Category: Data

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Data

How Data Analytics Is Positively Impacting Transportation Management

The transportation industry faces numerous challenges that disrupt the seamless movement of people and goods. Logistical complexities, inefficient route planning, and escalating costs collectively impede the sector’s efficiency, negatively impacting commuters, businesses, and consumers alike.

To address these issues, policymakers and transport sector leaders can leverage data analytics to enhance the core components of the transportation sector. Data analytics offers powerful solutions that revolutionise the transportation management process, offering industry stakeholders the following benefits:

Real-Time Route Optimisation

Traditionally, planning routes for fleets of vehicles involved relying on static schedules and forecasts. However, these often lead to inefficiencies and delays when faced with dynamic factors such as traffic congestion or unexpected road closures. With the advent of advanced data analytics techniques, the process of route planning has dramatically evolved. Today, transportation managers harness the power of big data to continuously monitor and analyse a myriad of variables that influence travel times and conditions. This includes immediate traffic updates, weather forecasts, historical patterns, and even social media feeds for immediate reports on incidents affecting roadways. Leveraging these diverse data sources and employing sophisticated algorithms enables transportation operators to swiftly compute the most efficient routes for vehicles in transit.

For instance, logistics companies can utilise data-driven transportation management solutions to anticipate traffic patterns and adjust delivery routes accordingly to avoid bottlenecks and minimise idle time. This not only reduces fuel consumption and operational costs but also enhances delivery speed and reliability. Moreover, real-time route optimisation contributes to sustainable practices by minimising organisational carbon footprints. Unnecessary mileage and idle time are reduced, resulting in less emissions released into the atmosphere.

Improved Maintenance and Asset Management

Another significant area where data analytics is revolutionising transportation management is in maintenance and asset management. Vehicle maintenance schedules were often based on periodic inspections or mileage milestones, which could lead to either over-maintenance or insufficient upkeep—both of which impact operational efficiency and safety. With the integration of data analytics, transportation sector stakeholders can adopt a proactive approach to maintenance by equipping vehicles with sensors that monitor various performance metrics (e.g., engine health, tyre pressure, and fuel efficiency) in real time. Predictive analytics algorithms can analyse these data streams to forecast maintenance needs accurately, recommending servicing based on actual usage and performance rather than arbitrary schedules. This predictive maintenance strategy not only extends the lifespan of vehicles but also minimises downtime — thus improving fleet performance.

Furthermore, data-driven insights into asset management enable organisations to optimise fleet composition and allocation. Analysing usage patterns can help transportation managers right-size their fleets and ensure that the right number and types of vehicles are deployed where and when they are most needed. This leads to cost savings through reduced capital expenditure on unnecessary vehicles and improved efficiency in resource utilisation.

Enhanced Passenger Experience

Across various modes of transportation, the ability to gather and analyse vast amounts of data is revolutionising how services are delivered and perceived by users. Transportation providers can examine historical data on passenger volumes, travel patterns, and seasonal trends to better anticipate peak times and adjust service levels accordingly. This allows for smoother operations, reduced overcrowding, and improved scheduling accuracy, leading to less waiting time and greater convenience for passengers.

Real-time analytics also enable operators to provide timely and accurate information to passengers. Whether it’s updates on departure times, gate changes, or service disruptions, passengers can stay informed through mobile apps, digital displays, or automated announcements. This transparency builds trust and satisfaction among passengers, who are likely to appreciate being kept informed and empowered to make informed decisions about their travel plans.

What’s more, data analytics can be used in personalising the passenger experience. Transportation providers can use data collected from customer preferences and behaviour to tailor their services and offers to their customers’ individual needs. For example, airlines can offer personalised travel recommendations based on past bookings, while public transport operators can suggest alternative routes or modes of transport based on current traffic conditions and user preferences.

Safer Transport Services

Transportation operators can harness data from various sources and apply advanced analytics to the safety of their services. They can use onboard sensors, GPS tracking, and even video surveillance to ensure their drivers are following the rules of the road. Analysing the data enables them to identify risky behaviours and issues such as speeding, harsh braking, or fatigue. This can lead to timely intervention, thereby promoting safer driving practices and reducing the likelihood of accidents.

Better Transportation Infrastructure Development

Through the power of data, transportation planners and policymakers can make informed decisions that optimise infrastructure investments, improve connectivity, and enhance overall mobility within communities. They can easily identify areas of high traffic density and inefficiencies in current road networks, enabling them to employ targeted interventions to alleviate congestion and improve traffic flow. Examples of these solutions include road expansions, traffic signal optimisation, or the introduction of alternative transportation modes like bus rapid transit (BRT) systems, cycle lanes, and rail systems.

Moreover, data analytics facilitates predictive modelling for future transportation needs based on population growth projections and economic trends. By forecasting future demand for transport services, planners can anticipate infrastructure requirements and allocate resources effectively. This proactive planning ensures that transportation networks can accommodate future growth while minimising disruptions and cost overruns.

Data analytics is revolutionising transportation across the globe. It’s giving policymakers, transport sector leaders, and even transportation operators the tools to make significant improvements in the efficiency, safety, and sustainability of their transportation management processes. With the proper use of data, the transport industry can build a positive future and meet the evolving needs of communities.

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Advertising & Marketing Business Data

Why Using A Customer Data Platform Will Take Your Customer Experience to the Next Level

Why Using A Customer Data Platform Will Take Your Customer Experience to the Next Level

There’s no shortage of marketing tools that capture and analyze customer data. The problem? When businesses analyze customer data, each data set is usually treated as a standalone. But siphoning through data set after data set can be costly and inefficient. 

So, how do marketers combine different data sets into a single customer view? 

They use customer data platforms (CDPs). 

What are CDPs?

CDPs are data platforms that capture data from various sources and display it in a single unified customer database. In other words, they consolidate and integrate customer data into one central platform. This way, businesses can pull insights on a specific customer or prospect during various points of the customer journey. 

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Like all marketing tools, businesses use CDPs to understand their customers’ unique preferences and behaviors. 

