Pitch deck

1. PREDIKAT
“Yes, we reduce SaaS
(Software as a Service) churn”

2. Problem

3. How we solve the problem in a logical order

4. Analytics Actions
• Predictions score/Accuracy
rate
• Categorizing customers by
churn risk level
• Churn signals
• Churn timing at
individualized level
• Which product’s features to
tweak
• Improving the product’s
navigation
• The “right” set of churners
to target
• How to optimize customer
support
We give you ACTIONS not just
numbers

5. Actionability Rapid
integration
Technology
designed for
churn data
High
predictive
accuracy
Predikat ✔ ✔ ✔ ✔
SAS ✗ ✗ ✗ ✗
Gainsight ✔ ✗ ✔ ✗
BigML ✗ ✗ ✔ ✗
The Opportunity
Predikat vs Competitors
North-American predictive analytics market : USD 2.5 bln*
Customer success solutions share: $ 750 mln* - Predikat’s
category

6. Rapid integration
Our
Customer
3rd parties analytics tools store our customer data

7. Why we’re unique
Deep Machine Learning
 Predictive technology
designed to drive up
short term retention.

 High predictive accuracy
based on Deep learning
 technology Technology tells “Why” the
customer will leave.

8. SaaS Business model

9. Our Milestones and Team
Beta test with 2 pilot customers
One partnership
Goal: MRR – $20K by Q3 2016
Data science team - 3 people: 2 founders, 1
employee (PhD math)
Industry and related R&D and technology experience

10. Next
Preparing for “SAP Startup Focus” – a Program for
predictive analytics startups
**
*Image
source:
www.startupfocus.
saphana.com/

11. Our Ask
Seed funding: $400K :
 Whole product deployment costs
 Integration costs with partners
 Sales and marketing costs (outbound)
 G&A costs
 Runtime: 18 months