USING BIG DATA TO UNDERSTAND BUYER

BEHAVIOR AND FILL UP CARTS

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

Request a demo to discover the power of Strands Retail.

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recommendations. Offering the right

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