Tuesday, November 6, 2012

Big Data & Survey Data: Like Peanut Butter & Jelly

Image courtesy of Chapendra
Today I am pleased to present a guest post by Sarah Simon.

Big data = customer behavior; survey data = customer sentiment.  Combine the two for comprehensive customer insights to drive your informed customer experience strategy.

Introduction: Why the fuss?
Lately, there have been a lot of skirmishes in the unnecessary battle between survey data (customer sentiment) and big data (customer behavior).

Those on the far corner of the big data camp argue that the survey will and should die a slow, painful death and that big data is the insight mode of the future.  Surveys are prone to bias of all sorts (non-response, compliance-bias, inside-out bias); customers hate surveys; and surveys are just so… 2000s.

Those on the far corner of the survey camp argue that big data is messy, overwhelming and lacking in statistical validity.  Databases full of CRM nonsense, mountains of transaction records, tsunamis of social media chatter. One might as well bring a soothsayer to the C-suite to read the tea leaves.

Evolution: A lot has changed!
The last few years have seen an evolution of both big data and survey data in response to business insights needs as well as the availability of changing technologies and customer preferences.

Big Data
Years ago, I worked as an in-house market researcher for a software company when every business model worth its salt was prefaced with “eTHIS” and “eTHAT.”  Somewhere, we got the idea to “mine” our customer database for insight.  Fumbling with SQL queries and SPSS Syntax statements, I struggled along with the database management team to try in vain to pull data from the system in a usable format.  We didn’t call this “data mining;” we didn’t call it “big data.” This was 2001, and we were just trying to pull something (anything!) out of the database jungle to give to marketing to guide decision making.  Frustrated with the limited technological tools at our disposal and short on bandwidth (and with no clear goals guiding our efforts), we gave up before the initiative yielded a single nugget of useful information.  And to think…this was well before big data had swelled to the much more massive size (and growing!) of today.

I like to think that today’s analyst has available far better tools to extract, organize, and analyze large volumes of data, and that such efforts benefit from stronger business case support and business objectives guidance.

Survey Data
Surveys used to be much more ad hoc and freestanding in nature; even tracking studies, as we called them, were primarily ad hoc studies that just happened to take place more than once for trending purposes.  Traditional market research leveraged sophisticated sampling plans of the general population in the broader “market.” To survey one’s customers was considered a “poor man’s” approach to research – a money-saving compromise that would result in myopic survey results biased by the fact that these respondents already decided to do business with you. 

A lot has changed, as many companies have come to rely less on traditional market studies and emphasize Voice of Customer feedback gathering.  We are now regularly building customer feedback architectures – coordinated, comprehensive sentiment-gathering machines comprised of multiple surveys mapped to the business model.  To mirror this service model, a foundation is set with a broad relationship assessment tool, augmented by post-event surveys (triggered by the occurrence of a specific transaction) and lifecycle surveys (triggered by a customer reaching a particular milestone in his relationship with the company).  Feedback mechanisms like these are typically “evergreen,” or ongoing throughout the year, and because we are speaking with customers, we have more data about their relationship with our company than we would with a “general sample” respondent from a purchased list.  These customer insights systems become living, breathing strategies feeding an ongoing effort to optimize the experience your customer has with your company. 

Image courtesy of BreatheLA
Harmonious Outcome
The evolution that has occurred means the time is right to combine customer sentiment data (survey results) with customer behavioral data (“big data”) to turbo-charge customer insight and customer experience initiatives.  Relying on just one of these sources of data means missing out on tactical calls to action and service recovery opportunities (through data-triggered alerts) as well as missing broader patterns that can be analyzed to guide product and service strategy and improvement.

Combing the two data sources empowers you to align customer sentiment with customer behavior, uncover more meaningful trends, and more accurately predict behavioral outcomes.  This opportunity is one I would have given an arm and a leg to have 12 years ago, as I floundered through disjointed customer data and survey results.  You have the chance today to combine customer sentiment with customer behavioral data to gain optimal insights to drive customer experience strategy – why settle for just one or the other?

Sarah Simon is a career insights professional with 16 years of experience in the feedback industry. Specialties include VoC architecture, journey mapping, developing linkages to business performance, reduction of customer defection, results analysis and communication, with expert survey design skills.  She is the survivor of a botched early-generation "big data mining" operation and is happy to live to tell about it.

3 comments:

  1. Mostly Big Data developments today have traditional techniques to process the huge amount of data that must be processed.

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    Replies
    1. Hi Benslin. Thanks for your comment. Is that a good thing or a bad thing? How do we move thinking forward with regard to big data analysis?

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  2. A survey method is a data collection tool used to gather information by asking questions to people. Look for additional posts on this topic soon. Thanks for sharing this info.
    Client Surveys

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