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What is Big Data? Why Big Data Analytics Is Important? Data is Indispensable. What is Big Data?

Is it a product?

Is it a set of tools?

Is it a data set that is used by big businesses only?

How big businesses deal with big data repositories?

What is the size of this data?

What is big data analytics?

What is the difference between big data and Hadoop?

These and several other questions come to mind when we look for the answer to what is big data? Ok, the last question might not be what you ask, but others are a possibility.

Hence, here we will define what is it, what is its purpose or value and why we use this large volume of data.

Big Data refers to a massive volume of both structured and unstructured data that overpowers businesses on a day to day basis. But it’s not the size of data that matters, what matters is how it is used and processed. It can be analyzed using big data analytics to make better strategic decisions for businesses to move.

According to Gartner:

Importance of Big Data

The best way to understand a thing is to know its history.

Data has been around for years; but the concept gained momentum in the early 2000s and since then businesses started to collect information, run big data analytics to uncover details for future use.  Thereby, giving organizations the ability to work quickly and stay agile.

This was the time when Doug Laney defined this data as the three Vs (volume, velocity, and variety):

Volume: is the amount of data moved from Gigabytes to terabytes and beyond.

Velocity: The speed of data processing is velocity.

Variety: data comes in different types from structured to unstructured. Structured data is usually numeric while unstructured – text, documents, email, video, audio, financial transactions, etc.

Where these three Vs made understanding big data easy, they even made clear that handling this large volume of data using the traditional framework won’t be easy.  This was the time when Hadoop came into existence and certain questions like:

What is Hadoop?

Is Hadoop another name of big data?

Is Hadoop different than big data?

All these came into existence.

So, let’s begin answering them.

Big Data and Hadoop

Let’s take restaurant analogy as an example to understand the relationship between big data and Hadoop

Tom recently opened a restaurant with a chef where he receives 2 orders per day he can easily handle these orders, just like RDBMS. But with time Tom thought of expanding the business and hence to engage more customers he started taking online orders. Because of this change the rate at which he was receiving orders increased and now instead of 2 he started receiving 10 orders per hour. This same thing happened with data. With the introduction of various sources like smartphones, social media, etc data growth became huge but due to a sudden change handling large orders/data isn’t easy. Hence a need for a different kind of strategy to cope up with this problem arise.

Likewise, to tackle the data problem huge datasets, multiple processing units were installed but this wasn’t effective either as the centralized storage unit became the bottleneck. This means if the centralized unit goes down the whole system gets compromised. Hence, there was a need to look for a better solution for both data and restaurant.

Tom came with an efficient solution, he divided the chefs into two hierarchies, i.e. junior and head chef and assigned each junior chef with a food shelf. Say for example the dish is pasta sauce. Now, according to Tom’s plan, one junior chef will prepare pasta and the other junior chef will prepare the sauce. Moving ahead they will hand over both pasta and sauce to the head chef, where the head chef will prepare the pasta sauce after combining both the ingredients, the final order will be delivered. This solution worked perfectly for Tom’s restaurant and for Big Data this is done by Hadoop.

Hadoop is an open-source software framework that is used to store and process data in a distributed manner on large clusters of commodity hardware. Hadoop stores the data in a distributed fashion with replications, to provide fault tolerance and give a final result without facing bottleneck problem. Now, you must have got an idea of how Hadoop solves the problem of Big Data i.e.

Storing huge amount of data.

Storing data in various formats: unstructured, semi-structured and structured.

The processing speed of data.

So does this mean both Big Data and Hadoop are same?

We cannot say that, as there are differences between both.

What is the difference between Big Data and Hadoop?

Big data is nothing more than a concept that represents a large amount of data whereas Apache Hadoop is used to handle this large amount of data.

It is complex with many meanings whereas Apache Hadoop is a program that achieves a set of goals and objectives.

This large volume of data is a collection of various records, with multiple formats while Apache Hadoop handles different formats of data.

Hadoop is a processing machine and big data is the raw material.

