Relational database ebook

Fresh Ideas for

Engaging Subscribers

with a

RELATIONAL DATABASE

Fresh Ideas for Engaging Subscribers with a Relational Database • 2

TABLE OF CONTENTS

LET’S TALK DATABASES

FLAT VS. RELATIONAL: DATABASES DEFINED

SMART MARKETING = RIGHT DATA + AUTOMATION + PERSONALIZATION

DIGGING DEEPER BY INDUSTRY

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In mid-January 2014, we released a significant new feature to help

you market smarter: a relational database.

We’re enthusiastic about this feature because we love email, and

relational data simplifies the process of sending smart, personalized

emails even more. And that’s what this resource is all about:

Walking you step-by-step through the inner workings of a relational

database so sending timely, relevant messages becomes a snap.

LET’S TALK DATABASES

We’re sure you’ll not only go gaga over what a relational database

can help you do, but you’ll fall in love with email, too.

If you have any questions about converting or storing relational

data at the end of this resource, don’t hesitate to email us at

[email protected].

Without further ado, introducing a relational database!

mailto:support%40whatcounts.com?subject=Relational%20Databases


It’s easier to understand how a relational database works if you first

understand flat databases.

A flat database is the traditional, standard way of collecting and

storing subscriber data to use in your email campaigns. It is limited

in function and scope. This basically means you can’t do a lot with

it if you want to get creative with whom you send different emails

campaigns.

Flat data is stored in records and fields. If you picture an Excel

spreadsheet, a row is the same as a record, whereas a column in

the spreadsheet is the same as a field.

In a flat database, you barely have any flexibility. You can have at

most a single record for each subscriber. The record can contain

unlimited fields, but you are limited to just one record.

If you’re tracking multiple actions per user, you’re going to have

many, many fields in one record. This creates a large – even

potentially unmanageable – database.

It’s easy to take whatever data you’re storing now and move it over

into relational tables. But we’re getting ahead of ourselves. First,

let’s dive a little deeper into how a flat database works.

FLAT VS. RELATIONAL: DATABASES DEFINED


WHAT DOES A FLAT DATABASE LOOK LIKE?

You’re constantly monitoring your inventory and customer

behavior. This data gets stored in a system, which lists each action in

a separate field in a record. Consider this snapshot of a restaurant’s

database, where a record is created for a subscriber and fields for

each action the customer took.

Trying to store and use flat data presents multiple issues. First, how

many similar fields are you going to retain per subscriber?

For example, this restaurant data could have endless fields per

record for Meals. Too much historical data becomes unfeasible to

work with.

Some subscribers will have data populated in a certain field, while

that space will remain blank in other subscriber records, e.g. Jan

(above). You must decide what to do about the absence of data in

these fields.

Customer Meal1 Date1 Cost1 Meal2 Date2 Cost2J. Smith Steak 2/1/2013 $ 20.00 Lobster 2/3/2013 $ 25.00

JanR. Doyle Veal 3/1/2013 $ 30.00 Shrimp 5/10/2013 $ 20.00

E. Pengler Steak 2/5/2013 $ 20.00 Steak 7/8/2013 $ 20.00

TABLE 1 - Example of a flat database

Another gray area when it comes to flat data is how to update the

data when a repeat customer orders the same exact product or

service he or she did before. You have to either overwrite the

current field or shift fields to make room for the new data.

The final problem comes when you run out of columns in which to

store your data. Most flat databases only allow a limited amount

of columns, so you have to prioritize what information you want to

keep.


Deep segmentation is out. Personalization allowing you to query

any aspect of a user’s data is impossible. Reporting is a pain in the

neck.

Smart Marketing goes out the door. You may have data, but you

aren’t able to use it to automate personalized emails.

In order for flat data to be converted into a relational format, it must

be structured. Before we cover what this means, let’s walk through

what relational data tables look like.

Is this similar to how you’re collecting and storing data? Enormous

amounts of records are stored in one database, and your job is to

wade through all that information to find relevant details about

customers. You have to prioritize and make cuts. Instead of having

every piece of your customer’s data available, you’re left with only a

percent of it you hope is the most relevant. Talk about inconvenient,

incomprehensible and nearly impossible.