But what sets CDPs apart from other tools is their ability to help businesses create customer-centric experiences.

CDPs give businesses the data they need to create relevant messaging — all in one place, in real-time. This helps them create messages that are custom-tailored to their customers’ preferences. When customers feel like a company knows them, they’re more likely to stick around. 

Let’s take a closer look at how you can use CDPs to take your customer experience to the next level.

Building a sales process 

Using a customer data platform can be invaluable to building a sales process that entices your customers to say “yes” without resorting to slimy tactics. 

Below, we’ve listed a number of ways businesses can use CDPs. 

1. Map out the buyer’s journey

Buyer journeys aren’t simple straight lines that lead to a sale. They zig, zag, turnaround, and zig again. 

Today, a buyer’s journey could start on one channel and toggle between several steps. Knowing how that journey ebbs and flows gives your organization a chance to ensure no customer slips through the cracks.

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That’s why it’s essential to map out your company’s buyer’s journey. 

Every step and interaction a customer goes through during the buyer’s journey produces customer data. 

Businesses that study these interactions and look for patterns can predict future buying habits. Understanding this behavior shows you what customers align with and what turns them away. 

To sum up: Understanding what actions customers complete before or after purchasing helps you nurture leads and create more enjoyable customer experiences.

2. Craft customer-centric sales funnels

Mapping out the buyer’s journey and analyzing customer behavior gives you a competitive advantage. 

When you can predict customer behavior, you’ll be able to:

  • Understand buying habits
  • Share relevant offers
  • Personalize content 
  • Build long-term customer relationships 
  • Speak to customers in their preferred communication style 

You’ll also be able to craft customer-centric funnels. 

Customer-centric funnels use the data you mapped in your buyer’s journey to take customers through a personalized sales experience. 

Each stage of the funnel is personalized for your customers. Some people will need more nurturing before making a purchase, while others won’t need much coaxing at all.

For instance, some customers will need a whole series of offers while others will buy after just two. 

Funnels also take some of the pressure off your sales team and create passive business revenue. 

If done well, they also help you build long-term customer relationships and repeat sales. 

3. Analyze detailed customer profiles

The most valuable part of a CDP is the personalized customer profiles. These in-depth, single customer view profiles are what set CDPs apart from other systems. 

Profiles detail each person based on data pulled from an array of channels. This means wherever your customer is — you are. Whether they’re in person or online, you have a compilation of their behaviors and preferences. 

This helps you craft unique experiences you know a customer will love. 

In the end, customers want to be seen as individuals, not as lead prospects. It can get cloudy when you’re analyzing data. Sooner or later, prospects look like sales targets on a spreadsheet. But CDPs have the distinct ability to create comprehensive profiles that feel human.

Every data point serves a purpose, is cleaned, and deduplicated. Next, the datasets are grouped together by theme. Finally, the data generates a unified customer profile. 

In a CDP customer profile, you’ll see details such as:

  • Their behavior 
  • Their engagement 
  • How they feel about your business
  • If they’re a frequent user
  • If they’re likely to re-engage 
  • Their likes and dislikes

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Personalizing marketing content and promotions 

Understanding customer behavior and preferences is essential to personalizing the content that enhances the customer experience. 

CDPs help businesses such as SaaS content marketing agencies personalize content and marketing promotions in a number of ways. 

1. Relevant messaging 

Customers don’t engage with campaigns that aren’t personally relevant. The data you collect from a CDP helps create relevant messaging your customers will connect with. Relevant messaging through target campaigns increases customer experience and loyalty.

For instance, Millennials prefer different messaging than Baby Boomers. Baby Boomers prefer different messaging than Gen Zers.

2. Content optimization

Understanding your customers also helps you optimize content to match a customer’s search intent. This helps you produce the right content at the right time.

Picture this:

You own a simple skincare brand, and you’re looking to increase your product base. Before you start getting creative in a lab, you analyze customer data in your CDP.

After noticing a series of patterns, you realize that your customers are searching for:

  • Sun protection sets
  • Bath bombs
  • Jade rollers
  • Beard oil

Not only does this show you what products to create, but it also shows you which target phrases to optimize your content around.  

You use those target phrases to plan out content briefs, research secondary keywords, and prepare SEO plans. 

By the end of your preparation, you’ve planned out three months worth of:

  • Topics to target
  • Secondary keywords
  • Frequently asked questions to answer
  • Content scores to target
  • Sales copy
  • Blog article outlines
  • Social media content 

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Along the way, you continue to keep an eye on your customer data. If you see a change in pattern, simply adjust your content as needed. 

This commitment to custom tailoring content to customers’ preferences enhances their experience with your company.

Segmenting customers 

While every customer has their own preferences and behaviors, it’s common for many to exhibit common patterns. 

CDPs give you tools to define your audience by segments based on these shared attributes and behaviors. Segments are based on rules, or they’re built using machine learning and AI. With these tools, you can enrich customer profiles with data you wouldn’t be able to gather on your own.

With the segmenting features, you can:

  • Predict customer churn 
  • Deliver relevant recommendations based on buying history
  • Identify customer advocates and frequent buyers
  • Identify similar patterns
  • Identify upsell and cross-sell opportunities 
  • Segment your customers using common attributes 
  • Tailor messages to those segments

Businesses can use CDP segmentation tools to optimize the entire customer journey from discovery to advocacy. 

To analyze and segment profile data, look for a CDP that has:

  • Prebuilt code
  • Visualizations that feel intuitive 
  • Out of the box features
  • 24/7 customer support 

An example of audience segmentation 

Let’s imagine that your business sells digital courses on personal and career development. You just set up a CDP, and you’re looking forward to trying the audience segmentation features. 

After identifying customer patterns and behaviors, you notice you have three main types of customer patterns:

  1. Frequent buyers that mainly buy career development courses
  2. Infrequent buyers that mainly buy personal development courses 
  3. Moderate buyers that buy a mix of both

 

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This insight helps you segment your customers into the following customer avatars:

  1. Career development customers 
  2. Personal development customers
  3. Career and personal development customers 

Later, you improve your marketing strategy to cater to these three customer avatars. You have three different kinds of social media campaigns, email newsletters, landing pages, websites, and paid ads. 

