Technical information is also available
This is a non-developers' guide to using our Insight data; for a more technical account of insight data see our guide for developers.
|What is insight data?|
|What can I do with this data?|
|How is insight data stored?|
Insight data is storable information related to your customers’ purchasing histories and habits. It allows you to store collections of arbitrary data against your contacts via our API.
So they’re additional contact data fields, extending the contact data fields we already store?
Yes – but with the prime difference being these are transaction-based ones. Any additional keyed data you have for a contact can be stored as insight data, with very little restriction on the type of data it can be. As a broad rule, you can store anything that is serialisable to JSON.
Just as you can currently segment upon contact data fields such as gender, age and geography, insight data gives you the ability to segment upon types of items purchased, regularity of purchases and amount spent on purchases, for instance. As ever, the quality of your insight data will dictate how well you can segment address books and personalise your campaigns and offers.
Here are some examples of datasets that could be stored as insight data:
- For an auction site, a list of all bids a user has made and if they succeeded or not, including the date and time of the bid, the amount of the bid and the category and name of the item bid on
- For a travel agent, a list of destinations a user has visited via bookings on the site, including the number of bookings for the destination (solo, couple, family, etc.), the amount spent and the type of accommodation
- A list of a user’s likes and dislikes
The key point is that many different types of insight data can be stored for each contact, and structured in a way of your choosing.
Once insight data is stored against your contacts, you can use the website to write queries to segment your contacts using this data and create new address books. This will enable you to send targeted, personalised campaigns based upon your contacts’ transactional history and habits. Using the above examples, you'll be able to run segmentations based on bids made, countries visited and likes and dislikes.
For more on segmenting insight data, read our article on insight data segmentation.
This gets rather technical! But if you’re game and want to know more, read this developer-oriented guide on insight data. It’s more likely, however, that you'll probably want to leave it for your developer to read and then explain to you in much friendlier terms!
Given the nature of insight data storage, you'll probably want to spend some time with your developer thinking about how your insight data needs to work for you. A little bit of planning will go a long way! What sort of data do you want to store? How might you want to extend this data as you move forward?
The point to remember is that you don’t want to become backed into a corner by initiating a schema that will prove constrictive.
For example, you'll probably want to avoid entering your product ID as an integer. This won’t give you very easily identifiable information and you won’t be able to change it once it's uploaded. To change it, you would need to start again with a new schema and re-upload all of your data to conform to it. This is definitely something to be avoided!