Sia is cognitive-
ONE product, THREE solutions.

Sia

sia insights

Uses cognitive technology to understand all types of unstructured data around your customers and marketing assets.

  • Feature: Custom entities, concepts and categories, Custom sources & data, API integration, Chatbot UI
  • Use Case: Competitive analysis, Product intelligence, Audience insights, Regional insights
Insights

ENGAGE

Who am I talking to?
How much do I know about him/her?
How do I engage them?
What’s my story?


DISCOVER

When are the personalisation decisions made?
How is the personalised message selected?
Where is the conversation happening
How connected is it to other conversations?

DECIDE

What gets personalised?
Who do we go after?
How do we scale?




Personalized content

sia PERSONALIZE

Hyper-segments your target audiences for fully automated distribution of assets across channels.

  • Feature: Unlimited personalization across social, web and newsletters, API integration, Chatbot UI
  • Use Case: Run a full marketing campaign from awareness to conversion across social and newsletters

68%

Of firms have made delivering personalized experiences a business priority.

51%

Of firms rely on email marketing to achieve their personalization goals.

53%

Of firms lack the right technology to personalize experiences.

sia PREDICTION

Relies on cognitive analytics to predict success of campaigns and assets for guaranteed ROI

  • Feature: Predict a transaction based on historical data, Custom sources & data, API integration, Chatbot UI
  • Use Case: Predict & optimize marketing campaigns, keywords, & up-sell
Marketing Predictions

CHURN

Predict how users will respond to what type of campaign to reduce churn

Churned Users x Average LTV = Revenue generated by reducing churn

QUALIFY

Qualify targets by predicting their likelihood to engage and convert

Churned users x Cost per acquisition = Money spent acquiring users who needlessly churn

cac

Utilize your resources and $$ efficiently by predicting what will work and what won’t

Money spent acquiring users who needlessly churn + Revenue generated by reducing churn = Total money lost