You can also repurpose content over time by launching a podcast or producing videos, for example. After this: This helps you produce the right content at the right time.

For your career development customers, you:

Focus your content on career advancement, job skills, and networking tips.

For your personal development customers, you:

Focus your content on personal growth, coping skills, and building self-confidence.

For your career and personal development customers, you:

Focus your content on how personal growth contributes to career growth.  

At this point, your planned content strategy is laser-focused and serves your three most common types of customers. 

Over time you keep an eye on behaviors and make adjustments when needed. You also keep an eye out for new customer avatars and buying habits.

Isn’t a CDP just a CRM?

CDPs and CRMs both work with customer data, but the two are pretty different. 

You may have wondered, “Why do I need a CDP? Isn’t a customer relationship management tool (CRM) the same thing?”

It’s actually not. 

While CRMs contain data you already know (i.e., name, email, and zip code), CDPs collect data you wouldn’t know over a specific time. 

But there are more differences between the two tools. 

Here are some major differences between a CDP and a CRM.

1. Data capacity 

CRMs were intended to keep track of customer and prospect interactions to automate the process for sales teams. They’re great for sales and marketing teams that need to pull customer information quickly. 

CDPs are great at handling large amounts of data from various channels. 

2. Known data 

CRMs only contain known data — they won’t be able to pull anything on potential customers you’ve never met before. 

CDPs work with both known and unknown data making them more valuable than most martech tools. 

3. Storage information 

CRM data stores simple information into a few fields — almost like a flashcard. It includes basic customer information such as name and contact information. 

CDPs have detailed information about a customer’s buying patterns, online and offline activity, and behaviors.

4. Data format

CRMs can’t handle data in a free-flowing manner. The system can only recognize data if it’s formatted in a specific way (i.e., a CSV file). 

CDPs take information from several sources and act as a central hub for customer data. They can handle both simple and robust information while also making sense of complex data. This includes online and offline data and behaviors. 

5. Monitoring and engaging 

CRMs are helpful for monitoring and engaging with customers and prospects throughout all phases of the buyer’s journey. They work well at managing contact information and also have automated workflows and reports. Solopreneurs and small teams often use CRMs. 

CDPs are helpful for tracking all aspects of customer behavior on and offline. They pull information from various sources for customers you know and don’t know alike. They help you segment audiences and refine your messaging. Startups, mid-size, and large companies use CDPs.  

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Which tool is better for customer experience — a CRM or a CDP?

While CRMs are helpful for engaging with customers at various parts of the customer journey, they’re still limited on information. 

The more detailed information a company has about their customers, the more insight they have as to what those customers crave.

First, you have to know what their pain points are. Then, you have to craft messages that align with customer needs. But a CRM can’t help you with that. It can help you manually look up customers and engage with them directly, but it doesn’t use machine learning or artificial intelligence to scour customer behaviors.  

That’s why, in the end, CDPs are better tools for enhancing the customer experience. 

Ready to transform your customer experience to the next level? 

Customer experience affects every aspect of success, making CDPs invaluable to a business. 

With a CDP, you’ll understand how your customer thinks, what they’re looking for, and what they like. You won’t need three tools, and multiple data set extraction tools. Instead, your CDP will pull information from various channels for you. 

Not only does this save you time, but it also helps you understand how to reach your customers in a profound way.

From knowing how to craft messages to building long-term relationships, a CDP is every business’s trusted tool for up-leveling the customer experience. 

 

Related: read more about location data for advertising and marketing.

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Data

What Is Technographic Data and How Do You Use It?

In an overwhelmingly competitive digital business landscape, modern businesses depend on their ability to collect, process, and analyze data. There are many types of B2B data, and technographics is one of them. 

It’s especially beneficial to IT companies and businesses selling the latest tech solutions. Technographic data or technographics give your business insight into competitors’ tech stack and provide information on the current market demand for your products. 

This information could be a competitive edge for a young business looking for a secured position in an overcrowded marketplace. Let’s discuss what technographics are, how to collect them, and how they can benefit your company.

 

What are technographics?

So, what is technographic data? 

It is a corporate term for business-to-business data that shows users which software tools and technologies their competitors and potential prospects use. 

Technographics give you a full scope of information on the type of data platform, SaaS solutions, software, and hardware your competitors and potential customers use. 

Sales teams use such insights to create more competitive, customer-centric digital marketing campaigns and drive better results. 

Technographics help businesses understand competitors’ technologies and how to improve their services to beat the competition, attract more customers, and expand their operations. 

Technographics also provide insights into competitors’ budgets, scalability, flexibility, and infrastructure.

How to gather technographics

The best way to collect technographic data on your prospects depends on your specific business needs. There are three common ways to gather technographics:

  • Third-party vendors – businesses can buy technographics from various data providers. This option is the safest way to collect accurate, up-to-date technographic data.
  • Web scraping – data and web scraping are excellent methods for collecting technographics. For example, you can use them to gather details from the source codes of competitors’ websites to ascertain the kind of software solutions they use.
  • Surveying – distribute surveys by email or phone to ask prospects for feedback on tech solutions they use.

 

How technographics help businesses

Technographic data gives you various insights into complementary and competitor technology. However, it can do more than that:

  • Technographics offer insights into the level of expertise of your competitors;
  • You can tap into the budgets of your competitors to find out how much they’re planning to spend on innovative tech solutions;
  • You can find out how tech-savvy your competitors are and whether they need additional training or not.

Gathering the latest tech insights on your competitors and their target audiences allows you to adjust your digital marketing approach according to the market demand. 

You can combine technographic data with compatible technology to develop solutions for targeting different demographics across markets and provide customers with real-life solutions to their problems.

