With the development of technology, the amount of data has increased exponentially. Traditional tools can no longer handle automated data analysis and storage needs. Thus, special algorithms were created, which are commonly referred to as "big data."
Most often big data does not have a clear structure, so there's no way one can use it for any activity. Big data technologies are used to automate the analyzing process.
Three main big data properties are as follows:
Over the past few years, the popularity of big data has increased. This resulted in two extra properties: value and trustworthiness. The value is defined by each company in its own way. Company specialists assess whether the information obtained will benefit the business or not. As for the reliability, it shows whether the data used can be trusted and to what extent. Inaccurate information can harm the company and its operations.
Big data contains a lot of useful information. Based on the latter, companies create new opportunities and form business models. Working with Big data goes into three stages: integration, management, and analysis.
At this stage, companies integrate technologies and systems into their work. This way they will be able to collect large data volumes from different resources. Data processing and formatting mechanisms are introduced to simplify big data analytics. It is also worth paying attention to the security of your data, as the possibility of unplanned or launched leakage, always great and it can happen even by your employees. To do this, many companies use software for employee monitoring, so this is the best solution for the security of your data.
Obtained data needs to be stored somewhere, and you have to think about this in advance. The final decision is usually based on multiple criteria, with format and processing technology preferences being the most important. As a rule, companies use local storage, public and/or private cloud services.
Only after analysis, big data actually becomes useful. Machine learning, association rule learning, genetic algorithms, and other technologies are used for this. After this final interaction stage, only the most valuable data remains.
Which IT solutions are the most valuable today in different sectors of the economy? We can highlight three main pillars: intelligent process-control systems, artificial intelligence, and smart solutions for maintenance and repair. All of these technologies are based on Big Data.
Companies that implement advanced solutions to improve processes at the highest level win. This is especially true for decision-making, financial, asset, and staff management processes.
Simply collecting data is not enough. It is big data analytics that can make unstructured data organized, identify bottlenecks and growth points. It can even provide optimization recommendations.
According to Deloitte, only 15% of companies use their data correctly and understand how to benefit from it.
Meanwhile, deep data analytics and management are still major trends. Thus, software development or web app development, retail, telecoms, and banking sectors have already mastered this skill close to perfection.
Equipping everything with sensors will be expensive. You will have to develop a whole infrastructure for collecting and storing big data. This can result in spending millions of dollars only to find yourself thinking about what to do with all that data. Otherwise, such optimization simply will not pay off. Therefore, there's no universal answer to the question.
Everything depends on the equipment, operation mode, and developed/tested models. In each case, it all comes down to design. The probability of making a mistake here is much higher if you don't have any experience.
You need an expert who understands the production process and how the equipment is set up. To indicate which of the measured parameters most clearly reflect the performance of the equipment, tool, or app.
Today, the U.S. has every opportunity to provide the local market with secure and high-quality software solutions. They will contribute to the effective digitalization and development of various sectors of the economy.
In the post-pandemic times, businesses still have an urgent need to transform all software development processes. Without innovative smart solutions, it's getting more and more difficult for businesses to keep their margin levels, value, and temporary monopoly. However, the progress is unstoppable.
Software development companies need to know how to process big data in the most efficient way. Finding a technology partner that will help achieve specific business goals is crucial. The main thing is to approach this as intelligently and systematically as possible.
Great opportunities bring great difficulties. And big data is no exception.
The first difficulty that software development companies and their developers often face is that big data takes up a lot of space. Yes, storage technology is constantly improving, however, data volumes are rapidly growing, as well (in fact, 2x every 2 years). For example, for fleet management development, you should have a very huge database, since this is an important feature of the car market.
Buying a huge database will not solve everything. Simply storing your data will not do any good - you need to work with it. Hence another challenge - setting up the processing of collected big data.
Analysts now spend 50-80% of their time making the information acceptable to clients. Software development companies now have to hire a data expert or more specialists, which, in turn, increases costs.
Another problem is the rapid development of big data. New software tools and services (e.g. Hbase) emerge almost every month. Businesses have to spend a lot of time and money to stay trending and keep up with innovative software developments.
With that said, big data is a combination of technologies for processing large volumes of information (hundreds of terabytes or more). There's absolutely no doubt their importance, distribution, and popularity will only increase in the future. Subsequently, new technologies will be developed to automate big data analysis. This will allow for not only large but also medium-sized and small software development companies or web design agencies to work with big data.
Marketers use big data to predict the results of advertising campaigns. The analysis also helps find the most interested audience. A prime example of big data in marketing is Google Trends. The system receives a huge amount of data, and after analysis, users can estimate the seasonality of a particular product, job, or service.
Big data is a ubiquitous bestseller, used by specialists from different companies all over the world. However, studies show that its potential is still only 20-25% used.
Until recently, big data analytics production technology was only used by large companies to set up complex systems. Today, these tools are also used by medium-sized and small businesses.
Experts state that respondents do not have enough access to enterprise data. This can be the main reason for poor results. In their turn, company managers state that this is not the case.
