How to properly use online data

The biggest advantage of online marketing is that data can be collected, analysed and used for optimisation measures. The goal is to address the right target group at the right time in order to increase sales and leads. Many companies do not yet know, or have hardly any competencies within the company, how the data can be analysed and used to increase performance. The demand for data scientists, digital & web Analysts, etc. continues to increase as companies realize that they do not have a competitive advantage by just collecting data. It is the basis for analysis but not the goal.

However, before data can be analysed and used for recommendations for action, firstly the data collection (tracking) must be clean and complete, and secondly a certain amount of data must have been collected before meaningful analyses can be performed.

Due to legal regulations, classic cookie-based tracking is endangered and analysis tools such as Google Analytics are already partially banned. However, there are already a large number of tools that are GDPR-compliant and thus have a higher data quality than, for example, Google Analytics. More information on privacy-compliant tracking and tracking tools can be found here.

In the following article, we will show you selected options for analysing data in a meaningful and targeted manner. We have limited ourselves to data from the following areas:

The first part of our series will cover online data usage from search engine advertising. The other parts, which will be published gradually, will then deal with the use of online data from other areas.

Analysing online data from SEA

Through search engine advertising, advertisers collect a large amount of data, which can be viewed in the respective tools, e.g. Google Ads. As already mentioned above, the collection of data alone is not helpful, because the data must also be analyzed in order to get the best out of the ads. The classic data about Google Ads is collected, such as impressions, clicks, conversions, costs, etc., as well as the ratios from the data, such as click-through rate, conversion rate, cost per conversion, cost per click, etc…. However, what is interesting is on which dimensions these metrics are collected. The dimensions are:

Based on this plethora of data, different analysis can be conducted. You can increase conversion by answering the following questions:

Cost benefit analysis

A cost-benefit analysis provides information about the relationship between costs and sales. For this purpose, it is always advisable to also look at the industry values, since the ratio varies depending on the industry and products or services. The benchmark then serves as an orientation for your performance. In the next step, the SEA turnover can be considered in comparison to the total online turnover. In other words, how much revenue does the website generate via search engine advertising compared to the other channels. It must also be taken into consideration to what extent the website is SEO optimised. If the site does not have good organic visibility, the traffic or revenue share via SEA will most likely be higher.

To analyse the quality of the traffic, for example, the traffic share and the sales share can be compared. If the traffic share via SEA is 65% and the sales share is 10%, this is an indication that the “wrong” traffic is coming to the website via the ads.

In the next step, the turnover can be put into relation with the costs. The cost-turnover ratio (CTR) describes the relationship between the (ads) turnover and the ads costs. To do this, divide the costs by the sales and multiply by 100 (CUR = costs/sales * 100). The smaller the CUR, the better the performance of the channel, campaign or ad group. There are different levels that should be considered here:

It is a good idea to analyse from big to small, i.e. first look at the Ad Cur. If it is relatively high and not yet at or below the industry average, the individual campaign CURs can be calculated and the non-profitable campaigns identified. Once these are identified, the AG CUR can be used to analyze whether the entire campaign is not performing or just one or more ad groups. (The basis for this is a clean and sensible campaign structure!).

It is imperative to consider that SEA is only one channel and may not be the last point of contact (in the case of the “last click” attribution model) before the conversion. The assisted conversions can be displayed and analysed with common tracking tools. This means that if SEA is relevant as a second touchpoint, a budget reduction can also have an impact on the overall online performance, even if it seems at first glance that the channel is less relevant.

Possible optimisation measures:

Analyse the quality of your SEA traffic

The KPIs session duration, pages/session and bounce rate can provide information about the traffic quality. Industry averages and the comparison of traffic via other channels such as “direct” or “organic” can be used for interpretation. Since traffic via SEO or direct is not paid, but behaves naturally, these are good benchmarks. If the bounce rate via “organic” is 35% and with SEA 65%, it indicates poor traffic quality. In the best case, the traffic quality via ads is the highest. This can be achieved through targeted optimisation measures.

After the entire SEA performance has been analysed and classified, the next step is to look at the performance of the individual campaigns. This makes it possible to assess whether the SEA performance as a whole is still unsatisfactory or whether individual campaigns are responsible for this. Once individual campaigns have been identified, the next step is to look at the ad groups.

Possible optimization measures:

SEA potential analysis

An SEA potential analysis looks at and identifies the potential of the targeting options and the campaign/ad group set. Such an analysis makes sense, for example, if the CUR or SEA traffic analysis and the optimisation measures implemented as a result have not brought the desired performance uplift.

An SEA potential analysis checks, which cities, regions and countries achieved the highest CTR, traffic quality, direct and indirect conversion and conversion rate. Example: a campaign is targeted at the DACH region. The KPIs are above average for Germany, average for Austria and below average for Switzerland, either Switzerland can be excluded as a country or the budget for Switzerland can be reduced via bid adjustment

The same approach is suitable for the advertising times (days & time) and devices. This way, the budget can be used in a more targeted manner and performance can be increased.

Another way to identify the potential of the ads account is to look at the demand of the users and compare whether campaigns are already being run for the product (categories). The Google Keyword Planner can be used for this purpose. It helps with the demand and competition analysis of individual keywords. In addition, the average CPC of the keywords is displayed here, which in turn helps to estimate the costs incurred by adding new campaigns. There are also other tools, such as Sistrix, which give a very good indication of demand. However, especially in the B2B area, it is difficult to see which demand comes from B2C and which from B2B users. Therefore, we have developed the PD Analytics Tool, our in-house software, which helps marketers in B2B companies to easily analyse the demand from B2B platforms such as PartsCommunity or Traceparts and use it for potential analysis.

Possible optimization measures:

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