When you have so much data in a flat database, you may not have

the ability to do anything with it.


WHAT IS A RELATIONAL DATABASE?

Simply put, a relational database comprises multiple tables, each of

which has its own fields with its own records. These tables contain

defined relationships to each other, hence the phrase relational

database. Compare this to a flat database, which stores all fields and

all records.

Each table represents its own topic: customer data, order data,

supplier data. If you think of it in terms of a content area, each data

table represents its own field of content. There is one record per

customer, and additional records relating to it.

If you store a restaurant’s data in relational tables, instead of

having a separate record for each customer and fields for each

action he or she took, you have one record for the table. This record

would be a table with customer information. You would also have

another record with details about order information: when they

dined, what they ordered, how much it cost.

In a relational database, there are tables of similar data connected

to each other through a common field.

Consider the following relational tables.


The table on the left is the main data table; it stores all the basic

information about the customer. You may be wondering about the

abbreviations, such as PK. These letters identify this field in this

table as the Primary Key, which uniquely tags this record as

belonging to a certain subscriber. By definition, no two rows can

use the same PK value.

FK is the foreign key in each table, otherwise known as the

connector between the two tables. The FK is always the common

field connecting its table with the next relational table.

You’ll see in the middle table, OrderID FK becomes the Primary Key.

Imagine a line drawn between each of these tables connecting each

FK and PK. That line is essentially the telephone wire, allowing each

table to talk to each other and share information.

Customers Orders Order Items ProductsCustomerID PK OrderID PK OrderID FK ProductID PK

CustFirstName CustomerID FK ProductID FK ProductName

CustLastName OrderDate QtyOrdered ProductDescription

Street OrderAmount PricePaid ProductPrice

City

State

Zip

TABLE 2 - Example of relational database tables

If you’re already storing data relationally, you can most likely store

it in the same format in our database. No parsing out fields so

everything fits and no overwriting existing data.

If you’re storing information in a flat database, you may or may not

have the ability to convert it to relational data tables.

In order for flat data to be converted into a relational format,

it must be structured. Each separate source of data must have a

unique identifier. In order to tie different pieces of data together,

there must be a key in place in each source indicating how each is

related to the other. This key becomes the foreign key (FK) that is

essential to how relational data tables work.

You may think your data is relational, but it might not be so.

Consider the following Customers and Orders tables.


CustomersOrganization Email Name

XYZ Corp. [email protected] David HillWhatCounts [email protected] Joy UgiAcme Inc. [email protected] Jane Smith

OrdersName OrderDate OrderAmount OrderDescription

David Hill 3/1/2014 $ 99.00 1 widgetDavid Hill 3/2/2014 $ 199.00 1 XL widgetJane Smith 3/2/2014 $ 225.00 25 XS widgets

TABLE 3 - A structured flat database is still a flat database

This data may look relational. The customer table has a row for each

subscriber, and can be related to rows in the Orders table using

Name. But consider if a different person with the same name as

an existing customer bought something from you. Since there’s no

unique identifier in the Customers table, there’s no way to relate

rows in the Customers table to rows in the Orders table.

Another issue will arise if someone in the Customers table changes

his or her name. For example, imagine Jane Smith gets married, chan-

ges her name, and places another order. Using the current system,

the Jane Smith record would be overwritten by Jane’s new name.

The row containing the 25 XS widgets Jane Smith bought would be

orphaned because it would no longer relate to anything in the

Customers table. The history previously associated with Jane Smith

gets lost.

So how would you take these Customers and Orders tables and make

them relational? It’s a matter of adding unique IDs to each record.

In this format, the unique identifier is CustomerID, which

communicates to the Orders table which customer placed the order.

This unique ID never changes for a customer, which means if two

customers have identical names, they can still be differentiated

from each other both in the Customers and Orders tables.

If Jane Smith were to change her name now, her customer ID

wouldn’t change and her order history would be saved.