Now that we know what this data is, how Hadoop and big data work. It’s time to know how companies are benefiting from this data.

How Companies are Benefiting from Big Data?

A few examples to explain how this large data helps companies gain an extra edge:

Coca Cola and Big Data

Coca-Cola is a company that needs no introduction. For centuries now, this company has been a leader in consumer-packaged goods. All its products are distributed globally. One thing that makes Coca Cola win is data. But how?

Coca Cola and Big data:

Using the collected data and analyzing it via big data analytics Coca Cola is able to decide on the following factors:

Selection of right ingredient mix to produce juice products

Supply of products in restaurants, retail, etc

Social media campaign to understand buyer behavior, loyalty program

Creating digital service centers for procurement and HR process

Netflix and Big Data

To stay ahead of other video streaming services Netflix constantly analyses trends and makes sure people get what they look for on Netflix. They look for data in:

Most viewed programs

Trends, shows customers consume and wait for

Devices used by customers to watch its programs

What viewers like binge-watching, watching in parts, back to back or a complete series.

For many video streaming and entertainment companies, big data analytics is the key to retain subscribers, secure revenues, and understand the type of content viewers like based on geographical locations. This voluminous data not only gives Netflix this ability but even helps other video streaming services to understand what viewers want and how Netflix and others can deliver it.

Alongside there are companies that store following data that helps big data analytics to give accurate results like:

Tweets saved on Twitter’s servers

Information stored from tracking car rides by Google

Local and national election results

Treatments took and the name of the hospital

Types of the credit card used, and purchases made at different places

What, when people watch on Netflix, Amazon Prime, IPTV, etc and for how long

Hmm, so this is how companies know about our behavior and they design services for us.

What is Big Data Analytics?

The process of studying and examining large data sets to understand patterns and get insights is called big data analytics. It involves an algorithmic and mathematical process to derive meaningful correlation. The focus of data analytics is to derive conclusions that are based on what researchers know.

Importance of big data analytics

Ideally, big data handle predictions/forecasts of the vast data collected from various sources. This helps businesses make better decisions. Some of the fields where data is used are machine learning, artificial intelligence, robotics, healthcare, virtual reality, and various other sections. Hence, we need to keep data clutter-free and organized.

This provides organizations with a chance to change and grow. And this is why big data analytics is becoming popular and is of utmost importance. Based on its nature we can divide it into 4 different parts:

In addition to this, large data also play an important role in these following fields:

Identification of new opportunities

Data harnessing in organizations

Earning higher profits & efficient operations

Effective marketing

Better customer service

Now, that we know in what all fields data plays an important role. It’s time to understand how big data and its 4 different parts work.

Big Data Analytics and Data Sciences

Data Sciences, on the other hand, is an umbrella term that includes scientific methods to process data. Data Sciences combine multiple areas like mathematics, data cleansing, etc to prepare and align big data.

Due to the complexities involved data sciences is quite challenging but with the unprecedented growth of information generated globally concept of voluminous data is also evolving.  Hence the field of data sciences that involve big data is inseparable. Data encompasses, structured, unstructured information whereas data sciences is a more focused approach that involves specific scientific areas.

Businesses and Big Data Analytics

Due to the rise in demand use of tools to analyze data is increasing as they help organizations find new opportunities and gain new insights to run their business efficiently.

Real-time Benefits of Big Data Analytics

Data over the years has seen enormous growth due to which data usage has increased in industries ranging from:

Banking

Healthcare

Energy

Technology

Consumer

Manufacturing

All in all, Data analytics has become an essential part of companies today.

Job Opportunities and big data analytics

Data is almost everywhere hence there is an urgent need to collect and preserve whatever data is being generated. This is why big data analytics is in the frontiers of IT and had become crucial in improving businesses and making decisions. Professionals skilled in analyzing data have got an ocean of opportunities. As they are the ones who can bridge the gap between traditional and new business analytics techniques that help businesses grow.

