Social media has become a significant component of digital marketing in today’s digital world. It’s now a sizable part of the marketing budget, and it’s only getting bigger. The rise of social media as a marketing channel has necessitated analytics.
This isn’t a one-of-a-kind requirement. The same thing happened with the first email marketing campaign and the first business website: companies needed a way to track these mediums, so email and web analytics were born.
What Is Social Media Analytics?
The process of collecting and analyzing audience data shared on social networks to improve an organization’s strategic business decisions is known as social media analytics.
Businesses can benefit from social media sentiment analysis because it allows marketers to spot consumer behavior trends relevant to their industry and influence the success of marketing efforts.
A good analytics solution allows you to overview your social media performance, see where strategic opportunities exist and where threats need to be nullified with strategy adjustments, and daily tracking, monitoring, and analyzing metrics and data trends.
Many social media analytics tools and other specific platforms help marketers drill down into those metrics. One of the most popular tools for marketers is Azure Stream Analytics. This tool allows marketers to improve transparency, processes, and structure to make their marketing activities and strategies more sustainable and scalable.
What are the Steps to Run Social Media Analytics on Azure?
Azure Stream Analytics is a real-time and complex event processing engine for analyzing and processing large amounts of fast streaming data from multiple sources simultaneously. Information extracted from various input sources, including devices, sensors, click-streams, social media feeds, and applications, can be used to identify patterns and relationships.
These patterns can be used to start workflows and trigger actions like creating alerts, feeding data into a reporting tool, or storing transformed data for later use. Stream Analytics is also available on the Azure IoT Edge runtime, allowing IoT devices to process data.
The following are some scenarios in which Azure Stream Analytics can be used:
- Analyze IoT device telemetry streams in real-time.
- Clickstream analytics/weblogs.
- Geographical analytics for fleet management and self-driving cars.
- Remote asset monitoring and maintenance are essential for high-value assets.
- Inventory control and anomaly detection using real-time analytics on the point of Sale data.
The Working Process Of Azure Stream Analytics
An Azure Stream Analytics job has three parts: input, query, and output. Stream Analytics typically consumes data from Azure Event Hubs (including Apache Kafka Event Hubs), Azure IoT Hub, and Azure Blob Storage. The SQL query language-based query can easily filter, sort, aggregate, and join streaming data over time.
You can also add JavaScript and C# user-defined functions to this SQL language (UDFs). You can quickly alter the event ordering options and duration of time windows when executing aggregation operations using simple language constructs and parameters.
You can control what happens in response to the information you’ve analyzed because each job has one or more outputs for the transformed data. You can, for example:
- To trigger communications or custom workflows downstream, send data to Azure Functions, Service Bus Topics, or Queues.
- For real-time dashboarding, send data to a Power BI dashboard.
- To train a machine learning model based on historical data or perform batch analytics, store data in other Azure storage services (Azure Data Lake, Azure Synapse Analytics, and so on).
Benefits Of Azure Stream Analytics
Azure Stream Analytics is built to be simple, flexible, and scalable to any job size.
Some of the key capabilities and benefits of this social media analytics tool are as follows:
Completely supervised
Azure Stream Analytics is a PaaS (fully-managed) offering. You don’t have to set up any hardware or infrastructure, and you don’t have to keep your operating system or software up to date. The tool takes care of everything for you to concentrate on your business logic rather than the infrastructure.
It Can Run In The Cloud Or On The Intelligent Edge
Azure Stream Analytics can be run in the cloud or on IoT Edge or Azure Stack for ultra-low latency analytics for large-scale analytics. Azure Stream Analytics uses the same tools and query language on both the cloud and the edge, allowing developers to create truly hybrid architectures for stream processing.
Low Total Cost Of Ownership
Stream Analytics is a cost-effective cloud service. You don’t have to pay anything upfront; you only pay for the streaming units you use. You don’t have to commit to anything or set up a cluster, and you can scale the job up or down depending on your needs.
Ready For Mission-Critical Situations
Azure Stream Analytics is available in multiple regions worldwide and is designed to run mission-critical workloads while meeting the highest reliability, security, and compliance levels.
Reliability
Azure Stream Analytics ensures that events are processed precisely once and delivered at least once, ensuring that events are never lost. As described in Event Delivery Guarantees, the processing is guaranteed exactly once with selected output.
Security
Azure Stream Analytics tool supports TLS 1.2 encryption and encrypts all incoming and outgoing communications. Built-in checkpoints are also encrypted. Because all processing is done in memory, Stream Analytics does not store the incoming data.
Conclusion
The value of social media analytics can be seen in how it aids online businesses in connecting with their customers. The ability to better engage your customers based on specific statistics (provided by social media analytics) improves marketers’ ability to convert that engagement into monetary growth.
By leveraging the power of big data, Azure Stream Analytics allows customers to gain valuable insights and gain a competitive advantage.
