Broken links: Why analytics investments have yet to pay off, sponsored by ZS, draws on the survey findings, interviews with senior corporate executives and desk research to explore the current state of sales and marketing analytics.
Artificial intelligence, customer journeys, and paid analytics
Quest to be more data-centric and insights-driven
Data-driven CMOs drive omnichannel customer intelligence
Companies turn to paid analytics for enhanced capabilities
The power of now: customer journey analytics rely on integrated data
Harnessing AI for more insight-driven marketing and better customer experiences
The new ‘A and B’ of the Finance Function: Analytics and Big Data - -Evolutio...Balaji Venkat Chellam Iyer
Published in 2013, this White Paper discusses how the finance function would evolve with the combined forces of Big Data and Analytics and the levers that could help catalyze the change and has drawn upon the Global Trend Study conducted by Tata Consultancy Services (TCS) on how companies were investing in Big Data and deriving returns from it.
The economist intelligence unit: Voice of the customer, whose job is it, anywayAidelisa Gutierrez
In what areas should marketing focus investments in order to contribute most to your business in 3 years?
#1 Customer Analytic
#2 Customer Relationship Management
#3 Social Media
We conducted a groundbreaking survey of the UK’s data and business professionals to get a snapshot of the state of the world of data, uncover some of the issues facing the industry and get a sense of the changes on the horizon. The results were enlightening, and in some cases, very surprising.
Find out:
Why nearly a third of IT Directors feel their organisation uses data poorly
What the hybrid data manager of the future will look like
Why understanding customer behaviour remains the holy grail for so many
We conducted a ground-breaking survey of the UK’s data and business professionals to get a snapshot of the state of the world of data, uncover some of the issues facing the industry and get a sense of the changes on the horizon. The results were enlightening, and in some cases, very surprising.
We conducted a survey of the UK's data and business professionals to get a snapshot of the state of the world of data, uncover some of the issues facing the industry and get a sense of the changes on the horizon. The results were enlightening, and in some cases, very surprising.
Broken links: Why analytics investments have yet to pay off, sponsored by ZS, draws on the survey findings, interviews with senior corporate executives and desk research to explore the current state of sales and marketing analytics.
Artificial intelligence, customer journeys, and paid analytics
Quest to be more data-centric and insights-driven
Data-driven CMOs drive omnichannel customer intelligence
Companies turn to paid analytics for enhanced capabilities
The power of now: customer journey analytics rely on integrated data
Harnessing AI for more insight-driven marketing and better customer experiences
The new ‘A and B’ of the Finance Function: Analytics and Big Data - -Evolutio...Balaji Venkat Chellam Iyer
Published in 2013, this White Paper discusses how the finance function would evolve with the combined forces of Big Data and Analytics and the levers that could help catalyze the change and has drawn upon the Global Trend Study conducted by Tata Consultancy Services (TCS) on how companies were investing in Big Data and deriving returns from it.
The economist intelligence unit: Voice of the customer, whose job is it, anywayAidelisa Gutierrez
In what areas should marketing focus investments in order to contribute most to your business in 3 years?
#1 Customer Analytic
#2 Customer Relationship Management
#3 Social Media
We conducted a groundbreaking survey of the UK’s data and business professionals to get a snapshot of the state of the world of data, uncover some of the issues facing the industry and get a sense of the changes on the horizon. The results were enlightening, and in some cases, very surprising.
Find out:
Why nearly a third of IT Directors feel their organisation uses data poorly
What the hybrid data manager of the future will look like
Why understanding customer behaviour remains the holy grail for so many
We conducted a ground-breaking survey of the UK’s data and business professionals to get a snapshot of the state of the world of data, uncover some of the issues facing the industry and get a sense of the changes on the horizon. The results were enlightening, and in some cases, very surprising.
We conducted a survey of the UK's data and business professionals to get a snapshot of the state of the world of data, uncover some of the issues facing the industry and get a sense of the changes on the horizon. The results were enlightening, and in some cases, very surprising.
Unlock your content, FirstSpirit, CMS, e-Spirit AG, Best-of-Breed, Internet, Intranet, Extranet, Management, CIO, CEO, CMO, Digital Marketing, Integration of third part technology, SEO, Analytics, Strategy, Customer Experience
Marketing & SalesBig Data, Analytics, and the Future of .docxalfredacavx97
Marketing & Sales
Big Data, Analytics,
and the Future of
Marketing & Sales
March 2015
3McKinseyonMarketingandSales.com @McK_MktgSales
Table of contents
Business
Opportunities
Insight and
action
How to get
organized and
get started
8 Getting big impact from big
data
16 Big Data & advanced
analytics: Success stories
from the front lines
20 Use Big Data to find
new micromarkets
24 Smart analytics: How
marketing drives short-term
and long-term growth
30 Putting Big Data and
advanced analytics to work
34 Know your customers
wherever they are
38 Using marketing analytics to
drive superior growth
48 How leading retailers turn
insights into profits
56 Five steps to squeeze more
ROI from your marketing
60 Using Big Data to make
better pricing decisions
60 Marketing’s age of relevance 72 Gilt Groupe: Using Big Data,
mobile, and social media to
reinvent shopping
76 Under the retail microscope:
Seeing your customers for
the first time
80 Name your price: The power
of Big Data and analytics
84 Getting beyond the buzz: Is
your social media working?
90 How to get the most from big
data
94 Five Roles You Need on Your
Big Data Team
98 Want big data sales programs
to work? Get emotional
102 Get started with Big Data:
Tie strategy to performance
106 What you need to make Big
Data work: The pencil
110 Need for speed: Algorithmic
marketing and customer
data overload
114 Simplify Big Data – or it’ll be
useless for sales
54 McKinseyonMarketingandSales.com @McK_MktgSales
Introduction
Big Data is the biggest hame-changing opportunity for marketing and sales
since the Internet went mainstream almost 20 years ago. The data big bang
has unleashed torrents of terabytes about everything from customer behaviors
to weather patterns to demographic consumer shifts in emerging markets.
