The key difference between big and small data? Big data tends to refer to large caches of
information available to a company or data that is extemporaneous to the interaction with the customer.
Small data is more traditional in nature and tied to the immediate interaction between consumer and brand,
notes Jeff Hassemer, chief strategy officer, Alterian, a marketing software provider, and the owner of Play
it Again Sports in Aurora, Colorado.
The latter is more efficient for small businesses because “it doesn’t require an enormous up-front
investment, as opposed to big data, which requires huge data-warehouses, advanced data-mining software,
and highly trained data miners,” says Martin Lindstrom, branding consultant and author of Small Data:
The Tiny Clues That Uncover Huge Trends. “Small data can be easily gathered and executed on, and time
after time, has shown to be substantially more powerful than the conventional big data for small
In practical terms, Bonde’s reference to “everyday tasks” has real meaning for small business and the way
small data can be positioned and used. “Take for example, information about your customers, the
transactions that they perform with your brand, and the key data around that,” states Hassemer. “Small
data may include the date of their last purchase, the frequency with how much they purchase, or the
total dollars spent with your company (also known as Recency, Frequency, and Monetary, or RFM, data).
All are forms of small data that can be highly predictive to how a customer will interact with your
company in the future.”
From that perspective, small data works for small business simply because it is typically both captive
and easily accessible. Using Hassemer’s RFM example, most companies have a record of the total
transactions a customer has with their business. This data, he says, can be easily formed into RFM
segments, such as “those who have transacted within the last 90 days,” “those who transact frequently,”
and “those who spend more than $X per year with the brand.” These elements tend to be highly predictive.
Even in a large-scale, big-data model, the Recency factor of the transaction tends to be the most
predictive element regarding whether a consumer will transact again.
“Based on that information, a small business can easily send out a marketing event to everyone on their
list who has done business in the last 90 days with an incentive to do so again. Something like this can
have the highest response rate of any type of campaign,” says Hassemer.
THE DATA JOURNEY: PUTTING IT TO WORK
Simply knowing that you may have all the data you already need doesn’t mean you can
immediately put small data into play for your company. So where do you start? Essentially, you almost
need to reverse engineer a solution by “shaping” the data you need to mine.
Stephen Yu, database marketer and associate principal, analytics & insights practice lead at
process improvement consultant eClerx, refers to the process kickoff as “building a proper problem
statement.” In other words, you have to formulate the question or goal before jumping into data or
The key is not to try to solve a huge problem immediately, because, he says, “deriving insights out
of mounds of data is a step-wise process, while too many users want some silver bullet that takes
care of all the issues in one fell swoop. You must set the goals first, examine your data assets
through the lens of such goals, then move into analytical steps to distill insights out of
For instance, your problem statement might simply be, “I want to maximize the value of my customers
by building customer loyalty.” Extrapolating from there, the data you need to pull, says Hassemer,
• Customer information. Name, address, email, and phone.
• Customer preferences. Do they want email from you? How often do they like to
hear from you and in what form?
• Transactional information. When was the purchase made; what was bought; how much
was paid; was there a discount?
• Response information. Do they respond to marketing?
Yu adds that such data then should be transformed into “descriptors of each customer,” such as
customer’s preferred price range, maximum dollar threshold per product category, frequency of
purchase, shopping intervals, and channel preference.
“Such small nuggets of data then can be utilized for proper targeting and personalization via
advanced analytics, leading to increased loyalty,” Yu states. “These steps should all be performed
in a logical order, and it’s all based on a problem statement that clearly defines the goal. In
addition, data activities should stem from business goals, not just piles of big or small data. No
one should start any construction work without a blueprint.”
Clearly, small data can give you the information you need to inform and speed a range of processes. But
how do you put it into play and where can you see its advantages in real time? Webconnex, a Sacramento,
California maker of event registration and fundraising management software, uses a small data
application to help target customers and automate key functions.
“Our software sends raw data into the application, and in return, we get actionable insights that empower
us to send the right message at the right time to the right people, says Webconnex co-founder Eric
Knopf. “Furthermore, we get instant visibility into who who’s thriving, who’s ‘stuck,’ and who might be
Webconnex uses a small data application intercom.com, which is geared specifically
to sales, marketing, and support functions, to facilitate it’s customer-facing communications, including
lead capture and customer support/service. It helps the company:
• trigger onboarding sequences,
• send timely help articles based on a customer or prospect’s progress,
• collect feedback for new features,
• alert customers instantly that their account needs attention,
• congratulate customers as they progress through the lifecycle,
• identify who has stalled or may need help,
• identify key customer accounts, and
• send emails to check in after certain amounts of time.
Webconnex also ports its data out to other applications, (e.g., gotowebinar, Campaign Monitor, etc.) using the Zapier application automation program.
“Overall, our communication is streamlined in a way that is automated, yet personal, without having to
grow our staff,” Knopf says.
Depending on the functions you’re seeking to support, Knopf notes that there are a number of sound
providers in the small data space. For example, in the customer/engagement space, sample applications
—Analytics designed to
answer questions about how users engage with your products and/or services.
that optimizes go-to-market strategies.
—A social media
feedback platform designed to support brands, products, and services.
intelligence application designed to create overall transparency across the organization.
—Social media monitoring and analytics platform.
“Small data applications that can help you interpret your data into meaningful action can be
game-changing for your business,” states Knopf. “But capturing data is pointless unless you can extract
meaningful conclusions from it and act on it.”
Whether you’re talking big data or small data, it doesn’t have to be complex. In the case
of the latter, it’s all about narrowing things down, making the data more digestible and applicable, and
finding patterns. To make it work for your business, it’s about finding out what you really need—mining the
data that serves your specific needs. You’re not looking to solve a massive problem. Instead, it’s best to
focus on an issue you can easily grasp.
Set your initial goals through a simple problem statement, such as “I want to find my best customers” or
“I want to make it easier to deal with my company.” From there, small data applications can help you
easily find the information you need, much of which you already have. If, for example, you’re looking
for your ideal customer, data inputs about customer preferences, where they shop, how often they shop,
and how much they spend can deliver the picture of an optimal customer and where to find them.
At the end of the day, the job of small data is to help streamline the decision-making process. “Using
small data simply gives you the opportunity to identify and leverage the behavioral patterns you see,”
adds Lindstrom. “You need to make sure you have the tools within your business that help you find those
patterns in your data. Simple reporting tools or ad hoc analysis tools can answer key questions and
allow you to create new strategies as a small business owner to help make your business more