People are individuals. Regarding online shopping, this is a fact that has not been widely internalised. Different cues or opportunities speak differently to a varying number of online shopper types. Still the customers are not personally but generally addressed. This represents one of the reasons behind the low conversion-rate of online shops. Through predictive analytics users can be determined as certain types. A recent study at trbo has proven that there are five different online shopper types: the price sensible, the comparing, the hesitant, the spontaneous and the faithful online shopper.
Each of these types distinguishes themselves through different characteristics. And each of them can be reached at a different stage of the shopping process with diverse shopping incentives in order to convert online shop visitors to buyers or convince them to buy more. To show this exemplary two of the types are compared in the following paragraph. The study has investigated what the potential revenue uplift is when influencing a certain type with shopping incentives
How do we reach them?
The spontaneous type is in the majority female and often found in online-shops for books, clothes or pet supplies. These users look at a high number of product detail pages during one session and like to surf and shop extensively during bad weather. They spend a lot of time online and are not “quick” buyers. Most of the shopping process is thus spent in the awareness phase, where this user type searches for ideas, his attention wandering, with no real buying intent taking shape. When a product or an offer catches the user’s eye he is quick to decide, therefore the spontaneous user does not spent a lot of time in the consideration, interest or purchase phase. Consequently the spontaneous needs to be addressed during the awareness phase, the best option is to supply him with ideas to induce shopping impulses. This goal can be reached with product onsite targeting resulting in potential revenue uplifts of up to 15%.
Accordingly the spontaneous wants a special product experience, for this user type a striking presentation of products, product recommendations and product videos are of importance.
By comparison the price sensible is in the majority male and can be found very often in online shops for consumer electronics, telecommunication or drugstores. The structure of product detail pages is not relevant to this user type. Having a clear buying intent, a certain product that has already been chosen is focused. So this user is not open to product suggestions and does not spend time in the awareness phase. The price sensible needs to be addressed during the intent-to-purchase phase. Only the price influences his rational decisions. Onsite price targeting is therefore the right move to address this kind of customer and will result in potential revenue uplifts. Any kind of hidden cost (e.g. shipping charges) will have a negative effect.
Special offers, coupons or bundle product offers are significant options to get the attention of the price sensible user.
Online shops need to know what type of user is browsing their page and how to get the attention in the particular phase of the shopping process. Predictive onsite analytics evaluates different data points, systematically and anonymously, and determines the type of user, his individual needs and preferences, thus making it possible to engage them with personalised incentives. To address users personalised generates a distinct additional benefit: good shopping experiences create satisfied customers and more conversions.
Online shops that use personalised incentives can actively push user engagement. And this will result on one hand in raising conversions and shopping basket value and on the other reduce shopping basket drop-outs. “Engaging” users needs a diverse approach. Social engagement (e.g. social sharing), emotional engagement (e.g. scarcity incentives), product engagement (e.g. specific product recommendations) or price engagement (e.g. coupons) can be used.
The use of predictive onsite personalisation helps to address a single customer and therefore enables an online shop to get closer to the – customers desired – offline-shopping-experience. And it provides the possibility to convince any type of shopper.