The companies who are successful in turning data into above-market growth
will excel at three things:
ƒ Using analytics to identify valuable business opportunities from the data to
drive decisions and improve marketing return on investment (MROI)
ƒ Turning those insights into well-designed products and offers that delight
customers
ƒ Delivering those products and offers effectively to the marketplace.
This goldmine of data represents a pivot-point moment for marketing and
sales leaders. Companies that inject big data and analytics into their operation
show productivity rates and profitability that are 5 percent to 6 percent hight
than those of their peers. That’s an advantage no company can afford to
gnome.
This compendium explores the business opportunities, company examples,
and organizational implications of Big Data and advanced analytics. We hope
it provokes good and useful conversations.
Please contact us with your reactions and thoughts.
David Court
Director
David headed McKinsey’s
functional practices, and
currently leads the firm’s digital
in.
Bridging the Gap Between Business Objectives and Data StrategyRNayak3
Explore the fundamental elements of a robust data strategy that aligns with business objectives, from defining goals to prioritizing data architecture.
The study was conducted by Avaus Marketing Innovations, a leading data-driven marketing agency, together with ISS, a leading marketing research company. We asked Swedish and Finnish CMO’s, CTO’s and COO’s to assess the state of data and analytics in their companies, in four major areas:
1. Strategy and business objectives
2. Investments
3. People, processes and leadership
4. Tools and technologies
Download the full study for free here: https://www.avaus.fi/en/state-of-analytics/
The data directive is an Economist Intelligence Unit (EIU) report which seeks to explore the degree to which the ongoing data revolution within business is delivering truly strategic change within companies, as opposed to more incremental optimisation gains. Although many of the issues discussed here stray into the realm of so-called “big data”, this report is not explicitly focussed on that topic and not deal with any technology-related issues. Instead, it seeks to explore how the wider trend toward a greater reliance on data is affecting the strategic management of businesses at a C-suite level, across a range of industries. There is also a supplement to this report available that focuses on the strategic impact of data on CFO's and the finance function. http://bit.ly/thedatadirective_mt
CGT Research May 2013: Analytics & InsightsCognizant
A new survey conducted by Consumer Goods Technology (CGT) and sponsored by Cognizant explores how consumer goods companies are approaching data management strategies and usage.
Views From The C-Suite: Who's Big on Big DataPlatfora
he way that big data pervades most organizations today creates a dynamic environment for C-level executives to explore how it can and should be used strategically to add business value.
While each C-level executive views big data through a unique lens, a strong consensus exists among them about the need for effective big data analytics across their organizations.
This Economist Intelligence Unit report shows that senior executives are optimistic about both the capabilities of big data and the impacts such data can have on their businesses.
Download the report to get the whole story.
In June and July 2015, with sponsorship by SAP, The Economist Intelligence Unit (EIU) carried out a survey of more than 300 executives who are familiar with their company's data and analytics practices. The goal was to assess trends in the use of market-facing advanced analytics.
To add insights to the survey findings, the EIU conducted interviews with several advanced analytics practitioners. This executive summary describes the top findings of this research.
The use of Big Data is becoming a key basis of competition and growth for individual firms. In most industries, established competitors and new entrants alike will leverage data-driven strategies to innovate, compete, and capture value from deep and up-to-real-time information.
Unlock your content, FirstSpirit, CMS, e-Spirit AG, Best-of-Breed, Internet, Intranet, Extranet, Management, CIO, CEO, CMO, Digital Marketing, Integration of third part technology, SEO, Analytics, Strategy, Customer Experience
Marketing & SalesBig Data, Analytics, and the Future of .docxalfredacavx97
Marketing & Sales
Big Data, Analytics,
and the Future of
Marketing & Sales
March 2015
3McKinseyonMarketingandSales.com @McK_MktgSales
Table of contents
Business
Opportunities
Insight and
action
How to get
organized and
get started
8 Getting big impact from big
data
16 Big Data & advanced
analytics: Success stories
from the front lines
20 Use Big Data to find
new micromarkets
24 Smart analytics: How
marketing drives short-term
and long-term growth
30 Putting Big Data and
advanced analytics to work
34 Know your customers
wherever they are
38 Using marketing analytics to
drive superior growth
48 How leading retailers turn
insights into profits
56 Five steps to squeeze more
ROI from your marketing
60 Using Big Data to make
better pricing decisions
60 Marketing’s age of relevance 72 Gilt Groupe: Using Big Data,
mobile, and social media to
reinvent shopping
76 Under the retail microscope:
Seeing your customers for
the first time
80 Name your price: The power
of Big Data and analytics
84 Getting beyond the buzz: Is
your social media working?
90 How to get the most from big
data
94 Five Roles You Need on Your
Big Data Team
98 Want big data sales programs
to work? Get emotional
102 Get started with Big Data:
Tie strategy to performance
106 What you need to make Big
Data work: The pencil
110 Need for speed: Algorithmic
marketing and customer
data overload
114 Simplify Big Data – or it’ll be
useless for sales
54 McKinseyonMarketingandSales.com @McK_MktgSales
Introduction
Big Data is the biggest hame-changing opportunity for marketing and sales
since the Internet went mainstream almost 20 years ago. The data big bang
has unleashed torrents of terabytes about everything from customer behaviors
to weather patterns to demographic consumer shifts in emerging markets.
The companies who are successful in turning data into above-market growth
will excel at three things:
ƒ Using analytics to identify valuable business opportunities from the data to
drive decisions and improve marketing return on investment (MROI)
ƒ Turning those insights into well-designed products and offers that delight
customers
ƒ Delivering those products and offers effectively to the marketplace.
