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How Big Data Can Help You Understand Your Customers and Grow Your Business

What’s the main purpose of a marketing campaign for any business? You’re trying to convince the customers you offer exactly what they need. What do you do to get there? You find out what they need. This is where big data gets into the picture. Big data is a general term for all information that allows you to understand the purchasing decisions of your target consumers That’s not all. Big data also helps you create a sustainable budget, find the best way to manage your business, beat the competition, and create higher revenue. In essence, big data is all information that helps you grow your brand. The process of analyzing and successfully using that data is called big data analytics. Now that we got the definition out of the way, let’s get practical. We’ll help you realize how you can use big data to understand the behavior of your customers and grow your brand. Where Can You Find Big Data? This is the big question about big data: where do you find it? When you’re looking for data that you could immediately turn into useful information, you should start with the historical data of your business. This includes all information for your business you collected since it was formed. The earnings, revenues, stock price action… everything you have. That data is already available to you. You can use it to understand how your business worked under different circumstances. The US Census Bureau holds an enormous amount of data regarding US citizens. You can use the information about the population economy, and products to understand the behavior of your target consumers. gov is another great website to explore. It gives you data related to consumers, ecosystems, education, finance, energy, public safety, health, agriculture, manufacturing, and few other categories. Explore the field relevant to your business and you’ll find data you can use. This information is for US citizens. If you need a similar tool for the EU, you can explore the European Union Open Data Portal. Facebook’s Graph API gives you a huge amount of information about the users of the platform. How to Use Big Data to Your Brand’s Advantage Collecting big data is not that hard. Information is everywhere. However, the huge volume of information you collect might confuse you. For now, you might want to focus on the historical data for your business. That should be enough for you to understand the behavior of your customers. When you understand how the analytics work, you can start comparing your historical data with the information you get from governmental and social media sources. These are the main questions to ask when analyzing big data: The average amount your customers spend on a typical purchase. This information helps you understand their budget and spending habits. Did they spend more money on an average purchase when they used promotions? What’s the situation with conversion? How many of the social media followers follow a link and become actual customers? These rates help you determine the effect of your marketing campaign. When you understand it, you’ll be able to improve it. How many new customers did you attract through promotions? Did those activities help you increase the awareness for your brand? How much have you spent on marketing and sales to attract a single customer? Divide the total amount of expenses for promotional activities with the number of customers you attracted while the campaign lasted. You’ll get the acquisition cost of a single customer. If it’s too large, you’ll need to restructure your promotional activities. Compare historical data to identify the campaigns that were most and least successful in this aspect. What do your customers require in order to stay loyal to your brand? Do they ask for more support or communication? How satisfied are your customers with the products or services you offer? What’s the difference between the categories of happy and unhappy customers? When you determine the factors that make your customers happy, you’ll be able to expand on them. When you identify the things that lead to dissatisfaction, you’ll work on them. Every Business Benefits from Big Data You feel like you have to own a huge business to get interested about big data? That’s a misconception. It doesn’t matter how big your company is. You still have tons of data to analyze, and you can definitely benefit from it & bigdata solve problems easily Collect all data described above and compare it with the way your customers behaved in the past. Are you growing? If yes, why? If not, why? The key to understanding the behavior of your customers is to give this information a human face. Connect the numbers with the habits and spending behavior of your real customers. When you relate the data to actual human experience, you’ll be able to develop customers personas. You’ll increase the level of satisfaction your consumers get. When you do that, the growth of your business will be inevitable.
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How Big Data Can Help You Understand Your Customers and Grow Your Business

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How Big Data Can Help You Understand Your Customers and Grow Your Business

What’s the main purpose of a marketing campaign for any business? You’re trying to convince the customers you offer exactly what they need. What do you do to get there? You find out what they need. This is where big data gets into the picture.

Big data is a general term for all information that allows you to understand the purchasing decisions of your target consumers That’s not all. Big data also helps you create a sustainable budget, find the best way to manage your business, beat the competition, and create higher revenue. In essence, big data is all information that helps you grow your brand. The process of analyzing and successfully using that data is called big data analytics.

Now that we got the definition out of the way, let’s get practical. We’ll help you realize how you can use big data to understand the behavior of your customers and grow your brand.

Where Can You Find Big Data?

This is the big question about big data: where do you find it?

