Data Analytics

Data Analytics

Data analytics is the science of analyzing raw data to make conclusions about that information. At Silico, we provide the best data analytics from best professionals across industry. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption. Key points are

A.           Data analytics is the science of analyzing raw data to make conclusions about that information.

B.           Data analytics help a business optimize its performance, perform more efficiently, maximize profit, or make more strategically-guided decisions.

C.           The techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.

D.          Various approaches to data analytics include looking at what happened (descriptive analytics), why something happened (diagnostic analytics), what is going to happen (predictive analytics), or what should be done next (prescriptive analytics).

E.           Data analytics relies on a variety of software tools ranging from spreadsheets, data visualization, and reporting tools, data mining programs, or open-source languages for the greatest data manipulation.


Understanding Data Analytics

Data analytics is a broad term that encompasses many diverse types of data analysis. Any type of information can be subjected to data analytics techniques to get insight that can be used to improve things. Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize processes to increase the overall efficiency of a business or system.

For example, manufacturing companies often record the runtime, downtime, and work queue for various machines and then analyze the data to better plan the workloads so the machines operate closer to peak capacity.

Data analytics can do much more than point out bottlenecks in production. Gaming companies use data analytics to set reward schedules for players that keep the majority of players active in the game. Content companies use many of the same data analytics to keep you clicking, watching, or re-organizing content to get another view or another click.

Effective revenue management depends on integrating data from various systems and sources and analyzing the information to implement the right strategies to optimize resource use and revenue. A LinkedIn article lists the key areas where data analytics impacts revenue management in the hotel industry as:

•            Pricing Decisions: Data analytics helps revenue managers make pricing decisions. By studying past trends and comparing competitor pricing patterns, they can determine the ideal prices for rooms in each category.

•            Channel Management: Analyzing data is important to identify which channel is performing well, seasonality of bookings, the region from where more bookings are coming, the sale prices of various room categories on different channels, and more. This information can be utilized to maximize revenue from each channel.

•            Booking Patterns: Analyzing data on demand for different room types based on region, season, and market segment, managers can forecast occupancy and design appropriate pricing strategies.

•            Data Filtering: Analyzing and processing relevant data will help in the generation of accurate and timely reports for informed decision making.

However, today, revenue management tactics have gone beyond pricing and inventory management. The conventional method of analyzing historical demand patterns such as booking lead times, booking patterns by segments, and occupancy trends by season, day, week or month to determine the best possible room rate at a given point of time has become obsolete. Analyzing performance and estimating demand and demand patterns have become unpredictable as they are dependent on multiple external factors.

The hospitality and airline industries are particularly vulnerable to disruptions such as economic crises, wars and epidemics. Though these sectors usually recover fast from such disasters, the COVID-19 pandemic has badly affected their resilience and ability to bounce back. Revenue managers have to rethink their data and analytics strategy to optimize operations and profits.

New Elements in Data Analysis for Revenue Management

For hotels to survive and stay competitive in the COVID world, experts recommend that data analytics for revenue management should include the following:

•            Real-time Market Demand Indicators: According to a hospitality net article, it is essential to have a complete understanding of the competitor landscape for competitive pricing. Hotel prices for every room type and individual options available for each room type should be analyzed 365 days in advance. Knowing these real-time market demand indicators is important to ensure optimal pricing for every potential new booking.

•            Inbound Search Volume by Source Market: Examining real-time trends in flight and booking search volume can provide an idea of how demand for a destination grows over time, notes Atomize. Predicting which dates will see increased demand over time before reservations begin can help hotels adjust rates in advance and get bookings at optima rates.

•            Macro Perspective of Demand: Various factors impact demand pressure for a destination: travel agent hotel booking searches, how often hotel prices change, hotel cluster search analytics, and flight search data trends. Understanding these factors is important to understand market demand and ensure appropriate pricing.

•            Online Reputation Ranking: Before they book a hotel room, most customers will compare the online reputation scores of hotels available across various online booking platforms. People typically rank their stay experience based on factors such ambience, cleanliness, comfort, location, facilities, staff, value for money, etc. While a hotel will not have a direct control over its reputation score, its service delivery standard will impact these factors. Revenue management strategies need to incorporate reputation data analytics to understand what’s important to potential guests.

•            Data on Flight Search and Potential Travel Patterns: In the COVID-19 scenario, analyzing flight search data is especially important for hotel revenue management. Hotel bookings and stays are closely linked to flight travel patterns. Understanding flight search data is crucial to identify travel intent and hotel booking prospects.

Effective data analytics depends on having clean data in the required format. Data entry outsourcing can ensure that even complex data is organized properly so that different visualizations and analysis can be performed effortlessly. Outsourcing companies also provide document conversion services to convert unstructured content into data that is ready for analysis. Relying on these solutions can make the data analytics task easier for revenue managers in these challenging times.

How Big Data Analytics Helps Businesses Increase Their Revenue

Big data analytics isn’t just a bright and shiny new thing in the marketing space — it’s a more efficient, innovative approach that connects the art and science of marketing. Data-driven companies don’t leave creativity behind; they find even greater opportunities. Rather than spending countless worker hours attempting to determine consumer behaviors by traditional means, data-driven companies have understandable and evolving data sets at their fingertips.

