Quais são algumas das métricas de publicidade de pesquisa e erros de relatórios mais comuns e como você pode evitá-los?

Clicks and impressions are the metrics that directly link to the advertising spend you invest within your advertising account. Therefore, it is important to find the right balance between quality and quantity and preferably focus on the context of the clicks and impressions by evaluating the search terms that trigger your search ads within the Search Engine Results Page (SERP). For example, you can get a lot of impressions and clicks by bidding on very broad keywords that align with your business for 1-2% of the cases the user has searched for them. This does mean that you have a high quantity of impressions and clicks on the one hand (if your messaging is aligned) but a low quality on the other hand.

This is something you want to consider based on your campaign goal. If your goal is just impressions and clicks because this is an awareness campaign, you want to optimize for maximum reach and minimize cost-per-click (CPC) to lower your spend. In this case, you don't need to worry that much about the conversion rate because your conversion is the visit/click. On the other hand, if this is a performance campaign that wants to convert a lead, action, sale, we need to check the conversion rate of the campaign to make sure we're not paying for clicks that are not converting. Segment by term and creative to find out those who are working and the ones that aren't and optimize based on that.

If your CTR is above average, then it's fair to say your ad is effective. However, if you're landing page metrics are poor, e.g. bounce rate, CVR, dwell time etc... it suggests a disconnect between the user expectation and reality. The challenge for digital marketers is to focus on an integrated approach. Ensure there is no disconnect and optimise the landing page content and experience not just your media accounts.

The simplest way to solve attribution without having to invest in data lakes, machine learning and data driven models (not that these aren’t great) is to set a macro commercial KPI to govern your marketing efforts. For example, a site wide ROI target. All off your cost vs all of your spend over a given week, month, quarter etc. With this in place you can then free your approach up to look at other sets of KPIs that might better match the expected behaviour. Top of funnel activity may be far better reviewed by optimising CTR and landing page engagements – Did they scroll the through whole page? Did they stay long enough to read the amount of content on offer? Did they watch the video? Etc

A common misconception is treating Quality Score as a direct performance indicator, whereas it's a diagnostic tool providing insights into ad relevance and landing page experience. It's crucial to interpret Quality Score in context, understanding it reflects comparative performance, not absolute quality. Similarly, Ad Rank should be viewed dynamically, as it varies with each auction based on multiple factors including bid amount and search context. To mitigate errors, focus on enhancing the components influencing these metrics, such as refining ad copy for relevance and improving landing page user experience, rather than solely aiming to increase these scores

The biggest mistake people make with Return on Ad Spend (ROAS) and Cost Per Acquisition (CPA) is focusing on them in isolation, without considering the broader business context. ➜ High ROAS but low profit margins: Your ads might generate revenue, but not enough to cover costs. ➜ CPA misalignment: A low CPA doesn't always mean profitability if the customer lifetime value is overlooked. ➜ Ignoring industry factors: Seasonality and industry norms can skew metrics. Always set realistic targets based on margins and business goals, and optimise accordingly!

Assuming the numbers are always accurate without investigating the root cause is a common issue. Regularly verify your data sources, align tracking methods, and investigate discrepancies to ensure reliable insights! ➜ Inconsistent tracking: Different tracking methods or settings across platforms can lead to mismatched data. ➜ Outdated reports: Processing delays or technical issues can result in incomplete or inaccurate data. ➜ Ignoring time zone or filter differences: These can skew performance metrics.

When working with multiple sources of truth, it's important to set expectations with your team and clients around the data being shared. One way to avoid major discrepancies is setting the same lookback window on conversions when using different platforms like Google Ads and Google Analytics. There may still be some variance given the different platforms but it will align the campaigns to relatively the same window of tracking. Additionally, checking the health of your conversion tags is important to catch any discrepancies early on. Keeping clients up-to-date on their data and any issues is important. Being transparent about platform limitations and the status of their tags helps inform and provide context on the data they are seeing.