How do you test and experiment with different DSP optimization and bidding methods and tactics?

Tips For Gaining Alignment: 1. When working with your client, remember they are your partner in this. So ask questions like: How can we make you look good? What are you being tracked against? What goals are important to your vertical? 2. It may be beneficial to meet with both the marketing and sales team to create an atmosphere of synergy and identify any loose ends.

Set targets, KPIs, target audience, segments, regions, devices, and creatives to evaluate DSP optimization and bidding approaches. A well-defined baseline is achieved by comparing results and identifying the best optimization and bidding strategies.

A few things to consider: 1) Vertical strengths is crucial when considering DSPs to work with. Some are gaming strong and some are more on non-gaming, which result in different levels of maturity for optimization 2)The industry has changed the way to work on single optimization goals, CPI/CPC, CPA, ROAS D1~D30 can all be campaign goals. Which means we can try different things to test if you wanted to balance the results. 3)blocklisting and allowlisting is still a thing when optimizing the performance

Clearly define your cross-device campaign objectives, whether it's increasing brand awareness, driving website traffic, or boosting conversions. Establish key performance indicators (KPIs) to measure success, such as click-through rates, time spent on site, or purchase completion. Ensure your objectives and KPIs are specific, measurable, achievable, relevant, and time-bound.

To test DSP optimization, start with defining your goals and KPIs, like CPM or CPC. This step is crucial for measuring success. Experiment with different tactics and keep a close eye on performance metrics. It's a process of trial and error, but with patience, you'll find the best strategy for your campaigns.

Granular KPIs by Funnel Stage: Instead of just setting a broad CPA or ROAS goal, break KPIs into funnel stages. For example, use Impression Share or Viewability Rate for awareness and Add-to-Cart Rate or Purchase Rate for conversion stages. This approach helps refine tactics. Set Micro vs. Macro Objectives: Alongside overarching goals (e.g., 3x ROAS), establish micro-objectives like improving Dayparting Performance or Ad Fatigue Reduction. These help track minor yet impactful campaign improvements. Example: If your macro goal is a 10% lift in CTR, a micro-objective could be reducing bounce rates on landing pages.

Testing and experimentation are key to DSP optimization and bidding. To that end, there are several approaches for advertisers to consider.  - A/B testing is a great starting point for comparing two strategies directly, but it's limited to analyzing only two scenarios at once.  - Incremental testing, on the other hand, measures the incremental impact of your DSP efforts, providing a clear picture of additional value created. When deciding on what to test — be it bid strategies, modifiers, or pacing — it's crucial to focus on one or a few variables at a time to clearly understand their effects.  When choosing, the type of test and variables for experimentation, align the test with your goals, resources, and quality of data within your reach.

Consider the inventory your tests will run on including site lists, publishers and PMPs. This is as important to the outcome of your testing as is the bidding methods themselves.

Select a testing method, such as A/B testing or multivariate testing, that aligns with your campaign goals. Identify the variables you want to test, such as ad creatives, targeting, messaging, or device types. Determine the sample size and statistical significance needed to draw meaningful conclusions from your experiments.

Factorial Design Testing: For campaigns with multiple variables (e.g., creatives, bid modifiers, audience segments), use factorial design to see how combinations influence performance. It saves time over multivariate testing. Control Variables for Consistency: Lock certain variables, like geography or device type, when testing others to isolate effects. This ensures clear attribution of performance changes. Example: When testing bid strategies, keep audience targeting consistent to determine if Target CPA performs better than Maximize Conversions.

A few quick hints to make it easier for you: While setting up campaigns on your DSP platform: - Create separate campaigns for test and control groups - Use clear, consistent naming conventions - Apply proper tagging for easy tracking Ensure similar conditions across campaigns: - Same budget allocation - Identical targeting parameters - Consistent scheduling Allocate sufficient traffic and runtime for statistical significance. It's important to monitor test without interfering during the whole test period.

