Data-Driven Recommendations for Meta Ads PDF

Make data-driven recommendations Introduction Measurement is an iterative process. As your marketing strategy evolves, it’s important to continue to test different variables to determine which strategy is most effective at driving results. How ads appear across Meta technologies It’s important to...

Make data-driven recommendations Introduction Measurement is an iterative process. As your marketing strategy evolves, it’s important to continue to test different variables to determine which strategy is most effective at driving results. How ads appear across Meta technologies It’s important to know how the ad delivery system works so you can make effective recommendations, such as adjusting your bid strategy or targeting. Meta gives you many opportunities to show ads. The delivery system determines which ad is shown using the following three components: Ad auction ● ● ● Determines the best ad to show to a person at a given time The winning ad maximizes value for both people and businesses. Can help you understand your ad performance Performance optimization ● ● Spends your budget evenly over the schedule of your ad set. Budget and bid strategy determine pacing Advertiser controls Strategic levers to consider before launching a campaign including bid, budget, audience, creative, placement and optimization MAKE DATA-DRIVEN RECOMMENDATIONS 53 Total value To ensure that all ads are evaluated in a consistent way and the winning ad maximizes value for both people and businesses, Meta assigns a total value to every ad that competes in the auction. The total value is based on the amount you bid, how likely it is that showing your ad to a person will lead to your desired outcome and ad quality, along with how relevant your ad is to the targeted individual. The ad with the highest total value wins the auction for the targeted individual. Maximize advertiser value Advertiser bid x estimated action rates Optimize the customer experience + Ad quality = Total value Advertiser bid The amount the advertiser is willing to pay to achieve their desired outcome, such as a conversion. A bid can be the same as or less than a budget, which is the total amount of money an advertiser is willing to spend through the life of a campaign. Estimated action rates The probability that showing an ad to a person leads to the advertiser’s desired outcome. The desired outcome is aligned with the advertiser’s campaign objectives. Ad quality A measure of the quality of an ad and how interesting a person will find it. Buying types There are two main buying types for Meta ads: Auction Reach and frequency Auction buying offers more choice, efficiency and flexibility, with less predictable results. Ads can be placed across Facebook, Instagram, Messenger and Audience Network. Reach and frequency buying lets you plan and buy your campaigns in advance, with predictable ad delivery and more control over your frequency settings. MAKE DATA-DRIVEN RECOMMENDATIONS 54 Performance optimization system The performance optimization system uses machine learning to predict which ad auctions give you the best value for your money. Pacing is a budget optimization process that ensure your budget is spent as evenly as possible over the lifetime of your ad set. It helps prevent you from spending your budget too quickly on inefficient results. Pacing enables flexibility to help get you the best available results for your goals by enabling adjustments. Budget pacing Bid pacing The aspect of pacing where we may increase budget if there’s an opportunity to get many optimization events with costs aligned with your bid strategy The aspect of pacing where we adjust your bid or which auctions we enter based on how much budget and time are left for your ad set. MAKE DATA-DRIVEN RECOMMENDATIONS 55 Bid strategies There are five bid strategies to choose from. You can also choose not to enter a cost control. Highest volume Select the highest volume bid strategy if you want to maximize delivery and conversions you can get from your budget. This bid strategy is best for spending your budget as efficiently as possible. However, the cost of your ad results will fluctuate more, as changes are made to get the highest volume. For example, if auction competition decreases, the cost of your ads may go down and if auction competition increases, the costs of your ads may go up. Highest volume optimized for value Select the highest volume optimized for value bid strategy if you want to spend the entire budget by the end of the schedule of an ad set while maximizing the amount of value you get from purchases. This automated bid strategy is available for ad sets optimizing for purchase value. Value optimization uses machine learning to predict how much ROAS you may generate for a business over a one- or 7-day window. This prediction is then used to bid for your highest value customers. By bidding more for people who are likely to spend more, you can maximize the ROAS for your campaigns. Cost per result goal Select the cost per result goal bid strategy if you want to maximize cost-efficiency and need to keep cost within a specific threshold. This bid strategy enables you to provide a benchmark cost for the results you care about. This helps limit your cost per conversion while maximizing the number of conversions. The cost per result goal reflects how much you are paying on average for results. Not all optimization goals are available for cost per result goal. Learn more about best practices for using cost per result goal. ROAS goal Select the ROAS goal bid strategy if you want to keep return on ad spend around an average amount of the course of your campaign, providing you with more command over the value a campaign brings to your business. When you set a ROAS goal, we’ll try to deliver against that over the campaign’s lifetime, dynamically bidding as high as needed to maximize results. To use this bid strategy, you'll need to optimize your ad set for purchase value. Bid cap Select the bid-cap bid strategy if you want to set a maximum bid across auctions and reach as many people as possible at that bid. This bid strategy maximizes volume at specified maximum bid and can increase competitiveness against other advertisers targeting similar audiences. Bid controls are less flexible, which means they're more likely to constrain delivery than cost controls because the bid cap limits bids in every auction. MAKE DATA-DRIVEN RECOMMENDATIONS 56 These bid strategies are divided into three categories. These are