How Can You Successfully Grow Your Business Using Big Data?

How Can You Successfully Grow Your Business Using Big Data?

When it comes down to business development, it is essentially an ever-changing route.

Even though this has been quite evident, the different twists and turns of current times have easily baffled even the most intelligent and experienced entrepreneurs and marketers. From what every person can easily observe, one needs much more than mere creativity.

Basically, creativity yields ideas. However, they are merely stuck in quite a theoretical plan in case they aren’t executed properly. Hence, audience segmentation, artificial intelligence, multimedia-based audio and visual marketing and different other methods have captured almost all of the web development services

Irrespective of how impactful all of these trends are, basically, they are inseparably connected to a much larger concept of big data. Most of the prominent and influential companies actually base their own business model on the big data and use it in order to achieve the best possible results while employing minimum workforce, mistakes and finances. 

Here, we focus on the concept of big data and assist you in understanding the importance of it and we will be dissecting the term as well as analyse why it needs to be embedded deeper into your own business strategy.

Big Data

When it comes to defining big data, its most basic and literal form refers to a large amount of information. In order for information to be considered to be big data, it has to come from at least two or more separate sources. Anything can easily be big data and can readily come in different forms. Browsing history, satellite imaging, credit records, etc. all these things can easily be tracked and even stored. Owing to the recent controversies as well as data leakages, it might even seem scary. However, there is nothing to be afraid of. Collected big data can lead to creating mini behaviour profiles of the customers all over the Internet irrespective of the website. Essentially, the purpose of these profiles can easily be a wide range of things.

Also, improving customer support, better efficiency on websites and better ad placement along with more are all possible due to the current existence of the big data and big data solutions. In order to understand it completely, you need to know how can you easily gather this big data.

Data Gathering

Essentially, big data can easily be gathered in various ways. There are three ways to do so. They are:

  • Asking the users directly using a lead or opt-in form for permission.

Among the three ways used, this particular one is quite outdated. However, EU-imposed regulations have again imposed the businesses to include it in their own terms and conditions along with privacy policies.

  • Utilizing tracking software

At present, there are a multitude of apps as well as software which can track the smartphones and computers of the users. With it, companies can easily gather big data corresponding to users’ browsing habits as well as other details. Also, through tracking software, an e-commerce website can easily see that you have actually spent just 30 seconds browsing their own sneaker section. Cloud computing services should employ applications to track user behaviour.

  • Utilizing cookies

Utilizing cookie is most probably the most popular and familiar method to anyone. Owing to different breaches in recent times, EU countries have actually made their entire content-related laws a lot stricter. Due to this, a business that wishes to allow EU citizens to browse the pages need to notify the user about the usage of these cookies. Also, a notification can appear in different forms, generally through the classic “I Agree” button or a complicated and unconventional one.

In case you choose to use big data as an essential part of your own business plan, then you can easily incorporate all of the three wats of data gathering. Now, it is time to discuss in deep about the different methods of easily growing your business with big data.

Improvement of Customer Profiles through Big Data

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Among all of the methods of use, the improvement of existing customer information remains to be the most essential as well as the most straightforward one. Data analytics is the most crucial aspect of every business plan since it gives a basis to each and every action. Also, the more information you have, it is much less likely that you make a mistake and to focus your efforts on the wrong customer groups.

Essentially, a typical customer profile contains information such as name, location, emails sent and purchasing history. With the assistance of big data, you can easily find out much more. Addition of cookies to your website may even allow you to easily collect big data in a different form.

  • The device used by Customer

With this relevant information, you can easily adjust the page format for their smartphone or even tablet model. Also, adjusting for mobile is quite important, but not every OS and model are the same. A SaaS development company needs to make its application compatible for such adjustments.

  • Average time spent by a user on the page
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If you are posting videos or blogs, then big data can easily allow you to view where things can easily go wrong. Why did a particular customer just watch a few seconds of the videos? Why did the customers only spend a few seconds on a given page? Such questions can be great assistance of improving the entire customer experience if they are answered utilizing big data.

  • Browsing behaviour

Basically, big data allows you to find out the average browsing behaviour or habits of the customers. Also, a large section of experts suggests that browsing behaviours are the crucial piece of information that a marketer should know.

Whenever utilizing big data, you can easily pinpoint the actual time people spend online. Such a revelation can easily assist you to post the right things at the best time. Knowing this additional information about the customers can lead to much better and deeper analytics. 

Delve Deeper

Generally, big data is just considered as categorizing as well as collecting textual data, but basically, it is much more than it appears. Companies are now using big data to strengthen their own call centre’s functioning. Instead of settling on simple level information, they are now orienting themselves in the direction of sentiment analysis.Cloud infrastructure solutions need to incorporate this as their feature.