This goldmine of data represents a pivot-point moment for marketing and
sales leaders. Companies that inject big data and analytics into their operation
show productivity rates and profitability that are 5 percent to 6 percent hight
than those of their peers. That’s an advantage no company can afford to
gnome.
This compendium explores the business opportunities, company examples,
and organizational implications of Big Data and advanced analytics. We hope
it provokes good and useful conversations.
Please contact us with your reactions and thoughts.
David Court
Director
David headed McKinsey’s
functional practices, and
currently leads the firm’s digital
in.
Bridging the Gap Between Business Objectives and Data StrategyRNayak3
Explore the fundamental elements of a robust data strategy that aligns with business objectives, from defining goals to prioritizing data architecture.
The study was conducted by Avaus Marketing Innovations, a leading data-driven marketing agency, together with ISS, a leading marketing research company. We asked Swedish and Finnish CMO’s, CTO’s and COO’s to assess the state of data and analytics in their companies, in four major areas:
1. Strategy and business objectives
2. Investments
3. People, processes and leadership
4. Tools and technologies
Download the full study for free here: https://www.avaus.fi/en/state-of-analytics/
The data directive is an Economist Intelligence Unit (EIU) report which seeks to explore the degree to which the ongoing data revolution within business is delivering truly strategic change within companies, as opposed to more incremental optimisation gains. Although many of the issues discussed here stray into the realm of so-called “big data”, this report is not explicitly focussed on that topic and not deal with any technology-related issues. Instead, it seeks to explore how the wider trend toward a greater reliance on data is affecting the strategic management of businesses at a C-suite level, across a range of industries. There is also a supplement to this report available that focuses on the strategic impact of data on CFO's and the finance function. http://bit.ly/thedatadirective_mt
CGT Research May 2013: Analytics & InsightsCognizant
A new survey conducted by Consumer Goods Technology (CGT) and sponsored by Cognizant explores how consumer goods companies are approaching data management strategies and usage.
Views From The C-Suite: Who's Big on Big DataPlatfora
he way that big data pervades most organizations today creates a dynamic environment for C-level executives to explore how it can and should be used strategically to add business value.
While each C-level executive views big data through a unique lens, a strong consensus exists among them about the need for effective big data analytics across their organizations.
This Economist Intelligence Unit report shows that senior executives are optimistic about both the capabilities of big data and the impacts such data can have on their businesses.
Download the report to get the whole story.
In June and July 2015, with sponsorship by SAP, The Economist Intelligence Unit (EIU) carried out a survey of more than 300 executives who are familiar with their company's data and analytics practices. The goal was to assess trends in the use of market-facing advanced analytics.
To add insights to the survey findings, the EIU conducted interviews with several advanced analytics practitioners. This executive summary describes the top findings of this research.
The use of Big Data is becoming a key basis of competition and growth for individual firms. In most industries, established competitors and new entrants alike will leverage data-driven strategies to innovate, compete, and capture value from deep and up-to-real-time information.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
1. Using customer
analytics to boost
corporate performance
Marketing Practice
Key insights from McKinsey’s DataMatics 2013 survey
January 2014
2. Contents
Cross-referencing tools
Indicates that additional
information is available in the
documentation part
Indicates that the chart
can be enlarged
The following icons help readers
to navigate in this report
Introduction
Part I: Executive summary
Extensive and best-practice users of customer
analytics outperform their competitors
So how do the top performers do it?
„
„ Analytics is not an IT but a strategic
business topic
„
„ A truly integrative approach is key
„
„ High performers hire C-level executives with
the “data gene” in their DNA
Outlook – Implications for CEOs and CIOs
Part II: Documentation
Methodology and sample structure
Detailed results
„
„ Objectives and value contribution
„
„ Capabilities and challenges
„
„ Trends and future investments
„
„ Organization and governance
Contacts
4
5
8
8
11
12
16
17
20
20
24
26
28
29
2
3. Overview of key insights
It creates value! Extensive use of
customer analytics has a large impact
on corporate performance. Companies
that use customer analytics extensively are more
likely to report outperforming their competitors on
key performance metrics
1.
2.
3.
4.
5.
Successful companies outperform their
competitors across the full customer
lifecycle. Focusing on acquisition and
profitability gives the greatest leverage, yet none of
the customer-relevant key performance indicators
must be missed. Goals need to be set for both
strategic and tactical indicators
Having a culture that
values and acts on
customer analytics is
critical. Investments in IT
and skilled employees are also
important, but investments alone
will not deliver value. Leadership
that expects fact-based
decisions and an organi
zation
that can quickly trans
late those
facts into action are more likely
to win than those companies that
focus mostly on IT
Success requires senior-
management involvement
in customer analytics.
High-performing companies
are led by data-savvy C-level
executives who understand
the importance of customer
analytics and take a hands-on
approach to the topic
Integration of customer
analytics across functions
and channels is the
top trend to focus on.
Enabling integrated multichannel
marketing that leverages real-
time data and frontline access
is seen as more important than
leveraging new sources or types
of data
Extensive and best-practice users of
customer analytics outperform their
competitors
So how do the top performers
do it?
Outlook – Implications for CEOs
and CIOs 3
4. Introduction
Corporations across the world and across industry
sectors are increasingly approaching their busi
nesses from a customer-centric perspective,
amassing vast quantities of customer intelligence
in the process. But harnessing this data deluge is
a huge challenge. Even after drawing on sophisti
cated software systems to portion big data into
viable segments, countless companies are finding
their expectations dashed. Big data often fails to
deliver the big insights that were hoped for because
com
panies are not tackling the topic opti
mally.
To do this, it would be of huge benefit to know
whether one can actually identify a corre
lation be
tween the use of customer analytics and cor
porate
performance. And if so, how can this be evidenced
and quantified? Does the impact of customer
analytics differ by industry? What capa
bilities and
investments are needed? What is the impact at
stake? What are the most important levers?
