Our vision of the Sales bots is to go beyond chat bots. Creating bots that provide high customer relevant sales, appealing to both the logical and emotional side of the customer
Our objective here was to build a sales bot which can handle both inbound and outbound calling on scale.
During this process we use a combination of audio analysis in AI from speech to text, text to speech, intent classification, sentiment classification as well as other feature extractions.
We handle complex situations, which includes
Our objective was to complete a solution which provided:
We provide a high level of interaction for the users, able to highlight key information and interact with the web site. Collecting information about the user through the experience to customised the use.
We are looking to extend the sales bots to include more complex relations, unlike chat bots which are designed for responsive nature, we intend to use as triggers for alternative and customer sales interactions
Current, this is in Beta testing. Where we can handle Questions and Answers through the conversations for both the voip and web bot
There is a large overlap of gamification and salesification. In the initial phases, we will be creating a deeper understanding of the user. From performing sentiment analysis on the audio rather than on the associated text. It is our objective to extract
Use these as models for A/B testing on response and to create strategic optimisation for sales conversions.
This is an exciting concept where we view sales as an optimisation solution. Here we view sales as the following
The solution is a combination of creating customer problem clusters and research to find the strength of problems and associated value of the problems.
The transitive values will be designed to appeal to their logical sense, backed up with emotional side. Missing out, Epic Growth, Peer Pressure and other emotional to drive sales for the users and create the needs.
The output of this models is a meta representation of information extracted, which would be passed into a copy writing model to develop the copy based on industry specific users.
The concept of A/B testing is not new, but there has been some amazing research and models created for Journey based optimisation. An example of this is seen in poker models. In the game play actions which may on the surface seem like a bad move later can have a more optimal solution.
The same is said for sales, individuals all have their own emotional triggers and logical triggers. The sequence, timing and messaging can be optimised for each individual to get the right results.
The technology which is being planned to optimise these strategies is a reinforcement learning model, NN of the user profile as well as BPMN modelling as the optimisation strategy.