In the case of sentiment analysis, it is directly connected to AI or artificial intelligence. By means of collecting voice recordings of the conversations of employees with customers, these companies are now analysing the details of the voices of their customers. AI programs can easily harness vital information about emotions, voice pitch, as well as voice tone. In case you have a call centre for your business, you can easily use this heads-up information.

Achieving Success

Any business can be considered profitable in case it makes advances. Essentially a good advance as per people in the industry actually revolves around reducing the time as well as effort duly spent. But, a truly exceptional advance in case of business success is increasing the overall profit as well as the general success of the company. In this regard, big data can assist you in achieving that.

With the minimum amount of effort invested, you can easily watch the data getting collected. Whenever it is time to take any action, you will be duly ready.

Also, rolling out campaigns is performed much more smoothly with the assistance of big data. Knowing enough about your customers will assist you in focusing the marketing as well as the products. Also, the greatest changes can be seen in PPC campaigns.

Of course, the money is allocated efficiently through this without any major risk of wrong users clicking on the ads. Even when you manage to attract new customers, the big data can assist you in retaining them.

Attraction and Retention

Bu means of improving the marketing campaigns, and big data has reflected that it can easily attract new customers. A lot of business owners focus on this, while at the same time, they are basically losing customers by simply not listening to them. In this regard, big data can assist you in reading the online sentiment pertaining to your product as well as the brand. Most of the people have the habit of talking about the products and services they have essentially used. Knowing the public consensus will certainly aid you in better understanding of the customer.

The Internet can be a massive echo chamber, but big data can still help you explore and understand its mysteries. Having a better and clear insight into public opinion regarding your company is one of the ways to perform it. Essentially, the knowledge that you acquire will assist you in preventing problems before they appear or worsen. Also, you can easily highlight what works and even make different things easier for you, your team and business.Big data development services can assist you in this regard.

Efficiency

Disregarding the popular belief, an efficient system is quite important. Due to the overwhelming use of AI and different other programs in the world, this statement might not bode well for some. 

Big data actually highlights the importance of the system’s efficiency by doing most of the work for the team. Owing to large analytical data being collected, one doesn’t have to utilize valuable resources to estimate. Each decision will certainly take lesser time to be executed.

Utilizing big data allows you to focus on the different tasks which can’t be easily automated. Also, the creativity of you, as well as your team alike, can be allocated to different things which matter—improved energy allocation results in business growth.

Big Data: Explained

Big Data: Explained

For years, people have asked all-knowing Google how big data can help businesses to succeed, what big data technologies are the best, and other important questions. A lot has been written and said about big data already, but the term itself remains unexplained. To be fair, we do not count a widespread definition “big data is big.” This concept raises another question: what are the measures for “big” – 1 terabyte, 1 petabyte, 1 exabyte or more?

Here, our big data consulting team defines the concept of big data through describing its key features. To give a complete picture, we also share an overview of big data examples from different industries, enumerate different sources of big data and fundamental technologies.

Short summary:

What is big data

Big data defined

Here’s our definition:

Big data is the data that is characterized by such informational features as the log-of-events nature and statistical correctness, and that imposes such technical requirements as distributed storage, parallel data processing and easy scalability of the solution.

Below, you can read about these features and requirements in more detail.

Informational features: In contrast to traditional data that may change at any moment (e.g., bank accounts, quantity of goods in a warehouse), big data represents a log of records where each describes some event (e.g., a purchase in a store, a web page view, a sensor value at a given moment, a comment on a social network). Due to its very nature, event data does not change.

Besides, big data may contain omissions and errors, which makes it a bad choice for the tasks where absolute accuracy is crucial. So, it doesn’t make much sense to use big data for bookkeeping. However, big data is correct statistically and can give a clear understanding of the overall picture, trends and dependencies. Another example from Finance: big data can help identify and measure market risks based on the analysis of customer behavior, industry benchmarks, product portfolio performance, interest rates history, commodity price changes, etc.

Technical requirements: Big data has a volume that requires parallel processing and a special approach to storage: one computer (or one node as IT gurus call it) is not sufficient to perform these tasks – we need many, typically from 10 to 100.

Besides, big data solution needs scalability. To cope with ever-growing data volume, we don’t need to introduce any changes to the software each time the amount of data increases. If this happens, we just involve more nodes, and the data will be redistributed among them automatically.

Big data examples

To better understand what big data is, let’s go beyond the definition and look at some examples of practical application from different industries.