To find answers to these questions, McKinsey
recently conducted a global survey on big data,
inter
viewing over 400 top managers of large inter
national companies from a wide variety of indus
tries. The data obtained consisted of com
panies’
self-assessment of their own position and capa
bil
ities. A subsample of these results was then
substantiated with objective performance criteria.
The validation phase evidenced a signi
fi
cant
correlation with the companies’ return on assets.
Key aspects of the survey
The DataMatics 2013 benchmarking survey was
conducted from May to June 2103 with 418 senior
executives of major companies distributed
equally across Europe, the Americas, and Asia.
All the organizations had revenues of between
around EUR 500 million and over EUR 50 billion;
the majority had revenues of over EUR 5 billion.
Most respondents were from analytics-intensive
sectors within 10 major industries, including Retail,
Banking, Insurance, Media, IT, and Energy. The
topics covered were the objectives and value con
tribution of customer analytics, its capabilities and
challenges, trends and future investments, and its
organization and governance.
This report describes the results of McKinsey’s
2013 DataMatics survey, explaining the cham
pions’
secrets of success. It is divided into two sections.
The first is a report covering the results of the study
and some of the learnings that can be derived from
it. The second section is more statistics driven,
showing the data on which the report is based.
Extensive and best-practice users of
customer analytics outperform their
competitors
So how do the top performers
do it?
Outlook – Implications for CEOs
and CIOs
Key insights from McKinsey’s DataMatics 2013 survey
Text box 1
Please refer to the
Documentation section
in Part II for further
details on the survey.
4
5. Extensive and best-practice users of
customer analytics outperform their competitors
Use of customer analytics appears to have an im
mense impact on corporate performance (Exhibit 1).
The likelihood of generating above-aver
age profits
and marketing earnings is around twice as high
for companies that apply their customer analytics
broadly and intensively as for those who are not
strong in customer analytics. The effect from
sales is even greater: 50 percent of customer
analytics champions are likely to have sales well
above their competitors, versus only 22 percent of
laggards. These champions are also almost three
times as likely to generate above-average turnover
growth as competitors who evaluate their data
only sporadically (i.e., 43 percent of champions
manage to do so, compared to only 15 percent
of laggards). Return on investment shows roughly
the same picture: companies making intensive use
of cus
tom
er analytics are 2.6 times more likely to
have a significantly higher ROI than competitors –
45 percent versus 18 percent.
It is not just along such vital KPIs that these com
pa
nies are very likely to outperform their compe
titors: they reveal a markedly higher likelihood of
above-average performance across the entire cus
tomer lifecycle. In terms of strategic KPIs, some of
the findings are quite extraordinary. Intensive users
of customer analytics are 23 times more likely
to clearly outperform their competitors in terms
of new customer acquisition than nonintensive
users, and 9 times more likely to do so in terms
of customer loyalty. Our survey results also show
that the likelihood of achieving above-average
profitability is almost 19 times as high for customer
analytics champions as for laggards. Even more
impressive is their likelihood of migrating an
above-average share of customers to profitable
segments, at 21 times that of non-intensive users
of customer analytics (Exhibit 2).
Extensive and best-practice users of
customer analytics outperform their
competitors
So how do the top performers
do it?
Outlook – Implications for CEOs
and CIOs
Extensive users of
customer analytics
are more likely to
outperform the market
Extensive use of customer analytics has a large impact on
corporate performance
Percentage of companies above competition
SOURCE: McKinsey, DataMatics 2013
1 Based on "Please describe the performance of your firm/business unit in the following areas relative to your average competitor". "Above competition"
defined as 6 to 7 on a 7-point scale: 1 = Well below competition, 7 = Well above competition.
2 Based on "Please indicate how much you agree or disagree with the following statement: 'We use customer analytics extensively in our firm/business
unit'." Scale of 1 to 7: 1 = Strongly disagree, 7 = Strongly agree. Comparison of items assigned 1 or 2 vs. 6 or 7.
22
20
15
22
49
45
43
50
+132%
+186%
+131%
+126%
ROI
Sales growth
Sales
Profit1
Extensive use of customer analytics
No extensive use of customer analytics2
Exhibit 1
5
6. A third of all survey participants rated customer
analytics as extremely important for business
success, positioning it among the top five drivers
of their marketing. They consider it as important
as price and product management, and only a
few percentage points below service and actions
to enhance customer experience, far ahead of the
management of advertising campaigns (which only
20 percent view as a key driver of success).
These results also differ considerably by industry
sector. Banks have the greatest analytical skills,
media companies are particularly strong on imple
men
tation, while the retail trade – surprisingly –
lags furthest behind. Although they have an un
precedented wealth of transaction data available,
most retailers began deploying customer analytics
comparatively late: industries such as financial
services and telecommunications are far ahead.
Extreme cost consciousness has held the majority
of retailers back from making major investments
in the field. Many also appear to lack awareness
of how great the impact of customer analytics
can be. While the best performers across all
industries rate the use of customer analytics and
other customer-oriented initiatives as the no. 1
contributor to their success, retailers whose
performance is average view general marketing,
pricing, and campaign management as the key
factors for success – incorrectly. McKinsey’s
benchmarking has shown that intensive data
evaluation has the greatest impact on performance
in the retail industry. In the European retail trade,
benchmarking shows that the economic impact
of customer analyses is in fact more than twice
that in the banking sector, and around three times
higher than in telecommunications and insurance.
Extensive and best-practice users of
customer analytics outperform their
competitors
So how do the top performers
do it?
Outlook – Implications for CEOs
and CIOs
Successful companies outperform their competitors across the full
customer lifecycle
1 Based on "Please describe the performance of your firm/business unit in the following areas relative to your average competitor". "Above competition"
defined as 6 to 7 on a 7-point scale: 1 = Well below competition, 7 = Well above competition.
