Generate Insights PDF

Generate insights Introduction Data-driven insights can inform the next steps in your marketing strategy. Through your analysis, you may have learned which marketing actions, campaigns and campaign strategies had a positive effect. You can use these insights to make media planning and buying decis...

Generate insights Introduction Data-driven insights can inform the next steps in your marketing strategy. Through your analysis, you may have learned which marketing actions, campaigns and campaign strategies had a positive effect. You can use these insights to make media planning and buying decisions or decide on your future campaign strategies. Synthesize results from statistical and data analyses. After completing an experiment and analyzing marketing performance across multiple tools, it’s time to draw insights to develop media recommendations. Contextualize the results by taking the following actions: Combine campaign insights with research. Leverage all insights and research available to inform your marketing recommendations. GENERATE INSIGHTS Identify a common thread across studies, platforms and/or channels. For example, an advertiser ran a series of Conversion Lift tests against different strategies. Results showed that including video creative in addition to static creative drove incremental results across all strategies. Because of this, the advertiser considers prioritizing the development of video assets when new creative is developed. 48 Generate insights. Insights can be captured from the following sources. From clients First-party data Insights derived from a client’s efforts to learn and gather information, including campaign data in Ads Manager or results from a lift test or actual sales data and qualitative customer data. For example, an analytics team identified customers with the highest lifetime value. Testing the incrementality of a lookalike audience based on this segment showed a significant increase in incremental conversions compared with their current strategy in a multi-cell Conversion Lift test. From Meta Foresight A robust offering of studies designed to help leaders navigate cultural shifts, challenge convention and prepare for what’s next. Through meta-analysis, Meta Foresight-commissioned surveys and industry research, Meta Foresight is able to formulate three types of insights: ● People insights: trends among consumer groups across demographic interests and behaviors ● Advertising insights: information about behaviors in different digital spaces, such as messaging, video views. These insights are derived from an analysis of aggregated campaign and test results within a specific period of time, across advertisers. ● Industry insights: include vertical and market-level insights and support broader stories and pitches. From third-party resources There are a variety of free and paid online hubs where you can gather more insights and research to flesh out your story. Insights should be based on data and can incorporate a variety of dimensions, including but not limited to: Dimension Example Budget Doubling the campaign budget made ROAS less efficient. Inter-channel allocation A brand sees that overall incremental return on ad spend increases by 10% when running with a media allocation of 60% on channel A and 40% on channel B, vs. 50% on channel A and 50% on channel B. GENERATE INSIGHTS 49 Dimension Example Intra-channel allocation A brand sees that overall incremental return on ad spend increases by 10% when allocating 30% of their budget to upper-funnel campaigns, versus allocating 50% of their budget to upper-funnel campaigns. Reach Reach across both TV and Meta technologies has more impact on in-store purchases compared to reach on each platform alone. Bid strategy Utilizing cost cap drove business outcomes with the most efficient returns compared to target cost. Buying strategy ROAS was more efficient when using reach and frequency buying instead of auction buying. Audiences Targeting women ages 18-24 brought more value than targeting women ages 18-55. Note: ● Core audiences: uses information that people provide to Meta, such as interests. ● Custom audiences: uses information that the advertiser provides to Meta, such as activity from a pixel or a list of email addresses from a customer database. Placement Selling products is more effective on Instagram and Facebook combined, as compared to Facebook alone. Creative Using Creative A outperformed Creative B. Test duration Running a four-week campaign resulted in better outcomes than a two-week campaign. GENERATE INSIGHTS 50 Provide or disprove a hypothesis. Approach your research with your proven or disproven hypothesis in hand, and let that hypothesis focus your research. Interpret the significance of your test results using relevant metrics, such as: P-value The probability of obtaining test results at least as extreme as the results actually observed during the test, assuming that the null hypothesis is correct. ● Null hypothesis rejected when p < alpha ● Not rejected when p > alpha where alpha is determined by the analyst ● ● R2 ● Known as the coefficient of determination The proportion of the variance in the dependent variable that is predictable from independent variables Typically used when evaluating goodness of fit of multiple models to determine most accurate model Construct a narrative. Explain why the hypothesis was true or false by contextualizing the data, determining the caveats and acknowledging that the consumer path is complex. Stay clear on the how and why of the data, as well as the tools and research that yielded it. Evaluate the success of a measurement approach by determining whether it adequately measured your KPIs. There are issues that could come up that would prevent you from adequately measuring your KPIs, such as: ● ● ● ● Dilution during a test Not accounting for offline transactions during a Conversion Lift test Insufficient statistical power Contamination between the test and control cells of an experiment Adjust the measurement approach if the original plan failed to meet the intended measurement goals by considering either testing a new hypothesis or retesting a hypothesis. Retest a new hypothesis if the test results were contaminated, the test results were inconclusive or you want to validate the results. Example The p-value of a statistical test showed that there was insufficient evidence to reject the null hypothesis, but the analyst uncovered a data issue with the pixel that led to conversion volume being drastically lower than it should have been. During a re-test, the team needs to ensure that the proper data is flowing into the experiment so that the results are as accurate as possible. GENERATE INSIGHTS 51