  1. When you’re looking for data that you could immediately turn into useful information, you should start with the historical data of your business. This includes all information for your business you collected since it was formed. The earnings, revenues, stock price action… everything you have. That data is already available to you. You can use it to understand how your business worked under different circumstances.
  2. The US Census Bureau holds an enormous amount of data regarding US citizens. You can use the information about the population economy, and products to understand the behavior of your target consumers.
  3. gov is another great website to explore. It gives you data related to consumers, ecosystems, education, finance, energy, public safety, health, agriculture, manufacturing, and few other categories. Explore the field relevant to your business and you’ll find data you can use. This information is for US citizens. If you need a similar tool for the EU, you can explore the European Union Open Data Portal.
  4. Facebook’s Graph API gives you a huge amount of information about the users of the platform.

How to Use Big Data to Your Brand’s Advantage

Collecting big data is not that hard. Information is everywhere. However, the huge volume of information you collect might confuse you. For now, you might want to focus on the historical data for your business. That should be enough for you to understand the behavior of your customers. When you understand how the analytics work, you can start comparing your historical data with the information you get from governmental and social media sources.

These are the main questions to ask when analyzing big data:

  • The average amount your customers spend on a typical purchase. This information helps you understand their budget and spending habits. Did they spend more money on an average purchase when they used promotions?
  • What’s the situation with conversion? How many of the social media followers follow a link and become actual customers? These rates help you determine the effect of your marketing campaign. When you understand it, you’ll be able to improve it.
  • How many new customers did you attract through promotions? Did those activities help you increase the awareness for your brand?
  • How much have you spent on marketing and sales to attract a single customer? Divide the total amount of expenses for promotional activities with the number of customers you attracted while the campaign lasted. You’ll get the acquisition cost of a single customer. If it’s too large, you’ll need to restructure your promotional activities. Compare historical data to identify the campaigns that were most and least successful in this aspect.
  • What do your customers require in order to stay loyal to your brand? Do they ask for more support or communication?
  • How satisfied are your customers with the products or services you offer? What’s the difference between the categories of happy and unhappy customers? When you determine the factors that make your customers happy, you’ll be able to expand on them. When you identify the things that lead to dissatisfaction, you’ll work on them.

Every Business Benefits from Big Data

You feel like you have to own a huge business to get interested about big data? That’s a misconception. It doesn’t matter how big your company is. You still have tons of data to analyze, and you can definitely benefit from it & bigdata solve problems easily

Collect all data described above and compare it with the way your customers behaved in the past. Are you growing? If yes, why? If not, why?

The key to understanding the behavior of your customers is to give this information a human face. Connect the numbers with the habits and spending behavior of your real customers. When you relate the data to actual human experience, you’ll be able to develop customers personas. You’ll increase the level of satisfaction your consumers get. When you do that, the growth of your business will be inevitable.

Robert

Robert Morris

Blog Author

Robert Morris is a freelance writer. He writes articles on business, career, marketing, technology and currently is working as a blog editor at the educational blog <a href="http://askpetersen.com" />askpetersen.com</a>.

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How to install Apache Spark on Windows?