Benefits of Big Data Analytics

Cost Reduction

Better Decision Making

New product and services

Fraud detection

Better sales insights

Understanding market conditions

Data Accuracy

Improved Pricing

How big data analytics work and its key technologies

Here are the biggest players:

Machine Learning: Machine learning, trains a machine to learn and analyze bigger, more complex data to deliver faster and accurate results. Using a machine learning subset of AI organizations can identify profitable opportunities – avoiding unknown risks.

Data management: With data constantly flowing in and out of the organization we need to know if it is of high quality and can be reliably analyzed. Once the data is reliable a master data management program is used to get the organization on the same page and analyze data.

Data mining: Data mining technology helps analyze hidden patterns of data so that it can be used in further analysis to get an answer for complex business questions. Using data mining algorithm businesses can make better decisions and can even pinpoint problem areas to increase revenue by cutting costs. Data mining is also known as data discovery and knowledge discovery.

In-memory analytics: This business intelligence (BI) methodology is used to solve complex business problems. By analyzing data from RAM computer’s system memory query response time can be shortened and faster business decisions can be made. This technology even eliminates the overhead of storing data aggregate tables or indexing data, resulting in faster response time. Not only this in-memory analytics even helps the organization to run iterative and interactive big data analytics.

Predictive analytics: Predictive analytics is the method of extracting information from existing data to determine and predict future outcomes and trends. techniques like data mining, modeling, machine learning, AI are used to analyze current data to make future predictions. Predictive analytics allows organizations to become proactive, foresee future, anticipate the outcome, etc. Moreover, it goes further and suggests actions to benefit from the prediction and also provide a decision to benefit its predictions and implications.

Text mining: Text mining also referred to as text data mining is the process of deriving high-quality information from unstructured text data. With text mining technology, you uncover insights you hadn’t noticed before. Text mining uses machine learning and is more practical for data scientists and other users to develop big data platforms and help analyze data to discover new topics.

Big data analytics challenges and ways they can be solved

A huge amount of data is produced every minute hence it is becoming a challenging job to store, manage, utilize and analyze it.  Even large businesses struggle with data management and storage to make a huge amount of data usage. This problem cannot be solved by simply storing data that is the reason organizations need to identify challenges and work towards resolving them:

Improper understanding and acceptance of big data

Meaningful insights via big data analytics

Data storage and quality

Security and privacy of data

Collection of meaningful data in real-time: Skill shortage

Data synching

Visual representation of data

Confusion in data management

Structuring large data

Information extraction from data

Organizational Benefits of Big Data

Big Data is not useful to organize data, but it even brings a multitude of benefits for the enterprises. The top five are:

Understand market trends: Using large data and  big data analytics, enterprises can easily, forecast market trends, predict customer preferences, evaluate product effectiveness, customer preferences, and gain foresight into customer behavior. These insights in return help understand purchasing patterns, buying patterns, preference and more. Such beforehand information helps in ding planning and managing things.

Understand customer needs:  Big Data analytics helps companies understand and plan better customer satisfaction. Thereby impacting the growth of a business. 24*7 support, complaint resolution, consistent feedback collection, etc.

Improving the company’s reputation: Big data helps deal with false rumors, provides better service customer needs and maintains company image. Using big data analytics tools, you can analyze both negative and positive emotions that help understand customer needs and expectations.

Promotes cost-saving measures: The initial costs of deploying Big Data is high, yet the returns and gainful insights more than you pay. Big Data can be used to store data more effectively.

Makes data available: Modern tools in Big Data can in actual-time presence required portions of data anytime in a structured and easily readable format.

Sectors where Big Data is used:

Retail & E-Commerce

Finance Services

Telecommunications

Conclusion

With this, we can conclude that there is no specific definition of what is big data but still we all will agree that a large voluminous amount of data is big data. Also, with time the importance of big data analytics is increasing as it helps enhance knowledge and come to a profitable conclusion.

If you are keen to benefit from big data, then using Hadoop will surely help. As it is a method that knows how to manage big data and make it comprehensible.

