The answer is simple, more purchases result in higher turnover and that usually means more profit. However, not only the increased sales speak for shop optimization. Very often, this also results in increased customer satisfaction, as the usability of the shop usually provides a great lever for improvement.

Several actions can be taken to increase sales. Among the most frequently mentioned are SEA (Search Engine Advertising), SEO (Search Engine Optimization), and CRO (Conversion Rate Optimization). While the first two measures are designed to attract more customers to the store or your offer, a good conversion rate ensures that visitors make the purchase and convert to customers.

There are several ways to generate more traffic on your website. These include SEA (Search Engine Advertisement) and SEO (Search Engine Optimization).

SEA will run ads on the search engine as everyone probably knows. The classic example is a Google ad. Most often, the first and last results on a search results page are filled with ads. The price of individual search words varies, depending on how much the respective word (keyword) is in demand. It is always worth doing a keyword analysis (eg. with Searchmetrics) to find out which terms and pairs of words in the context in question are even searched.

Another option is SEO. Here, the content of the shop is optimized so the page can be found directly through the search results of a search engine. You get a good rating if you provide valuable and relevant content for the user.

Some optimization can be done already by the shop owners themselves. For example, ensuring that product descriptions provide sufficient information or that the visual presentation is detailed enough. If the customer is well-informed, feels comfortable with the purchase, and the return rate drops. Furthermore, clear communication is important, so the user always knows what they are getting into. This includes, for example, clearly presenting the shipping costs.

To optimize your shop optimally, it is key to understand your customers/users.

On one hand, quantitative analysis data (through an analytics tool) helps to identify bounce rates and locate the weak areas of the website.

Once the biggest levers have been identified, qualitative data (such as usability tests) can be an additional source in order to find out why customers bounce or exit at a certain step and identify potential points for optimization. In addition, qualitative data helps to better understand the users and the context of use to address their needs more precisely.

We recommend using both data sources to achieve the best result and to obtain a holistic picture of your users.

Several tools on the market offer shop optimization. On one hand, your analytics implementation helps immensely to provide an overview of relevant Key Performance Indicators (KPIs). On the other hand, there are tools (e.g. Hotjar or Crazyegg) that record user behavior in addition to the analysis data and visualize usage and interaction data as heatmaps. This makes it easier to identify which areas of the shop are relevant to the user and which may need to be reworked, are not recognizable, or are simply not relevant to the user. It should be noted that recording individual sessions can certainly be a big effort in the evaluation. Therefore, one should consider whether such tools are useful for a particular situation.

In order to sell well, it is important to show the product from the right point of view. In the buying process, people usually react emotionally and this can be influenced by presenting the products correctly. A precise product description paired with emotional images and the right tonality adapted to the target group plays a major role here. Another approach focuses on existing customers. For example, personalized emails with voucher codes are often used as triggers for customers who are in a long shopping break.

One step could be to analyze the success regarding the measures, taken from the analytics figures. If the conversion rate changes after an action, it can be assumed that this action is responsible (unless it is seasonal or other temporary influences) for the changes. With the help of goal-based tracking, it is easier to analyze the reasons for the improvements. An A/B test can be carried out even before the implementation of a measure to test in advance whether the action affects the user or usage.