers.
Like good medicine, good content also depends on trust and results. So, here is the ultimate guide for healthcare content marketing to produce tangible results.
Why is content marketing essential for healthcare?
A volley of medical terms could often lead to nausea. To be more specific, is your content easy to digest? Can your readers find their answers by reading your content?
Are you shaking your head?
In such a case, how do you connect and engage with them? What is the point of spending all that time, money, and effort on writing content that has failed to connect with your audience?
Healthcare content marketing helps you produce content that increases your customer engagement and retention. However, it depends on a robust digital marketing strategy that will increase brand awareness and organic traffic to your website.
With an increasing demand for healthcare content marketing, here are some tricks and tips that you can use for your business.
Five excellent healthcare content marketing tips
Do you know your audience! Whether it’s your patients or customers, who are they, or where are they from?
The first step is to build a buyer persona. This will give a clear knowledge of your target audience i.e their pain points, likes, dislikes, and behavior. Once you are clear, it will become easy to develop relevant content that makes a difference to them.
With this information, you can find out why they are interested in your product or service and create content based on their stage within the buyer’s journey. For example, if Rakesh has a sudden skin allergy and is trying to figure out the problem. Does your website have content that addresses his pain point? Akash knows which type of skin allergy he is dealing with but does not know how to treat it. Is your content providing specific and reliable information about the same? On the other hand, Shameet knows how to deal with his skin allergy problem but is unsure about the treatment. Does your content specify why your treatment is best to solve his problem?
By creating stage-focussed content, you can convince your audience to make the right decisions and move forward to the next stage. Since no one wakes up in the morning and decides to buy something. Instead, they go through a path of research before making a decision.
In addition, appropriate keyword research can give you the words people are using to search when they need your service, making it easy to attract potential customers.
Keep it real! Use user-generated content to engage with your potential customers and get quality leads.
In the healthcare world, it’s all about creating relevant and authentic content that gains the trust of your potential customers. But, how to do that? User-generated content has shown promising results in developing brand loyalty. Allowing patients to share their stories builds positive opinions of potential customers towards your brand and increases conversions.
Also, user-generated content builds brand awareness, engagement factor, and reputation of your brand. Potential customers are more likely to respond to content directly from a patient than from the brand trying to sell.
So, if you aim to target people with a skin allergy or abnormal hair loss, you can post a three-line review or a five-minute testimonial of your patients, telling a story. These stories can provide Rakesh, Akash, and Shameet with a sense of community and help them feel that they are not alone. Also, once they know that a healthcare solution can help their situation, it’s not surprising that they would want to share their story and inspire others. So, user-generated content creates a circle of trust that naturally attracts brand fans and advocates.
Boost your headlines for greater visibility
As per renowned copywriter David Ogilvy, 2 out of 10 people who see headlines click through the post. Yes, it’s true. Attractive and interesting headlines get a visitor to open your article and read the first sentence. And then they read the entire article.
While immunity boosters are becoming popular around the world, headline boosters can help your content become more visible. Within the headlines, you can talk about the benefits that your brand offers and provoke curiosity. But, be careful that you don’t give all the information away.
Write conversational style pieces to develop interest
There are many articles and papers that appeal to healthcare professionals on the internet, but they are too technical for a non-professional reader. Your potential customers may not be used to this kind of writing. Therefore, you must ensure that your content doesn’t sound like a thesis, especially when solving their health problems. It must be more conversational that answers questions of your audience and engages them.
But, how to write in a conversational style? The best way is to simplify the communication, explain ambiguous terms, and humanize your brand with the audience by showcasing inspiring healthcare stories. Also, you need to write in an active voice and make the reader feel that you are writing for him or her personally.
An example would be:
“Dermatitis is another word for skin allergy that occurs when the skin comes into direct contact with an allergen. The outcome of this is a red, itchy rash that can also lead to blisters or bumps. The rash comes out whenever the skin comes in contact with the allergen; basically, the substance attacking the immune system. The delay in treatment eventually results in a rash.”
When written in conversational style
“Skin allergy (Dermatitis) occurs when the skin comes into direct contact with an allergen, and the substance attacks your immune system. The delay in treatment results in a rash that eventually leads to blisters or bumps. “
The above paragraph is easy and attractive to read when it’s less complicated.
Video is King
When it is about promoting healthcare products or services, what type of content comes to your mind? Blogs and articles. Well, that’s not the only type of content. It is vital to explore different types of content like videos to engage with your audience.
Over the years, video marketing has been gaining prominence by healthcare professionals and practitioners as a major part of their digital marketing strategy. And why not? Video is a powerful tool to give your potential customers a peek behind the scenes and develop a trust relationship.
Think about it? Isn’t it effective to get a doctor or expert in front of the camera and let them talk on how-to’s, FAQs, educational pieces, etc., and promote it on social media?