There's also a lack of big data specialists. And those who can actually work with large data volumes still make mistakes. Experts who understand the big data importance point out that the level of expertise in the field is insufficient today. Less than 50% who work with data can group audiences by direction by segmenting data.
More than 50% of marketers surveyed admitted they want to be able to make real-world decisions by relying on customer data. Many company managers with evaluation and analytic skills want to develop a big data model to bring their business to success. Statistics, however, show that only 6% of all marketers can make full use of big data on their own.
This gap needs to be filled. New technology and professional education to work with big data should be introduced at the global level. The world is moving towards automation in all niches so this is a necessity for us today.
Only 17% of companies make data-driven decisions. Even though everyone knows the benefits of data-driven marketing. If you're not using data, you're missing out on a lot of opportunities. Thus, we recommend checking the following 6 examples of using big data to generate additional revenue.
Segment Your Customers
Segmenting customers is a simple but effective way to use data in email marketing. By capturing customer actions, purchases, and characteristics, you will understand what content will interest everyone. Make email a personalized channel of communication with your customers.
74% of marketers report an increase in engagement after segmenting newsletters. This can increase ROMI (Return on Marketing Investment) by up to 760%.
You don’t want to send the same email to your "warm" leads and new subscribers. If your lead has:
then you should send them a promotional email on your target product.
Send a newsletter with attractive content to your new subscribers. It will increase their interest and build additional value. Use email marketing to send newsletters that will move your leads through the sales funnel.
Increase Customer Loyalty
32% of CEOs prioritize customer retention. This is quite obvious since attracting new ones costs 5-25 times more than retaining your existing customers.
When improving customer loyalty, you need data. The more, the better. Analyze your sales, and you'll see what other products you can offer. For example, you have three similar products in your range, and one of your customers has been buying two of them. The probability that he/she will be sensitive to your third product ads is quite high.
In addition to personalized email marketing, ask some of your customers to follow you on social media, e.g. on Facebook. Then run social media ads to that audience and promote your product. These people already know your brand, which means conversions will be higher than when attracting new customers.
Set up triggers so that customers receive automatic emails on certain events, birthdays, or when placing their orders.
Do not consider your retention goal to be getting as much money out of your customers as possible.
Provide an amazing customer experience, send personalized content and offers. This will yield more benefits in the long run and is more profitable.
Visualize Your Data
90% of marketers hardly ever use data visualization in their work. This is wrong. Your site viewers will more likely appreciate images than pages full of continuous text. This allows you to get your message across faster.
Visualization and infographics are often considered synonymous. However, the former is a way to translate data into graphical form. Infographics, in turn, are a way of telling and explaining a topic in a coherent way. Visualization is one of the key elements of infographics but it's also self-contained.
Transforming your data into colorful visuals will be useful for big data analysis. Plus, it's much easier to study sales dynamics via graphs.
Develop New Software Products
Predictive analysis refers to the study of past data to calculate future probabilities. If you have a lot of information, predictive analysis can help introduce a new product/service.
Those in the software development or mobile app development and retail industries know that only a few products make the most profit, while the rest are illiquid. For this reason, expanding an assortment can feel like a gamble.
Thus, Netflix is a brand that is masterful in the art of predictive analytics. Based on vast amounts of data, Netflix establishes the hallmarks of a potentially successful movie or TV show.
Thus, the predictive analysis allowed Netflix to create the series "House of Cards," which has been a huge commercial success. Kevin Spacey (as a lead actor) and David Fincher (as a producer) were chosen for a reason. Big data analytics showed that Netflix subscribers are thrilled with their previous work.
Predictive analysis can't guarantee success when developing a new product. However, it will greatly improve your chances.
Reduce Churn Rate
You can make a list of customers who have a high probability of leaving. One of the churn factors could be a period of inactivity in your personal account.
Now develop and launch a return campaign. This could be offering special bonuses that show your customer your care.
Study customer buying patterns. This will allow you to make sales forecasts for the future.
Calculate customer metrics: the price of attraction, average check, and customer lifetime value. This way you'll understand how much revenue each new customer will bring in in the future. Otherwise, running effective marketing campaigns can become a challenge.
When forecasting sales, consider a more pessimistic scenario as well. A forecast is a probability and cannot be absolutely certain. Thus, you want to have a backup plan in case your sales don't go as expected.
Working with big data, analytics, and other related elements of data-driven marketing regularly make lists of top trends every year. However, the prevalence of this approach is still growing at a low rate.
Many marketers have little or no command of the statistical tool and have no idea how to work with big data. This situation applies to the United States, Europe, and other countries. However, the possibilities of the analytical approach in marketing are truly limitless. They will allow you to significantly increase the effectiveness of your marketing campaigns. You will be able to better justify your activities and identify previously unnoticed patterns.
Remember that marketing is not only about creativity - it's also pure math.
Published on Jul 16, 2021
WRITTEN BYWhatagraph team
The Whatagraph blog team produces high-quality content on all things marketing: industry updates, how-to guides, and case studies.
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