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From Big Data To Smart Data

Fight the Big Data Backlash and use Smart Data help you identify purchase intent

Big data is starting to experience some significant backlash. A ‘case in point’ comes from a recent popular article in VentureBeat: ‘Big data’ is dead. What’s next? The backlash is more to do with the buzz than the data but the reason relates to the difficulty of extracting meaningful insights from big data.

Born from the backlash comes another buzzword; smart data, a means of extracting these meaningful insights from big data.

Looking past the marketing hype, smart data is actually the metamorphosis of big data into something actionable. Here we look at recognizing purchase intent as an example of actionable data extraction.

Big data vs Smart data rundown Big data, strong signals, Smart Insights

The big opportunity for big data is how to extract a ‘strong signal’ from the noise. Collecting big data and mining it mercilessly is not the opportunity. The opportunity is leveraging ‘a strong signal’ data set and integrating it to label big data, thus making it immediately usable. This is where an information rich contextual data set can inform big data and turn it into smart data.

Let’s take a real example: Say you were trying to identify and target website visitors who intend to purchase. If you were to rely only on mining your web analytics data for this information you would have to sort through the entire data set looking for the behavioral traits of purchase intenders. This not only is difficult but could be wildly inaccurate. You would think that focusing on the shopping cart is all you would have to do to get a stronger signal of purchase intent, but there is more to the story. Data shows that for a typical e-commerce site only 44% of visitors that enter the cart actually have the intent to purchase while the remaining 56% represent all other intent types such as researchers.

By labeling your data set with a ‘strong signal’ such as visitors who are actually intending to purchase, you can segment and contextualize the web data illuminating the most important aspects of the data set.

Empowering your Big Data

Collecting visitor stated intent, or in other words the way someone describes their intention for visiting a website, provides a much stronger signal because it is the visitor who describes their intention.

iPerceptions research shows that a visitor who states that they intend to ‘purchase’ is 15 to 20 times more likely to do so than someone who describes their intent to ‘research’.  This powerful qualitative intent data paired with quantitative and descriptive data creates contextualized data sets, transforming your big data into smart data.

Putting it all together – Big and Smart Data

Big data is complex and vast but many of the benefits cannot be truly realized without adding contextual information. If these data sources are combined not only can you transform big data into smart data, but you can also provide enormous windfalls for consumers and companies alike improving the customer experience and the company’s ability to meet the needs of its customers. However having the right type of data is only half the story. To make personalization a reality and directly impact the customer experience, a real-time approach to leveraging this information must be taken so that the quickly eroding opportunities can be recognized and acted upon.

Top 10 Big Data Analytics Trends And Predictions For 2023

These trends in big data will prepare you for the future.

Big data and analytics (BDA) is a crucial resource for public and private enterprises nowadays, as well as for healthcare institutions in battling the COVID-19 pandemic. Thanks in large part to the evolution of cloud software, organizations can now track and analyze volumes of business data in real-time and make the necessary adjustments to their business processes accordingly.  

AI will continue to improve, but humans will remain crucial

Earlier this year, Gartner® stated “Smarter, more responsible, scalable AI will enable better learning algorithms, interpretable systems and shorter time to value. Organizations will begin to require a lot more from AI systems, and they’ll need to figure out how to scale the technologies — something that up to this point has been challenging.” While AI is likely to continue to develop, we aren’t yet near the point where it can do what humans can. Organizations will still need data analytics tools that empower their people to spot anomalies and threats in an efficient manner.  

Business intelligence adoption will grow in technology, business services, consumer services, and manufacturing

According to Dresner’s business intelligence market study 2023, organizations in the technology, business services, consumer services, and manufacturing industries are reporting the highest increases in planned adoption of business intelligence tools in 2023.  

Predictive analytics is on the rise

Organizations are using predictive analytics to forecast potential future trends. According to a report published by Facts & Factors, the global predictive analytics market is growing at a CAGR of around 24.5% and is expected to reach $22.1 billion by the end of 2026.  