1. Customer analytics

To create a 360-degree customer view, companies need to collect, store and analyze a plethora of data. The more data sources they use, the more complete picture they will get. Say, for each of their 10+ million customers they can analyze 5 types of customer big data:

  • Demographic data (this customer is a woman, 35 years old, has two children, etc.).
  • Transactional data (the products she buys each time, the time of purchases, etc.)
  • Web behavior data (the products she puts into her basket when she shops online).
  • Data from customer-created texts (comments about the company that this woman leaves on the internet).
  • Data about product/service use (feedback on the quality of the goods ordered, the speed of delivery, etc.).

Customer analytics is equally beneficial for companies and customers. The former can adjust their product portfolio to better satisfy customer needs and organize efficient marketing activities. The latter can enjoy favorite products, relevant promotions and personalized communication.

2. Industrial analytics

To avoid expensive downtimes that affect all the related processes, manufacturers can use sensor data to foster proactive maintenance. Imagine that the analytical system has been collecting and analyzing sensor data for several months to form a history of observations. Based on this historical data, the system has identified a set of patterns that are likely to end up with a machine breakdown. For instance, the system recognizes that picture formed by temperature and load sensors is similar to pre-failure situation #3 and alerts the maintenance team to check the machinery.

It’s important to mention that preventive maintenance is not the only example of how manufacturers can use big data. In this article, you’ll find a detailed description of other real-life big data use cases.  

3. Business process analytics

Companies also use big data analytics to monitor the performance of their remote employees and improve the efficiency of the processes. Let’s take transportation as an example. Companies can collect and store the telemetry data that comes from each truck in real time to identify a typical behavior of each driver. Once the pattern is defined, the system analyzes real-time data, compares it with the pattern and signals if there is a mismatch. Thus, the company can ensure safe working conditions (as drivers should change to have a rest, but they sometimes neglect the rule).

4. Analytics for fraud detection

Banks can detect an unusual card behavior in real time (if somebody else, not the owner, is using it) and block suspicious activities or at least postpone them to notify the owner. For example, if the user is trying to withdraw money in Spain, while they reside in Texas, before declining the transaction, the bank can check the user’s info on the social network – maybe they are simply on vacations. Besides, the bank can verify if this user has any linkage with fraud-related accounts or activities across all other channels.

Big data sources: internal and external

There are two types of big data sources: internal and external ones. Data is internal if a company generates, owns and controls it. External data is public data or the data generated outside the company; correspondingly, the company neither owns nor controls it.

Let’s look at some self-explanatory examples of data sources.

Internal and external big data sources

Autonomous system or a part of traditional BI?

Big data can be used both as a part of traditional BI and in an independent system. Let’s turn to examples again. A company analyses big data to identify behavior patterns of every customer. Based on these insights, it allocates the customers with similar behavior patterns to a particular segment. Finally, a traditional BI system uses customer segments as another attribute for reporting. For instance, users can create reports that show the sales per customer segment or their response to a recent promotion.

Another example: Imagine an ecommerce website supported by the analytical system that identifies the preferences of each user by monitoring the products they buy or are interested in (according to the time spent on a product page). Based on this information, the system recommends “you-may-also-like” products. This is an independent system.

Big data technologies: overview of good-to-know names and terms

Big data good-to-know terms

The world of big data speaks its own language. Let’s look at some good-to-know terms and most popular technologies:

  • Сloudis the delivery of on-demand computing resources on a pay-for-use basis. This approach is widely used in big data, as the latter requires fast scalability. E.g., an administrator can add 20 computers in a few clicks.
  • Hadoopis a framework used for distributed storage of huge amounts of data (its HDFS component) and parallel data processing (Hadoop MapReduce). It breaks a large chunk into smaller ones to be processed separately on different data nodes (computers) and automatically gathers the results across the multiple nodes to return a single result. Quite often Hadoop means the ecosystem that covers multiple big data technologies, such as Apache HiveApache HBaseApache Zookeeper and Apache Oozie.
  • Apache Spark is a framework used for in-memory parallel data processing, which makes real-time big data analytics possible. E.g., an analytical system may identify that a visitor has been spending quite a long time on particular product pages, but has not added them to the cart yet. To motivate a purchase, the system can offer a discount coupon for the product of interest.

Now you know what big data is, don’t you?

Our big data consultants created a short quiz. There are five questions for you to check how much you’ve learned about big data:

  1. What kind of data processing does big data require?
  2. Is big data 100% reliable and accurate?
  3. If your goal is to create a unique customer experience, what kind of big data analytics do you need?
  4. Name at least three external sources of big data.
  5. Is there any similarity between Hadoop and Apache Spark?

Well done! We hope that the article was helpful to you and that after reading it you’ve found the quiz easy.