Overview of AIRA dashboard

Artificial Intelligence Retail Analytics (AIRA) dashboard is Capillary’s platform to run machine learning models. AIRA dashboard offers self-serve personalization with several models available out-of-the box.  The AIRA Dashboard allows you to launch models for your org in just a few clicks and leverage valuable inferences from them.

AIRA dashboard abstracts the complexity of data science and makes machine learning models easily accessible to analysts. You can make business decisions based on the insights provided by the model. Since, processing and making inferences on data present in large volumes cannot be handled manually, machine learning provides an intuitive solution to the problem. The AIRA dashboard closely works on the principles of machine learning operations (MLOps). MLOps streamlines the data science outreach for an organization.

Benefits of AIRA dashboard

AIRA Dashboard aims to address the following benefits for running and managing machine learning models efficiently.

Ready availability of machine learning models

The AIRA dashboard fosters model discovery with all existing and upcoming models available on the dashboard. To know the list of models that can be used for running personalization campaigns, for example, brand users need not rely on Capillary Customer Success (CS) teams.

Seamless integration with Capillary application

Once the models go live on the AIRA dashboard, the results (predictions) can be utilized in Engage+ as filters to run personalized campaigns.

Likewise, users of Insights+ can leverage live models to generate reports that depict vital statistics. 

All model results can be visualized on Insights+ reports. However, the Insights+ team needs to setup KPIs for the reports to view results.


Improved productivity

The AIRA dashboard has been designed with the goal of making Artificial Intelligence (AI) self-serve. A marketer looking to run a personalized, churn killer campaign, for instance, need not rely on internal data science teams. Capillary’s Insights+ team intervention is also not required.

With the AIRA dashboard, the marketer can leverage the relevant model while utilizing predictions to run personalized campaigns. There is no productivity loss as there is no dependency on internal or external teams.

Easy management

AIRA dashboard provides a timely management of the machine learning models that an organization deploys. The dashboard validates and trains the model regularly in periodic intervals to ensure that predictions are accurate and based on recent data.

To get started with AIRA Dashboard, see here.


Use Cases

Model display nameUse cases of the model
Transaction Propensity
  1. The high propensity customers can be encouraged to try products that have been newly launched. Orgs can identify the customers that are most likely to transact in the next 30 days using the transaction propensity model and target these customers with specific campaigns. 
  2. Orgs can also identify the customers that are least likely to transact in the next 30 days and exclude them from campaigns. 
  3. Product preferences of customers who are most likely to transact in the next 30 days can be analyzed using Insights+. Use results generated by models in Insights+ for reports. 
Customer Churn Prediction
  1. Orgs can identify the customers who are likely to churn in the next 30 days and these customers can be targeted with aggressive churn killer campaigns. 
  2. Customers who are least likely to churn can be targeted with informational campaigns (instead of aggressive, personalized offers) to keep them engaged.

Campaign Response Prediction

  1. Orgs can identify customers who are most likely to respond to a campaign. For instance, customers not likely to respond to a festive campaign can be excluded and campaign costs can be saved.
CLTV Prediction
  1. Orgs can identify the most valuable customers for the brand using the CLTV Prediction model. Orgs can create an exclusive experience for such customers. 
  2. Orgs can identify the customers with low CLTV value and offer them loyalty services or additional discounts to upscale their value.
  3. Orgs can use a combination of CLTV prediction and churn prediction models to arrest churn of high value customers.