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Top 5 Data Analytics Trends That Will Reign in 2018

For IT companies, understanding the latest data analytics trends and devising strategies to deal with them is becoming critical. As a result, the use of a wide range of techniques and technologies like machine learning, big data, and data science is rising rapidly. A data analytics strategy that offers deep insights into system performance, customer behavior, and upcoming revenue opportunities, needs to be modified accordingly to fetch maximum benefit from the latest data analytics trends. To use a summarized version of data is one thing and having the knowledge to collect, compose, and propagate in best ways is another. This is where is the impact of new data analytics trends comes to the fore since it helps businesses and organizations to bring about changes in their strategy to optimize their performance and fetch maximum profits. But before exploring more of it any further, let’s pause for a bit to understand data analytics in simple terms.

What is Data Analytics?

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Digital Analytics Trends 2018: Our Top Picks

digital analytics

Web analytics is no longer as simple as it used to be a few years ago. In this digital age, capabilities such as social, cloud, mobile analytics, and associated data technologies have emerged as catalysts for core business disruption. The amount of data available today can easily prove to be overwhelming while formulating a marketing strategy. Moreover, there is a proliferation of devices and channels from which data has to be tracked, making digital analytics an even more daunting task for marketers. However, the developments in digital analytics have grown leaps and bounds in the past couple of years. In this age where companies follow a data-driven marketing strategy, digital analytics is the way ahead for forward-thinking companies. Let’s take a look Request Solution Demoat some of the top digital analytics trends that will dominate in 2018:

Prescriptive analytics

This year, companies will be upgrading from predictive analytics to prescriptive analytics. This means that marketers will now get insights on not only what will happen next but will also be enlightened on what course of action must be taken during a particular event. Automation is the new trend everywhere, and automated data-driven insights and decisions are definitely on the cards for digital analytics this year.

Multi-channel attribution

The number of devices and platforms that customers use online is growing with time. Ergo, it is the context of a particular content that decides which platform and device the users generally prefer. It is also affected by factors including the amount of time a person has, the task they want to achieve, their location, and even their state of mind. For instance, for a quick search for details regarding products, customers may rely on a mobile platform, but the actual purchase might happen on a desktop. So, top companies are undertaking digital analytics for different platforms before they formulate their next marketing strategy.

Data monetization

Modern businesses are blessed with an abundance of data, but the big question here is how to correctly leverage the data and turn them into useful insights for driving future action and cause a direct impact to bottom-line revenues. To undertake data monetization successfully, companies must assess data around customer transactions and interactions across devices and channels. This should be done either in the form of increasing revenue streams or using the data to create efficacies within the organization that would eventually result in reduced costs. E-commerce giants such as Amazon are already routing their efforts in this direction.

Return on analytics investment

Most companies have realized the importance of having an in-house digital analytics team. But since this requires a considerable investment, businesses must keep track of the return on analytics investment. It is the analytics teams that ideally measure the performance of other teams, but now they need to do the same for themselves, it’s a step towards being a cost center to a profit center.


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Why the Media Industry Needs to Upgrade to Big Data Analytics

Big data analytics has become an invincible tool to stay competitive in any business. In fact, there is hardly any sector that has been left unaffected by big data. The media industry, known to be hungry for innovative content and technology, has been one of the early adopters of big data analytics. Media companies may have better opportunities than most, as the very act of consuming content provides incredible amounts of data that can inform every aspect of content generation, packaging, and distribution.  With smartphones and associated digital media becoming the major source of entertainment, creators and distributors in the media industry must embrace advanced analytics capabilities to strike a chord with the customers. It is crucial for media companies to seize the opportunities this data presents—or watch their pure digital competitors extend their lead in consumer intimacy. HereRequest Solution Demo are some of the key use cases of big data analytics in the media industry and why it should not be ignored:

Predicting audience demand 

The entertainment and media industry gathers a large amount of data on an everyday basis. This data collected can be used by companies in the media industry to understand the demand of the genre of shows, music, content for a given age group on different given channels. When customers feel that the companies understand their needs and show them exactly what they want to see, they become more loyal to your brand. There is nothing that is as valuable as creating a loyal customer base.

Customer acquisition and retention 

With the help of big data analytics, companies in the media industry can decipher exactly what customers subscribe to and what they unsubscribe from. Based on this data, players and media creators can alter their content accordingly to suit the likes and dislikes of viewers. This will also help them in developing the best promotional and product strategies to attract and retain the audience.

Ad targeting 

Big data analytics provides media companies with a better idea of the digital content and the consumption trends of users across various digital platforms. Companies can leverage the traditional demographic data to personalize advertising for users. By offering micro-segmentation of customers to their advertising networks and exchanges, players in the media industry can also increase digital conversion rates.

Scheduling optimization 

Big data analytics provides insights to companies in the media industry about when customers are most likely to view content and what devices will be used for the same. With big data analytics’ scalability, this information can be analyzed at a granular ZIP code level for localized distribution.