ANATOMY OF AN ORDER

The information as on the order confirmation or invoice:

Ms. Jane Smith

[email protected]

Customer ID: 12345

Order ID: 140302789

Order Date: Sunday, March 2, 2014

Qty. Products Price

25x XS widget $ 225.00

The information as stored in a relational database:

CustFirstName CustLastName Email CustomerIDJane Smith [email protected] 12345

OrderDate OrderAmount CustomerID (FK) OrderID3/2/2014 $ 225.00 12345 140302789

QtyOrdered PricePaid ProductID OrderID (FK)25 $ 225.00 00101 140302789

ProductName ProductSize ProductPrice ProductID (FK)Widget XS $ 9.00 00101


Every email marketer’s dream is to see higher engagement

numbers, more people interacting with their emails. Gone are the

days when batch-and-blast strategy made this dream a reality. Now

the only way to reach subscribers is through relevant, timely,

personalized messages.

At WhatCounts, we call this Smart Marketing, and it’s the marriage

of right data, automation and personalization.

SMART MARKETING = RIGHT DATA + AUTOMATION + PERSONALIZATION

Part of collecting and using the right data is formatting it

relationally because the possibilities for deep segmentation and

personalization are endless. You can connect lists with relational

tables, and from there use your information to send targeted

messages to segments of users. Additionally, you can use relational

data inside email messages to send hyper-relevant content to

customers.


FIRST COMES RELATIONAL LISTSWhen data is stored relationally, you can create one-to-many

relational lists from the related tables. This means all the data

available in all the tables for a single subscriber is at your fingertips.

This is a powerful tool for email marketers because you can use

these relational lists to create segmentations.

Relational data tables imported as lists don’t even need to have an

email address field, and they can include entirely customized fields.

The only caveat is one of the fields in the table needs to match a field

on a mailing list or a field on a different relational data table.

All the table needs is a field that can be associated with a field on an

existing mailing list to connect the two.


NEXT COMES DEEP SEGMENTATIONOnce you’ve added a relational table as a list, you can use the fields

from both tables to create segmentations.

Say you want to send an email to customers who worked with a

specific sales representative who isn’t working with your

organization anymore. Sending a proactive message to these

customers is a good idea, and you can produce a segmentation to do

it using data in a relational format uploaded as a list.

You would choose to segment by the field in the table marked with

the representative’s name.

Another example would be reaching out to customers who bought

a product or service from you before, but haven’t purchased in a

while.

Using a purchase history relational table, we can see the informa-

tion about what the subscriber has bought. This includes details

such as what the product was, what day the subscriber purchased it,

what category the product falls under, and how much it cost.

Because the information is accessible in this relational table, you

can easily set up a segmentation of subscribers who haven’t

ordered a product in over 60 days over the price of $50. Sending

these subscribers a coupon to shop again is a good way to create

repeat purchasers.


USE RELATIONAL DATA IN EMAIL TEMPLATESThe factor most motivating subscribers to open your emails is

whether you can answer the question, “What’s in it for me?”

With so many marketing messages flying into subscribers’ inboxes,

yours has got to provide readers with an immediate benefit for

opening your email. Subscribers want to know they’re going to

receive something valuable and relevant to them if they open your

email.

Relational data makes it possible for you to correlate information

about subscribers in order to send them timely messages that

matter.

It’s always exciting to receive a coupon from your favorite

restaurant. But imagine receiving a coupon for your favorite meal

at your favorite restaurant for the time of day you go most often.

This kind of valuable, personalized content is what makes

subscribers more likely to open your emails, read your messages,

and evangelize your brand.

Relational data can be pulled into email content to create these

relevant messages. Once you’ve set up your relational list and

segmentation conditions, you can pull the appropriate fields into

your content.


No matter what industry you’re in, the WhatCounts relational

database supports the data you’re storing today. Remember,

relational data is just a way of organizing your data to better use it

for personalization in lists, segmentations, and email templates.

Relational data can be used to market smarter across all industries.

eCommerce, travel, financial services, media and publishing – you

name it and the data can be imported into a relational database if

it’s structured correctly.

And you don’t need to completely redefine the data you have now

in order to make it work in a relational database. For example, many

companies store data in a Customer Relationship Management

system (CRM) such as ZoHo or Salesforce. The data already existing

in this system that you could easily pull into a relational database

includes contacts, leads, opportunities, and more.

