customer 360

31
Customer 360 Understanding Your Customers In The Digital Era

Upload: dave-birckhead

Post on 14-Jul-2015

291 views

Category:

Marketing


0 download

TRANSCRIPT

Customer 360Understanding Your Customers In The Digital Era

2

Contents

• Background • Why Customer Data Matters • Data Warehousing and Big Data • Data Blending Solutions • Data Management Platforms • The Road Ahead

3

Background

‣ When most people think of marketing and advertising, they think of the archetype of the Mad Men era ad agency

‣ But with surprising speed, the rise of digital media (and the accompanying explosion of customer data) has revolutionized marketing

Moving from “Mad Men” to “Math Men”

Why Customer Data Matters

• We are in the Age of the Customer • Customers have more power, choice

and influence than ever before • What we think and feel about our

interaction with an organization’s products and services is increasingly important due to the rise of social media

• Consumer behavior has shifted dramatically in recent years: how we research, evaluate, purchase, and engage with brands has changed

Power is in the hands of the customer

4

5

Rapid Shifts In Customer BehaviorIn recent years, most people have changed…

How they watch TV

How they research

What they expect

How they communicate

How they shop

on-demand via NetFlix, Amazon,

HBO GO, etc

anywhere and anytime using smartphones and

tablets

based on experiences with Apple, Amazon,

Trader Joes, etc

using social media; Facebook; Pinterest

“Showrooming” and buying it cheaper

online

THIS IS A DEALBIG6

7

An explosion of new devices

An explosion of touchpoints

88

An explosion of content

9

Quantity of global digital data, exabytes

130 2005

1,227 2010

2,720 2012

7,910 2015

Source: EMC/IDC Digital Universe Study, 2011

An explosion of data

10

Farewell FunnelDuring the Mad Men era, the purchase journey was more predictable and linear

Customers

Prospects

Leads

11

Hello Decision Journey

Purchase

Prospects

LeadsOnline Review

Ask FB Friends

Online Chat

Online Search

Store Visit

Banner Ad

View Video

Purchase

Today, the consumer decision journey is non-linear, multichannel, and consumer-driven

12

56% of customer interactions happen during a multi-channel, multi-event journey

13Source: McKinsey & Co.

Business OutcomesUnderstanding customers and customers decision journeys helps companies drive significant business outcomes

Marketing & Advertising

Customer Service

Retention & Loyalty

Customer Experience

CSA

T Sc

ores

ROM

I

Waste

Call Reduction

Cro

ss S

ell

Churn

14

15

Customer DataCompanies have access to lots of data that can help them understand their customers and customer decision journeys

Online Review

Ask FB Friends

Online Chat

Online Search

Store Visit

Banner Ad

View Video

Purchase

Social Media

Retail

Mobile

Purchase

Call Center

Survey

Chat

Web Branch

16

The ChallengeMore often than not, customer data is fragmented and locked away in physical and organizational silos

Social Media

Retail

MobilePurchase

Call Center Survey

Chat

Web

Branch

MARKETING CUSTOMER SERVICE

SALES

16

17

Customer AnalyticsNew approaches have emerged to help companies unlock and analyze their customer data

Data Warehousing Solutions

Data Blending Solutions

Data Management Platforms (DMP)

traditional, batch-oriented ETL data

integration for reporting and analysis

real-time blending or mashing of data from different sources for

analysis

platform to collect, organize and activate

audience data from any source; integrated with

execution systems

18

Data WarehouseTraditional approach to integrating data for consistency and quality

• For many years, traditional business intelligence and data warehousing technologies and approaches have been used to capture and analyze customer data.

• Beginning in the 1990s, companies pulled data from their transactional systems into separate, centralized data warehouses to support reporting and analysis.

• The typical extract-transform-load (ETL)-based approach to data warehousing captures data housed in disparate source data systems, transforms the data, and then moves it into the data warehouse, where the data is arranged in a way to help facilitate access.

• By centralizing data in the warehouse, companies could create a "single version of the truth" and avoid the errors and discrepancies that often plagued them when reports were created from various transactional and source data systems.

19

Data WarehouseTraditional approach to integrating data for consistency and quality

ETL

Data CleansingData Sources Data Warehouse Example Use Cases

CRM

ERP

Operational System

Flat File Data Mining

Analytics

Reporting

20

Data Warehouse ChallengesThe explosion of data has strained the traditional approach & technologies

• Explosion of data, particularly unstructured data, generated in recent years has strained the traditional data warehousing approach and underlying technologies

• The foundational infrastructure of data warehousing has been the relational database, which stores data into tables (or "relations") of rows and columns and is used for processing structured data.

• As the volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources) of data has increased, relational databases often aren't able to provide the performance and latency needed.

21

Evolved Data WarehousingNext generation approaches and technologies for big data analytics

Cloud Computing Big Data Technologies

Data Visualization

Cloud computing decreases cost of computing resources and creates agility. Resources

spun up and shut down quickly and easily.

