Conversion optimization is one of the biggest challenges facing online shop operators. It’s so much easier to organize and acquire visits to your shop than it is to get those visitors to also order in the shop.
And each additional visit with an average or poor conversion rate is still a bad visit, because a low conversion rate drives marketing costs in ways that are no longer economical. A simple consideration of the important online marketing indicators, i.e. cost-per-order (CPO), the click or contact costs (CPC), and the conversion rate (CR) show this:
If we throw in some examples with three moderate values for the CPC, the click or contact price shows very quickly that a low conversion rate in online marketing leads to costs per order that exceed the usual economic framework of consumer goods in e-commerce.
So, what could be more obvious than to place conversion optimization in the focus of shop optimization? However, current market data on conversion rates shows that many online retailers struggle with optimizing the conversion rate such that online success is ensured with a greater probability than just a few percentage points.
Low conversion rates have several causes beyond just a struggle with low online sales. There are three main causes for this problem:
The first two are cardinal errors in online marketing and e-commerce. Visits for an online shop should be acquired as specifically as possible to reduce scatter losses. And in order to improve conversions, you must measure in advance and must not make any errors. A mistake here results in waiving the settlement of conversions regarding returns that must be deducted from the conversion rate.
While both cardinal errors can be solved comparatively easily through focusing and professionalization in online marketing and in shop analysis, the third cause for low conversions is a trickier subject. If errors in checkout and in the landing pages are the cause for low conversions, this is usually caused by a lack of understanding of the complex customer journey by the shop operator.
Thus, each e-commerce company finds itself with a conflict: on the one hand, the shop design should show individuality and be unique, but on the other, users have come to expect certain standards that make conversions easier.
Faced with this dilemma, the shop designer and operator can only resort to A/B tests of conversions for different shop designs. But A/B tests are very expensive and not always conclusive. Are there other ways that user behavior can be better optimized under conversion optimization?
These do exist and are introduced by psychological behavior models that describe universal patterns of behavior. These patterns of behavior among shop users facilitate optimization. I will now present two of these behavior models, because I have come to see them as being extremely helpful for improving the understanding of conversions.
The first model comes from André Morys, who, with the Frankfurt agency WebArts, numbers among the leading conversion experts in the German-speaking world. With his 7-stage model for conversion, he presents a better understanding of the psychological reasons behind conversions. The second model is more general and comes from the US-American entrepreneur, Nir Eyal. In his book “Hooked – How to Build Habit-Forming Products,” he shows how triggers and rewards can be used to generate routines that strengthen customer connections on digital platforms.
Both models complement each other. While the 7-stage model from Morys describes the micro-level of conversions, Eyal contextualizes conversions and the accompanying behavior in the design of business models and platforms. He thus describes the macro-level of the conversion. He also clarifies how you can begin with this model.
Eyal’s model starts with behavioral triggers that lead to actions. Triggers are stimuli that arise externally or internally and that lead to actions. External triggers are found in advertising, while internal triggers lie in the user’s emotions. Typical triggers are often negative emotions such as boredom, loneliness, frustration, or fear. To relieve these emotions, more and more people are turning to smartphones to find conversation, social context, confirmation, or information. Thus, your trade will be rewarded for using smartphones and suitable apps.
In the “Hooked” model, a reward follows the triggers and the action they generate. It is important that the reward is variable and not fixed; this is because of how our brain processes rewards. We are especially satisfied when we have to search for and find rewards. In an online shop, this satisfaction comes from browsing, scrolling, and comparing – “digital bargain hunting.”
When online users receive rewards because of their actions, motivation to invest in a platform and a long-term connection becomes the last step in the Hooked model. This investment comes from the reward phase: in exchange for the reward, users are motivated to take a next step with long-term effects. Users therefore expect that future rewards will turn up more frequently if they participate in the expansion of a platform. In effect, the investment of the user in the platform is controlled by the reciprocity effect: when we receive something without having to give something for it, we have to balance this disparity. We will reciprocate.