Let’s dig deeper into what kind of existing data can be pulled into a

relational database by industry.

ECOMMERCE

DIGGING DEEPER BY INDUSTRY

TRAVEL & HOSPITALITY

MEDIA & PUBLISHING

FINANCIAL SERVICES


RELATIONAL DATABASE IN PRACTICE: ECOMMERCE

If you’re in eCommerce, you have a lot of data fields and records about your customers.

They may have taken multiple actions on different days. Relational tables store all this information intelligently.


You have a whole slew of information about your customers and

their behaviors, which may include:

9 Customer data: ID, name, title, company name, address, phone

9 Order data: order date, shipped date, shipment address

9 Product data: name, category, quantity per unit, price, reorder

level

9 Supplier data: name, address, phone, contact name

Once you’ve imported this information into relational tables, you

can start using it to set up segmentations and send targeted

messages.

Let’s say you wanted to target subscribers who purchased a

particular product from you. You want to tell them the product has

gone on sale, or the subscriber is getting a special discount for the

product.

First, make sure your product table and the relational data table

holding purchase information are connected. Choose your mailing

list, ensuring it includes a field that connects to the purchase

information relational data table.

Once this is done, create a segmentation for subscribers who

purchased a particular product. Add a segmentation including

information about that product. Create your email and, using the

segmentation you’ve put in place, hit send.

In this case, none of the purchase or product data is in the mailing

list itself; instead, you’re pulling purchase and product data from

your relational data tables.

With all the information you’re storing as a retailer, there are many

possibilities when it comes to using relational data to create

segmentations. Importing your already-existing data into this

format opens the door to connecting with subscribers on a whole

new level, which leads to high engagement and even higher

return-on-investment.


RELATIONAL DATABASE IN PRACTICE: FINANCIAL SERVICES

A lot of email marketers in the financial services industry struggle with creating relevant messages to send to subscribers.

If you’re in this boat, you may think batch and blast is your only option. But that’s a myth.


You’re collecting lots of customer data now that can be used to send

relevant, one-of-a-kind emails to your subscribers. This data

probably includes:

9 Customer data: name, birthday, account number, address,

phone

9 Account data: date opened, agent, opening $, location

9 Account type: interest rate, first deposit, recent deposit

9 Mobile: browser, OS, first login, recent login

Import this data you’re storing in your flat database into relational

tables, and you’ll be able to send super-targeted messages.

If you’re a community bank and someone opened a new account at

one of your branches, you could send the person a thank-you email

from the teller who helped him or her. Simply connect the account

data table to your mailing list. Create a segmentation using the date

opened and agent fields. Then send your new customer thank-you

message, including the photo of the particular agent.

You can go as deep as you want with segmenting using relational

data tables. For example, if you just released a stellar version of your

mobile app for Android users, you’ll want to tell all your customers

who use this operating system about the update.

Using relational data tables, you could create a segment for every-

one who has an account (customer data table) and has used mobile

banking with an Android device, but not in the past three months

(mobile data table). Make sure your mailing list is connected to the

customer data table, which must also be connected to the mobile

data table.

Hopefully you see how your existing data can be organized into

relational tables, and by doing so, how simple it is for you to set up

segmentations and personalized emails.


RELATIONAL DATABASE IN PRACTICE: MEDIA & PUBLISHING

If you’re in the media and publishing industry, you may have a subscription service for your publication.


This means you’re collecting the standard subscriber data:

9 Customer data: name, address, phone

9 Subscription information: date subscribed, publication

subscribed to, renewal date, date unsubscribed

9 Order details: amount paid for subscription, discount

All of this can be pulled as-is into the relational database and used to

segment. If you have paying subscribers, you’ll want them to renew

their commitments to your publication on a regular basis. Sending

an email to people reminding them their subscriptions need to be

renewed is a snap when your data is organized in relational tables.

It just means connecting your mailing list to a field in the customer

data table, which connects to the subscription information data

table. Pull in the customer name and subscription information to

find ones expiring in a set period of time, maybe two or three weeks.