Technographics help businesses by providing five essential advantages:

  • Market segmentation – you can segment your target audience by prospects, specific needs, or the tech they use and base marketing campaigns on their preferences;
  • Customer-centricity – reaching more prospects with your marketing messages is much easier when you know the specific software tools they prefer. Use their preferences to start a conversation and promote your products/services;
  • Lead generation – capture quality leads from the conversations with prospects and prioritize the highest paying customers;
  • Shorter customer journey – reach more prospects with your marketing messages at the most appropriate time;
  • Competitive analysis – learn from your competitors’ business and marketing strategies and identify lucrative market opportunities before they do.

In the data-driven business landscape, technographic data help businesses identify the right audience, target prospects with personalized messages, and understand the type of technology they prefer. It lets your sales team understand the logic behind your customers’ purchasing decisions.

 

Conclusion

Technographics provide companies with valuable, accurate, and up-to-date insights into the technology stacks their competitors and target audiences are using. 

Businesses can purchase technographic data from third parties or use surveys and web scraping methods to collect the information. 

These technographic insights provide more effective solutions for sales, lead generation, list building, and conversion and make your digital marketing campaigns more effective.

The goal of gathering technographics, intent data, and chronographics is to create more effective, targeted marketing strategies for targeting prospects with personalized messages at the right moment. 

B2B data is vital to improving your business efforts and approaching wider target audiences with personalized products that can solve real-life problems and make your company stand out.

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Data

Is business intelligence the same as data science?

Data science is growing immensely in today’s modern data-driven world. Business intelligence and data science are two recurring terms in the digital era. These involve the use of data that are totally different from each other. Data science is a bigger pool that contains huge information; business intelligence can be considered as a part of the bigger picture. These are both data-focused processes, but there is some difference between the two. Business intelligence focuses on analyzing things, whereas data science aims to predict future trends. Data science requires an effective technical skill set as compared to business intelligence. 

Power BI certification allows interested candidates to explore Power BI concepts such as Microsoft Power BI desktop layout, BI reports, dashboards, power BI DAX commands, and functions. Microsoft Power BI is a widely used business intelligence platform, and this follows a hands-on applied learning approach. 

 

Business Intelligence:

This is a means of performing descriptive analysis of data with the help of technology, skills for allowing one to make informed business decisions. The tools which are used for business intelligence collect, govern, and transform data. This allows decision-making by enabling data sharing between internal and external stakeholders. The main aim of BI is to derive actionable intelligence from data. BI enables acton such as gaining a better understanding of the market, uncovering new revenue opportunities, improving business processes, and staying ahead of competitors. This has shown its impact on cloud computing. Cloud has made it possible to collect data from resources and use this efficiently. This deals with the analysis of structured and unstructured data, which paves the way for new and profitable business opportunities. Business intelligence tools enhance the chances of enterprises entering a new market as this helps in studying the impact of marketing efforts. 

Importance of business intelligence:

As the data volume is increasing, business intelligence is more essential than ever in providing a comprehensive snapshot of business information. This provides guidance towards informed decision-making and even identifies the area of improvement, which leads to greater organizational efficiency and even increases the bottom line. 

 

Data science:

Data science mainly involves extracting information from datasets and creating a forecast. This involves the use of machine learning, descriptive analytics, and other sophisticated analytics tool. This is a process of collecting and maintaining data. Further, this involves the process of data via data mining, modeling, and summarization. After this, data analysis is conducted, etc. After analyzing the data, the patterns behind the raw data can be discovered to forecast future trends. Data science is used in different industries. Companies can use a devised approach to develop new products, study customer preferences and predict market trends. Here high volume of data can be collected from electronic medical records and individual fitness trackers. 

Importance of data science:

Data science in different companies is able to predict, prepare and optimize their operations. Data science plays an important role in the user experience; for many companies, data science is what allows them to offer personalized and tailored services. 

 

Business intelligence vs. Data Science: Is it the same or different?

Business intelligence and data science play a key role in producing companies’ actionable insights. Let us check on some common attributes between the two:

  • Perspective: business intelligence focuses on the present, while data science looks toward the future and further predicts what will happen next. Business intelligence works with past data in order to determine the responsible course of action, while data science creates predictive models which recognize future possibilities. 
  • Data types: business intelligence works with structured data, which is typically data warehoused or stored in data silos. Data science works with structured data and further results in greater time, which is dedicated to cleaning and improving the data quality. 
  • Deliverable: reports are used when it comes to business intelligence. Different deliverables for business intelligence include creating dashboards and performing ad-hoc requests. Data science deliverables have similar end goals and focus on long-term projects. These projects include creating models in production instead of working from enterprise visualization tools. 
  • Process: the difference between the processes of both comes back to the time, same as how this influences the nature of deliverables. Business intelligence mainly revolves around descriptive analytics. This is the first step of analysis and sets the stage for what happened in the past. Here non-technical business users can understand and interpret data via visualization. Data science would take the exploratory approach and means investigating the data via its attributes, hypothesis testing and exploring different trends, and answering questions on a performance basis. 
  • Decision making: business intelligence and data science are used for driving decisions, and this is central to determining the nature of decision-making. The forward-looking nature of data science is used at the forefront of strategic planning and determines the future course. These decisions are preemptive instead of responsive. Business intelligence aids in decision-making based on previous performances which have occurred. These fall under the umbrella of providing insights, and this supports business decisions.

 

Conclusion:

Both business intelligence and data science have differences, but the end goal of these are ultimately aligned. It is important to note the complementary perspective of both. From the company perspective, both data science and business intelligence play similar roles in business processes that provide fact-based insights and support business decisions. Data science and business intelligence are facilitators of each other, and it is said that data science is best performed together with BI. These are required to have an efficient understanding of company trends which are hidden in the large amount. 

In order to summarize simply, data science and business intelligence are not the same things, but this represents the evolution of business intelligence; thus placing data into introspective plays a central role in the business. Data science and business intelligence are equally vital roles on the same team. The individual roles are different, and when together, they serve the broader business analytic world. Though there is a difference in the way data science and BI handle objective tools, the end game is the same. 