Product development 

Additional revenue generation is one of the main advantages that big data analytics provides for media and entertainment companies. Media companies can incentivize consumer behavior and reveal the true market value of the generated content using accurate data. If companies find any gap in the existing content and the popular consumer demand, they can use this data in the development of new content.


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Web Analytics Vs Customer Analytics- What’s the Difference?

Web analytics and customer analytics are often confused with one another. However, both these terms are distinct from one another. Web analytics refers to the collection and analysis of web data to maximize the usage of the site. Web analytics measures things that are considered by the webmaster. This includes factors such as the number of views, page loading time, time spent by the visitor on the site, and other factors. The performance of the website can be greatly improved using web analytics. It also helps to measure the effectiveness of advertisement campaigns. Customer analytics, on the other hand, is the process of identifying the customer information, which will help companies to deliver and meet their needs. Customer analytics forms the backbone of any marketing activity for a business. It includes techniques such as data visualization, predictive modeling, and segmentation. Here are some of the key parameters that distRequest Solution Demoinguish web analytics from customer analytics:

End users

Data and product teams are the end users of the data gained from web analytics. It does not have anything to deal with customer-facing departments. Customer analytics, on the other hand, includes customer-facing departments, who play a major role in implementing customer knowledge into action in the organization.

Customer insights

Web analytics is not designed to provide information at an individual customer level. It is used to identify trends and patterns in the behavior of a group of customers. Customer data analytics provides in-depth insights on each and every individual customer of the business. This can help businesses track the traits of customer’s individually and target the right customers especially for marketing and ad campaigns.

Forecasting

Web analytics does not have the capability to help companies predict the future based on the traffic driven to the site or by the number of customers visited the site.  It can merely depict how successful the marketing campaigns of the company has been. Customer data analytics involves understanding customer behavior before reaching out to them. This behavioral study will let the company predict future leads, new customers, and references. This tool is really helpful for companies who have a huge number of customers like B2B, insurance, and real estate.

Information provided

Marketing and data teams obtain information about the website traffic with the help of web data analytics tools. Customer analytics tools provide contact and business information to sales and customer support teams who face the customers directly. This helps the customer-facing teams to get in-depth behavioral insights about the customers.

Data organization

Data is organized around the website like page views, landing pages and others in the case of web analytics. Customer data analytics, on the other hand, organizes data about the customers. Instead of focusing on the website data, if we concentrate on the customers’ data then the data becomes easier to control. This is because data related to customers are simpler to understand and use.


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4 Retail Industry Challenges That Predictive Analytics Can Solve

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What Are the Retail Analytics Trends to Be Prepared for in 2018?

In past couple of years, the retail industry has been going through a paradigm shift. Most retailers today understand the need to monitor in-store analytics and are investing in various analytics platforms based on video cameras, beacons, thermal imaging, and 3D sensors. However, the data that is captured using these platforms is an aggregated data that might not necessarily prove to be actionable. This is where appropriate in-store analytics come into the picture. Companies in the retail industry can make use of the latest analytics trends to understand their customers better. By understanding customer behavior using data insights from different channels, retailers can better execute marketing campaigns and personalize the shopping experience for their customers. Let’s take a look at the analytics trends in the retail industry that 2018 has in store for us:Free demo

Rise of wi-fi analytics platforms

Retailers will not be able to differentiate a first-time visitor, repeat visitor, or a loyalty member just by looking at them. An appropriate in-store wi-fi analytics platform can provide such precision. Using such a platform, retailers can identify each customer by asking them to opt-in via captive portal using their name, email, phone or loyalty number. This will provide companies in the retail industry with more accurate and authentic details of their customers. In fact, according to research experts, the analytics trends such as wi-fi analytics is expected to rise from USD 2.94 Billion in 2017 to USD 10.72 Billion by 2022.

Predictive to prescriptive analytics

Predictive analytics is widely used in the retail industry for everything ranging from forecasting demand & footfalls to personalizing the customer experience. But with the increasing pressure from competition in the market, pricing remains one of the most significant challenges for retailers. With the help of analytics trends like predictive analytics, companies can overcome this challenge. Prescriptive analytics provides the best course of action for any given situation. Analyzing different types of data such as product availability, customer trends, resources, time, and geolocation, can help retailers to optimize profit margins and capitalize available opportunities

Product assortment analytics

Product assortment is one of the key analytics trends that has had a significant amount of impact on in-store conversions and sales. Earlier, retailers who have either neglected or poorly planned their product assortment have seen devastating results on their sales. Product assortment can be used to maximize sales by reviewing shopping patterns to understand correlated products that are bought together. In-store analytics will help players in the retail industry to integrate in-store customer behavioral data with purchase history from POS to discover shopping patterns.

Analytics and loyalty programs

Despite the growth of e-commerce in the recent times, research suggests that retail stores still account for roughly 94% of the retail sales. This means that a large number of customers are still relying on brick and mortar stores. It also establishes the fact that beyond price discount, customers experience is what drives purchases. By understanding in-store customer behavior, brands are focusing on personalizing customer experiences to drive customer loyalty.


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What Are the HR Analytics Trends to Watch out for in 2018?

