using big data to understand buyer behavior and fill up carts
TRANSCRIPT
WHAT DATA USED TO LOOK LIKE
In the days before digital everything, the data companies
collected on their customers and prospects was pretty
straightforward.
Everyone took down a name, address and telephone number.
Next came data that might be collected in response to a sales
call or an online survey.
All this data could be easily managed within a database and
extracted for targeted marketing efforts and other internal
processes.
Then, information technology exploded, and the wealth of data
that companies had access to was virtually unlimited.
The “Big Data” of Today
Today companies can collect, manage and interpret enormous
amounts of data that is well beyond anything that can be
managed in a simple spreadsheet.
Big data is a broad term for data sets so large or complex
that traditional data processing applications are inadequate.
These data sets include massive amounts of structured, semi-
structured and unstructured data.
Structured Data
Machine-generated: Sensor data, web log data, point-of-sale
data, financial data.
Human-generated: Input data, click-stream data, gaming-
related data.
Semi-structured Data
Semi-structured data is data that is neither raw data, nor typed
data in a conventional database system.
Some examples of semi-structured data would be BibTex
files or a Standard Generalized Markup Language (SGML)
documents.
Unstructured Data
Text, video, sound and images
HOW DO MARKETERS USE BIG DATA?
Understanding the audience
Big data gives marketers a deep understanding of an
audience, user or buyer in real-time and enables marketers to
adjust dynamically to them as their needs change. This deep
understanding allows marketers to improve relevancy, increase
engagement, drive sales and boost ROI among other things.
Right now your 30-something, 80-100k income, left-handed buyer
from Brazil is engaging with your message from their smartphone.
Thanks to big data you are ready with this information and can
target your marketing efforts accordingly.
Where Does Big Data Come From?
Big Data is both static and dynamic and resides in a multitude of
public and private locations. The following are some common
data sets and sources marketers, businesses and analytics
solutions leverage to gain a deeper understanding of their
audience, user or buyer.
The following are common types of data sets marketers use...
Large data sets open to the public
Want access to the database of 22,000 dreams collected by
a Stanford sleep researcher? How about Uhaul rates between
U.S. cities? If it is public information, chances are very likely you
can access these large data sets online.
Demographics
Demographics include statistical data of a certain population
and can include things like location, age, income, and education.
Companies use tools like Google Analytics to infer user
demographic and interest using cookies that follow a single
user’s engagement across the Internet.
Firmographics
Businesses use firmographics to define their target market to
better focus their marketing and sales efforts on who will be
most receptive to the message or most likely to purchase from
them. Firmographics are to businesses and organizations what
demographics are to people. They describe businesses, non-
profits, and governmental entities.
Some common business attributes or firmographics uses include:
From purchasing transactions, to chatter from social networks, to web server logs, to satellite imagery, it’s estimated that we now create—every two days—as much information as we did from the dawn of civilization up until 2003.
What’s more, it’s expected that the amount of data currently available will double every two years worldwide as virtually everything becomes digitized. Mark Van RijmenamThink Bigger. Developing a Successful Big Data Strategy for Your Business.
Solutions for deriving additional sets of relevant data for use in marketing applications include:
Market intelligence analyzes big data to provide insight into
a company’s existing market, customer, problems, competition
and growth potential for new products and services. IDC is a
longstanding market intelligence provider.
Sales intelligence includes the collection, integration, analysis,
and presentation of information to help salespeople keep up
to date with clients, prospect data and drive business. Lattice
Engines is a provider of both predictive marketing and sales
applications.
Social media intelligence includes the tools and solutions
that allow organizations to monitor social channels and
conversations, respond to social signals and synthesize social
data points into meaningful trends and analysis based on the
user’s needs.
Finally, Big Data can also be gathered by individual retailers
via customer interactions with their brand.
Industry: which industries are really buying from
your company?
Size of company: What size are you best served
pursuing?
Geography: Where are your best prospects
located?
Annual revenue: Do they really have enough
money to buy what you are selling?
Executive title: Which title(s) are the most likely
to need what you are selling.
Average sales cycle: How long does the average
sale take too close?
Companies like MeritDirect provide marketers paid access to
proprietary B2B firmographic information defining more than
50 million anonymous business profiles.
WHAT KIND OF CUSTOMER DATA CAN INDIVIDUAL RETAILERS COLLECT DIRECTLY?
Easily collectible data include...
k Name and contact detail
k Transaction history
k Profile
k Spending habits
k Birthdays
k Whether or not they pay on time
What methods can online retailers use to collect this data?
In order to keep customers from becoming irritated with
your requests for data, it must be either unintrusive (doesn’t
interfere) or incentivized (promises them a return).
Some vehicles for collecting customer data include...
k Orders
k Surveys
k Competitions
k Monitoring online activity
k Leaning on formal intelligence research.
How can big data help fill my customers’ carts?
Using solutions, Like STRANDS Recommender, that analyze
and act on Big Data, online retailers are able to make insightful,
perfectly-timed recommendations for purchases your
customers will be most receptive to when browsing through
offerings, adding items to cart, checking out engaging with
email marketing efforts.
Imagine this
You’re attending an occasion for “Dave” (a friend of a friend)
and it’s not like you to show up empty-handed. You’d prefer to
give a more personalized gift but you have nothing to go on but
a first name.
What does he need? What does he like? Is he into a particular
sport or hobby?
You call your friend to get a better read on Dave and he replies
with helpful insight: “My friend Dave is very similar to your older
brother.” Now you have something concrete to go on because
you know your brother’s tastes and preferences inside and out.
In an effort to add value, build customer engagement and
increase sales, online retailers are tackling this “Dave problem”
too. Only instead of calling a friend, retailers look to Big Data
for insight into what the Daves of the world respond to.
Though big name data analytics platforms are
popping up all over the place with the power
to pull in, analyze and report on mythical
proportions of big data, individual retailers
still have a part in mining big data from their
own customer interactions and applying
it accordingly to improving their sales and
marketing efforts.
USING RECOMMENDATION ENGINES TO ANALYZE CUSTOMER BEHAVIOR
Marketers have a few choices when analyzing customer behavior to get the most relevant results:
k Follow a single “Dave” on his buying journey, making
recommendations based on his digital movements
including purchase history, time on site, items left in cart
and how long.
k Follow millions of users like Dave and treat them as
a single customer segment to aggregate and analyze
behavior on a larger scale that will help predict what Dave
(and other Daves) might do—or what they might buy next.
k Follow both single Dave and the customer segment he fits
into.
Strands Recommender does both.
Using Big Data and machine learning algorithms, Strands
Recommender can group a particular customer to the segment
or segments they belong to based on what they actually do on
the site.
Some of the actions that signal belonging in one customer
segment over another include:
k movements,
k click-stream data,
k what gets placed on a wish list,
k what is added to a shopping cart, etc. This infographic illustrates just how big “big data” is. See it here.
PRODUCT RECOMMENDATIONS WORK
From our Barcelona HQ and offices in San Francisco, Miami, Madrid & Buenos Aires, we serve market leaders like Panasonic, Disney, Ashley Stewart, Chewy.com, Markafoni
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