Cloud-native analytics solutions will be necessary Self-service analytics will become even more critical to business intelligence

The demand for more fact-based daily decision-making is driving companies to seek self-service data analytics solutions. Jim Ericson, research director at Dresner Advisory Services, recently observed, “Organizations that are more successful with BI are universally more likely to use self-service BI capabilities including collaboration and governance features included in BI tools.” In 2023, more companies will adopt truly self-service tools that allow non-technical business users to securely access and glean insights from data.  

The global business intelligence market will be valued at $30.9 billion by 2023

According to research by Beroe, Inc., a leading provider of procurement intelligence, the global business intelligence market is estimated to reach $30.9 billion by 2023. and key drivers include big data analytics, demand for data-as-a-service, demand for personalized, self-servicing BI capabilities.  

60% of organizations report company culture as being their biggest obstacle to success with business intelligence

Dresner’s business intelligence market study 2023 revealed that the most significant obstacle to success with business intelligence is “a culture that doesn’t fully understand or value fact-based decision-making.” 60% of respondents reported this factor as most damaging.  

Retail/wholesale, financial services, and technology organizations are increasing their BI budgets by over 50% in 2023

Retail/wholesale, financial services, and technology organizations are the top industries increasing their investment in business intelligence. Each of these industries is planning to increase budgets for business intelligence by over 50%, according to Dresner’s business intelligence market study 2023.  

63% of companies say that improved efficiency is the top benefit of data analytics, while 57% say more effective decision-making

Finances online report that organizations identify improved efficiency and more effective decision-making as the top two benefits of using data analytics.  

The global big data analytics in the retail market generated $4.85 billion in 2023 and is estimated to increase to $25.56 billion by 2028, with a CAGR of 23.1% from 2023 to 2028

The Data Transfer Project’s Big

The Data Transfer Project addresses one pain point we all experience on our phones: moving our stuff around. While it’s certainly gotten easier over the years to share individual photos, songs, and files from one app to another, shifting large chunks of data or entire libraries and histories between services is often an exercise in futility, even with hundreds of gigabytes of cloud storage at our disposal.

But while the four founding members are certainly big enough to get the Data Transfer Project off the ground, it’s missing the support of the biggest player of all: Apple. And without the iPhone maker on board, it’s going to be a tougher sell than it should be.

Share and share alike

On the surface, the Data Transfer Project has a very simple goal that all providers and developers should support: portability, privacy, and interoperability. In the announcement, Google, Facebook, Twitter, and Microsoft served up this clear mission statement: Making it easier for individuals to choose among services facilitates competition, empowers individuals to try new services, and enables them to choose the offering that best suits their needs.

IDG

iPhone users should get the same Data Transfer experience as Android users.

The timing of the announcement isn’t accidental. While the group was officially formed last year, 2023 has been a troubling year for data and privacy, particularly with regard to three of the companies here. Facebook, Twitter, and Google have each taken very public lumps over the handling of user data. Most recently, the European Union implemented a stringent set of laws governing privacy rights and adding layers of transparency for users.

If nothing else, the Data Transfer Project is a public commitment to free users’ data from any one service and respect the right to move it between apps. In simple terms, your Facebook photos are just photos, so when the next big social thing comes along, you won’t need to rebuild your entire digital profile.

The benefit applies to non-social situations as well. As the group explains in its white paper: “A user doesn’t agree with the privacy policy of their music service. They want to stop using it immediately, but don’t want to lose the playlists they have created. Using this open-source software, they could use the export functionality of the original provider to save a copy of their playlists to the cloud. This enables them to import the playlists to a new provider, or multiple providers, once they decide on a new service.”

Opening the walled garden

The aim of the Data Transfer Project is something that simultaneously agrees and disagrees with Apple’s core philosophies. On the one hand, Apple promotes ease-of-use and interoperability among all of its products. The company is constantly working to break down barriers so our data can jump seamlessly from one device and app to the next.

Apple

If Apple is truly serious about privacy, it needs to sign on board with the Data Transfer Project.