2 Based on "Please describe the performance of your firm/business unit in the following areas relative to your average competitor." Aggregate index
derived from the dimensions Sales, Sales Growth, Profit, ROI. Comparison of bottom vs. top quartile.
Tactical KPIs
3
9
14
11
81
80
71
69
x 5.8
x 9
x 6.5
x 23
Customer
satisfaction
Customer
loyalty
Customers
retained
Customers
acquired
High performer2
Low performer2
5
3
74
4
10
63
76
75
x 21
x 15
x 18.8
x 7.4
Migration to
profitable
segments
Value
delivered to
customers
Customer
profitability
Sales to
existing
customers
Performance index1
Strategic KPIs
SOURCE: McKinsey, DataMatics 2013
Exhibit 2
6
7. Methodology
The methodology used was to ask the participants
to give full details of their context (position, indus
try, geography, revenues, and number of staff), as
well as their customer base and com
pe
titors. They
were then asked a series of questions, rating their
responses from 1 (Strongly disagree) to 7 (Strongly
agree). These fell into various catego
ries, such
as the value contribution of cus
tomer analytics,
their objectives in using cus
tomer ana
lytics, and
their use of it along different dimen
sions, whether
IT, analytics skills, or execution. Their evaluation
of upcoming trends was also re
quested, as well
as their opinions on forthcoming challenges
and opportunities. Investments, value chain
management, and staff/governance were further
aspects that were analyzed in detail.
An extract from the McKinsey survey (below) is a
brief example of the way many of the questions
were structured.
Extensive and best-practice users of
customer analytics outperform their
competitors
So how do the top performers
do it?
Outlook – Implications for CEOs
and CIOs
Please refer to the
Documentation
section in Part II for
further details on the
methodology.
8
CS010
Why does your firm/business unit deploy customer analytics?
Strongly
disagree
1 (1)
2
(2)
3
(3)
Neither agree
nor disagree
4 (4)
5
(5)
6
(6)
Strongly
agree
7 (7)
To acquire new customers (1)
To increase sales to existing
customers (2)
To increase customer profitability
(3)
To increase customer satisfaction
(4)
To improve the value delivered to
customers (5)
To increase the proportion of loyal
customers (6)
To increase the number of
customers retained (7)
To migrate customers to more
profitable segments (8)
To reduce customer acquisition and
servicing costs (9)
For cross-selling purposes (10)
To better understand the needs and
wants of the customer (11)
Other (please specify)
(998)____________
Other (please specify)
(999)____________
SO HOW DO THE TOP PERFORMERS DO IT?
The findings showed that winners take a truly integrative approach, seeing
analytics not as an IT but a strategic business topic. Hiring C-level executives that
take a hands-on approach to customer analytics is also vital – they need to have
Text box 2
7
8. Extensive and best-practice users of
customer analytics outperform their
competitors
So how do the top performers
do it?
Outlook – Implications for CEOs
and CIOs
The findings showed that winners take a truly
integrative approach, seeing analytics as a strate
gic rather than purely as an IT issue. Hiring C-level
executives who take a hands-on approach to
customer analytics is also vital – they need to have
the skills themselves, and be able to appreciate
their importance. These three factors play a large
role in the astounding spread in results between
high performers and laggards evidenced in the
previous section.
Companies need to understand that it is not the
IT that counts so much as what you do with it.
Many managers associate customer analytics
with complex IT systems and expensive analysis
tools. It is true that no company can leverage their
customer data successfully without IT investment.
But relying on technology alone is not the answer.
How companies actually make use of customer
information is what makes the difference, and the
organizational changes they implement to realize
these changes.
A narrow focus on technology and tools rather
than staff and processes is another common
failing. Competence is what counts – the ability
to swiftly translate data into concrete action
(Exhibit 3). That is where the retail industry – as
just one example – falls down. It does not invest
sufficiently in in-house expertise, staff skills, and
the development of proprietary analysis models.
McKinsey conducted a diagnosis on the advanced
analytics capabilities of one of the leading global
online companies, and outperformed their existing
analytics in various algorithms during the following
pilot phase. The new algorithms were fully imple
So how do the top performers do it?
Analytics is not an IT but a strategic business topic Exhibit 3
59
72
73
77
77
81
82
95
95
100
Broad use of customer analytics 91
Actionability of insights 92
Use of appropriate techniques 93
Management expectations
Fast translation to action
Fact-based decisions
Interlinked IT systems
Software accessibility
360°perspective
Automated data processing
Speed of model development
Automated analytics
Quality management of analytics
In-house expertise 84
Management attitude 91
Analytics valued by the front line 91
Having a culture that appreciates and acts on
customer analytics is critical for value creation
Execution and organization
1 Pearson correlation of respective capability with value contribution of customer analytics (based on agreement with the statement "The use of customer
analytics contributes significantly to our firm's/business unit's performance", with the highest scaled to 100%).
Analytics
IT
Capability
Importance for value contribution
through analytics1
Percent
Objective
Understand the most
important capabilities that
enable an organization to
gain value from customer
analytics
Method
Correlate level of capability
to perceived value
contribution of customer
analytics and index highest
correlation of 65 to 100%
Average for category 94
86
72
SOURCE: McKinsey, DataMatics 2013
8
9. Extensive and best-practice users of
customer analytics outperform their
competitors
So how do the top performers
do it?
Outlook – Implications for CEOs
and CIOs
mented across multiple levers and channels,
which led to a positive earnings impact of around
EUR 1 billion per annum. This has substantially
Example:
A leading retailer uses thousands of automatically
built predictive models to forecast future demand
on an SKU level, and link it with their customer
datamart.
Automating as much of the analytics process
as possible means the data scientists can focus
their time on defining the analytics process and
mission-critical QC and validation tasks.
contributed to making the company a trail-blazing
pioneer in the industry, epitomizing the “data to
strategy” shift.
