Recommend Measurement Solutions PDF

Recommend measurement solutions Introduction Meta measurement tools unlock the potential to optimize your ads, understand your audience and grow your business. Measurement solutions like Brand Lift and Conversion Lift tests can help your business find answers to many of the questions you have abou...

Recommend measurement solutions Introduction Meta measurement tools unlock the potential to optimize your ads, understand your audience and grow your business. Measurement solutions like Brand Lift and Conversion Lift tests can help your business find answers to many of the questions you have about ads with the help of rigorous scientific testing. Using randomized control groups, you’ll be able to see how much your Meta ads lead to conversions, which campaign causes the lowest-cost conversions and much more. Design a test. Meta offers a variety of solutions to test your hypothesis. You’re ready to design a test once you have the following: ● ● ● ● A business goal A primary KPI A strong hypothesis A variable to test RECOMMEND MEASUREMENT SOLUTIONS 21 A/B test Use this type of test to test different treatments of one of the following variables: Ad creative Delivery strategy Placement Product sets Target audience After you choose the variable you want to test, we’ll divide your budget to equally and randomly split exposure between two versions. An A/B test can then measure the performance of each strategy on a cost per result basis or cost per conversion lift basis with a holdout. When to use an A/B test To determine best practices To perform day-to-day tactical decision-making To see results based on last ad attribution When you are sure that baseline levels between A and B groups are similar When looking for a test that is easy and quick to set up Example A marketing strategist at Wind & Wool, a fashion retailer, wants to test the impact of a “Learn More” call-to-action button, as compared to a “Shop Now” button. Both buttons direct the audience to the promotions page on their website. With an A/B test, Wind & Wool learns which text is more effective and uses that knowledge to refine their future campaign strategies. Ad A Best performance Group A Group B Ad B RECOMMEND MEASUREMENT SOLUTIONS 22 Lift test Effective measurement starts by understanding incremental business outcomes, such as brand equity and conversions, that your ads can affect. When to use a Lift test To measure incremental outcomes by comparing actions of people who have seen your ad with people who haven’t To see how Meta ads affects the outcome of your ads To learn about cost per lift point, which allows advertisers to optimize their spend in the most efficient way possible Lift tests with statistically significant results can infer causality and accurately measure incrementality, unlike proxy metrics, like clicks and likes, which are indirect approximate measurements that may not be correlated with actual business value and can result in suboptimal business decisions. Test group Exposed to variable Hypothesis Compare conversions between the two groups to determine the true incremental value of the strategy. Control group Not exposed to variable RECOMMEND MEASUREMENT SOLUTIONS 23 Example of a Conversion Lift test An analyst at an ecommerce business develops a test hypothesis that states: Using automatic placements increases incremental sales for business as compared with Facebook placements alone. They run a Conversion Lift test to compare sales from Facebook with sales from automatic placements. Results show with 99.9% confidence that the use of automatic placements resulted in additional conversions, compared with Facebook alone. Example of a Brand Lift test Radiance, an online jewelry business, wants people to get to know their brand and think of them for special occasions. Because they are a new business, it is critical for them to attract and dazzle their prospective customer base, which is identified as men and women currently in relationships. Radiance’s goal is to see a 20-point lift in ad recall by mid-February, after Valentine’s Day. They run a Brand Lift test to quantify the value of their advertising on ad recall. RECOMMEND MEASUREMENT SOLUTIONS 24 Design a Lift test. There are two ways to design your lift test: Single-cell test Multi-cell test This option is best used to get a baseline understanding of incremental brand or conversion outcomes your campaign is currently driving. Compare two competing strategies to understand which leads to greater incrementality. In general, keep Lift tests as simple as possible. The more cells you add, the more complex the test becomes. You can also perform multiple tests. For example, you can use an A/B test to choose between two creatives, and in addition, a Conversion Lift test to understand the incremental conversions that result from this new creative strategy. Single-cell Was your campaign a success? Multi-cell How can you optimize? Target B Target A Ads on Facebook No ads on Facebook vs. Broad vs. narrow targeting vs. Creative 2 Creative 1 Influencer vs. brand Feed vs. Stories vs. Freq. A Freq. B vs. RECOMMEND MEASUREMENT SOLUTIONS Freq. C vs. Was the buy frequency sufficient to deliver creative impact? 25 Measurement solutions You can choose from the following solutions to measure your ad performance. Attribution Strengths ● ● ● ● Limitations ● ● ● Outputs ● ● ● Example Measure the performance of your ads across channels (paid vs. organic), publishers and devices. Learn about your consumer’s journey to purchase. Choose from two types of models: rule-based and statistical-based. Can be a useful complement to lift tests. Since there are a variety of attribution models, it will take time and experimentation to find the one that best fits your business. The length of the attribution window may limit results. Results may not reflect performance from all marketing efforts. Metrics for variables related to the media channels used in marketing, such as paid, organic and direct Metrics for variables that relate to the desired action, such as visits, conversions and sources Return on ad spend (ROAS) An advertiser who uses a mix of prospecting and retargeting campaigns