Improving Customer Experience through Predictive Behavioural Analytics Meetup with TLA DataTech and SYNTASA

We had a great evening with DataTech Events, TLA DataTech and an engaging crowd as we welcomed Syntasa to our Meetup as they talked us through their predictive behavioural analytics platform on Wednesday, 21st September in London.
 
Shawn Zargham, Syntasa CTO, started off by explaining the differences between Analytics (better apps & sites), Enterprise BI (better functioning business) and Behavioural Analytics (making happy customers) which provided great context to the audience.
 
Next up was an explanation of the Behavioural Analytics path to success which looped through “Personalise”, “Realise” & “Analyse” and driving back to “Personalise”. This is where you build personalised experiences, run test & learn initiatives to drive customer satisfaction to get better conversion, retention and revenue. As any good Data guru knows, this then requires the analysis of the data which then goes back to inform “personalise”…and so on.
 
Shawn then went into a live demo, always tricky when presenting something new, but the wi-fi worked great, thanks to Cooley’s who hosted our Meetup, and so was the interactive demo of reporting and insights.

The visuals were great & the way their predictive analytics tool enables informed recommendations for buyers based on continuous data analysis was impressive.

We liked that their platform sits in a Hadoop environment and can also be used by Data Scientists as access to the schema is granted so you can work offline & carry out your own statistical analysis & modelling.

Another great feature was the integrations they have such as the Adobe Analytics plugin which brought in props and eVars using an api directly from Adobe. There were other integrations available with saleforce and SAP to name a few to join up diverse data sets.
 
It was great to see a relatively new tool expanding into the global market and addressing the predictive and behavioural analytics space right here in London.
 
Kam