spend-based bidding, goal-based bidding and manual bidding. Spend-based bidding Goal-based bidding Manual bidding Focus on spending your full budget and getting the most results or value possible. Set a cost or value you want to achieve. Control how much you can bid across ad auctions. Use the table below to learn more about the bid strategies. Spend-based bidding Goal-based bidding Manual bidding Highest volume Highest volume optimized for value Cost per result goal ROAS goal Bid cap ● Spend the full budget. ● Maximize the value of conversions, rather than simply increasing the number of conversions. ● Keep cost per result below a certain amount, regardless of market conditions. ● Exercise more control over the purchase value generated from ads than can be achieved with highest value bidding. ● Use internal bidding models or lifetime value models. ● Get the most results possible from your budget. Budget goals ● Don’t have a specific cost per result goal. ● Don’t aim for specific goals to measure or define success. ● Your cost per result may fluctuate. Considerations ● Costs may go down if auction competition decreases, or costs may go up if auction competition increases. ● Spend the full budget while focusing on getting higher value purchases. ● This requires an even distribution of values across different products. ● This requires data on purchase value to be passed to Meta. MAKE DATA-DRIVEN RECOMMENDATIONS ● Control how much can be bid in auctions. ● Break even on your ad spend and reach a certain return. ● Spend may be slower than when using highest volume bidding and budget may not be entirely spent. ● If Ads Manager can’t reach your ROAS floor, then delivery may stop and your budget will not be spent in full. ● Costs may be higher during the learning phase, but delivery should stabilize after the learning phase. ● Requires Meta Pixel or Facebook SDK to pass back purchase values. ● A bid control that’s set too low might result in a campaign under delivering. ● Bid caps don’t control the cost per action you see in reporting and require more frequent bid changes. 57 Advertiser controls Adjustments can be made on the following dimensions to help improve your ad performance: Campaign objectives Budget Placement Audience Bid Creative Make recommendations for future campaigns based on insights Once you have assembled and contextualized your data, insights and research, compose a holistic story, including recommendations for future marketing efforts. Make your recommendations using insights that are anchored to the levers you can pull to maximize performance. For example, given a set of insights, determine the optimal media buy adjustments by considering: Short-term impact Long-term impact If the goal is to decrease cost per action, increase ROAS or both If the goal is to increase brand awareness, brand equity or both MAKE DATA-DRIVEN RECOMMENDATIONS Inter-channel allocation Intra-channel allocation How you allocate media budget between different marketing channels, such as Meta, Search and Video How you allocate budget against different marketing tactics within one channel, such as audiences, creative and optimization 58 Cross-channel and single-channel recommendations What it can do Available solutions Cross-channel Single-channel Enables advertisers to use the same KPI to measure performance across distinct marketing channels Enables advertisers to measure different placements and strategies within Meta products and technologies ● ● ● Cross-Publisher Conversion Lift Attribution Cross-Channel Brand Lift Can measure performance between channels. Consider which metrics you use to compare performance between these channels, such as cost per action, return on ad spend, brand metrics and reach. Can test different variables including: ● ● ● Conversion Lift Brand Lift A/B testing Form a powerful story. To give effective recommendations that guide future marketing decisions, identify what stakeholders know, feel and do to form an impactful story. Tailor the recommendations provided based on the role of each stakeholder who could take action based on the insights from the test. To create a compelling story with recommendations, ask yourself: ● ● ● What should the stakeholders know? Include a range of insights and facts from your research. What should they feel? Be as audience-specific as possible, and speak to your audience’s unique position. Leverage visuals to inspire emotions. What should they do? Be clear about what actions you want your stakeholders to take. MAKE DATA-DRIVEN RECOMMENDATIONS 59 Identify iterative measurement opportunities based on insights. It’s important to have a test-and-learn mind-set. Testing is a science and an art. After adopting your findings, identify new opportunities to test and learn from: Retest your hypothesis. Repeat the same test or a similar one. Use your conclusions to inform a new test for a new hypothesis. Determine potential new variables for iterative testing. Evaluate new approaches to test the same variable using either an A/B test or a lift methodology. Example Historically, an advertiser has only allocated budget targeting previous customers and has seen this audience lead to a 10x ROAS based on their last touch model. However, they have a hypothesis that many of these customers are already loyal and would have converted without seeing any ads. They decide to use a different method, lift, to measure the same variable, audience, and determine if this strategy is leading to a significant lift in sales. Identify opportunities beyond the scope of the measurement approach. Given a situation, recommend new approaches for future analyses. You can: Address data blind spots. Isolate which variables and strategies are most effective at achieving outcomes. Develop a strategy to measure cross-channel performance. Consider depth, breadth and scope. Example An advertiser runs a multi-cell test to determine if allocating budget against a Lookalike audience and a remarketing audience can lead to a higher lift than just targeting a remarketing audience. The lift test estimates that the Lookalike and remarketing audiences drove a significantly higher lift compared to solely targeting the remarketing audience. The insight is that allocating budget against an acquisition audience can increase incremental outcomes. The new opportunity is to allocate budget against an ever more broad, prospective audience and re-run the measurement test to determine if a broader audience can result in even more incremental outcomes. MAKE DATA-DRIVEN RECOMMENDATIONS 60