It goes without saying that the workforce is vital to the success of any company. If HR managers can analyze and predict staff needs accurately, then it would prove easier for them to make the office environment more productive, improve career development, and implement human resource processes. Research indicates that future HR leaders will be those who understand what technology can do, know how to mine intelligent insights from the technology, and apply them to their workplace to achieve their business goals. Advanced technologies such as HR analytics help managers to understand their workforce better. Therefore, workforce analytics is something that no progressive organization can afford to overlook. Here are four HR analytics trends that are going to breFree demoak new grounds for companies at large:

Utilize blind hiring techniques

Sometimes managers find a hard time in making a choice between different candidates. Sometimes there are also reported instances of unfair or biased judgment made. Companies can avoid such incidents with the help of a blind hiring process. Businesses can use HR analytics software that is entirely bias-free for screening and preselecting candidates based on their actions and answers, using algorithms to calculate a candidate’s likelihood to succeed in the role they apply. In 2018, we can expect more companies to resort to advanced analytics techniques while hiring employees for different job roles.

Increased measurement of the employee lifecycle

This year, HR analytics is all set to take a sharp turn towards utilizing available data for measuring the entire employee lifecycle — from pre-hire to exit. Progressive companies are likely to emphasize more on using data for measuring the employee lifecycle and reduce voluntary turnover. Each phase of the life cycle can give unique insights and value to the company regarding voluntary turnover. Several survey firms are already moving in this direction.

HR analytics is here to stay

The benefits offered by workforce analytics have been tried and tested by various top organizations. The majority of midsized to large organizations are trying to invest in and build robust HR analytics capabilities. However, a significant dilemma remains for most organization whether to buy or build workforce analytics for their company. The decision indeed varies according to each businesses’ capabilities. Also, from past experiences, many companies have identified that having a strong strategy is essential while implementing workforce analytics. Otherwise, the data just remains a tracking dashboard, which is simply not analytics.

Increases use of IoT

Cloud computing’s application in HR has skyrocketed more than ever before in the recent years, and this trend is expected to continue in 2018 as well. Storing data on clouds means that it would be easier to access these data and use them for workforce analytics. Even small and medium-sized companies are increasingly incorporating IoT capabilities into their business. Cloud computing offers several other benefits as well to companies, which include automatic software updates, so that the staff can access applications anywhere and at any time, and also enhanced data protection.


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7 Analytics Tools That Every Manager Should Know About

In today’s business scenario companies have access to large volumes of data. Manually categorizing and deriving value from these variables could prove to be a strenuous task. Thanks to various data analytics tools available today, business managers can easily classify complex data and use them for efficient decision making. Data-driven companies consider their data as corporate assets and leverage it for getting a competitive advantage. Successful business analytics depends on a variety of factors such as the quality of data, skilled analysts who have good knowledge of the technologies and the business, and an organizational commitment to data-driven decision-making. But with a large number of analytics tools available today, managers might be put into a dilemma about which ones they should ideally be using for the desired resultsHere are some of the top analytics tools that are widely used by businesses today:Free demo

 Correlation analysis

Correlation analysis is a statistical technique that allows you to identify if there is a relationship between two separate variables and how strong that relationship may be. It is especially useful when you have conviction suspect that there is a relationship between two different variables and you would like to test your assumption.

Visual analytics

One of the simplest ways to analyze data is to create a visual or graph and look at it to spot patterns. It is an integrated approach that combines data analysis with data visualization and human interaction. Such analytics tools become extremely helpful in understanding information when the volume of data is huge.

Regression analysis

It is a statistical tool that is used for investigating the relationship between variables. For example, the relationship between price and product demand. If you believe that a variable is affecting another and you want to establish whether your hypothesis is correct or not, this is the go-to tool.

Sentiment analysis

Sentiment analysis is also known as opinion mining, is one of the most recent and popular analytics trends. It seeks to extract subjective opinion or sentiment from text, video or audio data. The basic idea of using this analysis is to determine the attitude of an individual or group concerning a particular topic or overall context. In short, this tool is to be used when you want to understand the stakeholder opinion.

Monte Carlo simulation

This is a mathematical problem-solving and risk-assessment tool that approximates the probability of specific outcomes, and the risk of certain outcomes, with the help of computerized simulations of random variables. understand the implications and consequences of a particular course of action or decision.

 Linear programming

This is a set of advanced analytics tools, also known as linear optimization. Linear programming involves identifying the best outcome based on a set of constraints using a linear mathematical model. It helps companies to solve problems involving minimizing and maximizing conditions, such as how to maximize profit while reducing costs. This tool is highly beneficial if a business has a number of constraints such as time, raw materials, etc. and they want to know the best combination of directing resources to maximize profit.

Neural network analysis

Neural network analysis is on the advanced analytics trends to watch out for. A neural network is a computer program modeled on the human brain, which can process large volumes of information and identify patterns in a similar way human beings do. This is a technique of analyzing the mathematical modeling that makes up a neural network. This tool is particularly useful for companies that possess large amounts of data.


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