The investment in the connection to the online shop can be very valuable. A user who has been previously rewarded can subscribe to a newsletter, invest in a customer account, participate in a customer loyalty program, or write reviews. For conversion optimization in an online shop, this means including the users’ internal triggers and offering them retail possibilities. Examples for these strategies are social media platforms such as Twitter, Facebook, or Pinterest. Newsfeeds and image galleries that consistently deliver new items or social confirmations can offer rewards that will help bind the user to the online shop. A stronger connection to the online shop greatly increases the chance of a conversion. Amazon Prime, as one of the most successful customer loyalty programs, demonstrates this. Evaluations of the conversion rate with Amazon Prime lie beyond the astronomical value of 50%.
While the Hooked model offers a foundation for creating customer loyalty and thus has a strategic character, the second model presented here provides operative access to conversions. André Morys’ model of the “7 stages of conversion” is based on many years of experience with user tests and conversion optimization. In his established behavior model, he establishes the motivation that emanates from the phases of behavior. From this and from his own experience, Morys has derived the 7 stages of conversion. These are:
With the help of these 7 stages, or factors, that influence the decision and the behavior of online shoppers, each online shop can optimize itself with the goal of additional conversions. This optimization begins with relevance.
The stage of relevance is best explained with the quality factor of Google Adwords. This describes a congruence between the intentions of Google users, the statement of Adwords, as well as the contents of a landing page. This ingenious principle to ensure relevant advertising can also be transferred to other online marketing channels. Thus, keywords, meta descriptions, and landing page contents in the shop must be unified.
The stage of trust describes a “conditio sine qua non” in e-commerce. Online shoppers have learned to browse a shop according to signals of trust. If these are missing, mistrust will lead to immediate cessation.
In addition to trust, sufficient orientation is a significant factor. The smaller the screen, the clearer and simpler the click path must be. If there are breaks and gaps in usability, these can lead to a termination, even with improper orientation.
Online shoppers often find themselves engaged in a complex decision-making process. After all, they must compare a lot of online shops and an even greater number of offers. Seen in this light, it is clear that on the level of stimulation, the shop must differentiate itself according to why its own offering is optimal.
Finally, online shoppers also expect comfort; the online purchase must be as simple as possible. Entering the state in the address field? Illegible captchas in search inquiries? These and other “conversion killers” should themselves be “killed” during conversion optimization.
In 2017, the encryption of websites and online shops is becoming even more standard, thus belonging to the expected security of the purchase. In addition to trust in the performance of the online retailer with regards to product and delivery quality, the checkout and the transfer of address and payment data must be seen as secure. You should not forget that online shoppers know a lot of large, or even super shops, in which these factors have been around for a long time.
Even the end of the conversion path can support successful purchasing. This is especially important when the return quota is high. A compelling “thank-you site,” loaded immediately after clicking on the “purchase button” and perfect and clear communication during processing of the order are important instruments of “evaluation” in the 7-stage model of André Morys.
This model can be used as a guideline for ongoing conversion optimization. Thus, in a workflow of Goal – Analysis – Optimization, you should check again and again if changes in the user behavior and technology also require a change in the conversion path. Furthermore, the 7 stages present a framework for observing your competition. Large online shops set standards with which online users are familiar. These standards can be summarized, implemented, and developed with the help of the 7-stage model.
The Hooked model from Nir Eyal and the 7-stage model of conversion of André Morys can be used as complementary models for operative and strategic conversion optimization. While “Hooked” is a model that focuses on the connection to a shopping platform, the 7 stages deliver process-oriented notes for the operative implementation of a conversion. Both models may be very helpful in the context of the “mobile revolution,” which is already leading to great challenges and sinking conversion rates in e-commerce.
Published on 04/25/2017 by Dominik Große Holtforth.
Who writes here
Prof. Dr. Dominik Große Holtforth teaches business studies and media management at Fresenius University of Applied Sciences in Cologne. He is also head of the e-Commerce department which deals with strategy-related questions, the controlling of key performance indicators as well as competition strategies in online marketing and e-Commerce. Prof. Große Holtforth is co-founder of the e-Commerce agency Warenkorb.com and founder of the online plant shop “Meine Orangerie.” This is how he combines scientific expertise and practical experience.Become a guest author »
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