The power of data-driven decision making

poder del dato en toma de decisiones

In today’s technology-driven environment, organizations are faced with an incessant flow of data. This is where information-based decision making has become essential, especially in the field of digital marketing. Added to this need is the analysis of lead management, which allows efforts to be optimized and focused on demand generation and subsequent conversion into loyal customers.

Key aspects of lead analysis include:

1.Use of advanced tools and technologies

The use of platforms focused on the creation of dashboards and analysis, such as Power BI, allows for a more complete and effective data visualization. Similarly, Machine Learning and Data Analytics, both fundamental pillars of Big Data, are used to obtain knowledge and patterns from the data collected, which facilitates the identification of both opportunities and threats in the market.

2. User clustering

According to studies conducted by Ascend2, 68% of marketers consider lead segmentation to be the most effective tactic for improving results. The clustering of users allows grouping into more precise categories, according to profiles and characteristics, which in turn enables a more effective personalization of messages, increasing the probability of conversion and loyalty.

3. Attribution of conversions

According to a Forrester report, 74% of marketers consider attribution to be a significant challenge. Conversion attribution has a huge impact on advertising spend as it has many advantages.

Firstly, it provides a clear and accurate view of ROI, identifying which campaigns and ads are generating results. Likewise, by knowing which audiences respond best, efficient optimization of resources can be achieved. This valuable information not only improves the effectiveness of advertising campaigns, but also helps to understand target audience behavior and adjust strategies for better results in the future.

4. Predictive analytics

Lead scoring is positioned as a fundamental tool in lead analysis and its relationship with predictive analytics is very close. According to a Gartner report, it is projected that by 2023, 50% of organizations will incorporate predictive analytics into their business operations.

This approach enables companies to accurately identify leads that have a higher probability of converting to sales. By using lead scoring, organizations can strategically allocate resources, thus maximizing the efficiency of their marketing and sales efforts.

5. Data Intelligence and Business Intelligence

The efficient use of Data Intelligence and Business Intelligence becomes indispensable. Establishing a centralized repository of information and having a specialized BI team capable of creating clear and meaningful dashboards will allow companies to interpret the impact of their actions in real time and anticipate future actions.

In this context, Artificial Intelligence (AI) and Machine Learning (ML) emerge as fundamental technological processes. In data analysis, they provide faster and more accurate results, as well as predictive analytics that significantly improve business performance. Thanks to these processes, anticipatory answers can be obtained and repetitive tasks can be eliminated.

In conclusion, the analysis of leads through tools such as Power BI, the use of Machine Learning and the effective use of Data Intelligence and Business Intelligence, are considered essential competencies for companies seeking sustained growth and informed decision making based on accurate and meaningful data.


Originally published in our LinkedIn newsletter.

Latest posts

Elige tu sector

Choose your industry