How can e-commerce platforms gain meaningful insights into customer requirements?
E-commerce merchants know surprisingly little about their customers needs and wants.
This is a bold statement that runs somewhat counter to common wisdom, and it is true that e-commerce generates a lot of data originating from visitor’s search queries, mouse movements, page views, filter selections and purchase histories. But this data provides only an observation of how the consumer is engaging with the available product selection and the existing site structure, including the product selection mechanism (such as filters or search bars).
An example – how does an appliance webshop know that a consumer is looking for a quiet large-load washing machine with a low energy consumption? Or that the customer’s primary criteria is in fact the noise level, and that they are willing to settle for a smaller wash load if it helps bring the noise level down? This is not easy to deduce from the data that is currently collected. The consumer is unlikely to enter this level of detail into a textual search query; purchase histories are not very helpful; and mouse movements and page views are difficult to interpret due to their very low signal to noise ratio.
Hence the surprising truth is that e-retailers often have little insight into their consumers needs and wants compared to brick and mortar shops. Physical shops are able to gather information through personal interaction with shop assistants, creating a much more direct line of communication with users.
Up until now, one of the few methods of feedback that e-commerce sites can use is the product selection mechanism. Unfortunately (and as we’ve discussed in a previous blog post) customers have to a large extent learnt to avoid this feedback mechanism – the filter search system – due to its poor user experience, meaning that sites miss out on capturing potentially useful information. And when they do use it, the data that is gathered is often shallow and focused primarily on elementary characteristics like size, colour and brand while missing out on the long tail of customer interests.
This noisy and shallow data is then used to deduct trends and build a picture of what really interests and moves customers. Unfortunately, it’s rubbish in – rubbish out, no matter how much you spend on machine learning and AI.
So how can retailers capture the full breadth of a customers’ many and varied needs and wants?
To get a more detailed understanding of the customer’s thought process, it’s critical that a consumer can express the full breadth of their requirements as they interact with your site. Here at Traverz we’ve found that the key is to implement simple and unobtrusive feedback mechanisms, enable the consumer to provide indications of relative importance, and to expand the set of interactive features and options to capture the ‘long tail’ of consumers search requirements (see here and here for how we implement this). Through these changes the consumer not only has a better search experience, but the merchant obtains a far deeper and less confused insight into consumer needs and wants.
The resulting data flow provides detailed insights into customer interests that need no further deduction – the data directly tells the story. This enables merchants to be better prepared to:
- Stock the right products at the right time as they know what their customers need and want
- Market their products in a more efficient and much more targeted way because they know exactly what it is about a product that their customers like or don’t like
- Send engaging and tailored marketing messages to customers without noisy data and generalisations but instead that are tailored to match true needs and likes and can deliver accurate recommendations.
- Get a deep understanding of the customer and their shopping (not just buying!) behaviour, and provide relevant information which helps to build trust & loyalty, creating higher customer value.
To find out how you can build an e-commerce platform that delivers better insights and greater brand loyalty from customers, get in touch with us.