Here are some successful healthcare content marketing examples.
Blogs are the best way for brands to demonstrate thought leadership within their industry. And, Mayo clinic does it beautifully. The hospital staff writes their blog library, and that’s why it stands out from the crowd. All the information features their experiences in managing issues during practice. Being ahead in the curve, their user-generated content has a separate microsite with clear guidelines that makes sharing seamless. So, patients and staff can quickly write about their experience with Mayo clinic and share it across social media.
Arkansas Children’s Hospital
When it comes to promoting healthcare companies, social media marketing plays a significant role. And Arkansas Children’s Hospital. Along with regular blog posts, they run effective video campaigns across its media channels where they share stories. These pieces motivate parents and children who are suffering from the condition.
Over the years, content for healthcare companies has evolved and made tremendous advancements since the time pamphlets were issued at your doctor’s clinic. Hence, it would be best to have a healthcare content marketing agency guide you in the right direction.
How can Vajra Global help you?
At Vajra Global Consulting Services, we strive for perfection through expertise, experience, and excellence. Our relevant and result-driven content builds businesses rather than just links. We help brands understand their audience expectations and translate this understanding into insightful words.
As a healthcare content marketing agency, we strive to deliver creative and insightful content to brands to enable them to attract and retain customers through enriched content experiences. Our goal is to empower brands to connect with the right audience with the right choice of words. Our personalized content marketing services are engineered to accomplish your business goals.
If you are still confused and don’t know where to start, get in touch with us. We use a robust content approach to rekindle curiosity.
4 Facebook transformations that will stimulate the E-Commerce industry
Globally, Facebook has 2.6 billion monthly active users, while Instagram has 1 billion users. With this vast customer base, it is not surprising that E-Commerce is a massive success on both platforms. Further, amid COVID-19 induced movement restrictions, there has been a dramatic rise in online retail sales. By 2022, the global sales of the retail E-Commerce sector are slated to grow to 6.54 trillion.
Today, virtual shops on Facebook and Instagram draw over 300 million monthly visitors and have over 1.2 million monthly active users. However, competition is at its peak, and so is market saturation. Thus, despite the increasing number of social media users, small businesses are clambering to establish their presence.
To retailers’ respite, barely a year after launching these e-Shops, Mark Zuckerberg announced tools to provide an impetus to E-Commerce. Realizing the increased focus on E-Commerce, the Facebook CEO has gone beyond the shop concept to release a range of shopping and discovery tools.
Let’s deep dive into the tools and how they will transform the E-Commerce landscape.
In a significant move, Facebook has decided to extend the Shops format to its messaging app – WhatsApp. Various attempts have been made to mold it into a revenue generation platform as it is a widely used app. However, this has been challenging as people do not enjoy being bombarded with promotional content in a private messaging application. Even placement of advertisements in story form did not garner much interest.
Facebook’s Marketplace is a platform for users to list and identify products without any payment option. In contrast, Facebook Shops are meant for companies to sell their products with integrated payment options. Now Facebook is extending its marketplace platform to include not only products but also Shop listings. This option will facilitate a more organized shopping experience as well as better networking between buyers and sellers. Further, if an account on Facebook Shops is already in place, no additional efforts have to be made to list products on marketplaces.
Currently available in the US, this format is still undergoing trial. However, these experimental efforts are with the intent to encourage more browsing and buying activity. Naturally, with Shops comes promotions, and hence personalized Shop ads are also expected to be introduced. This introduction comes as a change to the ad-hoc advertisements by displaying items based on an individual’s registered interests and past behaviors.
Instagram’s Visual Search Tool
Many users will be familiar with the Lens option in Pinterest and Google, which acts as a visual search tool to find products in a particular image. This AI (Artificial Intelligence) enabled tool is now integrated into Instagram’s shopping experience. By scanning or uploading a photo of an actual product, the user will find similar products. Facebook is currently being tested on the Instagram platform to transform casual shopping into habitual behavior.
Facebook Augmented Reality (AR) ads
While Facebook already has advertisements with product tags, it is running a pilot test on AR Dynamic Ads and AR try-on partnerships. These advertisements will appear in the news feed allowing users to experiment with the camera effect and engaging with the product. Further, clickable options will be available within the ad leading the user to a purchase option.
Mark Zuckerberg never wanted to create a company when he started this initiative. His goal was to make a mind-blowing change in the world. The Facebook CEO has been known to introduce continuous innovations. From a social networking website to an E-Commerce platform, his vision has been progressive. Tapping into consumer behavior and the existing huge customer base, he and his team have transformed and integrated social media platforms to benefit users globally. A combination of these new tools and options will no doubt strengthen the E-Commerce landscape. Here’s to small steps in Social Network creating the stairway to virtual and robust businesses.
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