With the supersonic development of the technologies users have access to, there is so much more to e-commerce these days than buying and selling online. Efficient strategies for user engagement and customer retention are those that put at the centre of e-commerce the user – the empowered user brands are striving to build meaningful and smooth experiences for. On the Web, as much as these memorable experiences are related to the creativity of the editorial content, they are also inextricably linked to the data behind this content.
Think of it, on the Web the interactions between a user and a business are also interactions between a user’s data and an enterprise’s data. The reason that matters for e-commerce is that when thinking about attracting customers to your “virtual window” you need to also consider the data underpinnings of their journey towards your product. How are your product pages connected? Are they represented in a universal way for other machine applications to read them? Are they also interlinked to other products, to editorial content, generally to knowledge outside your website or to knowledge?
Meet Joe: The social customer struggling to connect the dots of your e-commerce content
Meet Joe! Your social, truly empowered customer. Being connected and having realised that he can ask more from companies and know more within a couple of clicks, Joe’s expectations throughout his customer journey are rising. Yet, no matter the fact that he has lots of tools to engage with a brand or navigate information to satisfy a need, he is still struggling.
He jumps across multiple sites, checks for inventory, checks for product availability, delivery, details. In a nutshell Joe does all the leg work which, as you will see, you and your relevant data strategy, can spare him.
If we take a closer look at the touchpoints and the activities of Joe, we will see that the question of engagement and consistency, especially when it comes to e-commerce interactions and transactions is a question of providing data that is required for platforms to understand what we are selling to Joe and what content Joe needs across his journey.
As we see from the exhaustive customer journey map the authors of Orchestrating experiences took the time and consideration to create, this is not about one single, discrete search. It is about multiple steps during which Joe will come back several times before making the final move of purchase: he would look for customers’ feedback, qualified reviews, imagery, FAQs, advertising and a lot more before making the transaction. That means that all our e-commerce content and data need to be in sync across all the platforms, devices and spaces (Google Shopping, Google Search, Google Image Search, Bing Shopping) Joe visits.
And it is at that challenging point where the power of a product knowledge graph for e-commerce comes into play.
Getting data right. With a Product Knowledge Graph.
A Product Knowledge Graph is an e-commerce specific form of knowledge graph built to improve product findability and end-user experiences by enriching a brand’s content with data. It consists of data about products, brands, product categories, product features, reviews, hi-res images, shipping data, FAQs and a lot more. Made of structured data and extended product mark-up, injected across both editorial and product content, a product knowledge graph is built on top of the product database to link all data together combining both structured information, (for instance, the list of products for a brand) or unstructured (for example the descriptions related to a collection of products).
Simply put, a Product Knowledge graph bridges the currently existing gap (hindering Joe’s smooth experience and our brand’s opportunities to connect to Joe where he most needs it) between product content and editorial content.
Consider this scenario: Joe, the empowered customer we met above, is looking for new sunglasses. The ones that he bought before do not suit his new exercise routine – running. So he tries to get some models that might fit his needs. He is not aware that there are special models of sunglasses that are created for running, yet he is very well aware of his need – glasses that fit well, don’t bounce up and down, are water resistant, maybe with high UV protection etc.
So here’s Joe, starting his navigation on the Web with a query.
He might try to search for tips for running with sunglasses. Or what models of sunglasses fit best for running. What unites all these queries is their navigational nature. That is, their essence implies the intent of Joe – not to buy right away but to explore various options. In contrast, transactional queries would look differently, this is where Joe will know what he is searching and will be now comparing prices and quality and availability. These two different queries (with a different intent behind them) map the uneven, meandering way Joe, or any other given person, reaches a product.
In the context of e-commerce, it is our goal (if we want to share with Joe the information and the products we have and offer in a meaningful way) to link our editorial content (the one that explains what models of sunglasses are good for running, what types of these models exist for what kind of running and what are the benefits of having glasses for running, etc.) to purely transactional information, such as prices, availability, types, product number, payment methods, delivery information.
And this is exactly what a Product Knowledge Graph does for our data and clients. It makes possible the curation and creation of editorial content as a network of data which you serve not only to Joe (and customers) but also to different types of machine applications that Joe (and other customers and leads and prospects) use to navigate your content (search engines, chatbots, personal assistants etc.). As an example, think about the Product Knowledge Panel in Google, probably the most dynamic and smart rich snippet on the search result pages.
Okay, You Got a Product Knowledge Graph and Now What?
What a Product Knowledge Graph knowledge does for e-commerce websites can be summarized in three general benefits (the ones users will thank you for!)
- Visibility & SEO
- Immersive content
To unpack these, let’s look at each and connect them to smooth customer experience on one side and well connected data on the other side.
Visibility: How structured data helps people (and search engines) “see” you?
The reason a Product Knowledge Graph makes your content visible is because it gives it machine-readable descriptions that are highly interlinked. Just like Google Knowledge Graph.
The Product Knowledge Graph though is not a knowledge graph, as it is not generic, but well targeted to a specific need: discoverability. It is a dedicated graph built to provide answers with the ultimate goal of being robot-readable. Focused on discoverability, a Product Knowledge Graph allows you to level up your ecommerce marketing through providing the data structure for hyper-targeted content, richly interlinked content, enriched with product.
Immersive content: why content needs data as to power better user experiences?
In order for content to create the immersive environments we all want, it needs not only the editorial touch but also a way for content to be assembled dynamically and thoroughly connected.
The Product Knowledge Graph, for example, connects blog posts with products and product categories. Thus you can build all kinds of meaningful content experiences. For example, a Navigator and Product Faceted Search, can help your user navigate seamlessly editorial content and at the point when they are ready painlessly move to buying what they decided to, instantly seeing prices, prices, availability, types, delivery information, etc.
Interoperability: How to use data to sell more?
Like any enterprise these days, e-commerce activities are more and more dependent on working with data that can be easily shared across different devices, platforms, softwares. It is mission-critical to “publicize [data] to a community of possible consumers, and make it available via many channels: the web page itself, but also via search engines, personal assistants, mash-ups,review sites, maps, and so on” [Semantic Web for the Working Ontologist, page 21]
With a Product Knowledge Graph we are able to provide APIs to other parties, for example search engines and retailers. We are able to feed mission-critical “hubs” gathering and serving data to users, such as:
- Google Merchant Center & Google Manufacturer Center
- Bing Merchant Center & Bing Manufacturer Center
- Free listing on Google Shopping
- Google Shopping Actions (US & France only)
- Alexa Skills for eCommerce.
Epilogue: What does a Hungarian Psychologist Have To Do with with interoperable data and a Product Knowledge Graph?
Think of a Product Knowledge Graph for e-commerce as the technological means of helping your customers and prospects reach a state of flow while navigating the Web (your databases and inventory included). This flow was described by Mihaly Csikszentmihalyi. It is a state where we are completely absorbed by what we are doing and in which we fully involved with the activity at hand, feeling engaged, fulfilled and rewarded.
And in the 1990s the flow was wisely linked to the shift marketing communications are going through in hypermedia environments. According to the researchers Novak and Hoffman, flow characterizes virtually every aspect of the interaction between the user and the environments they navigate through hypermedia. It is this concept of flow that can help an e-commerce website stand out by providing information and value. A flow which is efficiently facilitated for the user through the smart use of linked, interoperable data – to cater for the needs of human users and their machine applications.
Curious to know more about Product Knowledge Graphs? Read the next post of the Product Knowledge Graphs series on “Physical SEO”.
Do you want to know how you can leverage
e-commerce data to help you orchestrate smoother and better experiences for your user?
Let’s talk about it.