At this point data is captured on almost all products and services. Latency varies. Quality varies. Value varies. Use varies. But event data is collected even if it is just sales volume. However, not many products collect data in order to work. The ones that do of course have taken over the world. This is no coincidence.
I’d argue best data for a product to work well is contextual data. The best, most useful products understand, even anticipate the context in which they are used. In fact, they often can’t be useful without that understanding. You are not going to take a telescope to the opera. You are not taking your 65” TV on your commute. The same rules apply to digital products.
Context then helps define the purpose for the product.
Digital products use contextual data well for purpose. In fact, this data is often used in the same place it was created. Your location is an essential part of many digital products. So is history. So is device type. Time of day has always been great context for a product purpose. Minute Rice? Instant coffee?
In addition to products whole verticals have been created because of understanding context and anticipating people’s needs. Food services, transportation, the list goes on and on.
But web data has always been kind of funny. It’s been very hard to contextualize and even harder to use web data in the context it was created in. Look how personalization has failed since Peppers and Rodgers coined the term “One-to-One Marketing” in 1993. Look at the rise of cookie data. It’s shit because it lacks context.
Take site analytics.
Let’s take a product page as an example
<insert your product page here>
It’s great to have the data point that someone has visited this page. But what does that information do for me? How can it be productized? How does it help the customer?
It’s the context about that page view that creates value.
That product belongs to a category. It has a price. The price has its own dimensions. The product has a style, a color, a brand, it is wanted in a location, by a certain class of customer.
Imagine that context about the product was being collected, tabled and counted along with the page view. And now imagine it was being used to make the product better. In this case that product is both the store and the item itself.
That data enriches what was a meaningless page view count into real customer insights. Instead of days of analysts working to try and understand what products appeal to what customer at what prices, where and when, those insights are readily available. Those insights are actionable. Those insights can be used to make more relevant products with better experiences.
This level of customer insight is not available easily if at all right now. But it’s needed. We’ve been counting pages when we need to be counting people’s interests and needs.
There’s no analytics product where I can rank product page views by customer LTV but for crying out loud it is 2019, why isn’t there a product where I can rank product PVs by price bands? I’ll tell you why, because the contexts related to price are missing data points! E-commerce is but one example and an obvious one, which is why I used it but these data needs extend to all products and consumer interaction with products.
IoT is an interesting area for this type of exploration. Imagine a toilet manufacturer understanding when people use the toilet. How long they use it for. How much #1 vs #2. How often it is not in use. IoT makes this information possible. This level of consumer / product data will change all products previously made with focus groups, prototypes and assumptions. When products become contextually aware new products will flourish. Better products.
If you haven’t guessed by now I believe the ability to contextualize event data is a very big deal. Seminal even. Yes, this is has been done for a long time by data creators who also own the product. Search is a great example I mentioned earlier. Amazon is another. It’s no wonder these products have a hold on customers. But we are quickly moving to a world where any company of any size can contextualize data events.
JSON has and will continue to be a huge factor in the growth of contextually aware products and services. In past two years we have started to see the contextualization of data become available. Tableau started accepting JSON 2 years ago. GeoJSON was also started 2 years ago. IBM has become a big proponent of JSON for use in IoT. Here is a decent whitepaper they wrote last year on the subject. And only 2 months ago Couchbase launched Couchbase Analytics for JSON allowing for faster data pipelines that can apply contextual events to massively paralleled processing.
We are living in an incredible time with data collection and use. Privacy gets a lot of attention and rightly so when it comes to customer data but data renaissance is really about products and they will forever get better because as consumers our event/use data can now be contextualized.
If you’re working on any interesting things using contextual data to create better products (digital or non-digital) I’d love to talk with you.