E-commerce companies are increasingly data-driven in managing and optimizing their webshops. The idea: the more data we collect, the more insights we can gain. We use these insights to tweak the customer journey, which ultimately generates more money on the bottom line.
All collected data is written to a Customer Data Platform (CDP), from where the marginal gains are sought. The small, incremental improvements to help specific customers (just a little) better and convert (just a little) better. Think of the personalization of a newsletter or category page, sending abandoned cart emails or retargeting using ads.
To do this, the shop can use four different types of data. Conveniently numbered from 0 to 3 🤔:
Zero party data → Data that the customer has voluntarily and knowingly left on a web shop.
Example: the customer indicates that she is a woman.
First party data → Data produced by a customer through click, search and purchase behavior on a web shop. This can be further divided into behavioral data (all digital interactions of a customer such as visiting a page, logging in, placing products in the shopping cart, scrolling, filtering: actually every mouse click, keystroke or swipe falls under this category) and transaction data (data of interactions and transactions such as purchases, but also returns and customer service interactions).
Example: the customer browses the webshop for different 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/sells it to the web shop.
Example: the customer books a flight and the airline shares his data, with permission, with "selected partners", so the customer receives an offer from a car rental company without having to fill in his data again.
Third party data → Data collected (usually) by a data company with no direct relationship to the customer. Examples are Acxiom, Bluekai, Lotame, Datalogix and Experian - parties most people have never heard of. They collect data based on cookies and aggregate it to create comprehensive targeting 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 it 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 available in abundance. First party data has a smaller scale - it is limited to the traffic on your web shop - 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 category page 'Ladieswear' (first party data based on click behavior)
A customer buys a product for women (first party data based on transaction)
A customer indicates in her profile that she is a woman (zero party data based on 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. This is data that, as the name suggests, is created without any explicit input from the customer. It is a process that happens in the background, frequently and in many places.
Smart algorithms try to filter the needles out of the haystack of derived data. The challenge is further increased by the use of multiple devices (by the same customer), being logged in or not and accepting or not accepting cookies. Ensuring a uniform customer view with a high predictive value is therefore an extremely big challenge.
Therefore, in the hierarchy of valuable data, zero party data is the holy grail. Zero party data is a form of declared data (provided data). This is data that has been shared voluntarily and explicitly by a customer. For example, by filling out a login form or a survey. Because customers share this data consciously and of their own accord, it 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 a web shop. After all, it's not Facebook! Usually there is 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 their use is limited. The collection of zero-party data has therefore not yet taken off properly within e-commerce. A customer may have entered his gender, age and/or address, but that is usually all. Retrieving other, more contextual customer data such as preferences and wishes remained the domain of derived data. Or guesswork.
A good system, a currency, to support the interaction between customer (data) and webshop was lacking until now. What can a web shop offer so that a (non) customer is willing to share information about his wishes, situations and needs?
Guided selling as a goldmine for zero-party data
Retailers are condemned to derivative data because customers are condemned to rudderless web shop searches. If a customer's search is made more relevant, personal and human, it will be rewarded with a customer view that is more relevant, personal and human.
Retailers are condemned to derivative data as customers are condemned to rudderless web shop searches.
An online shop is a large digital warehouse, filled to the brim with products. Customers wander independently through the aisles, looking for the product that solves their problem. But they don't know exactly what they're looking for, and the shop doesn't help them very much, so they have to wade through dozens, if not hundreds, of products and pages. The result - in addition to low conversion rates - is an enormous amount of user data that is impossible to make sense of.
When a web shop focuses on the customer (his wishes, needs and situation) instead of the products, the customer is not only helped better, but the web shop is also rewarded with much more useful and 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 plowing through hundreds of products, just a simple product finder with some easy questions about your usage and situation:
How often will you use the e-bike per week?
What area will you be hiking through?
What type of bath are you looking for a cleaning robot for?
Do you get hot in bed quickly?
That 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 derived data points about a customer who visited the categories drill/screwdrivers, impact drills, nail guns and soldering irons, that same customer simply uses a "Find the right machine for your job"product finder. Et voila: we know he is an amateur handyman looking for the right way to install a drywall in the bathroom.
Instead of sifting through 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 use in the city, which will fit groceries and 2child seats.
No polonaise about hiking boots-related customer data, just a woman looking for a hiking boot in size 39 for a multi-day hike 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 there. The customer shares some data and in return gets a customized product recommendation in a few clicks. The customer is greatly helped by this, as the webshop has saved him valuable time and effort. And the webshop is also helped enormously. Not only because of the higher conversion rate, but also - precisely - because the customer can be understood on a much deeper level.
It is then 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 out of this. Instead of the umpteenth irrelevant retargeted ad, the customer is helped in a relevant, personal way in the moments that matter. In this way, customer data becomes a game of maximum rather than marginal gains.
Want to read more about guided selling and how other web shops are using product finders ? Check it out: