Looking for an Easy Way to Use Big Data?

Big Data and its potential are becoming a topic of greater interest for organizations of all types and sizes. However, many organizations do not know where to start when it comes to using Big Data effectively. Integrating Big Data sources with traditional data sources can also be a challenge.

InetSoft has provided a solution that makes integrating Big Data into your BI a more easy and manageable task.

As a leading edge BI provider, InetSoft is meeting the markets demand for Big Data connectivity head on. InetSoft's Style Intelligence can access data platforms like MapR, SAP HANA, and Hadoop, as well as standard relational databases that have become too large to effectively run analytics on without advanced BI tools such as InetSoft's.

The software also features a a data source connector for the Apache Hive data warehouse system for Hadoop, enabling the easy integration of this popular Big Data software.

InetSoft Makes it Easy to Work With Big Data

You won't be limited to just what's in those Big Data sources, however. InetSoft's software features an advanced data mashup engine that can pull data from a huge variety of disparate sources in real time.

Once IT has defined the data sources, end-users with only excel-level skills can use a drag-and-drop interface to create and customize their own dashboards and ad-hoc reports.

With InetSoft's solution, business users can benefit from the sheer volume of data in Big Data sources, without having to deal with the complexity of handling those sources directly.

How Do Advertising Agencies Use Big Data?

  1. Audience Segmentation:

    • Big data enables advertising agencies to segment audiences with precision. By analyzing large datasets, agencies can identify specific demographics, behaviors, and interests of potential customers. This allows for highly targeted advertising campaigns tailored to individual segments.
  2. Customer Profiling:

    • Agencies use big data to create detailed customer profiles. These profiles include information such as purchasing history, browsing behavior, social media interactions, and more. This helps in understanding customer preferences and predicting future behavior.
  3. Personalized Messaging:

    • With the insights gained from big data, advertising agencies can craft personalized messages that resonate with specific audience segments. This personalization can significantly increase engagement and conversion rates.
  4. Predictive Analytics:

    • By analyzing historical data, agencies can use predictive analytics to forecast future trends and consumer behavior. This enables them to stay ahead of the curve and adjust campaigns accordingly.
  5. Optimizing Ad Placement:

    • Big data allows agencies to determine the most effective channels and platforms for placing advertisements. Through analysis of consumer behavior, agencies can identify where their target audience spends the most time online.
  6. Real-Time Analytics:

    • Big data tools provide real-time insights into the performance of advertising campaigns. Agencies can monitor metrics such as click-through rates, conversion rates, and engagement levels in real time, allowing for immediate adjustments to optimize results.
  7. Competitor Analysis:

    • Big data allows agencies to monitor and analyze the activities and strategies of competitors. This information is crucial for identifying market trends, understanding consumer sentiment, and gaining a competitive edge.
  8. Content Optimization:

    • By analyzing customer interactions and feedback, agencies can refine and optimize the content of advertisements. This ensures that the messaging is resonating with the target audience.
  9. Attribution Modeling:

    • Big data helps in understanding the customer journey and attributing conversions to specific touchpoints. This allows agencies to allocate resources more effectively, focusing on the channels and strategies that have the most impact.
  10. Social Media Monitoring:

    • Big data tools can track and analyze social media conversations, mentions, and sentiment related to a brand or campaign. This information is invaluable for understanding public perception and addressing potential issues.
  11. ROI Measurement:

    • Through big data analytics, agencies can accurately measure the return on investment (ROI) of advertising campaigns. This allows for data-driven decision-making and helps in allocating budgets more effectively.
  12. Ad Fraud Detection:

    • Big data tools can identify patterns indicative of ad fraud, ensuring that advertising budgets are spent effectively and efficiently.
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Read how InetSoft was rated as a top BI vendor in G2 Crowd's user survey-based index.

How Do Tobacco Product Companies Use Big Data?

  1. Market Research and Consumer Insights:

    • Tobacco companies analyze big data to gain deep insights into consumer preferences, behaviors, and trends. This information helps in understanding the target audience and designing products that resonate with them.
  2. Product Development and Innovation:

    • Big data can inform the development of new products or the modification of existing ones. Through consumer feedback and market trends, companies can refine their offerings to meet changing consumer demands.
  3. Regulatory Compliance:

    • The tobacco industry is subject to strict regulatory guidelines and compliance requirements. Big data can be used to track and ensure adherence to these regulations, helping companies avoid legal issues and maintain a positive reputation.
  4. Supply Chain Optimization:

    • Big data analytics can be applied to optimize the supply chain, ensuring that the production, distribution, and delivery of tobacco products are efficient and cost-effective.
  5. Marketing and Advertising Strategy:

    • Similar to advertising agencies, tobacco companies use big data to segment their target audience and personalize marketing messages. This helps in maximizing the impact of advertising campaigns.
  6. Retailer Relationship Management:

    • By analyzing sales data, tobacco companies can identify high-performing retailers and develop strategies to strengthen these relationships. This includes inventory management, promotional activities, and incentives.
  7. Predictive Analytics for Demand Forecasting:

    • Big data allows companies to forecast demand for their products. This is especially important in an industry with seasonal fluctuations and changing consumer preferences.
  8. Brand Loyalty and Customer Engagement:

    • Data analysis helps in understanding customer behavior and preferences, which can be used to develop loyalty programs, promotions, and engagement strategies to retain customers.
  9. Health Research and Risk Assessment:

    • Some tobacco companies invest in research related to the health effects of their products. Big data analysis can be used to study health trends, assess risks, and develop harm reduction strategies.
  10. Competitor Analysis:

    • Companies monitor competitors' activities through data analysis. This includes tracking product launches, marketing strategies, and consumer sentiment towards rival brands.
  11. Price Optimization:

    • Through data analytics, companies can determine optimal pricing strategies for their products, taking into account factors like consumer willingness to pay, competition, and production costs.
  12. Smoking Cessation Programs:

    • Some tobacco companies are involved in smoking cessation efforts. Big data can be used to understand the effectiveness of these programs and make necessary adjustments.
why select InetSoft
“Flexible product with great training and support. The product has been very useful for quickly creating dashboards and data views. Support and training has always been available to us and quick to respond.
- George R, Information Technology Specialist at Sonepar USA

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