Looking at data with a hawk’s eye

I recently have been working with a peer in the creation of a next wave company called RytePath.  Both of us come from the enterprise CRM and real time scoring space in the 2000’s.   Many of the early pioneers in the CRM, including Siebel and Unica, offered clients the potential to manage onmi-channel programs against a core customer database model that recorded all contact touches.  The real time analytics space allowed us to pre-score prospects by their buying propensity across outside databases like Experian and Acxiom.  The idea, in those days, was to push communication to worthy prospects, who in turn were likely to buy.  One constraint of these early systems was the massive amount of data that needed to be processed via ETL to make the data useful.  None of the systems could scale into the millions of records.  All required long batch processing times.

Move forward to 2016.  The Big Data revolution has brought forth the next wave of in memory indexing tools.   This allows us to read in any database, and then,  understand the underlying demographics and the channel options associated with each database in real time.  I can now merge together complete prospecting databases by their area of strength, around the needs of a vertical.   We understand the customer header records, so we know which source can support call center, email, digital, social or direct mail, by vendor. The in memory tools allow us to cross-index all the data points, so we can hover over the data like a hawk and understand all the water fall relationships between individual elements.  The cool thing is we can address trillions of fields associated with billions of records.

We have just begun testing with several alpha clients.   It is great to swoop down on an intended target in seconds.  This is one of the most exciting eras now for marketing decision science, something we would have dreamed about having two decades ago.

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