Big data technologies support greater variety, volume, and velocity of data. They also

speed the time it takes to mash up different data sets.

Data visualization provides user-friendly visual analysis and

helps decision makers move from insight to action.

Data BlendingApproach to blending data from different sources for analysis

22

• Historically, analysts used tools like Microsoft Excel or Access in situations where they needed to analyze data not available in the data warehouse.

• But, in recent years a new type of solution, data blending (also sometimes referred to as data discovery), has emerged.

• Using data blending tools, analysts themselves can access, cleanse, and blend data from multiple sources without having to write a line of code.

• These tools allow customer data to be blended together from multiple internal sources as well as external sources immediately to support a more agile approach to customer analytics.

• This is increasingly important because if companies know what their customers are doing better than their competitors, or can get to those insights faster, then they have a very distinct advantage.

Data BlendingApproach to blending data from different sources for analysis

Internal Data Sources

CRM

ERP

Operational System

Flat File

Data BlendingExternal Data Sources

Market & Customer Data

Example Use Cases

Analytics

Reporting

Data Mining

23

24

Data Management PlatformsApproach to collecting, organizing and activating customer data

• A DMP allows companies to centralize data, both their own online and offline data as well as third party data, and use it to create target audiences and optimize their online advertising.

• Using a DMP, companies can measure how campaigns perform for different customer segments and optimize their media buys and creative elements over time to improve effectiveness.

• DMPs differ from data warehouses since they more provide more rapid data integration and are tied to execution systems, such as digital ad execution, content management and marketing automation systems.

• DMPs are optimized to allow marketers to define target audiences and then activate campaigns to reach those prospects and customers.

25

Data Management PlatformsApproach to collecting, organizing and activating customer data

Internal Data Sources

Display (Ad Server)

Web Analytics

CRM

Data Management PlatformExternal Data Sources

Market & Customer Data

Example Use Cases

Email/ Inbound Campaigns

Targeted Display Advertising

Email Database

Ad Execution

Mktg Automation

Advanced Customer Analytics

26

DMPs Support Demand-Side PlatformsDMPs support programmatic approaches to targeting specific audiences

Marketers

Demand-Side Platforms & Data Management Platforms

Exchanges (Supply-Side Platforms)

Publishers (Websites)

Audiences

DSP/ DMP

DSP/ DMP

DSP / DMP

DSP/ DMP

DSP/ DMP

DSP/ DMP

DSP/ DMP

27

How Do They Compare?Each approach has benefits and limitations

Data Warehousing

Data Blending

Data Management

Platforms

• Integrated data to provide a “single version of the truth” for reporting and analytics

• Minimizes any performance impact to operational systems

Benefits Limitations

• Long cycle times to integrate new data sources

• Business user-driven approach

• Speeds time to integrate and analyze new data sources

• Increases risk of data quality issues due to user errors

• May impact performance of operational systems

• Enables real-time activation through integration with execution systems

• Speeds time to integrate and analyze new data sources

• Bringing offline data online results in data loss

The Road Ahead

• Customer analytics is not about the data or technology, but about the business decisions that the insights enable.

• Customer insights have maximum value when the focus is on real-time insights connected with front-line execution.

• Many customer insights can be found by mashing up different data pools. But, it is important to begin with whatever data is available today.

• The best approach is business question or hypothesis-driven. Often the biggest challenge is to follow the 80-20 rule and identify the 20% of the data that provides the right insights.

• Where possible, begin with simple and then evolve to more sophisticated approaches. For example, is it possible to approach early attempts at multi-channel, multi-touch marketing attribution with heuristic approaches? Can you begin predictive modeling using simple, linear regression models that are easy to understand and implement?

• Keep people, your prospects and customers, constantly in mind in terms of improving their experience and meeting their needs and expectations.

• Don't just focus on customer acquisition and retention data. There is additional value in insights derived from the full life cycle of prospect and customer touchpoints.

Your 90 Day Plan: Recommendations to Consider

28

The Road Ahead (cont.)

• Gain an outside perspective. Consultancies can help provide an assessment of where you are today and recommend roadmaps and best practices based on their experience with other clients.

• Rather than approach customer analytics in terms of a single business use case, consider a full range of uses when determining appropriate levels of investment and communicating the full strategic value.

• Make learning and talent development a key part of the agenda.

• Take an agile, iterative approach to managing, analyzing and activating data.

• Approach customer analytics as a journey rather than a one-time project. Most companies require cultural, organizational and process change to become more data-driven--not just a new data store or technology--and this evolution takes time.

• Success with transforming to data-driven marketing also requires executive support and involvement. Persuade senior executives to champion and support these efforts.Let me know what’s working in your workplace

Your 90 Day Plan: Recommendations to Consider

29

30

31

Contact Me !

@DaveBirckhead www.davebirckhead.com