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Data

How Smart Cities Can Help Defend Against Pandemics

As the world remains gripped by the threat of COVID-19, questions over community spread, and prevention tactics loom large. Did cities and representatives do enough to stop the spread of the virus, or could they have done more?

An estimated 68% of the world’s population will live in cities by 2050, which makes urban development’s role in disaster response essential for protecting the population.

Cities and other population-dense areas have been flagged as one of the major issues impacting the virus’ community spread. Yet, smart cities may have also helped slow the spread of the virus in Chinese communities such as Wuhan.

China has 500 smart cities currently underway, and while the question of whether this smart technology has helped slow the spread of the virus in Chinese communities remains up for debate, here are some ways the urban planning of smart cities could help defend against pandemics.

 

Disease tracking

Disease tracking can be one of the biggest defenses against virus spread as it allows scientists, researchers, and city officials to analyze real-time data to make informed recommendations. So how does it work? This data-tracking system uses artificial intelligence to track the spread of infectious diseases.

Big data and natural language processing make it possible by allowing companies to track the spread of information from hundreds of thousands of sources.

A disease tracking company based out of Canada known as BlueDot was the first to sound the alarm about the novel coronavirus, even before any world health organization informed the public. On December 30, it noticed a cluster of “unusual pneumonia” cases in Wuhan and alerted its customers of the outbreak.

 

Autonomous delivery

One lesson the world is learning from COVID-19 is the importance of social distancing. Because COVID-19 can live on some surfaces for up to 3 days, restricting access to public areas has been deemed essential in cities across the globe.

Autonomous delivery systems would eliminate the need for drivers to deliver supplies and goods, such as food. Technological advancements such as delivery drones and driverless trucks will make this process more seamless in years to come. Incorporating these technologies into smart city management will give local governments more control over delivery systems to prioritize needs, for example delivering medicines before goods.

 

Geolocation

Many of our phones are already tracking our location-data, but what if our local governments used this information to make predictions during pandemics? Data companies such as Tamoco make tracking and predicting behaviors easier than ever for marketing and intelligence purposes.

One example of how geolocation data can be useful during a pandemic is by analyzing popular shopping times. It can even be used to understand which houses are for sale in Chatsworth California. City officials could use data around when people are most likely to shop for groceries and goods to help make decisions about store hours and restrictions, and when to establish senior hours.

 

Drone surveillance

While the application of robot surveillance is controversial, it has helped countries like China monitor citizens during the coronavirus outbreak. Rather than sending police officers out to monitor cities and streets where shelter-in-place orders are in effect, cities can send out drones to survey the areas and make sure citizens are abiding by city ordinances. Insect drone technology is fast becoming a powerful tool for cities to utilize.

 

Thermal cameras

Another technology the city of Wuhan used in its fight against the coronavirus is thermal cameras. While the technology hasn’t been perfected yet, these cameras, which also feature facial recognition software, can detect body temperatures in citizens passing by.

Since a fever spike is one of the most common symptoms of the coronavirus, these thermal cameras could, in theory, alert city officials when infected members of the population were out walking the streets, potentially spreading the virus.

 

Internet connectivity

In the age of information technology, the best way to stay dialed in is by staying online. Cities such as New York City have already activated smart technology that gives all citizens internet access in public places. This allows citizens to stay informed during times of crisis, no matter how much they make or whether they pay for WiFi at home.

Giving everyone access to the Internet allows for local city leaders to communicate with their citizens and get public information out faster. Many smart cities also have information kiosks and robust telehealth services that can be updated instantly to spread awareness as situations develop.

One of the scariest parts about the novel coronavirus is the lack of data researchers, scientists, and leaders have. Smart cities that collect data in real-time can revolutionize pandemic responses by making data available faster, and we’ve seen examples of this in action as China utilized their city technology to help fight the spread.

Smart cities can also help cities conserve energy, manage traffic congestion, optimize waste removal, and improve water and energy management. To learn more about how smart cities work, head to The Zebra. 

 

This post is a contribution by Karlyn McKell

Karlyn is a writer who specializes in the technology and insurance spaces. She believes the best ingredients for success are passion and purpose.

Categories
Data

How Big Data Is Changing The Event Planning & The Events Industry

Big data plays a crucial role in many industries, and event management is just one of them. People all across the world are relying more and more on their smart devices, and it has become increasingly easier for event organizers to gather and use data to cater more directly to event attendee’s experiences.

Event planners are leveraging big data to deliver highly personalized events and boost attendee engagement. In this article, we’ll look at some of the ways you can use big data to provide better event experiences and learn more about your audiences’ needs and expectations.

Let’s put everything into context before we begin.

 

What Is Big Data?

Generally speaking, big data refers to the enormous volume of data that is collected daily. The data itself doesn’t necessarily have to be useful on its own. It’s the insights, trends, and patterns the data reveals that event planners are most interested in.

For event organizers, big data analysis involves collecting vast amounts of data from their attendees, sponsors, and target audience. This might be in the form of emails, surveys, tweets, photos, or location data.

Event planners can use big data to organize attendee-centric events, increase attendee engagement in real-time, and deliver enhanced event experiences.

The good news is that implementing data analytics in your event planning doesn’t have to be complicated. You can work with the data sets that are already available to you and extract valuable insights from them.

If you’re not collecting any data from your target audience or attendees, the first step is to identify the key data points you’re most interested in. For example, if you’re hosting a business conference, you will need to know where attendees are traveling from so you can provide each attendee the best advice for lodging and commuting to the event venue.

Now that you know what big data is and why it’s important for event organizers, let’s look at how you can implement big data in your event management business.

 

4 Ways Big Data Is Driving the Events Industry

Below we discuss some ways big data can help event organizers plan for successful and engaging events.

#1: Improving Targeted Promotions

Promoting your event takes up a lot of monetary resources, which is why you need to make sure you can cost-effectively maximize your audience outreach. By gathering and analyzing data on your attendees, you’ll be able to find ways to connect with them.

For example, attendee surveys might indicate that most of the respondents first learnt about your event from your social media pages. So, instead of focusing on company spending on offline marketing strategies, you might consider spending more on your social media campaigns.

