As digital interactions between businesses and their customers become more commonplace than person-to-person interactions, recommender systems have become an integral part of the sales process. The adoption and use of recommenders have been wholly invited into almost every business to combat market saturation.
The building blocks of recommender systems came in the form of cognitive science and information retrieval in the 1970s. The first practical implementation of a recommender system occurred in 1979 with the computer librarian, Grundy. The system interviewed users about their preferences, grouped users into ‘stereotypes’ and recommended the same books to all users in a specific group.
Recommender systems with behavioral science
Since, recommenders have become far more sophisticated, including the adoption of economic behavioral constructs such as nudges, and other personalization techniques. ecosystem.Ai has the technical expertise to build highly reliable recommender systems, fusing psychological and sociological elements to improve the effectiveness of a recommender, all acting in real-time.
Traditionally, recommenders were based almost entirely on “like” trends. Offers are made on the basis of ‘those who liked this thing, also liked these’, namely that they are recommending items of similar attraction to people of similar tastes – a process called collaborative filtering.
However, the current day demands a higher level of personalization – one that sees beyond demographics and treats each customer as a multi-dimensional individual, whose desires and preferences change over time. Uncovering this nuance requires a combination of several capabilities. For example, behavioral economic techniques such as nudging can be used to test the offer environment, enabling restructuring where preferences change.
Other behavioral elements have been integrated into recommenders that enable dynamic learning about customers’ tastes and their desires; as well as helping to filter out the noise of what products are not relevant to them. However, in some instances, this has been found to be restrictive for the user, as humans have a yearning for new experiences too.
Spotify is a great example of a company that has employed the use of multiple recommenders to balance meeting users’ preferences while keeping them excited. For example, with Spotify’s new Only You playlists – the majority of them will feature artists or songs you know well, peppered with new songs based on rhythms and beats you like. Introducing novelty keeps users engaged and interested. While this leaves the risk that some songs miss the mark, the feeling the user gets from exploring, and having the power to choose whether they like or dislike certain music, provides excitement.
Applying behavioral constructs to recommenders
ecosystem.Ai takes a similar approach to recommendation systems, but in a business context. Using recommenders in a business environment is useful in a number of ways. For example, ecosystem.Ai algorithms can conduct scoring in real-time to determine a customer’s propensity to churn. This can in turn trigger an up-sell, cross-sell or down-sell recommendation as intervention. There are a number of behavioral elements that work to enrich the productivity of recommenders. One approach that ecosystem.Ai employs involves chaining together a series of recommendations in order to produce the clearest possible solution. With multiple recommenders strung together, the system iterates through each of these recommenders, selecting the most appropriate option to build out offers based on an accurate behavioral customer profile.
Digital personality for effective recommendations
Personality models can also be used to enrich the recommendation process. Personality is described as the combination of characteristics or qualities that form an individual’s distinctive character, and is often based on individual motivations. Human personality has gone through many versions of this definition, and although there is not one agreed-upon explanation, there are a few things we do know: personality as it relates to the individual, is an anchor point of human decision-making.
The reason a human’s actions or emotions can be attributed to the collection of attributes that make up their personality. The OCEAN personality model outlined in contemporary psychology assigns individuals a position on spectrums of five traits: Openness, Conscientiousness, Extroversion, Agreeableness & Neuroticism. Each of these factors is determined by the different patterns of behavior in various divisions of human life. Neuroticism as an example, is determined by the measure of an individual’s handle on their emotions – as most will know from the negative connotation attached to the word “neurotic”, those high in this factor are generally quite sensitive and particular.
The point here is that most psychological theories are built on interactions with environments, and the internal processes that determine action and motivation within that environment. The same can be said for personality in an AI context, namely that the patterns of interactions and actions a customer exhibits in a digital environment can provide a picture of their personality. This picture comes from the various data points that make up a customer’s entire interactive profile, in any given sector of business and usually in the form of transactions.
In an effort to further improve the capabilities of recommender systems, ecosystem.Ai has taken what we know about the technical implementations of recommenders in various business environments, and combined it with personality and behavioral constructs. This enables the system to provide output that is tailored by attribute associated with the style and rhythms expected by each personality.
Recommender systems do more than just clear up the noise produced by an over-saturation of options in contemporary human society. Recommenders provide companies with the means to communicate more effectively with their customers too. The right communication methods and modes are an invaluable resource that every business needs to utilize in order to remain relevant in this rapidly expanding digital network. The vast opportunities that recommenders with personality afford to businesses of any size and in any sector is an invaluable fact, there is no improvement without the right recommendation system.
