My sessions on SQLBits 2015

SQL Bits 2015 (XIV) is close to happen in March from 4th to 7th in London.

I have submitted some sessions in upcoming SQLBits, and I hope to get one or more accepted. SQLBits usually picks sessions based on community’s voice. so please go to link below and vote for any session you like to see in this conference;

https://sqlbits.com/information/PublicSessions.aspx

 

Presentation Material SQLSAT352 Sydney

Finished presentation on Data mining approach in SQLSAT352 in Sydney, 25th October

https://www.sqlsaturday.com/viewsession.aspx?sat=352&sessionid=24344

“Show Me Potential Customers “ : Data Mining Approach

In the most marketing departments, the tactical question is about who are going to buy our products. It is more cost effective to identify and spend money on highly potential customers (than those who are not likely to purchase). This also affects the advertisement strategy. Potential customers and their traits can be identified by analysing previous purchasing information. Management experts can predict who is going to be their new customers by analysing their current customer purchase information. There are many data mining algorithms which can help with this task. Microsoft Business Intelligence employs data mining algorithms that are deployed in an easy to use environment. This demonstration based session will show how to use previous customer purchase information to predict potential customers. We will discuss how to set data sets and use different data mining algorithms to get predictive results and then demonstrate how to find the best predictions.

Presentation Material SQLSAT337 Oregon

Thanks for attending my session, I just finished my presentation in Oregan SQLSAT 337 in Oregan

please find the below link to access the presentation power point

https://www.sqlsaturday.com/viewsession.aspx?sat=337&sessionid=25169

 

“Show Me Potential Customers “ : Data Mining Approach

In the most marketing departments, the tactical question is about who are going to buy our products. It is more cost effective to identify and spend money on highly potential customers (than those who are not likely to purchase). This also affects the advertisement strategy. Potential customers and their traits can be identified by analysing previous purchasing information. Management experts can predict who is going to be their new customers by analysing their current customer purchase information. There are many data mining algorithms which can help with this task. Microsoft Business Intelligence employs data mining algorithms that are deployed in an easy to use environment. This demonstration based session will show how to use previous customer purchase information to predict potential customers. We will discuss how to set data sets and use different data mining algorithms to get predictive results and then demonstrate how to find the best predictions.