When setting up your test and experiment campaigns in your DSP platform, implement a consistent naming convention and tagging strategy. This enables accurate tracking and analysis. Ensure your campaigns are configured to run under identical conditions and garner sufficient traffic and conversions for statistically significant results. I would also recommend segmenting your audience to isolate variables your team would like to test. Avoid any modifications during the testing phase to maintain the integrity of your results. One more thing: schedule regular checkpoints to monitor progress and make adjustments if necessary after completing the testing period.

Carefully design your test campaigns, ensuring consistency in branding, offers, and user experience across devices. Implement tracking and attribution models to accurately measure the performance of your cross-device efforts. Allocate budget and resources to support your experiments, and establish a clear timeline for execution.

Use Dynamic Naming Conventions: Include details like bid strategy, audience, and creative version in campaign names. Example: “Q4_TCPA_30-50F_Age18-34_VideoV1”. It simplifies tracking and analysis. Control for Timing Bias: Launch campaigns on the same day and time to prevent timing bias. Running experiments over weekends or during holidays may skew data. Example: Launching a campaign on Black Friday vs. a regular Friday can impact outcomes; controlling for such variables gives clearer insights.

Again, it's very important to monitor test without any interfering during the whole test period. After test, calculate key metrics for each test group and control group. Don't neglect statistical tests like t-test or ANOVA to determine if differences are significant to get real picture. Then identify any unexpected results or insights gained from the test. And only then decide how to implement successful variations at scale.

Continuously monitor the performance of your test campaigns, tracking key metrics and identifying trends. Analyze the data to understand the impact of your cross-device strategies, and identify the most effective approaches. Use data visualization tools to present your findings in a clear and actionable manner.

Automate Data Pulls for Consistency: Use scripts or automated tools to extract data at the same time each day. This reduces the risk of data variability due to real-time bidding fluctuations. Cohort Analysis: Analyze performance by cohort groups (e.g., week-by-week results) to understand long-term trends and avoid focusing only on short-term fluctuations. Example: If you notice a gradual increase in ROAS over weeks for a retargeting campaign, it may highlight a growing audience interest.

I would just add a few tips to improve optimization in learnings beyond using advanced tools and setting clear metrics: • Iterate Quickly: Make rapid adjustments to your strategies based on insights from segmentation and behavior analysis, ensuring continuous improvement and better results. • Small, Incremental Changes: Implement changes gradually to accurately gauge their impact without overwhelming your campaign. • Stay Updated: Keep up with the latest industry trends and algorithm updates to ensure your strategies remain relevant and effective.

Test at Scale Before Full Rollout: Implement winning strategies in a subset of your target audience first. If performance holds, then scale. This helps manage budget risk. Document Learnings with Context: Maintain a Testing Journal with results, learnings, and external factors (e.g., seasonality, competitive landscape). It’s helpful for future campaign planning. Example: After identifying that a specific bid strategy performs well for summer apparel, document the context for next year's summer campaigns.

Review the insights gained from your experiments and apply them to optimize your cross-device campaigns. Iterate on your strategies, testing new approaches, and continuously refining your tactics to improve performance. Share your learnings with relevant stakeholders and integrate them into your overall marketing strategy to drive long-term success.

Picture this: You're navigating a complex system with many useful functions, each of which holds immense potential. You can ensure you're making the most of every opportunity only via experimentation. Think of your DSP as a laboratory where you conduct experiments to find the winning formula for your ads. You can set up multiple scenarios: one with a captivating visual, another with a compelling message, and yet another targeting a specific audience segment at just the right time. Each test reveals valuable insights, helping you understand what resonates best with your audience. By continuously testing and refining your tactics, you stay one step ahead, ensuring your campaigns shine brightly amidst the noise of the digital landscape.

Utilize Industry Benchmarks: Compare your KPIs with industry averages to get a performance context. This can guide your expectations and highlight opportunities. Storytelling Through Data: Present your findings in a way that tells a clear story. For example, “Changing our pacing mode improved evening conversion rates by 15%, leading to a more efficient budget allocation.” Example: If you ran an A/B test that boosted CTR but saw no lift in conversions, analyze user journey data to optimize post-click experiences.