This not only frees up marketers’ time, it helps them harness it. A creative and progressive approach empowers marketers to build campaigns that simultaneously leverage valuable information, artistic vision, and company expertise. Forward-thinking marketers don’t leave their creativity behind as they reach for data analytics; they merge it together to design successful campaigns, strengthen their brand storytelling, and benefit their companies’ bottom lines.

Take Amazon, for example. Those constant price changes on their website are not random, nor are they merely an algorithm set to try different prices at different times with no informational underpinning. Instead, those price changes are heavily data-driven and personalized, enticing customers to purchase items they have looked at in the past but not bought, or to consider new items that match their previous purchases on the site. By leveraging big data to analyze customer interest, competitor prices, and inventory, Amazon can price its products in ways that attract and retain customers.

Data Analytics Empowers Companies to Set and Reach Their Goals

The team at DemandJump, a customer acquisition software platform, explains how data-driven marketing insights can strengthen goal-setting. For example, data can reveal population segments who share interests or behaviors. That knowledge can enable marketers to strengthen their messages according to their target consumers’ needs and desires. This approach yields better results and helps teams predict outcomes and determine the path to success.

Specifically, predictive analytics help marketers set and achieve clear goals. A Business Insider report found that “predictive analytics can support applications like scoring risk and preventing fraud, and provide insight into consumer behaviors like lifetime customer value and even affective states, like feelings toward a specific experience.”

Marketing Evolution identifies three predictive analytics models that stand to benefit marketers:

Cluster Models: These algorithms are used for audience segmentation based on past brand engagement, past purchases and demographic data.

Propensity Models: These evaluate a consumer’s likelihood to do something, such as convert, act on an offer or disengage.

Recommendations Filtering: This model evaluates past purchase history to understand where there might be additional sales opportunities.

With information like this at their fingertips, marketers can help their companies build on the past and present information to design the campaigns of the future.

Data Analytics Help Companies Carve out Niches in Saturated Markets

When UberEats entered the food delivery game, the market was saturated. So what set UberEats apart and helped them achieve tremendous success? They leveraged data from their ride-sharing service to predict masterful food delivery. UberEats modeled the physical world so they could find the most efficient and excellent plan that would deliver food that’s still warm, make customers happy, and increase profits. As the design and development company Bornfight observes, “What Uber Eats is doing is a textbook example of how Big Data and data analysis can help businesses expand their services and give them a clear advantage over their competitors.”

Data analysis also helps marketers and their companies predict and respond to changes in the marketplace and the expectations of customers. By leveraging data to understand customer trends and the ever-shifting landscape of the digital marketplace, companies can be proactive and creative in their approach to marketing rather than reactive.

But what about companies that are just getting started or want to stay small and local? While they face challenges like limited historical data, OMI points out that continuous cloud integration is a great starting point for leveraging data. With cloud integration, small businesses can adopt a cohesive system that puts data sets into communication with each other. This system can help small businesses make stronger marketing decisions and accelerate their growth as they identify patterns and opportunities in their company’s market.

Using data to drive business growth is possible for small businesses that don’t merely pay attention to sales or revenue statistics but ask insightful questions that help them make the most of their data. shares a few such questions, like “which vendors offer the most value?” and “which product lines need the most improvement?”

The answers to these questions will help small businesses implement data analytics in useful, sustainable ways that increase their revenue.

Data Analytics Give Marketers Information They Need to Design Successful Campaigns

One of the foremost companies using big data analytics in their approach to marketing campaigns is the multi-billion dollar streaming titan Netflix. The company implements algorithms and analysis technology that helps them understand what individuals are interested in watching, enjoy, and may want to see next. This way, they can design a personalized home screen for each of their over 200 million subscribers, increasing customer satisfaction and loyalty.

Netflix demonstrates a key finding in a McKinsey & Company report: personalization stands to gain five to eight times the return on investment of a company’s marketing spend. It can also increase sales by 10%, sometimes even more.

The retail banking industry uses data analytics to enhance their marketing strategies as well. International Banker gives the following examples of retail banks using big data to understand their existing customers in great detail so that they can market the most attractive and fitting products to them in the future:

Sending an offer to a banking customer because they used an app

Implementing retargeting to feature loan offers on other websites after a customer has searched for loan services on the bank’s website

Using credit-card data to promote products that fit well with something the customer already purchased

In the healthcare space, marketers are using data to drive business growth by studying consumers, patients, physicians, and geographic markets. Wearable technology and other forms of patient monitoring help marketers understand the needs of individuals, the trends of communities, and the products, programs, or services healthcare providers can offer to best serve their patients.

Lastly, consider the fashion industry. Fashion trend forecasting company Heuritech shares an example of a company that used data analytics to design and market a particular style of sneakers to a trendy segment of the marketplace. After using data to determine the optimal design, marketers used data analysis to determine the best time for the product’s launch. They then used consumer data to launch a marketing campaign that matched the values of their target customer. From product design all the way through to presenting a consumer with a trendy sneaker, data analytics joined together with creativity to both create and market an appealing product.

+91 7044686108
+91 7044686108