Location data {information about geographic positions of devices such as smartphones or tablets} allows event marketers to connect their digital marketing efforts to how prospective attendees behave in the real-world. As a result, event marketers can provide even more personal advertising to their target audience.

To attract more attendees, you need to ensure that you’re advertising to the right audience; otherwise, you might experience a drop in event attendance and audience engagement. Analytics can help you discover when your target audience is actively using social media, what is the best way to connect with them, and how you should craft your message to get their attention.

Big data can help you identify what your prospective attendees respond to, which you can then use to improve your event planning. Having access to this information will not only help you customize your ads to attract more attendees but will also be useful in delivering personalized event experiences to your guests.

 

#2: Gaining Insight from Analytics

Analytics can help you figure out the specific topics and themes that most interest your audience, the guest speakers they want to listen to, and any tools or presentation technology they’d like you to implement in your events.

By gaining access to the right information, you’ll be able to discover their pain points and what your guests expect from your events. Start by collecting online search data or using pre-event survey forms to gather this information directly from your attendees. Similarly, you can search through social media sites, community sites, as well as this you can make use of powerful location intelligence and analytics to reveal your attendees’ motivations.

Big data can help you identify the different factors that affect audience behavior and leverage them to your advantage. You’ll be able to predict trends months (or even years!) into the future. Predictive analytics lets you make near-accurate guesses and allow you to see which existing topics have growth potential so you can jump on them before your competition.

Google Trends is also an excellent tool for discovering what your prospective attendees search for. By comparing search terms with historical data, you can predict what will be popular in the future, and when you should host an event around it.

 

#3: Personalizing Attendee Experiences

The data you collect from your attendees gives you insights into how you can enhance attendee satisfaction. You can use technologies such as location data, RFID, VR, and beacons to deliver unique event experiences.

For example, you can send information to nearby devices using beacons, allowing your attendees to only focus on the booths they’re most likely to be interested in. This is also a great way to offer location-based experiences to your guests.

Pepsi organized a dance party at SXSW, where dancers wore wristbands that would gauge their reaction to a stimulus. The wristbands measured body temperature, the volume of music, body movement, as well as the physiological arousal through body sweat. This information was used by the event’s DJ to figure out what music people loved the most. In addition to this, it enabled the crowd to control lighting, bubble machines, and smoke machines.

 

#4: Crowdshaping

Another way big data can help you improve your event planning efforts is by letting you better manage crowd densities at the venue, also known as crowd shaping. It’s an effective way to make sure attendees can enjoy your events without openly influencing their behaviors.

For example, if you see a crowd of people around the book signing tables, you may decide to extend the time allocated for that particular activity in real-time. If a guest speaker is about to take the stage, you can send out notifications to let attendees know that they can get their books signed after the lunch break.

In addition to this, you can use data from past events to improve upcoming events. For example, if data from one event indicates that attendees like open spaces to walk around during breaks, you can use this information to book future venues with plenty of extra space.

In its simplest form, geolocation is capable of giving you information about the location of a person. Beyond that, it can be used by event organizers to gather crowd density data about events. This allows them to manipulate crowd flow more effectively.

Crowdshaping and big data come together to help you identify problems in your crowd flow and enable you to solve them by making proper adjustments quickly. In the long run, this information will allow you to make better event planning decisions (such as opening more check-in lines) and go for venues that meet your audiences’ specific needs.

Event organizer at C2 Montreal gave RFID badges to their attendees that collected their location data. This allowed organizers to see where the majority of the attendees gathered and which event places received less traffic. They found more crowds near food tables, which led them to send more food service staff to those places.

 

Conclusion

The data you gather on your attendees is a valuable resource that can help you improve your event experiences. You can always start small and use whatever data you have to learn about your audiences’ needs and expectations. With time, you’ll feel more comfortable collecting and working with big data.

Event Espresso lets you collect, control, and own all the data you collect for free. You can export your event attendees’ data into Excel or CSV format and use it however you want.

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Data

How Big Data Can Improve The Supply Chain

Big data has become the power that organizations need to harness to move their company forward at high speed. By the year 2020, the amount of data available in the world is estimated to be roughly around 44 zettabytes. Collecting all these datapoints will do no good unless it is utilized through the proper channels.

Various organizations from across the world are harnessing big data to create better business models and execute faster development strategies. Big data is playing a pivotal role in reducing operational costs through statistical and predictive analysis. In Supply Chain Management (SCM) too, there is a lot of data available. When this data is put together to create meaningful operations, it can make the entire supply chain more feasible.

While some organizations have identified what big data could do for their supply chain systems, there are a lot of companies which are yet to explore the possibilities. Here are some of the most crucial aspects through which big data can help to groom the SCM.

 

Be on Top of Your Demand

Predictive analysis is a huge part of making use of big data. With a considerable amount of data at their disposal, companies can avoid inevitable redundancies in their operations all the while making efficient use of the stocks. There are a lot of ways through which the predictive analysis with big data can help.

Companies can identify retailers who may need some stock sooner and the retailers who take longer to complete the stock order. Companies can analyze their orders and see if the cost of supplying the stock to some regions may be too higher than what they could afford. Companies can even predict which of their loyal customers may switch to their competitors and which of their competitors’ customers are looking to explore other options.

Much can be gathered from the predictive analysis of these datasets and enable companies to take precautionary measures and be ready for any potential surprises.

One good example of a multinational company using big data for the supply chain is PepsiCo. Replenishing the stocks of retailers is of significant importance for a customer-oriented product supplier like PepsiCo. Therefore, they make use of big data to predict the retailers’ stocks based on the data of the inventory they have, the time needed for the shipment, and the volume required to be shipped. This has helped PepsiCo to ensure that their products are never out of stocks from their retailers’ and at the same time, ensure that there is no high wastage. This has also been applied to retail, where the demand of designer swimsuits is predicted to ensure delivery in peak times.

In short, the predictive analysis with big data for supply chain helps companies to make the right volume of stock available to the right people at the right time.