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As historically, these are occupying significant market share. Apache Kafka Stream: Kafka is actually a message broker with a really good performance so that all your data can flow through it before being redistributed to applications. Kafka works as a data pipeline.Typically, Kafka Stream supports per-second stream processing with millisecond latency.  Kafka Streams is a client library for processing and analyzing data stored in Kafka. Kafka streams can process data in 2 ways. Kafka -> Kafka: When Kafka Streams performs aggregations, filtering etc. and writes back the data to Kafka, it achieves amazing scalability, high availability, high throughput etc.  if configured correctly. It also does not do mini batching, which is “real streaming”.Kafka -> External Systems (‘Kafka -> Database’ or ‘Kafka -> Data science model’): Typically, any streaming library (Spark, Flink, NiFi etc) uses Kafka for a message broker. It would read the messages from Kafka and then break it into mini time windows to process it further. Representative view of Kafka streaming: Note:Sources here could be event logs, webpage events etc. etc. DB/Models would be accessed via any other streaming application, which in turn is using Kafka streams here. Kafka Streams is built upon important stream processing concepts such as properly distinguishing between event time and processing time, windowing support, and simple (yet efficient) management of application state. It is based on many concepts already contained in Kafka, such as scaling by partitioning.Also, for this reason, it comes as a lightweight library that can be integrated into an application.The application can then be operated as desired, as mentioned below: Standalone, in an application serverAs a Docker container, or Directly, via a resource manager such as Mesos.Why one will love using dedicated Apache Kafka Streams?Elastic, highly scalable, fault-tolerantDeploy to containers, VMs, bare metal, cloudEqually viable for small, medium, & large use casesFully integrated with Kafka securityWrite standard Java and Scala applicationsExactly-once processing semanticsNo separate processing cluster requiredDevelop on Mac, Linux, WindowsApache Spark Streaming:Spark Streaming receives live input data streams, it collects data for some time, builds RDD, divides the data into micro-batches, which are then processed by the Spark engine to generate the final stream of results in micro-batches. Following data flow diagram explains the working of Spark streaming. Spark Streaming provides a high-level abstraction called discretized stream or DStream, which represents a continuous stream of data. DStreams can be created either from input data streams from sources such as Kafka, Flume, and Kinesis, or by applying high-level operations on other DStreams. Internally, a DStream is represented as a sequence of RDDs. Think about RDD as the underlying concept for distributing data over a cluster of computers. Why one will love using Apache Spark Streaming?It makes it very easy for developers to use a single framework to satisfy all the processing needs. They can use MLib (Spark's machine learning library) to train models offline and directly use them online for scoring live data in Spark Streaming. 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With Kafka Streams, spend predictions are more accurate than ever.Zalando: As the leading online fashion retailer in Europe, Zalando uses Kafka as an ESB (Enterprise Service Bus), which helps us in transitioning from a monolithic to a micro services architecture. Using Kafka for processing event streams enables our technical team to do near-real time business intelligence.Trivago: Trivago is a global hotel search platform. We are focused on reshaping the way travellers search for and compare hotels while enabling hotel advertisers to grow their businesses by providing access to a broad audience of travellers via our websites and apps. As of 2017, we offer access to approximately 1.8 million hotels and other accommodations in over 190 countries. We use Kafka, Kafka Connect, and Kafka Streams to enable our developers to access data freely in the company. Kafka Streams powers parts of our analytics pipeline and delivers endless options to explore and operate on the data sources we have at hand.Broadly, Kafka is suitable for microservices integration use cases and have wider flexibility.Spark Streaming Use-cases:Following are a couple of the many industries use-cases where spark streaming is being used: Booking.com: We are using Spark Streaming for building online Machine Learning (ML) features that are used in Booking.com for real-time prediction of behaviour and preferences of our users, demand for hotels and improve processes in customer support. Yelp: Yelp’s ad platform handles millions of ad requests every day. To generate ad metrics and analytics in real-time, they built the ad event tracking and analyzing pipeline on top of Spark Streaming. It allows Yelp to manage a large number of active ad campaigns and greatly reduce over-delivery. It also enables them to share ad metrics with advertisers in a timelier fashion.Spark Streaming’s ever-growing user base consists of household names like Uber, Netflix, and Pinterest.Broadly, spark streaming is suitable for requirements with batch processing for massive datasets, for bulk processing and have use-cases more than just data streaming. Dean Wampler explains factors to evaluation for tool basis Use-cases beautifully, as mentioned below: Sr.NoEvaluation CharacteristicResponse Time windowTypical Use Case Requirement1.Latency tolerancePico to Microseconds (Real Real time)Flight control system for space programs etc.Latency tolerance< 100 MicrosecondsRegular stock trading market transactions, Medical diagnostic equipment outputLatency tolerance< 10 millisecondsCredit cards verification window when consumer buy stuff onlineLatency tolerance< 100 millisecondshuman attention required Dashboards, Machine learning modelsLatency tolerance< 1 second to minutesMachine learning model trainingLatency tolerance1 minute and abovePeriodic short jobs(typical ETL applications)2.Evaluation CharacteristicTransaction/events frequencyTypical Use Case RequirementVelocity1M per secondNest Thermostat, Big spikes during specific time period.3Evaluation CharacteristicTypes of data processingNAData Processing Requirement1. SQLNA2. ETL3. Dataflow4. Training and/or Serving Machine learning modelsData Processing Requirement1. Bulk data processingNA2. Individual Events/Transaction processing4.Evaluation CharacteristicUse of toolNAFlexibility of implementation1. Kafka : flexible as provides library.NA2. Spark: Not flexible as it’s part of a distributed frameworkConclusionKafka Streams is still best used in a ‘Kafka -> Kafka’ context, while Spark Streaming could be used for a ‘Kafka -> Database’ or ‘Kafka -> Data science model’ type of context.Although, when these 2 technologies are connected, they bring complete data collection and processing capabilities together and are widely used in commercialized use cases and occupy significant market share. 
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