Operational AI (Beta)

Operationalise AI and drive business value with the help of data science and deep learning models

Operational AI Use Cases

We'd be here all day if we attempted to explain how operational AI works. Instead, below are a few of the common business cases that showcase what AI can do to improve marketing operational efficiency.

Sales Forecasting

Say goodbye to a lengthy sales forecasting process by using deep learning to predict sales forecasts with increased accuracy

Predict Customer Purchase Behaviour

Understand propensity to buy - how likely a customer is to purchase a product - to help optimise marketing communications and distribution

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Dynamic Pricing & Price Prediction

Suggest personalised pricing to customers in sectors with complex pricing models such as insurance. Predict prices for assets such as real estate

Improve Customer Experience

Conduct real-time sentiment analysis to gain insight during customer service interactions e.g. determine when to switch from an automated chatbot to a human operator

Predict Call Centre Volume

Predict when incoming calls are likely to go through peaks and troughs. Combine this with an employee absenteeism prediction to optimise resource scheduling

There are many other use cases for operational AI from detecting skin cancer in healthcare to assessing car damage in insurance claims. If you have custom requirements please get in touch.

Operational AI Process

1

Data preparation

We take a dataset, such as historical sales and market data, and organise it in a way that can be used for training an AI model.

2

Train your AI model

We plug your data into an AI to train it and begin building a model. The model is customised to your data and unique to your business.

3

Evaluate your AI model

Here we further experiment with the model to improve it by continuing further training if required.

4

Deploy your AI model

We launch the model and, depending on the requirements, build a front-end for users or integrate into existing applications.

Operational AI Integrations

Your AI model can connect with most apps you use in your operations, either directly or through Zapier

Microsoft Excel

Get predictions from you AI model directly into your Excel spreadsheet

Google Sheets

Get predictions from you AI model directly into your Google Sheet

Microsoft Azure

Import your data from your data warehouse such as Microsoft Azure Synapse and train AI models with it

Google BigQuery

Import your data from Google BigQuery and train AI models with it

Microsoft Power Apps

Deploy and visualise your AI models in Microsoft Power Apps or Dynamics

Zapier

Connect to many of the apps you use in your business every day