by Denae Luna | Oct 30, 2023 | Digital Marketing, Programmatic
The bidstream, also known as bid requests or bid opportunities, is a fundamental component of programmatic advertising. It refers to the stream of data generated during the real-time bidding (RTB) auction process, where advertisers and their demand-side platforms (DSPs) submit bids to purchase ad impressions on various ad exchanges and supply-side platforms (SSPs).
Here’s how the bidstream works in programmatic advertising:
- Ad Request: When a user visits a website or mobile app with ad inventory available for sale, an ad request is generated. This request is sent to an ad exchange or SSP, which acts as an intermediary between publishers and advertisers.
- Auction Initiation: The ad exchange or SSP collects information about the ad impression, such as the user’s demographics, browsing behavior, the content of the webpage, and more. This information is included in the bid request to help advertisers decide if they want to bid on the impression.
- Bid Requests: The bid request, which is often in the form of a JSON object, is then sent to multiple DSPs. Each DSP receives these bid requests and processes the data within milliseconds to make a bidding decision.
- Bidding Decision: Within the DSP, the bidding algorithm assesses the ad impression’s value based on the available data, the advertiser’s targeting criteria, and the campaign budget. The DSP decides whether to submit a bid and, if so, at what price.
- Bid Submission: If the DSP decides to bid, it generates a bid response. The bid response includes the bid amount and other parameters, such as the creative to be displayed if the bid wins. This response is sent back to the ad exchange or SSP.
- Auction: The ad exchange or SSP collects all the bid responses from participating DSPs. It evaluates these responses and determines the winning bid based on the highest price.
- Ad Delivery: Once the winning bid is determined, the ad impression is delivered to the winning DSP. The winning DSP’s ad is then displayed to the user in real-time.
- User Interaction: The user may or may not interact with the ad. If an interaction occurs (e.g., a click or view), the data is collected and used for reporting and optimization.
The bidstream, therefore, represents the flow of data from the initial ad request to the final ad delivery. It allows advertisers to evaluate and bid on ad impressions in real-time, enabling them to reach their target audience with relevant and timely advertising.
Advertisers and DSPs rely on the bidstream to make quick bidding decisions and optimize their ad campaigns. The bidstream is rich with data, and the analysis of bid requests can help advertisers make more informed choices about which impressions to bid on and at what price, making programmatic advertising a highly data-driven and efficient approach to digital advertising. Any degradation in the bloodstream can cause signal loss. For more on signal loss and how it impacts advertisers click here.
by Denae Luna | Oct 30, 2023 | Digital Marketing, Programmatic
Signal loss in programmatic advertising refers to the loss or degradation of data and information as it passes through various components of the programmatic advertising ecosystem. This loss can occur at multiple stages within the advertising process, from data collection to ad delivery.
Signal loss can have a significant impact on the efficiency and effectiveness of programmatic campaigns. Here are some key aspects of signal loss in programmatic:
Cookie Restrictions: Privacy regulations and browser restrictions have led to signal loss by limiting the availability and accuracy of cookies. This has made it challenging to track users and target them effectively.
Ad Fraud: Signal loss can be exacerbated by ad fraud, where fake or invalid data can be passed in the bidstream and impressions dilute the quality of data used in programmatic advertising. This makes it harder to distinguish genuine user behavior from fraudulent activity.
Data Transfer: Data transfer between different systems and platforms can result in signal loss if not handled properly. Data may be lost or altered during the transfer process. This can include user data, behavioral data, contextual data, IP address, and more.
Latency: Latency in the bidding and ad delivery process can cause signal loss. Delays in data transmission and decision-making can impact the relevance and timeliness of ad targeting. Bid auctions take place in milliseconds so it doesn’t take much of a glitch to create latency in the system.
Invalid Traffic and Impressions: Signal loss can occur when advertisers pay for impressions that are not seen by real users. Invalid traffic, such as non-human traffic (bots), can dilute the value of ad impressions.
Data Aggregation: Aggregating data from multiple sources for audience segmentation and targeting can lead to signal loss if the data is not consolidated accurately or if key details are missed.
Measurement Challenges: Signal loss can make it challenging to accurately measure campaign performance, making it difficult to understand the true impact of programmatic advertising efforts.
Retargeting Issues: Signal loss can hinder retargeting efforts, as tracking users across different devices or platforms may not be as accurate as desired.
