{"id":641516,"date":"2013-02-06T14:54:10","date_gmt":"2013-02-06T19:54:10","guid":{"rendered":"http:\/\/gigaom.com\/?p=607888"},"modified":"2013-02-06T14:54:10","modified_gmt":"2013-02-06T19:54:10","slug":"exclusive-causata-raises-7-5m-and-steps-up-its-game-in-targeted-ads","status":"publish","type":"post","link":"https:\/\/mereja.media\/index\/641516","title":{"rendered":"Exclusive: Causata raises $7.5M and steps up its game in targeted ads"},"content":{"rendered":"

I\u2019ll be frank: Causata\u2019s marketing software<\/a> is a little creepy in the level of personal data it collects and analyzes, but it also seems very good at what it does. Good enough for the company to close a $7.5 million Series C round from Accel Partners in December, bringing its total funding to $23 million (all from Accel) since launching in 2009. It\u2019s latest wrinkle: machine-learning algorithms that automatically figure out which campaigns are most likely to work on what customers.<\/p>\n

If you\u2019re not familiar with Causata, it\u2019s a true big-data application dedicated solely to stitching together customer identities so marketers know what they want. It collects first-party data \u2014 cookies, email addresses, usernames, site activity, customer service phone calls and everything it can, really \u2014 and stuffs it into an event store, from where users can run predictive algorithms against the data. Because it takes in such a wide variety of data, Causata stores everything in HBase, the NoSQL database that sits atop the Hadoop Distributed File System and is designed with such unstructured or semi-structured data in mind.<\/p>\n

\"IdentityGraph_01_0\"<\/a><\/p>\n

Previously, though, as VP of Marketing Brian Stone explained to me, analytics and predictive modeling within Causata were solely an offline function. Analysts used R, Tableau, Qliktech, plain SQL or their data-analysis tool of choice in order to work thr0ugh data, learn who\u2019s who among customers and then ultimately build their models. With the new machine-learning capabilities, the system is always looking at how companies are targeting consumers and how those consumers are behaving, and then generating models to predict how certain actions might influence behavior one or even many steps down the line.<\/p>\n

Once the data analysts figure out who\u2019s who and how particular microsegments are likely to respond to particular actions, the marketing team can put these models to work in their existing platforms for placing advertising, surfacing offers or whatever other methods they might use to try and reach consumers.<\/p>\n

About that personal data \u2026<\/h2>\n

Any time we\u2019re talking about personal data, though, a certain subset of consumers is likely to get creeped out \u2014 and rightfully so. It comes down to that now well-known tradeoff between how much we value personalization and how much we value privacy<\/a>. Not surprisingly, Stone says he\u2019s open to advertising when it\u2019s \u201cpersonalized, timely, relevant and intelligent.\u201d If his bank didn\u2019t \u201ccontinually misfire\u201d in trying to make him loan offers that don\u2019t match his situation \u2014 something they should know based on his account information, online banking and site activity \u2014 he might actually be willing to take it up on an offer.<\/p>\n

Besides, he noted, the only time a human being (at least using Causata\u2019s software) would ever really have reason to look at personal-level data is during troubleshooting or when trying to figure out better methods for segmenting customers. Ideally, this is by activity-based data such as price-consciousness or loyalty rather than classical demographic data such as age, sex or race. But in terms of actual ads or offers served, the system clocks your activity, runs a predictive analysis against your identity profile and returns a result in well under a second.<\/p>\n

This happens to be the same method, or at least a similar method, undertaken every time we see personalized ads online: No human being is sitting around, looking at our data and deciding we need hemorrhoid cream.<\/p>\n

\"MachineLearning_02\"<\/a><\/p>\n

Given the amount of digital data we\u2019re contractually giving away every time we use surf the web or use our smartphones, combined with the number of companies out there trying to help marketers make sense of it<\/a>, the personalization genie probably isn\u2019t going back into its bottle. Not that that\u2019s necessarily a bad thing.<\/p>\n

I\u2019m reminded of a conversation I had with IBM Fellow and overall identity-data genius Jeff Jonas<\/a> nearly three years ago. He explained his theory on how extensive data tracking will ultimately lead to a surveillance society<\/a> but we\u2019ll love it because we love optimization. \u201cIt\u2019s seemingly irresistible to us,\u201d he said.<\/p>\n

When someone actually gets targeted advertising right<\/a>, maybe it will be.<\/p>\n

To learn more about machine learning, privacy, Hadoop and everything else driving the discussion around big data, come to our Structure: Data event<\/a> March 20-21 in New York.<\/em><\/p>\n

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