Advertising Analytics: How to Use Data for Performance Marketing Success
Advertising analytics is a must-have for any digital marketer. It offers invaluable advertising insights into the effectiveness of advertising campaigns, helping businesses optimize their strategies for maximum impact.
In this blog post, we will walk you through advertising analytics from start to finish, from the benefits it offers to the core metrics you should track and the tools you can use
What Is Advertising Analytics?
Benefits of Advertising Analytics
Leveraging ad analytics offers significant advantages for refining and enhancing advertising strategies. Here's a look at the core benefits:
- Improved ROI: By understanding what works and what doesn't, you can allocate your budget more effectively, ensuring that every dollar spent contributes to your bottom line.
- Data-driven decisions: Advertising analytics arms you with actionable insights derived from data. This ensures strategies are not based on hunches but on solid evidence of what resonates with your audience. Most importantly, with real-time data, you can move away from post-campaign analysis and tweak campaigns on the fly, enhancing their performance and effectiveness.
- Enhanced audience targeting: By analyzing audience interactions and behaviors, ad analytics allows for more accurate targeting. This means your ads reach the people most likely to be interested in your product or service, increasing engagement and conversion rates.
- Effective and timely re-targeting: By understanding user behavior and engagement patterns, you can identify the optimal moments to re-engage potential customers. You increase the chances of conversion by keeping your brand top of mind and presenting tailored messaging that addresses previous interactions. It ensures that your advertising efforts are targeting users with the highest propensity to take action, thereby maximizing the efficiency of your ad spend and boosting overall campaign performance.
The Three Pillars of Advertising Analytics: Attribution, Optimization, and Allocation
In a broader sense, advertising analysis involves three main activities – attribution, optimization, and allocation.
By doing so, it enables a more strategic allocation of the advertising budget, focusing resources on the channels that yield the highest return on investment. Additionally, you can analyze the customer journey of your high-value clients and identify the most frequently occurring touchpoints, as well as optimize your campaigns to attract more prospects who match this profile.
Essentials for running attribution modeling
For marketing teams aiming to implement effective attribution modeling, certain foundational elements are critical:
- Unified tracking infrastructure: Establish a unified tracking infrastructure that captures every customer interaction across all touchpoints and channels. This should include clicks, views, and even non-digital interactions if they can be quantified digitally.
- Raw data storage: Save all raw interaction and advertising data in your data warehouse. This preserves the integrity and accessibility of your data for deep analysis. Besides, it allows for historical analysis and the flexibility to apply different attribution models as needed.
- Ad spend tracking: Implement solutions for tracking ad spend directly from ad platforms, incorporating the unique spend structures and mapping of each. This allows for accurate calculation of ROI across different channels and campaigns.
- State-of-the-art event streaming: Employ robust event streaming technology to capture and process data in real-time, ensuring a comprehensive view of customer interactions across all channels.
- Custom conversion tracking: Set up granular conversion tracking that goes beyond simple sales or lead captures, including micro-conversions such as email sign-ups or content downloads. This granularity helps in understanding the contribution of each touchpoint to the broader conversion path.
- Custom event integration: Ensure the capability to integrate custom events, such as product returns or customer service interactions, into the attribution model. This provides a more comprehensive view of the customer journey and its impact on conversions.
- Flexible identity resolution: Utilize flexible identity matching options to track user interactions across different identifiers – from individual contact to company, device, and even family grouping. This includes merging data across devices (web and mobile) for a unified customer view.
- Advanced segmentation: Apply advanced segmentation techniques to analyze how different audience segments respond to various touchpoints. This can highlight opportunities to optimize targeting and personalize ad content.
You can expect all these features from an Improvado-powered marketing attribution. The platform manages every step of the process, from collecting information on every touchpoint and mapping ad spend data to building single- and multi-touch models and enabling custom tracking.
Improvado is a marketing analytics platform, tailored for large enterprises and agencies. As a central hub, it integrates with over 500 Ad, MarTech, and CRM platforms, centralizing data into a cohesive marketing dataset. On top of this dataset, Improvado builds a marketing attribution dashboard tailored to your company's needs. Schedule a demo to get one step closer to precisely attributing conversions and seeing their impact on revenue growth.
The key to effective campaign optimization lies in the use of data-driven advertising insights.
With each ad platform having its own set of metrics and data points, delays in integrating offline and online data, complexity of measuring incremental reach and attributing sales, many marketers often opt for post-campaign analysis for strategy optimization.
Improvado solves this issue by addressing three main challenges:
- The platform aggregates data from the fragmented advertising landscape, offline and online touchpoints;
- And does it in near-real-time to enable timely campaign optimization.
- Improvado automatically prepares data for further analysis, by harmonizing disparate naming conventions.