 

Be in Control of Your Operations

Even when the external factors seem to be out of control most of the time, with big data analysis, you can be in control of your supply chain operations.

One significant problem that many supply chain managers face is the problems they face on the journey between the moment the product leaves their warehouse to the time it reaches the retailers. This is where a lot of external obstructions can affect delivery. When the data for the entire supply chain operations is collected over several years and updated frequently, companies can avoid and find a roundabout better way to ensure that the supply chain is not affected by these out-of-hand disturbances.

Moreover, analysis of big data will also enable companies to find better supply chain routes that are optimal and profitable. Based on the demand, expected revenue, the geoanalytics, and available modes of transportation, the analysis can to create improved and efficient ways of managing the supply chain, reducing cost and decreasing delivery time.

 

Be on The Constant Path of Innovations

Innovation is a necessary part of the supply chain, and big data doesn’t just stop at helping supply chain manageability, they also provide new avenues for achieving better efficiency.

You can idetnify new delivery channels that could half the cost or merge existing delivery channels for better optimization. For an organization to improve, they have to be continually innovating, and big data plays a significant role in these innovations.

Accenture found that including big data in their supply chain operations led to a 2.6x times increase in their supply chain efficiency. This consists of the innovations in the SCM through multiple channels.

A good portion of getting the best out of a big data or analytics company for SCM is by innovating as the company progresses. These innovative ideas can also help the supply chain managers in decision-making, having better control, and minimizing their problems.

Leveraging the data available at hand prevents unexpected supply chain issues and streamlines supply chain operations. Companies can save a great deal of money, cut down the time spent on managing the supply chain, and further assisting in future expansions.

 

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Data

Story Of Data Report – Consumer Trends In The 2018 World Cup

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Understanding consumer trends and behaviour during the 2018 World Cup

Download our free report to understand:

  • How footfall traffic changes to different venues during the World Cup
  • Which venues performed well during the tournament
  • On-trade and off-trade patterns
  • Busiest games, times and days of different categories
  • Trends in the behaviour of different demographics such as millennials.
  • How to use these insights to inform marketing and other business decisions

Read the introduction below.

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Introduction

We wait four years for a world cup to come around. The final is the most watched event in the world. That’s a whole lot of consumer interest – it’s one that businesses need to get right in order to maximise the effect of this once-every-four-year event.

The World Cup consists of 32 nations. IN the UK every game is broadcast on terrestrial TV. Globally around 3.4 billion people watch some part of the tournament. It’s a huge event for a number of industries, but none more than perhaps the beverage industry.

That’s why it’s important to understand consumer behaviour during these monumental global events. Understand how customers move and behave can help business perform well across a number of different functions, from advertising and marketing to insights and planning.

To identify alcohol and consumer trends during the World Cup we analysed visits to over 5000 venues across London during the 3-week tournament.

Our accurate first party data-set combined with our network of precise location sensors provides detailed insights into how consumers behaved during the World Cup. Our visit methodology provides a powerful way to reveal trends and behavioural information around consumers.

To keep up to date with the latest data trends, sign up to our monthly newsletter. 

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Data

How Big Data Is Changing The Finance Industry

The finance industry is a highly competitive space. It faces a new generation of disrupting banks and regulations. It’s an industry that needs to utilise big data to drive personalisation, boost customer loyalty, security and fuel everyday investment decisions.

The financial services industry has always been at the forefront of technical innovation. The availability of new datasets has provided a powerful way to understand behaviour and offers new directions for the financial industry to be predictive.

Big data application in financial services goes beyond predicting share prices. New data types are revolutionising the space.

We’re going to break down some examples of how big data is being used in financial services.

 

Personalisation

Personalisation has long been a priority when dealing with consumers. This is true both inside the finance industry and across other verticals with a strong consumer presence.

Disruptor banks have begun to establish themselves in the finance sector. One thing that these banks have done well is personalisation.

By understanding users spending habits and behaviour, they can offer more personal spending products and recommendations.

For example, in the world of digital banking, if a bank had the right data sets around its customers, it could provide services that truly bring value to the end customer. If a bank understands what their customers spend, where they go and where they work they can, for example, suggest that a travel card could save them a lot of money each month.

It’s in these areas that financial services can learn from disruptor banks. With the right dataset, the banking experience can be personalised for customers. This will allow financial services to boost customer loyalty and drive cross-selling of their products much more effectively.

 

Security

There are few industries where security and fraud are more of a threat than in the financial services.

As technology has advanced those in the space must be smarter and better adapted to the fast-changing tactics of those looking for weak points in sometimes outdated systems.

Adding location intelligence to the equation adds an extra layer of security for customers and allows financial institutions to instantly provide checks based on where a customer uses its products.

Location offers a new way for security teams to identify fraud. Location data can help to educate security systems on customer behaviour and can form a strong base from which to detect irregularities in financial behaviour.

Financial services continually process an incredible number of transactions. A location overlay which includes operational rules can help to determine when to flag records as fraudulent.

These processes enable financial services to provide better safeguards to their consumers and clients.

 

Investment

The rise of big data carries enormous potential for investors. Many have implemented predictive systems that are designed to understand data sets, digest vast amounts of data and then inform investment decisions.

These data sets have proved successful, but accurate location data is rarely used to optimal effect in this area. Location intelligence provides a more detailed understanding of trends and ingesting these precise datasets can help investors to stay ahead of the competition.

Understanding how populations move and behave en mass is readily available in the online world. Location offers something slightly different. It helps to understand how consumers move in the offline world.

Using location allows minimal lag between an aggregated understanding of consumer trends and investors being able to forecast the performance of portfolios and financial markets.

This allows investors to act quickly and decisively. Location is a fantastic indicator of market, brand or individual behaviour. With the progress made in accuracy by some in the space, this will prove to be one of the next steps in predictive analytics for the financial sector.

 

Insurance

For insurance location underlies everything. Many insurers can immediately benefit from quality location data to better model risk and dramatically improve their underwriting and pricing.