Ad Personalization: Signal loss can impact the personalization of ad content. Advertisers may struggle to deliver highly relevant ads to users if data is lost or inaccurate.
Addressing signal loss in programmatic advertising requires implementing data quality controls, using advanced targeting techniques, and being aware of the limitations imposed by privacy regulations and browser changes. Advertisers and marketers often work with data providers, ad tech platforms, and data management solutions to mitigate signal loss and optimize programmatic campaigns. Additionally, continuous monitoring, analysis, and optimization are essential to minimize the impact of signal loss and ensure the success of programmatic advertising efforts. For a deeper dive into DSP buying, check out our Buyer’s Guide: https://populationscience.com/demand-side-platform-buyer-guide/
by Denae Luna | Oct 26, 2023 | Digital Marketing, Programmatic
Attributing Return on Investment (ROI) to Connected TV (CTV), Digital Out of Home (DOOH), and Streaming Audio ads can be challenging but essential for assessing the effectiveness of your advertising campaigns in these channels. To attribute ROI to these ad formats, consider the following strategies (ranked in order of most straightforward to most advanced):
Unique Tracking URLs, QR Codes, or Landing Pages:
Create unique tracking URLs or landing pages for each ad campaign in these channels. This enables you to track website visits, conversions, and other user interactions specific to these campaigns. For visual campaigns like DOOH or CTV you can embed a QR code so people can walk up to the screen, scan, and land on the desired page and track conversions.
Conversion Tracking:
Implement conversion tracking pixels or tags to monitor actions that are relevant to your ROI goals, such as website purchases, form submissions, or app downloads. Ensure that these tags are correctly implemented across all channels.
Custom Promo Codes:
Assign custom promo codes or coupons for each channel or campaign. When customers use these codes during a purchase, it becomes a direct attribution to the specific ad campaign.
Surveys and Feedback:
Collect feedback and conduct surveys to gather information directly from customers. Ask them about their awareness of and response to CTV, DOOH, and streaming audio ads in your campaigns.
Incrementality/Lift Testing:
Conduct A/B testing or incrementality testing by running control groups that are not exposed to your ads and comparing their behavior with those who were exposed. This can help isolate the impact of your campaigns.
Geo-Fencing and Geo-Targeting: 
Leverage geo-fencing and geo-targeting capabilities. For DOOH campaigns you can attribute ROI by measuring foot traffic or visits to physical locations near displays. For CTV and streaming audio you can measure lift by only running one channel in specific geos and compare them to geos not supported by that channel.
Attribution Modeling:
Use attribution models to analyze the customer journey and assign value to each touchpoint along the way. Multi-touch attribution models can help determine the influence of CTV, DOOH, and streaming audio ads in the conversion path.
Cross-Device Tracking:
Implement cross-device tracking to understand how users interact with ads on different devices before making a purchase. This is important because consumers may see an ad on CTV, then switch to a mobile device to complete a transaction.
Advanced Attribution Solutions:
Consider using advanced attribution solutions like marketing mix modeling (MMM) or time-series analysis to determine the impact of CTV, DOOH, and streaming audio in conjunction with other advertising channels.
Keep in mind that accurately attributing ROI to these channels may require a combination of the above methods and tools. It’s essential to develop a robust measurement and attribution strategy to track the effectiveness of your advertising efforts in CTV, DOOH, and streaming audio to make informed decisions and optimize your campaigns.
Are you ready to implement your DSP? Check out this article with a great starting strategy: https://populationscience.com/how-to-implement-a-demand-side-platform-dsp/
by Denae Luna | Oct 26, 2023 | Digital Marketing
In digital marketing, cohorts refer to groups of users or customers who share common characteristics, behaviors, or attributes. Cohorts are used to segment a broader audience into smaller, more homogenous groups. These groups are typically defined based on specific criteria, and the members of a cohort are tracked and analyzed over time. Cohorts are an essential tool in digital marketing for understanding user behavior, improving targeting, and making data-driven decisions.
Here are some factors to consider when discussing the future of digital ad targeting with cohorts:
Privacy Concerns:
With increasing privacy regulations like GDPR and CCPA, as well as browser-level restrictions on tracking, cohorts offer a way to target users while respecting their privacy. This trend toward greater data protection is likely to continue, making cohorts a significant component of digital ad targeting.