But if Apple is truly committed to privacy—and not just Apple device privacy—it needs to take a stand here. While the lock-in inherent to Apple’s ecosystem is often derided, the fact of the matter is, a walled garden is a nice place to play. The devices all work well together, and they’re encrypted and secure and receive the latest security patches and updates. That’s why many people would be plenty happy to stay, even if Apple made it easier to leave by supporting the Data Transfer Project.

As it stands, the Data Transfer Project is an ambitious project that won’t see its full potential without the support of Apple. If the ease-of-use and privacy gains it delivers stops at the iPhone, the rest of the industry will be reluctant to join forces, even with the might of Google, Microsoft, and Facebook behind it. And Apple doesn’t need to tear down its walled garden to support it. It merely needs to put a key under the doormat.

Are Big Data Vendors Forgetting History?

With any new hot trend comes a truckload of missteps, bad ideas and outright failures. I should probably create a template for this sort of article, one in which I could pull out a term like “cloud” or “BYOD” and simply plug in “social media” or “Big Data.”

When the trend in question either falls by the wayside or passes into the mainstream, it seems like we all forget the lessons faster than PR firms create new buzzwords.

Of course, vendors within trendy news spaces also tend to think they’re in uncharted waters. But in fact there’s actually plenty of history available to learn from. Cloud concepts have been around at least since the 1960s (check out Douglas Parkhill’s 1966 book, The Challenge of the Computer Utility, if you don’t believe me), but plenty of cloud startups ignored history in favor of buzz.

And it’s not like gaining insights from piles of data is some new thing that was previously as rare as detecting neutrinos from deep space.

Here are five history lessons we should have already learned, but seem to be doomed to keep repeating:

It wasn’t that long ago that every time a cloud project or company failed, some tech prognosticator would sift through the tea leaves and claim that the cloud concept itself was dead.

The same thing is happening with Big Data. According to a recent survey, 55 percent of Big Data projects are never even completed. It’s hard to achieve success if you don’t even finish what you started, yet many mistakenly believe that this means Big Data is bunk.

Not true. Plenty of companies are reaping the rewards of Big Data, analyzing piles of data to improve everything from marketing and sales to fraud detection.

People mean many different things when they use terms such as “cloud” and “Big Data.” Are you talking about virtualized infrastructures when you say cloud? Private clouds? AWS? Similarly, Big Data can refer to existing pools of data, data analytics, machine learning, and on and on.

The Big Mistake with the term Big Data is that many use the term to mask vague objectives, fuzzy strategies and ill-defined goals.

Often when people use these terms loosely it’s because they not only don’t really know what the heck the terms mean in general, but they also don’t know what they mean to their particular business problems. As a result, vendors are asked for proposals that are a poor fit for an organization’s cloud or Big Data challenges.

If your CEO or CIO orders you to start investigating Big Data, your first question needs to be the most basic one: Why, specifically?

If you can’t answer that question concisely, you’re in trouble.

If you’re the person tasked with building out a Big Data architecture, then it’s fine to focus on details that won’t matter to anyone who isn’t a data scientist.

If you’re a business user or non-data scientist, it’s best to just ignore all this noise. It’ll sort itself out soon enough. I’ve seen this phenomena repeat with everything from CDNs to storage to cloud computing and now Big Data. Engineers and product developers often fall prey to “if we build it, they will come” syndrome, ignoring the real-world pain points of potential customers in favor of hyping their technical chops.

When they fail to find real-world customers for the resulting products, they then set their sights on technical minutiae, since it couldn’t possibly be a flawed go-to-market strategy that was the problem in the first place.

Take the recent news that Facebook is making its query analysis software, Presto, open source. Is this a win for Hadoop or for SQL? Does it mark the end of Hive?

Who cares?

Okay, if you’re reading this, you’re probably an early adopter or you’ve already placed some Big Data bets, so it matters to you. But for the rest of the world, it’s not even on their radar – nor should it be.

Why Consumer Behavior Is Important?

Why Consumer Behavior is Important for Business Managers?

