Going into business with a webshop can be hard. Cross and up-selling help to generate revenue, so how can we bring these sales methods to the fore?
Thorough planning means more turnover, and this cannot be more true than when planning and implementing best practice IT solutions.
One of the technologies to get better and more informative access to the ‘data hidden in your data’, are NoSQL databases like GRAKN.AI.
GRAKN.AI is a powerful database technology allowing complex relationships to be presented. To achieve this, GRAKN.AI comes with a custom (but straight forward) query language, Graql. Graql allows retrieval of data in a more native approach. To better understand all things GRAKN.AI, we will first need to define some things like ontology, entities, relations and roles.
In the simplest terms possible, an ontology is a model for describing the world. It has a set of types, properties and relationship types. It is also expected that the features of the model in an ontology should closely resemble the real world. According to blog.grakn.ai, Grakn uses four types in an ontology:
- An entity represents objects or things, for example: person, man, woman.
- A relation represents relationships between things, for example, a parent-child relationship between two person entities.
- A role describes the participation of entities in a relation. For example, in a marriage relation, there are roles of husband and wife, respectively.
- A resource represents the properties associated with an entity or a relation, for example, a name or date. Resources consist of primitive types and values, such as strings or integers.
The Importance of Ontologies
So why do we need an ontology? With proper descriptions and representations of the real world, it is possible for machines to recognise inconsistencies (known as validation) and to extract implicit information from data (known as inference).
In this example regarding family, let’s suppose there is a glitch whereby a person was incorrectly named as parents of themselves. In adding the data, Grakn would spot that a person entity was attempting to take both parent and child roles in a parentship relation, and would flag it up as an inconsistency. The same would be true about inconsistencies in customer data. Compared to simply tagging products, ontologies allow the machines to take over, using their abundance of logic to make the connections – automatically.
A Real World Example
Using these elements to build real-world solutions to better use your data can transform how you sell.
Let’s say a customer orders a Beatles LP from 1966. Your shop may be able to recommend another LP with guitar pop songs from the 1960s, but implementing good ontologies with Grakn would give you a list of customer recommendations that they may actually want/need to buy – like an obscure Gullivers People single produced in the same studio as the Beatles album was in 1966. In many shops these days, tags are used to provide searches over terms connected to products that are not directly part of the name or description. The advantage of ontologies is, that new knowledge can be reasoned through the product connections to other entities. So when a product is related to "Beatles", every fact saved for this term is now connected to the product.
As you can see from the examples, cross and upselling would be professionalised exponentially with the use of these methods. With the correct preparation and implementation, sales would generate themselves.