Dan Schaefer

Content Writer, Programmer, Marketing Consultant

December 4th, 2013

Marketing and the Promise of Big Data

Big Data, by Dan Schaefer.

(Note: This article has been published on the Business 2 Community website)

The integration of marketing and technology is changing many marketing departments’ modus operandi. Technology has brought a whole new way of thinking about marketing, as it creates a new thought paradigm motivated by the abundance of data and the ability to process it. Those that embrace this new paradigm are likely to make better, more efficient and productive marketing decisions. Contrarily, those that are slow to adopt will get caught flatfooted by their competitors.

This large abundance of data is what is commonly referred to as, “Big Data.” To quote Wikipedia, “Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.” From a marketing perspective, big data holds the promise of processing large amounts of data to facilitate intelligent decisions based on correlations discovered in customer behavior.

Some marketing managers may balk and hesitate to embrace the promise of big data. Their reasons may be based on the sense of being overwhelmed by the scope of the problem. For example, where do you get the data? And how do you differentiate the important data from the superfluous? And once you have the data, how do you analyze it? And finally, how should this analysis affect future marketing decisions?

Data Sources

Let’s start with where the data comes from. Though they may not realize it, marketing departments sit on a plethora of data sources. These data sources are wide and varied. For example:

  1. Spreadsheets sitting on a marketing manager’s personal computer
  2. Sales information available in online CRM systems, such as SalesForce
  3. Email databanks available in Demand Generation systems
  4. Call-to-Action and survey forms buried within websites
  5. Website analytic data, such as Google Analytics
  6. Customer sentiment expressed on social media, such as blogs and forums
  7. Internal resource allocation and scheduling software, such as AtTask
  8. Accounts receivable data from the Finance department
  9. Customer satisfaction surveys from the Support department
  10. Coverage in trade magazines

The above list is not exclusive; more sources of data can easily be identified and used. Thanks to communication technology, many data sources are connected via the Internet, so the acquisition of data is generally not a big problem. In fact, it is the abundance of data that has traditionally caused problems with the ability to process it. With so much data available, it may seem worthwhile to ask what data is important and what is irrelevant.

Pertinent Data

What data is important? Here’s an interesting answer: All of it! To be sure, there are some obvious data points that should be top priority. For example, customer satisfaction surveys provide pertinent data points, as they will likely affect the financial bottom line. Also, you can find a good list of recommended data points in the book, Data-Driven Marketing – The 15 Metrics Everyone in Marketing Should Know, by Mark Jeffery.

Besides the already-known data points, there may be some things that are not so obvious. LinkedIn, for example, can provide some useful insight to your customers. You may discover that a majority of your top customers are NFL Football fans. This might influence your decision to raffle off NFL Football tickets at the next trade show, rather than an iPad.

The bottom line is that you collect certain types of data as a matter of good marketing practice, but it’s worth experimenting with lots more data to see if there are some not-so-obvious correlations that may surprise and enlighten you.

Analysis

The analysis of large amounts of data – if performed at all – used to belong solely to data scientists, most of whom remained locked up in the IT department. These scientists were expensive and required lots of time and resources to provide answers to marketing questions. But times have changed, and new technology has emerged with new tools for acquiring and processing data from disparate sources.

Two of the tools that are finding predominant use are Hadoop and R. Hadoop is an open-source software framework that is used to acquire, store and process large data sets. It has the ability to act as a data liaison and effectively connect to disparate data systems while providing a standard interface to the user. The programming language called “R” is used to process the data and quickly perform statistical operations, such as regressions, clustering and graphing. Additionally, companies such as SAS, Alteryx and Qliktech add an application layer on top of the underlying code, further simplifying the user interface.

The bottom line is that useful analysis tools are now emerging – tools that simplify access and analysis of large amounts of marketing data.

Results

So how does analysis of big data affect future marketing decisions? The interesting thing about big data is its promise to predict future outcomes based on correlations discovered in past relationships. For example, a B2B customer’s increased inventory levels may indicate a future uptick in orders for your product, which may prompt you to instruct the account salesperson to be extra diligent against competition.

Also, analysis allows you to take proactive action when you see a problem on the horizon. For example, through data analysis, you quickly identify the early warning signs when individual customers are starting to turn against you. You can then take immediate proactive steps, such as arranging an impromptu meeting between your troubled customer and your CEO, to help reacquire precious customer loyalty.  It doesn’t require “Big Data” to discover customers who score alarmingly negative points on a survey, but by the time customers actually take the survey, it may be too late to reverse negative publicity.

In any case, the results of analysis must be viewed with human eyes and be subject to human judgment. The good thing is that the important information has been harvested from a mountain of data, and now humans can do what computers cannot: make good marketing judgments.

Conclusion

There are many ways that technology is enhancing the marketing department. One significant development is in the acquisition and analysis of big data. Leading-edge marketing departments are using big data to more accurately characterize their customers and discover new ways to enhance relationships and increase sales. Obviously, this leads to superior performance and helps keep the competition in check.

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