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Martech Ecosystem > Lecture 2 > Flashcards

Lecture 2 Flashcards

1
Q

Shoppers once relied on a familiar salesperson to find what they wanted. Today…

A

Today’s distracted consumers, bombarded with information and options, often struggle to find products

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2
Q

Salespeople would draw on their knowledge or quickly deduce about customer, locate the perfect product and suggest additional items. Today…

A
  • Shorthanded retailer floor staff can’t replicate the personal touch that shoppers once depended on
  • Consumers are still largely on their own when they shop online
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3
Q

IDC predicts that the “digital universe” (the data created and copied every year) will reach __ ___ (180 followed by 21 zeros) in 2025

A

180 zettabytes

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4
Q

Poor __ __ is enemy #1 to the profitable use of

machine learning

A

data quality

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5
Q

To properly train a predictive model, historical data must meet these exceptionally broad and high quality standards

A
  1. Data must be right (i.e. correct, properly labeled, de-duped)
  2. But you must also have the right data (i.e. unbiased over the entire range of inputs leveraged to develop the predictive model)
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6
Q

In a study involving 75 executives, only __ found that their departments fell within the minimum acceptable range of 97 or more correct data records out of 100

A

3%

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7
Q

How to Improve Data Quality

A
  1. Clarify your objectives and assess whether you have
    the right data to support these objectives
  2. Build plenty of time to execute data quality
    fundamentals into your overall project plan
  3. Maintain an audit trail as you prepare the training data
  4. Charge a specific individual (or team) with responsibility for data quality
  5. Obtain independent, rigorous quality assurance
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8
Q

Customer Data

A

1
Name, Date of Birth, Gender, Postal Address, Telephone, Email, Social Network
Profiles, Account Info, Job Info, Income

2
Transaction (offline & online), Communication, Online Activity, Social
Network Activity, Customer Service

3
Attitudinal Info, Opinion, Motivational, Interests

4
Family Details, Lifestyle Details, Career Info

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9
Q

How to Collect Identity Data

A

POS System & Online Transaction Database

Clienteling

Social Network Profile & Other Customer Profile Features

3rd party data sources

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10
Q

How to Collect Quantitative Data

A

Transaction Database

Web Analytics Tool

3rd Party Pixels

In Store Tracking

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11
Q

Google Analytics & Adobe Analytics are platforms that

A

collect online data and compile it into useful reports

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12
Q

To start collecting data you need to create an account and add __ __ to your site

A

javascript code.

Every time a user visits a page, the code will collect interaction data and other information from the browser (i.e. language, type of browser, device, etc…)

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13
Q

Web Analytics tools allow us to track every action the

customer is performing on a website:

A

Browsing Behavior

Search Data

Purchase History

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14
Q

The Challenge of Web Data

A

Unique visitors does not necessarily mean unique customers

One customer may be tied to multiple cookies (see slide 29 on how to solve it)

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15
Q

T/F

Online retailers know so much more about their customers than their offline counterparts

A

True

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16
Q

__ __ have created direct connection to their customers, which in turn allows them to collect massive amounts of data about them

A

Online retailers

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17
Q

Through AI, online retailers are able to create more-personalized…

A

…customer experiences, fostering levels of satisfaction, connection, and customer loyalty

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18
Q

Amazon has created a new digital marketing model based on a

A

1-to-1 relationship with the customer, informed by data collection, optimized with machine learning, and nurtured with other forms of AI

19
Q

3rd Party Pixel Tracking is a tool used to

A

track and analyze website traffic, individual user behavior, and ad impressions

Hidden in the background of a web page or email so that they aren’t part of the user’s experience

They are complementary to your web analytics tool

20
Q

New technology can track live foot traffic in a store and

A

break down shoppers into a variety of data segments

21
Q

In Store Tracking–Data is pulled from

A

IoT sensors, beacons and branded app

22
Q

In Store Tracking

Additionally, depending on the retailer, data is also taken and

A

joined to POS systems and online data

23
Q

On an average, consumers in the US use

A

4 devices each day

predicted to increase with IoT

24
Q

companies use multiple tools to store different customer attributes (i.e. CRM, Email, Ecommerce, POS, Social Media)

A

CRM, Email, Ecommerce, POS, Social Media)

25
Q

Businesses are left with isolated data sets and are paralyzed when it comes to connecting

A

data silos

26
Q

Most marketing teams don’t operate with systems that

A

talk to each other

27
Q

Most marketing teams manually analyze disparate data points to uncover insights. This data fragmentation is

A

costing marketers real dollars as they lose the ability to effectively optimize campaigns and fold learnings into future plans

28
Q

Customer Data Silos

A

Customer data collected from different sources but not connected to each other bc the systems don’t “talk” to each other.

29
Q

To eliminate silos, collect all data points into

A

a centralized system able to analyze them and surface actionable insights in real-time. Marketers can then start to roll out personalized content, translate strategies across all channels, and efficiently improve customers’ experiences.

30
Q

What is an Identity Graph

A

• •
What is an Identity Graph
A database that stores all identifiers that correlate with individual
customers, creating a unified customer view and breaking silos These identifiers could be anything from usernames to email,
phone, cookies and even offline identifiers like loyalty card number

31
Q

Across a consumer’s journey, multiple identifiers may be associated
with an individual
The ID graph collects these identifiers and

A

connects them to the customer’s profile and any related data points, including behavioral data like browsing activity or purchase history

32
Q

There are 2 types of customer profiles:

A

Authenticated

Non-Authenticated

33
Q

Authenticated:

A

uses durable IDs such as an email address or log-in ID

34
Q

Non-Authenticated:

A

uses shorter-lived identifiers that don’t translate across devices

35
Q

Authenticated Profiles

IDs include:

A

email addresses or customer IDs that
require a log-in

more durable than cookie-based data that
expires and is restricted to the Web

Upon authentication (ex: Site log-ins), the ID graph links this 1st party data to the various bits of data used to uniquely identify this person across channels or devices

With each customer interaction, the persistent profile accumulates data, increasing clarity and value over time

36
Q

Non-Authenticated Profiles are built from identifiers like

A

cookies or device IDs, which
are shorter-lived or don’t translate across devices

Work fine within a single channel or for a single campaign

Unable to continually collect and connect customer data

Results in just a partial view of a customer; essentially, a snapshot in time

37
Q

ID graphs use 2 different data matching methodologies:

A

Deterministic

Probabilistic

38
Q

Deterministic

A

uses known customer information (i.e. log-in data, hashed email addresses) to match and recognize individuals across devices with 100% certainty

39
Q

Probabilistic

A

uses anonymized data signals (ex: IP address, device, browser, location, OS) to create likely statistical connections across devices, achieving greater scale but lower accuracy

40
Q

What can marketers do with an ID graph?

A

Power people based targeting

Enhance customer engagement

Attribute and optimize performance

41
Q

What can marketers do with an ID graph?

Power people based targeting

A

ID graph collects/ connects all personal identifiers to one person, so a customer can be recognized and targeted with the right content and in the right context across screens

42
Q

What can marketers do with an ID graph?

Enhance customer engagement

A

Uploading and matching offline data with digital identifiers and behaviors within the ID graph delivers a 360-degree view of a customer, which can be leveraged to anticipate what consumers may need and strategize future interactions

43
Q

What can marketers do with an ID graph?

Attribute and optimize performance

A

Having all customer device, channel and behavior data in one place allows advertisers to accurately measure reach and frequency of their campaigns.

Martech Ecosystem (12 decks)

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