Consumer behavior is the study of how people respond to products and services, followed by their marketing and selling. It’s of huge importance to managers because the focus on consumers is the key contributor to the marketing of business practice. Business functions like accounting, production, or finance, don’t need to factor in the customer. Business managers, who truly understand their consumers, can come up with better products and services and promote them more effectively.

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Understanding consumer behavior is important for all companies, especially before the launch of a product or service. If the company fails to read the customer’s mind, it may end up in losses. Consumer behavior is usually very complex because each one has a different attitude towards purchase, consumption and disposal of a product. Understanding the concepts of consumer behavior helps in marketing products and services successfully. Besides, frequent study of consumer behavior helps in several aspects. There’s constant change in living standards, technology, fashion and trends, and customer attitude towards a product or service also changes. Marketing of a product is largely dependent on these factors and consumer behavior serves as a tool for marketers to meet their sales objectives.

What is Consumer Behavior?

Consumers, while buying a product or service, go through various steps. Studying consumer behavior helps companies to understand how the decision to buy was made and how they hunted for the product. These information help companies and business managers to know the reasons behind the purchase or rejection of a product or service by the customer.

To understand customer behaviour, marketing experts usually examine the buying decision processes, particularly factors that trigger customers to purchase a product. A recent study disclosed that an average shopper takes less than 20 minutes for purchasing groceries and covers only 23% of the store area, giving managers very little time for influencing customers. In fact, more than 58% of all purchases in a supermarket are unplanned. Business managers spend a lot of money and time to discover what compels customers to take such on-spot decisions. Researchers can obtain the most valuable data on customer buying trends through in-store surveys, and often introduce new products and services in some select stores where they expect to reasonably test an item’s success. In this way, a company can determine whether there’s a chance of the product to be successful when launched, before further investing into it.

Understanding Consumer Needs

Customers adjust their purchasing behaviour depending upon individual needs. On some levels, customer choices could be quite random. Every decision to buy, has meaning behind it, even though it may not always seem rational. Purchasing decisions could stem from social situations, personal emotions, values, and above all, goals.

People buy for satisfying various sorts of needs that may not be solely utilitarian. These needs could be biological or physical, for security, love and affection, to get esteem and prestige, for self-fulfilment, and a hundred other reasons. For instance, connecting products with a sense of belonging has seen success for many hugely popular campaigns like “Fly the friendly skies”, “Reach out and touch someone”, and “Gentlemen prefer Hanes”. Such a focus may link products and services either to attainment of belonging or link them with persons similar to those with whom others like to relate.

Prestige is yet another intangible need. People concerned about their status are ready to pay for that. Products and services appealing to such a need are considered high profile. Targeting this segment of the market means that the demand trajectory of luxury items is usually reverse of the standard i.e. a high-status product sells better at a higher price.

Some equate needs with a particular class of products. For instance, a desire to achieve something may drive people to carry out difficult tasks and invest in tools and self-improvement programs, and similar things.

Personality traits are also imperative for establishing how customers meet their needs. Pragmatic people would purchase what is useful and practical. Their purchases are mostly guided by durability and practicality of a product, rather than physical beauty. An aesthetically inclined customer will be drawn to items that forge a symmetry, beauty, and harmony. An intellectual will be more inclined to obtain more knowledge about a product or service and likely to be critical. They are likely to contrast and compare similar products, ahead of taking any decision. The politically motivated people would seek products and services to give them an “edge” regarding social power and position. More social people can be motivated if the company appeals to the humane angle and promotion that suggests kindness and empathy.

Customers also vary according to the demands they want to meet while buying a product or service. Are they more bothered to meet their own demands and buy what they require for themselves? Or do they give importance to others’ opinions for determining the products and services for their use? It helps the company to understand whether they will purchase a product simply because it’s new and the most popular product available, or because it’s truly something they need.

Customer behavior influences the way business managers brand their products. A wine seller, for instance, trying to cater to customers looking to whet their personal taste, would emphasize the superior vintage quality and fine bouquet. The same seller, marketing the same wine to people who want to satisfy others, would stress on how sharing the liqueur can lift up spirits in a gathering.