An important aspect of budget allocation is budget pacing. This involves monitoring and adjusting spend over the course of a campaign to ensure that the budget is spent evenly and effectively, avoiding underutilization or premature depletion of funds. Budget pacing allows for real-time adjustments based on campaign performance, ensuring that spending remains on track to meet both short-term objectives and long-term marketing goals.
You can choose between four approaches to budget pacing and allocation:
- Manual tracking, while offering control and a deep understanding of data, is time-intensive and more prone to errors, making it suitable for smaller businesses or less complex campaigns.
- In-app tools provided by platforms like Google Ads or Bing offer platform-specific insights, ideal for businesses focusing their efforts on a single platform.
- Automated tools offer a balance, providing efficiency and detailed advertising insights without the labor intensity of manual data aggregation and tracking. They are well-suited for medium-sized and enterprise businesses and marketing agencies or those managing campaigns across multiple platforms.
- AI-assisted tools are beneficial for companies and agencies managing complex, high-volume campaigns. These tools offer the ability to process large sets of ad data and provide budget-pacing insights on the spot.
Core Metrics in Advertising Analytics
These marketing measurements are crucial in understanding the effectiveness of advertising campaigns and their impact on business outcomes.
Here are the most fundamental metrics used:
- Click-through rate (CTR): Measures the percentage of people who click on an ad after seeing it. It's a direct indicator of how relevant and engaging an ad is to its audience.
- Conversion rate: The percentage of users who take a desired action (such as making a purchase or signing up for a newsletter) after clicking on an ad. It's crucial for measuring the effectiveness of ad campaigns in driving specific outcomes.
- Return on ad spend (ROAS): A measure of the revenue generated for every dollar spent on advertising. It's a key metric for evaluating the profitability of ad campaigns.
- Impressions: The number of times an ad is displayed, regardless of clicks or engagement. This metric is useful for understanding the reach of an ad.
- Engagement rate: Measures the level of interaction (likes, shares, comments) with an ad. High engagement rates can indicate strong resonance with the target audience.
Metrics related to different advertising models
To manage budget efficiency and optimize campaign strategies, you need a clear understanding of the metrics specific to various advertising models. Here are key marketing measurements tied to different advertising models:
- Cost Per Acquisition (CPA): Measures the cost associated with acquiring a new customer through an advertising campaign. It's pivotal for evaluating the financial efficiency of direct response ads.
- Cost Per Lead (CPL): Represents the cost of generating a lead that shows interest in a product or service. Lead cost is crucial for campaigns focused on lead generation, helping to gauge the cost-effectiveness of these efforts.
- Cost Per Thousand Impressions (CPM): The cost of having an ad seen 1,000 times. CPM is vital for campaigns aimed at increasing brand awareness, providing a measure of cost for ad visibility.
- Cost Per Engagement (CPE): Represents the cost incurred for each interaction with an ad, such as a like, share, or comment. CPE is used to assess the cost efficiency of campaigns designed to increase engagement.
- Cost Per View (CPV): Relevant for video advertising, CPV measures the cost of one view of a video ad. It's essential for understanding the expense related to engaging users through video content.
- Cost Per Click (CPC): Reiterates the cost for each click on an ad. While mentioned previously, monitoring cost per click is also integral to performance-based advertising models, offering insight into the cost efficiency of driving traffic.
Advanced metrics in advertising analytics
Beyond the basic and model-specific metrics, there are advanced metrics that offer deeper insights into campaign performance and audience behavior:
- Lifetime value (LTV): Measures the total revenue a business can expect from a single customer throughout their relationship. LTV is crucial for understanding the long-term value of customers acquired through advertising efforts.
- Share of voice (SOV): Represents the percentage of market visibility or brand presence within advertising channels compared to competitors. SOV helps gauge the effectiveness of brand awareness campaigns in capturing audience attention.
- Page views per visit: Offers insight into how engaging and compelling the website content is for visitors coming from ads. More page views typically suggest higher engagement and interest.
- Attribution window: Defines the period during which a conversion is credited to a specific ad or campaign. Understanding the optimal attribution window can help in accurately measuring the impact of advertising efforts.
Maximizing ROI Through Advanced Ad Analytics
Advertising analysis provides the insights needed to make informed decisions, optimize advertising spend, and enhance campaign performance across the board. From basic metrics like CTR and conversion rates to advanced measures like LTV and SOV, digital advertising analytics offers a comprehensive view of marketing effectiveness.
For marketers tasked with navigating this complexity and driving results, having the right tools at their disposal is crucial. Improvado stands out as a solution designed to streamline advertising analytics. It consolidates data from various advertising platforms into a single, cohesive dashboard, simplifying the analysis and enabling real-time optimization decisions. With Improvado, marketers can focus on what truly matters—leveraging actionable advertising insights to drive strategy and achieve a better return on investment.