In insurance, nearly every data point has a relation to location.

Crime data – understanding crime risk in specific locations including historical data and predictive solutions can provide significant advantages to insurers.

Catastrophe – using data analytics insurers can mitigate the risks involved with catastrophes based on a customer’s location.

Behavioural data – using new kinds of behavioural data, such as location, can allow insurers to understand consumer behaviour better. This helps to predict risk and assist with pricing.

These datasets are now available in large quantities. Precise location allows for more accurate insights and the ability to generate this data, as well as store and process it, has changed the game for the insurance industry.

 

New revenue

For financial businesses, consumer data also holds the key to new revenue. It allows companies to maximise revenue from existing channels.

For example, using precision data marketing financial companies can identify products and services that are a much closer match for specific customers. This provides more value for both customers and branded partners.

Understanding customers behaviour allows companies to understand which type of person is more valuable for their business.

Using this data, it’s possible to build lookalikes and use this to drive more targeted marketing activity to customers that represent a greater potential for higher revenue.

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Add data to your business

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Business Data

5 Amazing Ways Big Data is Transforming the Education Sector 

Big data and Education are closely interconnected as it helps school, colleges, universities, institutions and many more educational organizations to make the overall decision about in the organization’s overall outpace, scholastic outreach, and much more. 

Big data analytics in education will not only help in academic performance but also increases the efficiency and effectiveness of the faculty as well as technology. 

The educational Sector these days prefer big data in education as it makes sure to reduce business expenditure which will eventually be beneficial for the organization. 

Not only the present time, but data for education will enable you to predict different trends that will be adopted in the future in the respective industries. It leads the employees to have better planning which will help in the overall development of the education sector. 

Image Source: IntechOpen 

As rightfully said by Chris Lynch, an American Writer of Books “Big data is at the foundation of all the megatrends that are happening.” So, your educational institution and students will for sure benefit from big data. 

Importance of Learning About Big Data

Big Data helps all learners, students, professors, teachers and individuals involved in the educational sector to understand the overall strategies as well as the individualized learning process.  

Image Source: Springer Open 

Let’s get familiar with the importance of studying/researching big data in the education sector: 

  • Big data helps to map student learning by measuring the teaching-learning process of the classroom. 
  • According to Salt Lake Tribune data for education not only helps teachers and students but is also equally significant for financial and administrative management. 
  • In general, the cost-saving process will be quick, simple, efficient and easy to adapt with not much confusion. 
  • You can do individual manual searches or studies based on the initial search as per your own preferences and requirements. 
  • Along with big data and affordable writing services, you can increase accessibility and save costs. 

The list simply goes on and on so we have concluded the importance of learning about education data analysis in the best five points. 

Ways Big Data is Transforming the Education Sector

Even today many people think and assume that big data is significant when it comes to only business. But this is not a precise piece of information. 

Not only in business but big data is important for the financial market, detection of fraud, newly adapted smart traffic system, the healthcare industry, the government sector, media and much more. Along with all these sectors the Education sector is one of the most important sectors. 

As we already know about the importance of big data, let’s get familiar with all the ways big data is transforming the education sector. 

Find Out Strengths of Students 

Learning about big data doesn’t only solve short-term problems rather it focuses more on long-term things. It will help students to analyze, influence, discover and ascertain long-term goals. This will eventually help them out in the future. 

Big data can be helpful for students and teachers as it will help them gain better information about all the possibilities that are likely to appear in the upcoming days. This way they will get better to sort of thing and tackle all of the hassles that come through their path. 

Not only about the overall prospects but big data will also help them have a better understanding of themselves. 

Improve the Grading 

When an educational institution decides to let big data foreplay in their institution, they will be able to gain information about all the tracks and records of the students. 

This way the teachers will be able to gather all the fields that interest students through which creativity and collaboration will increase in the learning process. Students can gain what they want and teachers can get the proper feedback through which they can help students improve. 

Not only this but it has a long positive impact as this way they can gain all the necessary skills which will eventually help them in their career path. Hence, this way big data will help to transform the overall education sector. 

Targeted International Recruiting 

The application process is considered one of the most important factors in the education sector. More the number, the better as when more students enroll for better education more amazing students will gain quality education. 

This education will eventually help them to gain better jobs which will eventually improve their lifestyle and increase the overall economy of a nation. 

The application process can be made better using big data analysis in education by analyzing and guessing all the possible and essential factors. When analyzed better things can also be planned in a better way. 

Minimizes the Dropout Rate 

As everyone will get what they need, especially the students will be satisfied. This will eventually decrease the dropout rate of students from schools, colleges and universities. 

Sometimes even bad results discourage students, but that’s not the case once the education sector starts using big data. It is said that the analysis made with the help of big data will help both teachers and students understand problems and find out solutions without any hassle. 

There is less rate of error found after using the big data, not only that it will eventually help students to improve their overall academic performance. Hence it will improve their rank and performance which will eventually result in a deduction of the dropout rate. 

The Efficiency of Technology in Education 

In the 21st era, technology plays an important role as it provides opportunities for people to change their overall life. One without technology information can actually be a brick on the wall. Due to the use of big data in education, all the individuals involved in this sector will gain basic knowledge about it. 

Not only the teachers and students but other sectors of an educational institution like finance and administration can utilize them properly. 

This will eventually lead to bringing effectiveness, completeness and overall productivity in the overall education sector. Moreover, along with great learning, they will also get creative by predicting all the future aspects. 

Hence there are hundreds of way big data can transform the education sector, but we along with our research team has read and selected the 5 best ways. We made sure to include the most important ones so it turns out to be helpful for our readers. 

Wrapping Up: 

This is the end of our article ‘5 Amazing Ways Big Data is Transforming the Education Sector‘ We hope this article helped you learn about the importance and the way big data can bring positive impact in the education sector if used properly. 

Moreover, if you have any more points that are valuable, feel free to share it with us in the comments section below. 

Big data can actually be a precious gift in the education sector in the upcoming days if used properly. So, make sure to use the big data properly and shine out!