The need for business analytics to drive ad revenue has grown exponentially in the last five years. While the key custodians of this data management and associated analytics have been Ad Operations teams, in the past few years the skill set and competency required to address business needs for analysis has scaled within ops teams.
Today, with ad businesses handling large volumes of data on separate channels, processing and analysing data in real time requires significant computational resources and knowledge of how data works. There is now a pressing need for data analysts with the required skill sets and appropriate tools that can further enhance analysts’ productivity.
Key Challenges Analysts Face That Impact Productivity:
Disparate Data Sources: Analysts often have to pull data from various systems, including CRM platforms, ad servers, and audience analytics tools, leading to fragmented datasets. Integrating these diverse sources manually takes considerable time and effort. The lack of centralised data also increases the likelihood of inconsistent insights, as different platforms may have different ways of structuring information.
Custom Logic: Every ad business has unique KPIs and business logic that can’t be fully captured by out-of-the-box analytics tools. Analysts frequently need to create custom metrics or apply specific formulas to derive actionable insights. This custom logic, however, requires manual input and setup, often making the process prone to errors and time-consuming reworks, especially when scaling across multiple campaigns or departments.
Manual Management: The manual handling of reports and dashboards is a major bottleneck. Analysts often need to consolidate data, apply filters, and generate insights manually, which limits their ability to focus on higher-level strategic analysis. In the absence of automation, this not only lowers productivity but also risks inconsistencies in reporting when human error creeps in.
Lengthy Data Processing Time: Processing large volumes of data, especially in media and ad operations, is another challenge. Whether it’s extracting data from multiple platforms or running complex queries, the time it takes to process this information can delay critical decision-making. The longer it takes to generate actionable insights, the less agile the business becomes, ultimately impacting revenue growth opportunities.
People Attrition: With high turnover rates in analytics and ad ops teams, businesses often struggle to maintain continuity in knowledge and skill. As experienced analysts leave, new hires may face a steep learning curve, particularly when dealing with custom workflows or tools. This attrition can result in a temporary decline in productivity and disrupt the flow of actionable insights crucial for ongoing campaigns.
Data analysts in media and ad businesses face a range of challenges when it comes to extracting meaningful insights from the vast amounts of data at their disposal. One of the primary struggles is the reliance on generic reporting tools, which are often ill-equipped to meet the specific demands of ad monetisation workflows. These tools, while convenient for surface-level analysis, typically lack the flexibility needed to customise reports, dig deeper into data, and create metrics that align with business-critical KPIs. As a result, analysts are left grappling with fragmented datasets, manually piecing together insights, and struggling to communicate their findings effectively to business leaders.
What Data Analysts In Ad Monetisation Businesses Need Today
Off-the-shelf analytics platforms generally focus on providing standardised reports that work well for broad use cases. However, for media and ad businesses, these generic reports often overlook the unique complexities of ad monetisation. Analysts need the ability to track performance across multiple dimensions—audiences, ad placements, campaign performance, and revenue generation—and this requires more than just basic metrics. Following are some metrics that can improve analyst productivity:
- Unified Access to Disparate Data Sources
- Customisable Reporting Tools
- Automation for Efficiency
- Actionable Insights in Real Time
- Collaboration and Socialization of Data Outputs
- Scalability for Growing Data Needs
- Permission-Based Access for Data Security
How GRID Overcomes These Challenges To Deliver Value
This is where GRID comes in. Grid by Voiro is an AI-ready data stack that is purpose-built to serve the needs of custom analytics in ad revenue teams in a cost-effective manner. Built on years of Voiro’s experience working with leading media organisations, GRID is specifically designed to address the challenges that ad monetization teams face. GRID provides a high level of customisation, enabling analysts to design reports and build metrics tailored to their specific KPIs and easily socialise data outputs with sales and business leaders to power faster decision-making.
Following are some key benefits GRID by Voiro offers to data analyst teams.
GRID: Key benefits
Readily available data catalogue: Shorter time to set up and fast time to value by leveraging the data catalogue that has been market tested by large ad monetisation teams
Ability to create custom logic: With GRID, data analysts can go beyond basic, one-size-fits-all analytics. The platform allows analysts to dissect data across various dimensions and explore granular details that generic tools simply don’t offer. GRID enables analysts to generate custom metrics that directly align with their strategic objectives, leading to faster decision-making.
Conversational analytics: GRID offers conversational analytics that simplifies complex data points. By enabling users to interact with the data more intuitively (check out Genie for more on conversational analytics), GRID helps analysts uncover insights without getting bogged down in the details. This is particularly useful for ad monetisation teams, where understanding nuanced data can lead to strategic advantages.
Socialising Custom Data Outputs for Faster Decision-Making: A standout feature of GRID is its ability to streamline the socialisation of data outputs. Once insights are generated, sales and business leaders can access the information they need instantly, making it easier for them to make informed decisions. By providing a scalable way to socialise custom data outputs, GRID ensures that the insights generated by analysts reach the right people at the right time.
Conclusion
For data analysts in media and ad businesses, the challenges of accessing organised data and presenting actionable insights efficiently are all too familiar. Generic reporting tools fall short, leaving analysts with cumbersome workflows and delayed decision-making. GRID, with its deep customisation capabilities, automated reporting, and conversational analytics, offers a powerful solution. By saving time, generating strategic insights, and streamlining the socialisation of data, GRID enables ad monetisation teams to uncover actionable insights faster and ensure that businesses are always future-ready.
Unlock the power of custom analytics for ad monetisation teams with GRID. Book a demo to learn how to streamline your data analysis and uncover insights faster.