Even if you don’t have a subscription service, you’re most certainly

collecting information about advertisers. It’s important for you to

do so, as advertising is a significant part of what you do.

The existing data you have about advertisers may include:

9 Advertiser data: company name, ad name, size, publication

9 Publication data: placement, sales rep, medium

9 Medium: times viewed, times clicked

9 Times clicked: browser, OS, device

By importing this information into relational tables, you can drill

down to a granular reporting level to understand your advertisers

and advertisements better. With this information, you’re not only

able to update advertisers on the success of their business with

your publication, but you’re also able to judge for yourself which ads

work best.

For example, dig deeper into an ad’s success by company name

(advertiser data), placement (publication data) and times clicked

(medium). Compare the information in this report to similar ads’

click rates to judge whether or not this particular ad is doing well.

You could also run reports to see how well ads perform on mobile

devices, since this is the primarily appliance people are using today

to view media. Drill down into your various publications and see the

stats on which browsers, operating systems and devices your

subscribers are using to view your publication and the ads in it. This

is valuable information not only for your design team, but for

advertisers.


RELATIONAL DATABASE IN PRACTICE: TRAVEL & HOSPITALITY

Within the travel and hospitality industry, there are many different types and sizes of companies. CVBs, airlines, hotels, online travel agents –

if it has to do with traveling somewhere, staying somewhere or even moving somewhere, it fits within this industry.


There’s all kinds of information you’re collecting about your

customers that can be organized into relational tables:

9 Customer data: ID, name, address, phone

9 Purchase data: purchase date, price, discount

9 Travel data: from date, to date, destination, from state, to

state

That probably doesn’t even touch the amount of information you

have about your customers. But that means there may be so much

information it’s too difficult to use. This is where pulling it all into

relational tables can help. When organized properly, data allows you

to send timely emails to customers based on their past behaviors.

As an airline, you could use relational data tables to send emails to

customers based on where they like to fly. For example, if you’re

running a sale on flights to the Midwest, send a message to

customers who’ve flown there within the past year. Choose your

mailing list, and make sure a field in it connects to your relational

data table with customer information. That table would then

connect to the travel data table, which includes the destination field.

If you’re a hotel, B&B or another type of hospitality service, why not

reach out to out-of-state customers at special times of year with

discounts? Ask them to come back and stay with you if they’ll be in

your area for a special event. If your data is stored relationally, you

can simply create a segment using customer data connected to the

from-state field in your travel data table.

There are so many ways travel and hospitality organizations can use

relational data. It’s simple to import your existing data, as long as it’s

structured correctly, into relational data tables.


MAKE YOUR DATA RELATIONAL NOW!

No matter what industry you’re in, no matter what data you’re

storing now, and no matter what format that data’s in, you can use

relational data tables to start sending personalized emails to your

subscribers.

Relational data is a part of marketing smarter, which is a must for

email marketers who want to see higher engagement and ROI with

subscribers. Without smart, relational data, personalization and

automation aren’t effective. When you organize your existing data

relationally:

9 It makes your data much more manageable and easier to

understand.

9 It gives you the opportunity to build better lists.

9 It allows for deeper, richer segmentation.

9 It allows you to pull subscriber information into email creative

for personalized messages.

9 It allows for even more granular reporting and drilling down.

9 It allows you to easily modify data.

Want to learn more about how to make relational data work for your

company?

If you’re a WhatCounts client, contact your Strategic Account

Manager. If you’re not a WhatCounts client, let us show you how

relational data can help you market smarter. Reach out to us today!

http://www.whatcounts.com/email-marketing-company/contact-us/

WhatCounts loves email. That’s because it’s the only marketing channel providing a substantial ROI when marketing professionals deliver

smart, personalized messages to their target audiences.

Each day our team partners with over 800 customers to leverage email, social media, mobile design, and the web to drive revenue. We provide

enhanced data, flexible deployment options, content automation and professional services.

We’re headquartered in Atlanta, GA, with offices in Seattle, Sydney, Baltimore, and other regional locations. Find out more about why we love

email at www.whatcounts.com.

ABOUT WHATCOUNTS

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Relational database ebook