Key capabilities for maximum value creation
The key capabilities for creating value from cus
tomer analytics are
integrated deployment of IT systems, targeted analytics skills, and
smart execution/organization.
Ideal IT setup
A company’s IT – including all relevant legacy systems – is all
inter
linked. Silos are minimized by ensuring intensive cooperation
between the IT and business departments. A datamart with a
360° perspective on customers contains all customer data from
multiple interconnected sources, and is accessible for analytics
specialists from different business units. Data processing is fully
automated, as well as analytics processes for critical marketing
operations, with self-learning algorithms for stan
dard analytics.
All the requisite software is ac
ces
sible to analysts/analytics
decision makers. Business users have access to software
interfaces that allow them to run analytics on the go, without
programing know-how.
Text box 3
9
10. Extensive and best-practice users of
customer analytics outperform their
competitors
So how do the top performers
do it?
Outlook – Implications for CEOs
and CIOs
Optimal analytics skills
The company has in-house expertise for conducting advanced
customer analytics. Analytics leverages both structured and
unstructured data by combining data and text mining for ad
vanced predictive models. Analytics infrastructure enables rapid
development, evaluation and scoring of models from a single
integrated environment. Different types of statistical and predictive
algorithms are combined for maximizing the impact of analytics
(e.g., by moving from traditional CHAID decision tree models
to Random Forest or ensemble models to maximize accuracy).
The analytics staff are excellent at deploying the appropriate
analytics techniques and generating actionable insights from
data. Standards for end product quality and service are enforced;
predictive models are versioned and scores are tracked.
Example:
In the telecoms industry, analytics talent is a
scarce resource for many organizations: analytics
leaders score highest on superior quality manage
ment of analytics and streamlining their analytics
processes for maximum efficiency.
Knowing this constraint, a leading telco player
refrained from using the newest and most inno
vative algorithms. Instead, they concentrated on
finding the analytics talent able to apply the right
analytics method for a clearly defined business
question. Standards for end product quality are
also precisely delineated and rigorously monitored.
Highly efficient execution and organization
New analytics insights are quickly translated into value-creating
initiatives. Analytics assets are integrated into business processes
(such as real-time scoring where necessary). Virtually everyone in
the firm/business unit uses customer ana
lyt
ics-based insights to
support decisions. Predictive analyt
ics are used throughout the
organization. Top management ex
pects in
sights stemming from
customer analytics to sup
port key mar
ket
ing and sales decisions.
Frontline units value recom
mendations based on customer
analytics. Top management looks favorably upon using customer
analytics to reach informed decisions.
Example:
A major insurance company improved their profits
by integrating their customer analytics with their
fraud detection system. During the claims handling
process agents leverage customer analytics to fast-
track claims for customer segments with a low likeli
hood of fraudulent activities, simultaneously boost
ing satisfaction and reducing operational costs.
10
11. Extensive and best-practice users of
customer analytics outperform their
competitors
So how do the top performers
do it?
Outlook – Implications for CEOs
and CIOs
The integration of customer analytics across
functions and channels is instrumental. The survey
showed that enabling integrated multichannel
marketing using real-time data and frontline
access is more important than leveraging new
sources or types of data (Exhibit 4). Successful
big data players build an insight value chain that
incorporates all elements from design through to
the customer, end to end.
The six elements in this chain are data, analyses,
software, capabilities, processes, and strategy.
Any chain is only as strong as its weakest link,
so it is vital that each element is professionalized,
and that they are optimally interlinked. The data
have to be appropriately adjusted, structured, and
enriched. The analyses also need to be reliable,
supported by interactive and scalable software.
The findings should not be bundled at one central
site, but be made available to everyone involved
in making the related decisions, accessible at any
time. It is also important to continuously analyze
the data for new sources of value, and to feed
these into the value chain on a regular basis.
Some aspects of the integration that participants
rated as particularly significant are enabling
integrated multichannel marketing, expanding
customer analytics across the value chain,
embedding analytics on the front line and
processing real-time data. One of the UK’s major
retailers started a customer analytics journey in
the mid-1990s, further developing their analytics
capacity on a continuous basis. They learnt to
leverage analytics across a variety of marketing
and sales levers, broadening these applications
A truly integrative approach is key
Exhibit 4
Respondents perceiving the trend as "extremely important"1
Percent
Integration of customer analytics across functions and channels
is the top trend to focus on
14
17
19
21
21
24
27
28
29
Frontline embedding of analytics
Enabling integrated multichannel marketing
Sharing data in strategic alliances
Including third-party/external data
Analyzing unstructured data
Automating customer analytics
Generating automated commercial actions
Processing real-time data
Expanding customer analytics across the
value chain
1 What trends do you consider most important for customer analytics within the next 5 years?
SOURCE: McKinsey, DataMatics 2013
11
12. Extensive and best-practice users of
customer analytics outperform their
competitors
So how do the top performers
do it?
Outlook – Implications for CEOs
and CIOs
across their network to encompass targeted
communications, pricing, and merchandising.
Eventually, they even incorporated their suppliers
so that production was directly influenced by the
big data they were gathering. As a result, they
managed to save hundreds of millions of pounds
a year in promotion expenses while constantly
increasing their market share, and customer
com
plaints fell by 75 percent. They have also
seen a direct rise in customer loyalty: at present,
40 percent of their customers buy over 70 percent
of their groceries at the store. The company’s
unprece
dented ability to translate data to insight
to action created a sustainable competitive ad
van
tage, leading to clear market leadership, and
exemplifies the integrative approach that is the
secret to success.
