Move Beyond The Campaign

Rob Brosnan

Standing in an aisle of a big box retailer, I bought a new electric shaver from a competing retailer’s online store. The store’s shaving display reminded me that my razor was dying. Not knowing which to choose, I twitched for my iPhone, scanned a barcode, read several reviews, explored competing products, found the best price, and ordered it with free shipping. I saved $75 over the same model I could have purchased then and there.

My example is commonplace today. Perpetually connected customers – 42% of US online adults and 37% in Europe – can engage brands at any place, any time, and at any velocity. The technology trends that lead retailers to worry about showrooming touch every industry. Each brand must anticipate connected customers’ demand for information, reviews, and engagement. They must realign technology, processes, and talent to recognize customers in microseconds, using real-time signals to predict their needs and paths to purchase. And they must see that this problem can’t be solved with faster technology alone.

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MOVE BEYOND THE CAMPAIGN

Rob Brosnan

Standing in an aisle of a big box retailer, I bought a new electric shaver from a competing retailer’s online store. The store’s shaving display reminded me that my razor was dying. Not knowing which to choose, I twitched for my iPhone, scanned a barcode, read several reviews, explored competing products, found the best price, and ordered it with free shipping. I saved $75 over the same model I could have purchased then and there.

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Let Big Data Predictive Analytics Rock Your World

Mike Gualtieri

I love predictive analytics. I mean, who wouldn't want to develop an application that could help you make smart business decisions, sell more stuff, make customers happy, and avert disasters. Predictive analytics can do all that, but it is not easy. In fact, it can range from being impossible to hard depending on:

  • Causative data. The lifeblood of predictive analytics is data. Data can come from internal systems such as customer transactions or manufacturing defect data. It is often appropriate to include data from external sources such as industry market data, social networks, or statistics. Contrary to popular technology beliefs, it does not always need to be big data. It is far more important that the data contain variables that can be used to predict an effect. Having said that, the more data you have, the better chance you have of finding cause and effect. Big data no guarantee of success.
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