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Customer Retention. Automated

Extracting Big Data Gold at the WhatCounts Smart Marketing Roadshow

Posted by Andrew Pearson on Aug 23, 2013 9:55:00 AM
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NEWS FLASH: We're speaking about Big Data Gold at the next WhatCounts Roadshow in Washington DC on Sept 17.  Sign up today!

Windsor Circle CEO Matt Williamson recently spoke at the WhatCounts Smart Marketing Roadshow in Seattle, a half-day conference for some of the Northwest's most innovative digital marketers.  Matt and other speakers talked about how digital marketers are increasingly using their data to deliver dynamic, personalized messages through email, mobile, social and web channels.

On his return, I asked Matt to reflect on his experience. "The industry is rocketing towards the actual deployment of ideas driven by big data", noted Matt. "Those who think that big data is a buzzword are at a serious disadvantage to those who are actually using it to engage their customers and unlock revenue that they couldn't previously access." 

We're seeing the Big Data term entering into the eCommerce lexicon more and more.  It is in part due to the growing volume of data available to retailers from multiple platforms, and in part due to the complicated nature of extracting value from these diverse data-sets, some of which are structured (contact info, order history, demographic data) and some of which is unstructured and/or requires deep analysis (content consumption, social activity, predicted order dates, RFM).  But the ongoing success of companies who understood the value of collecting, analyzing, and using data from the beginning have proven its value.

"Everyone knows that Amazon has been doing it for years," says Matt. "What fewer people know is that retailers big and small are leveraging tools like Windsor Circle to deploy these kinds of programs today, and at a fraction of the perceived cost." 

The use of Big Data pays off: "We have IR500 clients who are generating 40x-90x ROI by targeting best customers, churning customers, those ripe for replenishment," says Matt. "What I see in the industry is that this is a zero sum game.  Those using big data today are beating retailers who are not using data.  It's that simple."

The most challenging aspect to smart marketing is getting clean data and then knowing what to do with it.  Matt reflects: "We see so many retailers with CRM or data mart projects who consider it a complete failure.  The problem is what I call 'marketing nirvana':  Marketing says 'we need smart marketing.'  CEO tells IT, 'make it happen.'  IT says 'well what do you want?"   Marketing says 'I want it all.'  This is a recipe for disaster.  The marketer starts with 'get me all of the data and I'll figure out the programs afterwards.' But this rarely happens."  A better approach is to go after actionable ideas, and then get the data required to execute just on those ideas.  There is a natural order to Data-Driven Marketing.  A gold mining analogy may help: Mining for Big Data Gold 1) Panning for Gold: Finding the Easy Data The easiest data to mine is that right at your finger tips: contact info a customer or subscriber has entered into your email marketing software.  Addresses.  Birthdays.   2) Digging for Gold: Getting at "Hidden Data" The next natural progression is to go after data that is not readily available directly in the marketing platform, but with the right tools, is fairly easy to get to.  This could include order data that can be imported into the marketing software, tied to an email address, and thus used to send out basic data-driven marketing messages, like a "Welcome Series".  You simply need to know the date of the customer's first order to trigger a series of emails that welcome them to the brand.
3) Extracting Deep Gold: Mining "Big Data" The third step involve the use of more sophisticated tools (akin to the massive digging machines, huge dump trucks, and mobile processors of large-scale mining) to extract and process large amounts of unstructured or hard-to-get-at data-sets to find the many nuggets of gold that are hidden by the massive volume of noise.  These data sets require analysis of purchase history, content consumption, demographics, social data, and more to find critical customer segments, personalized offers, and automation rules based on predicted lifetime value, replenishment dates, or optimal latency.

Fortunately, the revolution in big data analytics make these sophisticated machinery available to even small retailers - essentially, the ability to lease the tools rather than build yourself.

There are big shifts on the horizon, as we see companies who are collecting massive amounts of data, but not currently part of the eCommerce landscape, move that way.  "My biggest aha moment was when Allen Nance shared that Facebook has bought 3 payment processors," mused Matt. "Facebook is serious about moving from just big data into monetizing it.  Social commerce will simply morph into eCommerce... and big data driven marketing automation will be the engine that powers the future winners."

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