Actionable Insights from Big Data in Affiliate Marketing

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Big Data presentation from Performance Horizon Group and BrandVerity at the Performance Marketing Insights New York Conference.

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  • <read title> At some point data become more than just numbers, from 1’s and Zero’s it became, product and page views, customer info, membership info, product reviews, page shares on social networks, photo and video uploads, product pricing and promotions, customer service phone logs – I mean we like to record everything we can these days Let’s define big data generically… <read definition – only first part> Essentially - It’ s the things you do with the data as well… What changes data into big data is huge volumes, and the speed at which you can create, collect, and analyze those huge volumes If data’s goal is to answer a question – Big Data’s goal is to answer any and every question Lets look at an example…
  • Amazon IS Big Data <read> Yea that is a lot stuff – sold to a lot of people – and Amazon records info about every single one of them. And at first it was all just about selling and they collected lots of data. But with it they realized they can tell you things you might like to read or buy – sometimes things you didn’ t even know existed. Amazon is able to answer questions they weren’t aware anyone would be asking, but now are…like what a customer might want? They changed the game as we knew it. Now I get 10 emails a day telling me things I might like to buy. That’s the value of data – it helps in answering questions
  • <read title> The advent of new software is a big part of the reason we all hear about Big Data The days of a one size fits all solution for data are long gone – hence the term NoSQL Software is essential to making sure you can manage any data and now the software is readily available to mange big data Softwares like these are what power some of the biggest names on the web – like Google, Facebook and Twitter
  • <read title> So we asked why now but the truth is big data has been around for a while – since the 90’ s. It was a term used to describe statistics software. And while software has made it easier to manage big data it hasn’ t quite made it cheap <read not cheap unbolded>
  • <read data to big data> DATA HAS ZERO VALUE – but information now that has power – information helps us make decisions Data’s end goal has always been to answer questions. Big data’s end goal is to be able to answer any question. <read quant to qual> We have to remember we use data and the human factor to make our business decisions. Never forget the importance of the human factor and how we interpret the data as it applies to our business. Data, even big data, alone will never be enough to drive a business – its all about how we interpret that data. 3 things you have to remember when dealing with big data <next slide>
  • QA - Big Data is new for most IT, make sure they know how to collect it, store it, and make it easily accessible. Correlation – Make sure when you are making decisions based on your data they are sense checked. Test and retest the relationships you have identified before making big decisions. Intentions – Data isn’t always clear especially if you aren’t collecting the data for the reason you are using it. Example – you look at the data on all of your employees and you find out the person you thought was your best employee has missed the most deadlines. What data doesn’ t tell you is that you have given this employee the hardest projects – projects that were never expected to be done on time. Data doesn ’ t always have a clear understanding of all the variables.
  • <read title> <read big data and bullets> As targeting will become that much easier – expect online marketers to focus on retargeting – what keywords are really relevant in what parts of the country? Where should I be hiring my next sales person? Do I need to find new shipping routes?
  • <read hyper and bullets> Location based tech will move beyond the street-level and move to the aisle-level In store shopping as we know it today is going to change completely and with it the slow and steady demise of the brick and mortar store
  • Actionable Insights from Big Data in Affiliate Marketing

    1. 1. Actionable Insights fromBig Data in Affiliate Marketing ..
    2. 2. The Obligatory Bullets Slide • What Big Data is… • Why is Big Data suddenly a buzzword? • What Big Data is NOT… • What you can’t forget… • What’s Next for Big Data? www.PerformanceHorizon.com
    3. 3. What is Big Data? Data that is so large and complex it becomes hard to “process” But its more than just processing: Capture, collect, analyze, and visualize www.PerformanceHorizon.com
    4. 4. What is Big Data? Amazon sells 300K items a day In 10 countries Millions of Products Hundreds of Categories www.PerformanceHorizon.com
    5. 5. Why is Big Data suddenly a buzzword? NoSQL – Not Only SQL Hadoop Mongo Redis Cassandra www.PerformanceHorizon.com
    6. 6. What Big Data is NOT… Big Data is not a new term – it’s been around since the 1990’s Big Data is not cheap! Big Data is expected to drive $34B in IT spending in 2013 and create 4.4 million jobs by 2015 www.PerformanceHorizon.com
    7. 7. What Big Data is NOT… Data is not an answer and neither is Big Data Information leads to answers and data supports information and decision making Quantitative Data will lead us to Qualitative Decisions www.PerformanceHorizon.com
    8. 8. What you CAN’T forget… 3 Keys Quality Assurance Proper Correlation Data with Intention www.PerformanceHorizon.com
    9. 9. What’s Next for Big Data? Big Data will become smart data • Minute to minute sales data - by location, by SKU, by customer by location New data means: • Better targeting • Better hiring www.PerformanceHorizon.com
    10. 10. What’s Next for Big Data? Hyper local marketing • Buy bread, get a coupon for butter • Buy brand for $4, show generic at $2 Marketing moves to the aisle-level Selling moves ever more to the web www.PerformanceHorizon.com
    11. 11. Thanks for listening… Charles Calabrese VP of Operations Performance Horizon Group @ccalabrese3 Contact: Charles Calabrese Email: charles.calabrese@performancehorizon.com www.PerformanceHorizon.com
    12. 12. AppendixVideo via BBC - http://www.bbc.co.uk/news/technology-21535739Origins of Big Data - http://bits.blogs.nytimes.com/2013/02/01/the-origins-of-big-data-an-etymological-detective-story/?smid=tw-shareSize of Big Data -http://www.cio.com/article/728854/Big_Data_is_a_Solution_Looking_for_a_Problem?taxonomyId=600010What you CAN’T Forget - http://podcasts.infoworld.com/t/big-data/big-datas-pitfall-answers-are-clear-compelling-and-wrong-211647 www.PerformanceHorizon.com

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