Propensity to Purchase Algorithm
Our Propensity to purchase algorithm is a culmination of three core indicators; sentiment analysis, trigger/ keyword analysis,and past purchase history.
It’s important to firstly identify whether a user has the intent to purchase something that you’re offering. Do they have a positive opinion of your company? Does their behaviour suggest that they are open to being a customer of yours?
Sentiment analysis is basically the process of computationally identifying and categorising the opinions expressed by a user in order to determine whether the users attitude towards a particular topic, in this case your company or product is positive, negative, or neutral. This insight can determine when is a good time to target this user.
As well as utilising natural language processing, tagging keywords is part of a chatbots’ core functionality. Say for example you are a sports club and a fan asks your chatbot ‘what time is the game?’, this signifies that the user is either interested in buying a ticket which provides upsell opportunities or already has a ticket which provides cross selling opportunities.
There are a number of avenues that you can go down in relation to tagging trigger words. It’s important to ensure that whatever a user is enquiring about, the data is effectively assessed and used to optimise sales activity.
Past Purchase History
Traditional propensity to buy models score customers based on their past purchase history and although history is not always an accurate predictor of the future, it’s an important area to consider when it comes to the propensity to purchase algorithm.
Data is king and if your chatbot is integrated with your existing CRM system, you are able to gain a huge amount of insight into what a customers buying patterns are, what they tend to purchase, how often they purchase from you plus much, much more. Say for example a particular user has bought three pairs of red shoes over the past twelve months and you have decided to run a promotion on red shoes, the past purchase data for this user will signify that this particular promotion would be of interest to them.
Following this identification process, an automated promotional campaign can then go out to all users deemed appropriate as per the propensity to purchase algorithm at an optimised time with a personalised message.
In overlaying these three core datasets as well as utilising predictive analytics, you are able to effectively score each user based on their chatbot interactions and past purchase history. This results in a far more insight and data driven perspective which consequently leads to a higher number of engagements and conversions.
To learn more about our analytical capabilities and how you can take a more data driven approach to customer service whilst saving money, increasing sales opportunities and improving customer experience, book a demo today.