The results clearly indicate the importance of
senior-management involvement in customer
analytics. High-performing companies make sure
of this by hiring C-level executives who understand
the significance of customer analytics, and take a
hands-on role in its deployment. High performers
have 76 percent of their C-level executives involved
in customer analytics, whereas the figure is only
45 percent at low performers – a difference of
almost 70 percent (Exhibit 5). Perhaps unsur
pris
ingly given these results, 76 percent of com
panies
that extensively involve their senior man
age
ment
in customer analytics strongly agree that custom
er
analytics significantly contributes to per
for
mance,
while this is true of only 29 percent of com
panies
that do not. The likelihood of achiev
ing an above-
average return on investment is almost double
High performers hire C-level executives with the
“data gene” in their DNA
Exhibit 5
The most profitable companies have top-management involvement and
broader support for leveraging customer analytics
Percentage of each level involved in customer
analytics
Percent
Not/somewhat
important
15
25
20
43
25
30
Working teams 39
Team leaders 51
Dept. heads 70
2nd level 53
Other C level 54
CEO 49
Very/extremely
important
173
35
+394%
Very/
extremely
important
Not/
somewhat
important
How important is the use of customer
analytics to your commercial success?
Number of FTEs actively engaged in
supporting customer analytics
Companies that rate customer analytics as important are
more likely to have senior-level involvement and more
FTEs involved
SOURCE: McKinsey, DataMatics 2013
12
13. Extensive and best-practice users of
customer analytics outperform their
competitors
So how do the top performers
do it?
Outlook – Implications for CEOs
and CIOs
that of laggards at companies where senior
management is intensively involved in analytics,
while the probability of generating above-average
profits is more than twice as high (Exhibit 6). Sales
growth is another key area where champions
with senior-management involvement in customer
analytics have a clear advantage, with almost three
times the likelihood of outperforming competitors
who do not give importance to this factor.
Exhibit 6
Companies with
senior-management
involvement in
customer analytics
initiatives are more
likely to outperform
the market
Senior-management involvement in customer analytics
translates into business performance
Profit1
38
+113%
81
31
+161%
81
43
+86%
80
43
+91%
82
Sales
Sales growth
ROI
X% Percent of companies
above competition
1 Based on "Please describe the performance of your firm/business unit in the following areas relative to your average competitor". "Above competition"
defined as 5 to 7 on a 7-point scale: 1 = Well below competition, 7 = Well above competition.
2 Based on "To what extent is senior management involved in customer analytics initiatives?". Scale of 1 to 7: 1 = Not at all, 7 = To a great extent.
Comparison of items 1 or 2 vs. 6 or 7.
No senior management
involvement in
customer analytics2
Senior management
involvement in
customer analytics
SOURCE: McKinsey, DataMatics 2013
While it is crucial that management walks the
talk, it is also essential to anchor a culture of
customer data management throughout the entire
company, ensuring that analytics tools and their
results are always connected to the right data,
the right people, and the right marketing strategy.
Everyone in the organization should understand
the topic, and all decisions should be supported
by analytics.
13
14. DataMatics and big data in action:
All systems go
A consumer goods retailer had a CRM organiza
tion, but was using this on a reactive, ad hoc
basis, only occasionally mining big data. The
analyses were primarily descriptive – no use was
being made of advanced analysis techniques such
as customer value models or data mining.
McKinsey’s holistic diagnostic and subsequent
recommendations involved taking action in
all functional areas: Sales, Marketing, Pricing,
Category Management, and Inventory. Initiatives
included expanding the client’s existing CRM
system into an internal center of competence
for cus
tomer-oriented analytics and introducing
standard reports to the business functions. What
had previously simply been one technical tool
among others was now elevated to the status of
a strategic, predictive compass.
Other measures were target group programs,
intro
ducing guided selling devices for branch staff,
shopping apps for customers, and a Web shop
with personalized content, plugged in along the
entire customer journey. The project introduced
tailored Web-based marketing mix modeling
Extensive and best-practice users of
customer analytics outperform their
competitors
So how do the top performers
do it?
Outlook – Implications for CEOs
and CIOs
that statistically links marketing expenditure and
other drivers to sales (Exhibits 7, 8). This is not
just historical, but can also estimate the sales
impact of any stimulus, whether promotional
price, halo advertising, social media or competi
tive advertising. Pricing initiatives covered differ
entiation by price zone, and the optimization of
price guidelines for each category. Data-driven
Exhibit 7
Action
Target picture of big data competences after implementation
of actions
RETAIL EXAMPLE
1 Base level
5 Best practice
Improvement
+1
+1
Addressed by
prioritized actions
Data
Software
People
Processes
Strategy
Average
Analytics
Machine
room
Embed-
ding
2.7
3
3
3
3
3
3
3
2
1
2
2
2
3
3
3
2
2
3
Genera-
tion of
insights
Targeted
marketing
Space
Availa-
bility
4
4
4
4
4
4
4
4
4
4
4
Pro-
motion
3
3
3
3
3
3
1.5
2
2
1.5
2.5
3
2
2
3
2
4
3
Range
Marketing
mix
Pricing
3
3
3
3
3
3
4
3.3 3 3 2.4
+2
+2.5
2
+2.5
+3
+3
+1
0
0
0
0
+1
+1
0
0
0
0
0
+1
+2
+2
+2
+1
+2
+1
+1.5
+2
+1
+1.5
+1.5
+1
+1.5
+2
+1
+1.5
+2
+1
0
0
0
0
0
+1
0
0
0
0
0
0
0
0
0
0
0
+1.0 +1.7 +1.5 +0.2 0
Pur-
chasing
SOURCE: McKinsey, DataMatics 2013
Text box 4
14
15. Outlook – Implications for CEOs
and CIOs
evaluation and development of categories was
combined with out-of-range enquiries to produce
leading-edge category and inventory management.
The integrative aspect was key: the client found
the EBTDA improvements that this initiative was
likely to yield extremely compelling.
Extensive and best-practice users of
customer analytics outperform their
competitors
So how do the top performers
do it?