Platform Developments:
Major digital platforms, such as Google and Apple, are embracing the concept of cohorts. For example, Google is developing the Federated Learning of Cohorts (FLoC) as part of its Privacy Sandbox initiative. These platforms play a pivotal role in shaping the future of digital advertising.
Data Availability:
The availability and quality of data for creating cohorts can influence their effectiveness. The more data that can be used to create meaningful cohorts, the more valuable this targeting method becomes.
Performance and Relevance:
The success of cohorts in digital advertising will depend on their ability to deliver relevant and effective ads. Advertisers will need to continually optimize their cohort-based strategies to ensure they meet their campaign objectives.
Hybrid Approaches:
It’s likely that future digital ad targeting will involve a combination of cohort-based targeting and other techniques, such as contextual targeting, first-party data, and creative strategies. Advertisers will need to adopt a flexible and adaptable approach to meet their goals.
Regulatory Changes:
Ongoing and future changes in privacy regulations may further influence the role and capabilities of cohorts in ad targeting. Advertisers will need to stay informed and adjust their strategies accordingly.
In summary, cohorts are an important component of the future of digital ad targeting, particularly in the context of privacy-conscious advertising. However, the future is likely to involve a mix of targeting methods, with advertisers adapting to changes in regulations, technology, and user expectations. Cohorts represent a significant step toward a more privacy-focused and effective approach to digital ad targeting, but they are one piece of the broader targeting landscape.
Ready to implement your DSP? Check out our article that has a great startup strategy: https://populationscience.com/how-to-implement-a-demand-side-platform-dsp/
by Denae Luna | Oct 26, 2023 | Digital Marketing
Before making any changes or setting new goals, take the time to review and analyze your digital marketing campaign’s performance over the last year. This analysis should comprehensively examine key performance indicators (KPIs), such as website traffic, conversion rates, click-through rates, return on investment (ROI), and other relevant metrics. Pay special attention to what worked and what didn’t.
Specific steps to consider during this review:
Conversion Path Analysis:
We lead off with conversion path analysis because this is one critical aspect that many digital marketers overlook. Examine the customer journey and conversion funnel. Identify any bottlenecks or drop-off points in the funnel and strategize on how to improve the user experience. Many digital marketers focus on the ad funnel, but it’s vital to do conversion path optimization on your website. You need to A/B landing pages and understand customer behavior once they reach your website before you do any other analysis.
Channel Performance:
Evaluate the performance of different marketing channels (e.g., social media, email, paid advertising). Identify which channels were most effective in driving traffic and conversions. Effectiveness can be an elusive thing for marketing teams to agree upon. Too many teams get locked into last click attribution to conversions by channel. The reality is many attribution models are broken or at least less reliable in the age of cookie depreciation/privacy. The best approach to measuring channel performance is a mix of last click attribution, lift tests, surveys, advanced attribution modeling, and good old fashioned common sense when digging into campaign metrics.
Content and Messaging:
Analyze the content and messaging that resonated with your audience. Which types of content generated the most engagement and conversions? What messaging themes were successful? You might be surprised to find content that worked on one channel may not have necessarily worked on another. Optimize your content and messaging by channel but make sure you keep your main branding theme intact for some level of continuity, especially if you are retargeting across channels.
Audience Insights:
Understand your audience’s behavior and preferences. What segments of your audience performed the best, and which ones need improvement? Is your audience data up to date? Use analytics tools, CRM, and customer feedback to gain insights and cleanse any old data on customers that no longer engage or segments that are no longer profitable.
Competitive Analysis:
Look at your competitors’ digital marketing strategies and assess how your performance compares. Identify areas where you can outperform or differentiate.
Budget Allocation:
Review your budget allocation for various marketing channels and campaigns. Determine whether your spending aligns with the best-performing channels and where adjustments may be needed.
Once you’ve gathered and analyzed this data, you can make informed decisions about how to optimize your digital marketing campaign for the new year. This process sets the foundation for setting new goals, refining your strategy, and identifying areas where improvements are needed.
Thinking about next year’s strategy and haven’t considered programmatic advertising? Read this article before you write off the benefits: https://populationscience.com/dont-have-programmatic-in-your-2024-plan-heres-why-you-should-reconsider/
