By Markus Nagel, Head of Consulting, Webtrekk
Most recommended KPIs which are used by experts, marketers and analysts only present snapshots. They only look at a small section of available data and do not distinguish between different types of customers or their development stage in the user lifecycle.
However, the relevance of Conversion Rates and CPOs shouldn’t be underestimated. No business model can be successful without having a well-defined long-term strategy, and no reporting should exclude long-term effects. In the worst case, well-established KPIs point upwards – while actual sales numbers stagnate or even drop.
Webtrekk provides Cross-Device-Tracking and characteristics generated from the user’s history to enable long-term user developments to be analyzed in detail. These do not necessarily have to be based on comprehensive cohort analyses. Target-oriented KPIs can also be created. In the following blog post, I will explain seven of such KPIs, which can companies help break new ground in E-Commerce reporting.
1. Share of First Order Share
The proportion of initial orders already provide several insights into the long-term shop performance. It provides information on how strongly the number of orders and sales depend on new traffic, because first-time customers are usually relatively new on the website. If the proportion is very high, there is a high dependency on new traffic and little profit from loyalty effects such as lower marketing costs. However, the share should not become too low or fall too much – churn can only be compensated to a limited extent by new customers.
2. Customer Conversion Rate
The conversion of a new visitor into a new customer is a key moment. It shows a strong commitment and enables marketers to start more effective and potentially cheaper marketing activities.
The analysis of this conversion is hence quite crucial. If the customer conversion rate is very low, any investment in new visitors will have little effect. However, if the conversion rate is high, it is recommended to invest into targeting new visitors in order to see results rather quickly. The optimization goal should be to increase the customer conversion rate.
3. Returning Rate
The returning rate describes a component of the customer conversion. It shows the proportion of new visitors who come back to the website for another visit – a much more meaningful metric than the usual new vs. regular visitor comparison. With a low return rate, investment in new visitors is worthless. All optimization measures should then first focus on increasing the return rate. If this KPI ins reviewed in relation to the customer conversion rate, it becomes even more interesting. If you find a high return rate and low customer conversion cate, we recommend an in-depth examination of visitor engagement and UX.
4. Repurchase Rate
The repurchase rate is the economic equivalent of the returning rate mentioned above. The conversion into an existing customer is the second most important moment in a customer journey. Depending on the product and business model, the cost break-even is only achieved here – in some cases even later. At this point, reference should be made to the two well-known KPIs customer lifetime value and customer acquisition costs, which also provide good information on this. Consequently, the KPI can also be adjusted if, for example, the third order is of higher relevancy than the second.
5. Engagement Increase
In addition to the mere return of a new visitor, it is also essential to observe all changes in user behavior. An increased commitment is to be evaluated positively, since the user interact more intensively with the website and conversion probability is likely to rise. This metric therefore describes the most positive percentage change possible. If, on the other hand, the commitment drops, the churn risk increases. The new visitor did return but may not turn into a loyal long-term customer.
6. Order Value Increase
The development of the order value is much more relevant than engagement. Here, too, an increase in the average order value from the first to the second order is of course the ideal situation. However, the evaluation of this metric also depends strongly on the product and business model. For high-priced products, a decline in the order value is a more likely scenario than for consumer goods such as clothing. A sharp decline in the order value, on the other hand, always indicates that users are more likely to take advantage of upselling opportunities after the initial purchase and that a second order does not yet indicate long-term loyalty.
7. Returning Customer Visits Average
This metric serves primarily as additional information for evaluating the repurchase rate. Further orders can only take place if the new customers return to the site after their first purchase. Do new customers return with further product interest or do they never visit the website again after their first purchase? If the number of returning customers is very low, then orders are mainly spontaneous purchases without much potential for long-term loyalty. If the business model is originally aiming for long-term loyalty, this should be the first starting point for optimization. If, on the other hand, the number is high and the repurchase rate low, initially interested customers are not able to find any further articles sparking their purchasing desires.