MARKETING ANALYTICS Press Release

For Immediate Release

NEW BOOK – Marketing Analytics: A Practical Guide to Real Marketing Science

New Book Reveals When Your Customers are Most Likely to Buy

Available today, Marketing Analytics arms business analysts and marketers with the understanding and techniques they need to solve real-world marketing problems, from testing campaign effectiveness and forecasting demand to employing survival analysis to determine when your customers are most likely to buy. It outlines everything practitioners need to ‘do’ marketing science by following fictional analyst Scott as he progresses through his career and makes increasingly better marketing decisions.

The author Mike Grigsby has been involved in marketing science for over 25 years. He was marketing research director at Millward Brown and has held leadership positions at Hewlett-Packard and the Gap. He now heads up the strategic retail analysis practice at Targetbase and is an adjunct professor at the University of Texas at Dallas.

Part of the new Marketing Science series by Kogan Page, which makes difficult topics accessible by grounding them in business reality, Marketing Analytics helps readers refine their marketing skills so they can compete more effectively in the marketplace. It provides insight into the power of data analytics in the context of marketing problems; explains and demonstrates marketing data modelling techniques in a practical way, illustrates how data modelling methodology can be applied to a range of practical scenarios and offers advice and step-by-step guidance for ways to solve some of the most common situations, opportunities and problems in marketing.

Dr. James Mourey, Assistant Professor of Marketing at DePaul University in Chicago, has offered advance praise, declaring, ‘For those MBAs who barely passed their quantitative marketing and statistics classes without truly understanding the content, Marketing Analytics provides everything managers and executives need to know presented as a conversation with examples to boot! You’ll definitely sound smarter in the boardroom after reading this book!’

For a review copy (ISBN 9780749474171), a by-lined article or to arrange an interview with the author, please contact Megan Mondi: mmondi@koganpage.com or +44 (0)20 7843 1952.

http://www.koganpage.com/product/marketing-analytics-9780749474171

http://www.amazon.com/Marketing-Analytics-Practical-Guide-Science/dp/0749474173/ref=sr_1_1?ie=UTF8&qid=1433361116&sr=8-1&keywords=grigsby

 

 

The Required Spiel on B-I-G D-A-T-A

INTRODUCTION

Okay, this had to be done.  It’s time.

I’ve avoided it because Big Data (yes, you have to capitalize it!) is everywhere.  You can’t get away from it.  It’s in every post and every update and every blog and every article and every book and every resume and every college class anywhere you look.  It’s inescapable.  Big Data has become the Kim Kardashian of analytics.

So now it’s time to add to the fray.

 

WHAT IS BIG DATA?

No one knows.  I’ll provide a working definition here but it will evolve over the years.

First, Big Data is BIG

Duh.  By “Big” I mean many many rows and many many columns.  Note that there is no magic threshold that suddenly puts us in the “Oh my, we are now in the Big Data range!”  It’s relative.

This brings us to the second and third dimension of what is Big Data: complexity.

Second, Big Data is potential multiple sources merged together

The dimension of Big Data came about because of the proliferation of multiple sources of data, both traditional and non-traditional.

So we have traditional data.  This means transactions from say a POS and marcomm responses.  This is what we’ve had for decades.  We also created our own data, things like time between purchases, discount rate, seasonality, click through rate, etc.

The next step was to add overlay data and marketing research data.  This was third-party demographics and / or lifestyle data merged to the customer file.  Marketing research responses could be merged to the customer file to provide things like satisfaction, awareness, competitive density, etc.

Then came the first wave of different data: web logs.  This was different and the first taste of Big Data.  It is another channel.  Merging it with customer data is a whole other process.

Now there is non-traditional data.  I’m talking about the merge-to-customer view.  IN terms of social media the merge to individual customers is a whole technology / platform issue.  But there are several companies who’ve developed technologies to scrape off the customer’s id: email, link, handle, tag, etc. and merge with other data sources.  This is key!  This is clearly a very different kind of data but it shows us say number of friends / connections, blog / post activity, sentiment, touch points, site visits, etc.

Third, Big Data is potential multiple structures merged together

Lastly Big Data has an element of degrees of structure.  I’m talking about the very common structured data through semi-structured and all the way to unstructured data.  Structured data is the traditional codes that are expected by type and length–it is uniform. Unstructured data is everything but that.  It can include text mining from say call records and free form comments, it can also include video and audio and graphics, etc.  Big Data gets us to structure this unstructured data.

Fourth, Big Data is analytically and strategically valuable

Just to be obvious: data that is not valuable can barely be called data.  It can be called clutter or noise or trash.  But it’s true that what is trash to me might be gold to you.  Take click stream data.  That URL has a lot of stuff in it.  To the analyst what is typically of value is the page the visitor came from and is going to, how long they were there, what they clicked on, etc.  Telling me what web browser they used or whether it’s an active server page or the time to load the wire frame (all probably critically important to some geek somewhere) is of little to no value to the analyst.  So Big Data can generate a lot of stuff but there has to be a (say text mining) technique / technology to put it in a form that can be consumed.  That’s what makes it valuable–not the quantity but the quality.

 

IS IT IMPORTANT?

Probably.  As alluded to above, what multiple data sources can provide the marketer is insights into consumer behavior.  It’s important to the extent that it provides more touch points of the shopping and purchasing process.  To know that one segment always looks at word of mouth opinions and blogs for the product in question is very important.  To know that another segment reads reviews and puts a lot of attention on negative sentiment can be invaluable for marketing strategy (and PR!)

Just like 20 years ago click stream data provided another view of shopping and purchasing, Big Data adds layers of complexity.  Because consumer behavior is complex, added granularity is a benefit.  Beware of “majoring on the minors” or paralysis of analysis.

 

WHAT DOES IT MEAN FOR ANALYTICS?  FOR STRATEGY?

There needs to be a theory: THIS causes THAT.  An insight has to be new and provide an explanation of causality and of a type that can be acted upon.  Otherwise (no matter how BIG it is) it is meaningless.  So the only value of Big Data is that it gives us a glimpse into the consumer’s mindset, it shows us their “path to purchase.”

For analytics this means a realm of attribution modelling that places weight on each touch point, by behavioral segment.  Strategically, from a portfolio POV, it tells us that this touch point is of value to shoppers / purchasers and this one is NOT.  Therefore attention needs to be paid to those (pages, sites, networks, groups, communities, stores, blogs, influencers, etc.) touch points that are important to consumers.  The biggest difference that Big Data gives us is that now we have more things to look at, more complexity, and this cannot be ignored.  To pretend consumers do not travel down that path is to be foolishly simplistic.  When a three dimensional globe is forced into two dimensional (from a sphere to a wall) space, Greenland looks to be the size of Africa.  The over simplification created distortion. Same is true of consumer behavior.  The tip of the iceberg that we see is motivated by many unseen, below the surface, causes.

 

CONCLUSION

Big Data is not going to go away.  Like the Borg, we will assimilate it, we will add its technological uniqueness to our own.  We will be better for it.

The new data does not require new analytic techniques.  The new data does not require new marketing strategies.  Marketing is still marketing and understanding and incenting and changing consumer behavior is still what marketers do.  Now–as always–size does matter, and we have more.  Enjoy!