However, an error of interpretation can quickly occur, and its consequences can have severe implications for future decisions. To avoid this pitfall, the JVWEB team provides you with the seven commonly observed errors.
Limit Data Review To A Short Time
Attached to figures and statistics, marketers are sometimes in extremes: either they consult their data on a day-to-day basis or they analyze it over the month. Best practice indeed lies somewhere in between. Whatever the performance analysis period chosen, it is essential to give yourself time to step back.
Let’s say you have a monthly goal of 100 leads. It is doubtful that you will generate a strictly equivalent number of tips daily. The first week, you may collect 10, then 30, the next, then 20, and finally 40, the last week. If you readjust your actions based on the (wrong) results of week 1, your analysis is biased; likewise, if you rely on the last phase.
Your objective: proceed in two stages by monitoring daily what could cause a problem or even an opportunity. But also take the time for a broader analysis. On the month, but also by comparing it with the equivalent month of the previous year. Treat yourself to a more panoramic view, and your perspectives will be sharper!
Do Not Include The Effects Of Seasonality
Continuing from the previous point, keep in mind the weight of seasonality, which, depending on the market, can weight relatively heavily on overall results. In addition to the inevitable Christmas or sales periods, certain activities can experience peaks and troughs during, for example, vacation times. However, this is only sometimes an indication that we must change course.
For a relevant performance analysis, compile an annual performance history to identify sales peaks and quieter periods. You can then adjust your bids accordingly. This data is easily recoverable by combining your Google Analytics data, Facebook Analytics data and your internal sales data. The same analysis can be carried out on the evolution of the average basket and the flagship products.
Underestimating The Impact Of Offline Activity
The year 2020 gave us a most convincing demonstration that an event outside your ecosystem can have a substantial impact. Whether it’s news from your competitors, unusual weather or negative press coverage around your brand, it can be challenging to anticipate everything.
However, you must analyze it to measure its impact and adjust your model correctly. Likewise, stay alert and seize opportunities as they fly. Imagine your competitor launches a TV campaign on their product. It will benefit from solid visibility, bringing a new audience to your common subject.
It is a safe bet that this new target will be documented and compared and, therefore, that a significant part of the traffic will come back to you. However, for this, you need to identify the information, know how to react and adapt your investments accordingly. Conversely, if you notice abnormal activity on your account, an offline event may be the cause; do not neglect this source in your interpretation.
Minimize The Burden Of Multichannel Engagement
It is widespread to see marketers analyzing their performance lever by lever. As soon as they notice a discrepancy in one of them, it focuses all their attention. A lack of analysis is often accentuated by the last-click attribution model shared by several advertising platforms. However, today, few brands communicate in silos via a single channel. It’s as rare as users going directly from initial query to conversion!
This is why it is essential to measure the weight of multichannel engagement in your performance. To do this, carefully look at assisted conversions and complete your analysis by analyzing the conversion paths Google Analytics offers. Finally, corroborate your intuitions by comparing, via the Attribution section of Google Ads, the different attribution models. The key is a finer funnel breakdown and a better distribution of investments.
Aligning The Numbers Without Drawing Conclusions
You’ve struggled, but finally, you have your perfect reporting. First, ensure you compile the right KPIs, i.e. those that have a real impact. It is essential to distinguish between KPIs that influence the “technical” management of campaigns and those with direct business consequences. Our advice on this subject: adapt your reporting to your interlocutor. It is, in fact, very likely that the CEO is more interested in the evolution of the volume of conversions than in the bounce rate on the page, which has gained two positions in SEO.
With your small panel of KPIs, ensure they have something to say. A tip for this is to make sure you put a legend on each analysis table. Without going into litanies, provide some context and express the conclusion to be drawn from your analysis. This will help you better understand the data processed and make it easier for recipients to read the report.
Working On Bad Data
The recommendation may seem trivial. However, a poorly placed pixel or an incorrectly configured tag quickly arrives… The result is biased results, incorrect KPIs and, therefore, an analysis that is based on poor foundations. The correct option: set up a control routine with the people likely to interact on these different variables to ensure that everything is correctly configured, including outside of Google Analytics. Indeed, a URL is modified without being informed, or a piece of code is deleted, which disappears with a pixel, and your entire analysis is called into question.
Leave Out Backend Data
No matter how brilliant your performance analysis reporting is, you must take the data out of your Excel and compare it to the company’s reality. Especially when the purchasing cycle is long and very segmented, do not limit yourself to tracking conversions on a focused objective, such as lead generation. Go further and find out what happens to these leads. How did they convert once in the hands of your sales teams?
Quality UTM tagging and attribution allow you to measure the different stages of the sales process effectively. Well configured, they help you understand what revenue is generated by a campaign, via which ad, and which keyword. At the same time, compare this data with the results collected via your backend tools, starting with the CRM. You will thus be able to validate the integrity of the data, measure potential discrepancies and isolate new sales patterns.