Tag Archives: IBM

4 reasons why I am the perfect audience for analytics

I recently joined a small company after spending 8 years at IBM. While there I was fortunate enough to be exposed to some of the world’s best analytics offerings. I also had the opportunity to work with some really strong thought leaders like: Marcus Hearne, Erick Brethenoux, Olivier Jouve, Oliver Oursin and many others. Now that I have moved out of IBM I am feeling the pain many of my former clients feel.

I have become the target audience for many of the analytics vendors out there. IBM, Qlik, Tableau and others all have a shot at my business.  So why is that?  What is it that makes my company so typical and in such need for easy to use, fast, intuitive analytics that deliver a single version of the truth?

  • What business questions to I need to answer? – This is a fundamental issue.  Helping to identify the key business drivers has proven much more challenging then I would have thought.  Many in the business do not even know what they are trying to understand.  This obviously comes with experience and time.  But it is really something organizations must do a better job with unless they want to constantly be behind.
  • Metrics for the sake of metrics: We measure EVERYTHING and I mean literally everything in our sales organization.  On the one had, I am happy from a sales performance management perspective that we have some base metrics.  But on the other hand, we have an inability to influence or take action on the metrics.  We suffer from “so-what-itis”.  I cannot tell you how many times I have looked at some of the KPIs and metrics and asked “So what?”.
  • Data:  How do i put this politely?  Our core marketing and sales date is CRAP!  We suffer from duplication,a poor data capture approach and a lack of completeness and correctness.  When  asked: Who is responsible for the data quality?  The silence was deafening.  Data is the foundation for any organization.  Currently my foundation is made of wet, clay bricks.
  • Excel!  We are excel junkies.  All analysis is done in excel…literally everything. And guess what?  The visualizations are frickin awful.  There is no one, single, governed version of the results (quarter end was particularly enjoyable with finance.  They gave one version of the numbers and sales ops provided another.)  I personally want to answer: what WILL happen? Think Excel can do that?  Yep, you are right.  No way!

So what do we need to do to become more analytically driven?  For me it is not a question of whether or not an analytics tool will help me.  It is a question of how to best approach moving forward…

Come back next week to learn about the plan to address these areas…(Oh, and if you are IBM, Qlik or Tableau…I would send a sales rep!!!) Let’s see which vendor calls me first!

Finally! Predictive Analytics even I can use!

When I was running marketing operations, I had hired a statistics expert to come into the organization to help me make sense of all of our marketing data.  As I worked with this excellent “stats guy”, I realized that predictive analytics can be pretty darn complicated and required a more specialized skill set than I had originally thought.

This all changed on Tuesday June 11, 2013.  IBM announced the availability of IBM SPSS Analytic Catalyst – a statistician in the software – which makes predictive analytics on big data available to mere mortals like myself.  I was admittedly skeptical that this new offering could actual make predictive analytics accessible.  There had to be some gotcha….  well, I was wrong (not something I admit often!).

So, what does this thing do exactly? Let’s say you are a marketer and want to understand which customers would likely “churn” or leave for your competitor.  This is where Analytic Catalyst comes in.  There is a three step process to get the answers you need.

  1. Add your data (csv file)
  2. Select the field you would like to predict
  3. Review plain English results

Sounds simple?  Well it is.  Review a demo here to see how this can help marketers, customer service teams, sales and other organizations find the small data within the big data!

Big Data – big deal or big hype?

Big Data is a reality.  Big data is also big hype. So where is the real value in the Big Data hype?

Like it or not, Big Data is a reality marketing and other organizations are facing today and will face well into the future. According to the IBM Global Technology outlook, it is estimated that data growth will explode exponentially from 3,000 exabytes today to 9,000 exabytes by 2015. (I had to look up the word exabyte!) So what do we marketers do about incorporating this new source of data?  Where are the best use cases?

We are faced with a challenge – Big Data alone does not help us.  We need ways to get value out of the data.  The challenge is that traditional analytic tools are not exactly suited to take advantage of Big Data.  Analytics vendors are developing offerings in an attempt to keep up with the ever changing needs.  With that said, we need to be thinking about where it fits in.

For me, the best use case is squarely in the camp of customer analytics.  I love my data from the marketing automation system.  It tells me a great deal about the patterns and behaviors my customers and prospects display.  But it only gives me a piece of the puzzle.

As I think about my customer marketing initiatives, I need to be able to prevent customer defection (commonly known as “churn”). By leveraging unstructured data (call center notes, customer interactions, survey responses…) with internal structured data to detect & proactively mitigate factors that lead to defection,  I can better design marketing programs to avoid that “aha moment” when customers realize they no longer want to do business with satisfaction surveyus versus defecting to a competitor.  Conversely, applying the same approach can help marketers identify the “aha moment” which leads to improved customer loyalty

So, is Big Data hype or is it really something we marketers can benefit from?  If marketing organizations do several things, I believe it will improve marketing’s ability reach the target audience with more appropriate offers.

 

  • Big Data platform:  Do not look at Big Data as this mysterious beast that can solve world hunger!  It should be considered an additional data source which can augment existing data sources such as CRM, ERP or marketing automation data.  If you are a marketer, you should be connecting with your IT team to determine how to incorporate   this data into your existing data.
  • Analytics: Do not forget the analytics!  Discover, visualize and explore big data alongside traditional information to drive action and share your insights with others. Employ predictive analytics to identify patterns within the data leading to more accurate answers.
  • Answers!  Ultimately, it is what you do with the data versus the data itself! Use Big Data combined with traditional data sources to deliver more accurate answers by analyzing, predicting and automating decisions.

Connect with me to continue this discussion:  @BrendanRGrady