can track their consumer journey and attribute incremental value to all of their media touchpoints. This allows them to optimize budgets across publishers and tactics. RECOMMEND MEASUREMENT SOLUTIONS 26 Brand Lift Strengths ● ● ● ● Limitations Outputs Example Measure the incremental impact your ad has on people’s perception of your brand. Can be single-cell or multi-cell. See how your campaign performs against norms for campaigns in your industry and region. See lift by demographic breakdown (for example, age, gender). Can be performed with third-party measurement partners. ● You need to get at least 250 responses to one poll question in order for Meta to show results. A holdout is required for measurement. ● ● ● ● ● Poll results Brand lift percent for all responses Brand lift percent by demographic Cost per Brand Lift Test details: a summary of your test setup Confidence levels ● An advertiser uses Brand Lift to understand which tactics result in the greatest awareness of its new line extension. For managed Brand Lift studies, work with an account representative to set up the tests. RECOMMEND MEASUREMENT SOLUTIONS 27 Conversion Lift Strengths ● ● ● Measure the incremental impact your ad has on people’s perception of your brand. Use intent to treat (ITT) to manage the effect of potential error in the test results and more accurately ensure comparable audiences. Can be performed with third-party measurement partners. Limitations ● ● A holdout is required for measurement. Must ensure that the test has at least 80% statistical power in order to allow for statistically significant outcomes. Outputs ● ● ● ● ● ● ● Conversion lift Sales lift Cost per conversion lift ROAS lift Conversion lift percent Breakdowns by demographic and attribution window Confidence levels Example An advertiser uses Conversion Lift to understand which of its targeting audiences generates the greatest incremental ROAS. RECOMMEND MEASUREMENT SOLUTIONS 28 Marketing mix models (MMM) Strengths ● ● ● Limitations ● ● ● ● ● Outputs ● ● ● Example Quantify the impact of a large set of variables on sales. Understand what influenced past sales and predict what may happen as a result of future marketing. Understand how your marketing activity impacts sales. Requires high-quality data. Requires collaboration between modelers and an econometric model. Doesn’t help with in-channel optimization. Doesn’t infer causality, only correlation. Can take up to six months to fully implement. Marginal return associated with each marketing channel A report that details how much influence each of your marketing activities had on sales An overview of how your spending in different channels contributed to success An advertiser wants to cut its marketing budget by 10% and uses MMM to decide where to direct the cuts. RECOMMEND MEASUREMENT SOLUTIONS 29 A/B test Strengths ● ● ● ● Limitations ● ● Outputs Example ● ● ● Assess the correlation between different versions of your ads. Create multiple ad sets and test them against each other to see which tactical approach produces the best results. Understand which specific images, videos, placement, text and/or call to action performs best. Know which combination of variables (creative, audience, delivery optimization, product sets or placement) performs better at meeting your business goal. Understand the best allocation between full funnel stages. While split testing creates random, non-overlapping groups, it does not create a corresponding control group, like Lift studies do. As a result, A/B tests do not show incremental impact. If your split groups have different baseline levels, then results will be difficult to interpret, because differences in metrics are to be expected. Winning ad set Cost per result of each ad set Confidence level An advertiser hypothesizes that their ads get more engagement with Instagram Stories as compared to Instagram News Feed. They use an A/B test to understand which placement is more effective for their ads. RECOMMEND MEASUREMENT SOLUTIONS 30 Determine test feasibility. Now that you’ve chosen an approach, there are several factors to consider when assessing the likelihood of success of your chosen measurement solution, including: Potential reach A larger holdout increases statistical power because it increases the size of the control group that you are comparing against. Look for a big difference between the test group and the control group. It’s harder to detect lift with a small control group. If you’d like to increase the statistical power, you can either increase the reach or the control group. Both actions will give you more chances to detect lift. Conversely, with smaller reach, you’ll have a smaller holdout and less statistical power. Budget A higher budget can make for a more powerful test. Budget affects the media pressure to actually cause an effect, also called media weight. Although budget doesn’t technically affect statistical power, it does affect lift. If you spend more money, you’ll be able to expect a larger effect. If your test has a larger effect, it’s easier to detect lift. Because statistical power is the ability to detect lift, a greater budget will lead to greater statistical power. Data coverage and availability Does the platform have the ability to tie orders from all channels, such as an app, a website or offline, to impressions? Time constraints Consider whether your test duration aligns with best practices. For example, the ideal time frame for an A/B test is at least three days but no longer than 30 days. Technological constraints For example, does the platform in question have the capability to run a Randomized control trial (RCT) test, such as a lift tool? Maximize measurement validity Ensuring that your test is setup for success is a crucial step in any lift test. You may need to adjust the test and campaign parameters to maximize measurement validity. For example: ● Perform power calculations: This can maximize your chances of detecting the effect. Statistical power is a vital indicator of whether there will be enough data to report reliable results. ● Perform a preliminary analysis: Build a rudimentary MMM model with key variables. RECOMMEND MEASUREMENT SOLUTIONS 31