đź‘Ą Why guided selling is an indispensable source of zero-party customer data
Web shops are increasingly data-driven. But the vast majority of that data has low predictability and reliability. Guided selling helps your customer and helps you collect the holy grail of data: zero-party data.
Data-driven e-commerce companies
E-commerce companies are increasingly data-driven in managing and optimizing their webshop. The idea: the more data we collect, the more insights we can gain. With those insights tweaking we are the customer journey, which ultimately results in more money below the line.
All collected data is written to a Customer Data Platform (CDP), from where we search for the marginal gains. The small, incremental improvements to help specific customers (just) better and convert (just a little) better. For example, personalizing a newsletter or category page, sending abandoned cart emails or retargeting with the help of ads.
For this purpose, the webshop can use four different types of data. Handy numbered 0 to 3 🤔:
Zero party data → Data that the customer has voluntarily and consciously left behind on a web shop.
Example: The customer states that she is a woman.
First party data → Data produced by a customer through click, search and purchase behavior on an online store. This can be further subdivided into behavioral data (all customer digital interactions such as visiting a page, logging in, placing products in the shopping cart, scrolling, filtering: basically every mouse click, keystroke, or swipe falls below) and transaction data (data from interactions and transactions such as purchases, but also returns and customer service interactions).
Example: The customer browses the webshop for various barbecues, reads a blog about “backpacking in Italy” and buys a cookbook about pasta sauces.
Second party data → Purchased first party data: another website collects first party data and offers it/sells it to the webshop.
Example: The customer books a flight and the airline shares their data, with permission, with “selected partners”, so that the customer receives an offer from a car rental company without having to fill in their details again.
Third party data → Data that is (usually) collected by a data company without a direct relationship with the customer. Examples include Acxiom, Bluekai, Lotame, Datalogix, and Experian — parties that most people have never heard of. They collect data based on cookies and combine it to create comprehensive target group profiles, for example.
Example: the webshop runs an external advertising campaign to reach (new) customers who, on other websites, have previously shown interest in Italian cuisine.
Not all data is equally valuable
So there are different types of data, but not all of that data is equally valuable or easy to use. The rule of thumb is that the “further away” the data is collected, the less accurate it is — and the more of it is available. So third party data has low predictability and reliability, but is abundant. First party data has a smaller scale - this is because this is limited to the traffic on your webshop - but also a much higher predictability and reliability. An example to illustrate:
- A customer is a member of the Facebook group 'Girls' Night Out' (third party data based on login with social media account)
- A customer visits the “Women's Clothing” category page (first party data based on click behavior)
- A customer buys a product for women (transaction-based first party data)
- A customer states in her profile that she is a woman (zero party data based on the completed form)
Which of these data tells us with the greatest certainty that the customer is a woman?
Third, second, and first party data are all inferred data (derived data). This is data that, as the name suggests, is created without a customer explicitly providing input. It is a process that happens in the background, frequently and in many places.
Smart algorithms try to filter the pins out of the haystack of derived data. The challenge is made even greater by using multiple devices (by the same customer), whether or not you are logged in and (not) accepting cookies. Ensuring a uniform customer view with a high predictive value is therefore an extremely big challenge.
In the hierarchy of valuable data, zero party data is therefore the holy grail. Zero party data is a form of declared data (data provided). This is data that has been shared voluntarily and explicitly by a customer. For example, by filling in a login form or a survey. Because customers share this consciously and of their own accord, this data has the highest reliability and predictive value. Plus, the data is free of algorithmic guesswork.
But of course, customers don't just leave this kind of personal information on an online store. After all, it's not Facebook! There is usually something in return: for example, a customer shares his date of birth and receives a discount code on his birthday.
However, these are fairly basic rewards and are only used to a limited extent. The collection of zero-party data has therefore not yet taken off well in e-commerce. A customer may have entered their gender, age and/or address, but that's usually all. Retrieving other, more contextual customer data such as preferences and wishes has remained the domain of derivative data until now. In other words, guesswork.
A good system, a currency, to support the interaction between customer (data) and the webshop, has been missing so far. What can a webshop offer so that a (non-) customer is willing to share information about their wishes, situations and needs?
Guided selling as a gold mine for zero-party data
Retailers have been sentenced to derivative data because customers have been sentenced to search the web shop motionlessly. Like a customer's search is made more relevant, personal and human, then that is rewarded with a customer view that is more relevant, personal and human.
Retailers have been sentenced to derivative data because customers have been sentenced to search motionless in the webshop.
An online shop is a large digital warehouse, filled to the brim with products. Customers wander the aisles independently, looking for the product that solves their problem. But they don't know exactly what they're looking for, and they're badly helped by the web shop, so they have to go through dozens, if not hundreds, of products and pages. In addition to a low conversion, the result is an enormous amount of user data that is impossible to make sense of.
When an online store is shopper (his wishes, needs and situation) instead of the products If you focus, the customer will not only be better served, but the webshop will also be rewarded with much more useful and more valuable information.
There is no better way to do this than with guided selling.
Guided selling offers customers a short cut to their perfect product. So no more digging through hundreds of products, but a simple selection guide with some easy questions about your use and situation:
- How many times a week are you going to use the e-bike?
- Which area are you going to walk through?
- What type of bath are you looking for a cleaning robot for?
- Do you get warm in bed quickly?
The same short cut for customers is also a short cut for web shops. Because it is a direct route to declared data.
Instead of analyzing hundreds of derivative data points about a customer who visited the categories of drills/screwdrivers, impact drills, nail guns, and soldering irons, that same customer simply uses a “Find the right machine for your job” selection tool. Et voila: we know he's a amateur handyman is who is looking for the right way to a gypsum wall in the bathroom to assemble.
Instead of understanding a forest of click data from all kinds of e-bike pages, we know that the customer is looking for an e-bike for daily utilization in the city, where the groceries and 2 child seats fit.
No polonaise customer data related to hiking boots, but a woman who is looking for a hiking boot in size 39 for a multi-day trip in the high mountains with a chance of rain.
Zero-party data maximizes marginal gains
The reward of guided selling is not as literal as that of a discount code, but it is definitely there. The customer shares some data and, in return, receives customized product advice in a few clicks. This has helped the customer enormously, because the webshop has saved him valuable time and effort. And the webshop has also helped enormously. Not only because of the greater chance of conversion, but also - precisely - because the customer can be understood at a much deeper level.
It is no longer about “what product is the customer interested in?” , but about “what problem is the customer trying to solve?” Zero-party data enables web shops to play a much more critical role by entering into a dialogue with the customer and building a meaningful relationship from this. Instead of just another irrelevant retargeted ad, the customer becomes in the moments that matter helped in a relevant, personal way. In this way, customer data becomes a game of maximum rather than marginal gains.
Read more about guided selling and how other web shops use decision aids? Check out:
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