DATA ALCHEMY: Emerging Trends Conference





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Uploaded on Jun 18, 2015

Dean Abbott, Co-founder & Chief Data Scientist, Smarter HQ. He is presenting his views on emerging trends and predictive views analytic and business and he is talking about a predictive approach to Omni channel marketing.

He is telling about the strengths and weakness of the different tools from a practitioners standpoint. This case study talking about the behavioral marketing, a smarter HQ as an SAS company. We provide behavioral marketing capabilities for retailers primarily due to the segment and massage customer.

Why do you want to think broadly about the customers? It's because 80% of store shoppers check prices online. $1.1 trillion influenced by the web. Smart phones are the dummy and won't be long before the smart phone become the preferred transactional device of choice. Sales are two different $2 billion in sales just for e tail in 2013. Through lots of channels and ways, people interact with brands, so the question is how can we leverage the data? What is the data look like and how could we leverage these multiple channels of data, when we are making decisions.For example, we got a website data, desktop, and mobile that's why people interact with your brand, the mobile app. In stores, your behavior may be different in different stores near the store close to your house, office and the store you visit on your vacation, your behavior could be different. The different action with your brand as the retailer influences how you message somebody.

What is multichannel and Omni channel? It is an operational view that how you allow the customers to complete the transaction in each channel. Whereas Omni channel is viewing the experience through the eyes of customers, orchestrating the customer experience across all channels so that it is seamless, integrated and consistent. Omni channel anticipated that customer starts in one channel and moves to the other as they progress to resolution. There is revolution taken place in retail these days. The machine learning is becoming accessible enough so that we can leverage it in a way which is actionable data driven.

Big data is not smart, it's just big. There is we talk about data legs. There is lots and lots data there. But that data in itself doesn't do anything for us. We have to contextualize, manipulated it into a form which we can use for predictive modeling and analytic.

What data do we have?
- Visit behavioral (digital)
- Purchase behavior (digital)
- Digital data
- Demographics
- Analog data
- Purchase behavior

He also mentioned about the website of Avinash Kaushik. He found it very interesting.
Data driven analysis for large data sets are:
- Data drove to discover input combinations
- Data drove to validate models
Automated pattern discovery:
- Key input variables
- Key input combinations

You heard that 80% of time spent on the preparation of data. It's not the data you collect, it's also the data you create from the data you collect. That reflects the best understanding of what that customer looks like. So the goal f the conversation is we want to know who they are and what they do? The customer intelligence piece and then how do you use those predictions to define your programs. Are they near term purchases or are they long term purchases, what channels do they prefer to purchase in. Which will influence what message you give in to what channel

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