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While all these information may be of help to marketers, it’s equally imperative to recognize what compels a buyer to make a purchase, rather than just generating an interest. Some customers, for instance, only consider the price of the product and no other factors. Knowing the various elements that triggers a purchase activity helps business managers adopt appropriate sales techniques.

Chairperson of the marketing studies department at the Miller College of Business, Ball State University, Susan Powell Mantel, in a study on “attitude-based processing” and “attribute-based processing”, concluded that product attributes like size, price, durability, nutritional value etc. are usually compared disproportionately. Any one of the attributes assumes a key subject of comparison and elicits more consideration from the consumers when they decide on the “best” part of the brand. The order of the consideration is also important here.

Further complicating issues is the fact that buying decisions may not always depend on an “attribute by attribute” comparison. Customers often take decisions depending upon a general evaluation of their intuition, impressions, and knowledge gained from past experience. A learned attitude can also influence decisions. It’s seen that parents who loved Kool-Aid when they were children, usually buy the drink for their kids as well, largely because they have fond memories associated with it, or simply because of brand loyalty.

Each marketing strategy calls for time and effort. Attribute-based processing, however, requires much more effort on part of the consumer.

Buyer patterns are also dependent upon perceived roles that are acquired via social processes. The roles create a person’s need for things to enable them to carry out these roles, improve performance, facilitate attainment, or symbolizes a relationship.

Evaluating all the marketing information could be time-consuming if it’s done every time an individual visits a shop.

Interpreting Consumer Behaviour

It will be a mistake to depend upon traditional wisdom when business managers start to evaluate consumer behaviour, particularly when the actual activity can be studied. Where are they while buying certain items? What’s the time of use? Who accompanies them while purchasing an item? Why do they prefer a particular time to buy their stuff and not others? Business managers must determine the key needs satisfied by that service so as to sell it off.

There are two major ways for evaluating the motivation behind customer purchases: by intensity (how much they want), and by direction (what they want). Direction means what customers want from a product or service. For instance, if the customer is buying a pain reliever he/she may buy one that’s cheaper. But if what they want is a quick reliever, they may probably pay more that serves the purpose. Marketers have to understand the main motivation behind all types of product or service and zero in on the target group.

Intensity, the other way of evaluating customer behaviour, refers to whether the buyer’s interest in a product or service is enough compelling to make them go out and purchase it. Effective marketing can generate such intensity. The “Aren’t you hungry?” campaign by Burger King that aired on TV was compelling enough for people to venture out late at night and buy burgers. Understanding customer motivation is the ideal way for learning how to enhance buyer incentive.

While it’s easy to speculate all these elements, it’s much harder to research the motivating factors for a given product. It’s rare that customers’ reasons to but some product or service marketing campaign could be perfectly determined via direct questions. Researchers have to set up other ways to dig out the information. Questions like “What’s your opinion about your friends reacting to the marketing campaign?” Consumers are unlikely to admit the effects of marketing efforts on them. But they are often ready to speculate the effects on other persons. And they mostly answer their own responses.

Conclusion

Remember, marketing strategies can significantly impact the daily lives of consumers. They act as a source of information for new products and services that are introduced in the market. The strategies also influence the way people perceive things, their thoughts, attitude, beliefs, and finally their buying decision.

There are several ways by which a customer is exposed to various promotional and marketing tactics every day. TV alone accounts for more than six hours of commercials each week. Besides TV, customers can get information from other mass media like, newspapers, magazines, radio etc. What method the company adopts will depend upon the marketing strategy.

Consumer behaviour concepts and theories have the most important to marketers and salespersons. Products and services are devised to cater to the demands and needs of customers. Hence, they must be carefully marketed to successfully achieve the organization’s goals. Studying consumer behaviour helps companies to analyze the various factors that influence the buying decision of customers. Business managers who fail to understand the factors, won’t meet their targets. It’s imperative that companies evaluate consumer behaviour to a reasonable extent or the maximum possible. In the days when companies are cutting corners, unfruitful investments can severely dent revenues.

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