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Posts from the ‘analytics’ Category


Benefits of Big Data

Excited by my email update from ShopAkira, a Chicago based store selling brands similar to that of UrbanOutfitters, I immediately started my online shopping escapade after reading about their 50% special going on that day.  After filling my cart with 3-5 items, however, my conscious began to regain control of my shopping frenzied brain.  A quick glance down at the table where a copy of last month’s bank statement lay, further pushed my conscious mind to the forefront, as I regrettably forced myself to close the ShopAkira tab.  Thank goodness…crisis avoided…or so I thought.

You can image my surprise the next morning when I opened my inbox and found another email from ShopAkira.  The email not only provided a reminder of the items in my cart but further egged me on to finish my transaction by giving me and extra 10% off if I finished making my purchases with in the next 24 hours.  Needless to say online retailer 1, broke student 0… well played big data analytics…well played indeed. 

As mentioned in Precision Marketing, companies looking to take advantage of big data need to use “relevance to retain current customers, maximize revenue potential per customer, help to acquire new customers and build long-term customer loyalty.”  Gone are the days where customer loyalty could be built on mass e-mail spams and high general awareness.  In today’s fast-past, high tech world, online retailers need to send relevant, personal messages to their target segments, in order to build any credible kind of relationship that will result in brand loyalty and increased life time customer value. 

Not only can big data help drive brand loyalty and increase customer value, but it can also help to streamline retail practices/decisions and cut costs.  I recently read an article that talked about how ModCloth, another online clothing retailer, is using big data in new ways to personalize their consumers’ shopping experience.  For instance, their “Be the Buyer” program allows customers to vote on certain products with high-ranking products tagged as “Be the Buyer Picks.”  Each “Be the Buyer” poll generates an average of 6,700 votes and helps ModCloth more accurately predict what new product items will sell or what type of product should be reduced in future inventory purchases by product buyers. 

The article can be found here:


Big Data Strikes Again

AdWeek featured an article yesterday called “Three Brands That Used Data to Transform Their Media Strategies” that reflected many of the themes we have been learning about. In examining case studies of Proximity London, McCormick, UPS and Sprint, the article emphasizes 1) the overwhelming availability of data produced today and 2) how analyzing such data effectively is becoming a key means of realizing efficiencies and gaining a competitive edge. These four case studies reiterate the a key point of Precision Marketing, that “data a fundamentally important because data drive insight, insight drives relevance, and relevance drives customer loyalty” (85).

As a side note, this article also demonstrates the value of the analytics education we’re receiving this year in Marketing and Customer Value. Though SPSS can be overwhelming, it looks like ANOVA and chi-squared cross tabs really are the future of market research.


What If Big Data Is Wrong?

One of the many accounts I follow on twitter is @petechasmore of Mashable. Beside being a prolific tweet for the social new aggregate he is also I pretty funny dude. More than a couple of times a day I click on a link and browse at a Mashable post (or at least leave it in a lonely browser tab).

Today I came across this provocative blast suggesting that Big Data may not be all it’s cracked up to be. We’ve heard about the wonders of Big Data and the challenges to sift through it from S&S right up until our last class. We know that it helps marketers understand even the most seemingly trivial bit of minutia to laser pinpoint ads. And yet many of us have had that weird Facebook ad that makes us ask “Why did they think I wanted to see that!

While it’s not a big sample size (or really anything more than a thinly veiled advertisement for Enliken) it does pose an important question. What if all this data we’re collecting, that we’re spending all this money and time to analyze, isn’t actually correct? Curious to see what my data says about me I took the quiz too. According to Enliken advertisers are about 59% correct about my likes and dislikes. Some of them it’s pretty obvious why they are there. Sure, I google and read about finance–but it’s not an interest of mine. That’s pedantic, but some were really off base: I don’t speak Spanish, I’m not a car buff, or, gods forbid, looking for parenting advice.

I sure do like southeast Asian food, so they got that right.

Take the quiz here.


Omni-Channel Attribution Technologies

In class, we briefly discussed the challenge of measuring multi-channel attribution, but what are businesses actually doing about it? To quickly review, multi-channel attribution refers generally to the process of parsing out how, and to what extent, different consumer touch points influence consumers’ buying behaviors. However, different stakeholders have concentrated this definition differently. For clarity sake, we can distill these differences into three common focuses: (1) the impact of online communications on offline sales, (2) the consumer experience across multiple devices as it drives toward conversion, and (3) the consumer experience across multiple digital marketing channels as it drives toward conversion.[1]

All such articulations are plagued by the same underlying challenge. Although marketers in today’s omni-channel marketplace must accurately measure the value of different channels to strategically allocate their resources for the highest conversion rates, the cross-channel attribution technologies to do so are both underdeveloped and in short supply. Enter Adometry, a relatively young cross-channel ad attribution firm that has recently raised $8 million in funding to combat these challenges. According to Adometry’s CEO, Paul Pellman, “the company’s technology can track offline conversions and tie those to online ad activities—helping brands figure out the impact and value of multiple media channels.”[2] Similar firms have undoubtedly begun to follow suit and develop their own comprehensive analytics platforms to identify and quantify multi-channel performance drivers. However, as of date, Adometry appears to be ahead of the curve in this markedly immature industry (For more information, refer to the company website: That said, the influx of competition and R&D investment will likely speed the rate of innovation and fuel a more vigorous attribution measurement landscape in the coming years. Thereby, the everyday marketing toolbox will soon include fully-integrated attribution measurement technologies that provide realtime results and clarify the precise interplay and impact of disparate marketing investments.

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