Multiple actions are being taken to anchor this
transformation into the organization on the
dimensions of mindsets and behavior, skills and
organization, tools and process integration, and
data, IT, and analysis methods. Strong top-
management commitment is particularly crucial,
as well as ensuring that the analytical findings are
woven into decision making at every level.
Exhibit 8
EUR millions, Germany
Complete implementation of the big data road map has a
significant EBITDA impact
2014/15
RETAIL EXAMPLE
EBITDA before one-off costs
Total
Marketing mix
Targeted marketing
Purchasing
Availability
Space
Range
Promotion
Pricing
Generation of insights
2013/14
Relevance
of big data
Implemen-
tation effort
Low High
Actions
Strategic
relevance
SOURCE: McKinsey, DataMatics 2013
15
16. Extensive and best-practice users of
customer analytics outperform their
competitors
So how do the top performers
do it?
Outlook – Implications for CEOs
and CIOs
Whether termed “strategic analytics,” “business
intelligence” or “customer analytics,” smart inter
pretation of consumer data has already become
an absolute must, not simply a nice-to-have value
driver. From leveraging the converged cloud
with intuitive interfaces to prescriptive models
that match segments to campaigns, offers, and
content using every conceivable channel, analytics
is key to maintaining a competitive edge. In view of
these developments, one can assume that every
company will be performing customer analytics
as a matter of course in the near future. The call
to action for CEOs/CIOs of laggards will therefore
be to diagnose their status quo and catch up as
swiftly as possible.
But champions will not be able to rest on their
laurels. Since all the players will be in on the game
and improving by the minute, champions need to
prepare themselves for a pure-play strategy that
elevates them to operational excellence, with the
lowest costs coupled to the greatest possible
benefits. Operationalizing the insight value chain
along every link will be the key. Catapulting them
selves into the next dimension of integrative data
analysis that encompasses the entire ecosystem of
industry players will not be easy, but the rewards
will far outweigh the effort.
Outlook – implications for CEOs and CIOs
16
17. Methodology and sample structure
Methodology and sample structure Detailed survey results
Documentation
The DataMatics 2013 benchmarking survey was
conducted from May to June 2103 with 418 senior
executives of major companies from a wide
variety of industries and distributed equally across
Europe, the Americas, and Asia. The data obtained
consisted of companies’ self-assessment of their
own position and capabilities. A subsample of
these results was then substantiated via correlation
with objective performance criteria. The validation
phase evidenced a significant correlation with the
companies’ return on assets.
SOURCE: McKinsey, DataMatics 2013
Key parameters of the survey
What senior executives need to know about
customer analytics (examples)
▪ What is the value contribution of customer
analytics?
▪ What capabilities are essential to take customer
analytics to the next level?
▪ What are the top trends?
▪ What needs to be invested in customer
analytics to be competitive in the future?
▪ 418 senior and C-level executives participated in the online survey
▪ Field time was from May to June 2013
▪ The survey covers a variety of industries, including Retail, Banking,
Insurance, Media, IT, and Energy
▪ The survey was conducted globally, equally distributed across
Europe, the Americas, and Asia
Contacts
Exhibit I
The DataMatics benchmarking survey was
conducted to answer key questions of senior
executives on customer analytics (Exhibit I).
17
18. Methodology and sample structure Detailed survey results
Documentation
The sample covered key customer analytics topics
(Exhibit II) …
… as well as a broad range of industries and
geographies (Exhibit III).
Exhibit II Exhibit III
Region Industry Level of respondents
32
39
29
5
5
6
15
16
7
10
10
12
14
4
5
6
18
22
6
9
13
17
SOURCE: McKinsey, DataMatics 2013
IT/Software
Insurance
Pharma/Chem/Med
Media, Entertainment
& Information
Advanced Industries2
Telecom
Other1
Retail/Apparel
Energy
Banking and
Securities
CMSO
CSO
Head of Sales
CMO
CEO
Other3
Head of CRM
CCO
Head of
Marketing
Americas
Asia
EMEA
1 E.g., Hospitality, Logistics, Agriculture, Education.
2 Automotive, Manufacturing, Construction.
3 E.g., Sales Director, Head of Marketing Analytics, Manager, VP.
▪ Equal distribution of responses across Europe, the Americas, and Asia
▪ Majority of respondents from analytics-intensive industries
▪ Strong focus on C-level executives
Percent, n = 418
SOURCE: McKinsey, DataMatics 2013
Key questions asked
Topics covered
Capabilities and
challenges in
customer analytics
▪ Assessment of capabilities in IT, analytics, and execution
▪ Biggest challenges and opportunities
▪ Industry leaders in customer analytics
Trends and future
investments
▪ Most important trends
▪ Customer analytics as a share of the overall marketing budget
▪ Changes in spending
Organization and
governance of
customer analytics
▪ FTEs in customer analytics
▪ Level of senior management involved
▪ Performance indicators for customer analytics
Objectives and value
contribution of
customer analytics
▪ Overall importance
▪ Value contribution
▪ Objectives pursued
Contacts 18
20. Detailed survey results
Methodology and sample structure
Detailed survey results:
Objectives and value contribution
Documentation
For companies to tackle the topic of big data
optimally, the key is to know that there is actually a
correlation between the use of customer analytics
and corporate performance. But the answers to
other questions are vital, too. To what extent can
this be evidenced and quantified? How does the
impact of customer analytics differ by industry?
What capabilities and investments are needed,
what is the impact at stake, and what are the most
important levers?
McKinsey’s detailed survey results address
these topics, broken down into four categories:
objectives and value contribution, capabilities and
challenges, trends and future investments, as well
as organization and governance.
Objectives and value contribution
Extensive use of customer analytics plays a large
role in driving corporate performance (Exhibit V).
Extensive users of
customer analytics
are more likely to
outperform the
market
Percentage of companies above competition1
SOURCE: McKinsey, DataMatics 2013
1 Based on "Please describe the performance of your firm/business unit in the following areas relative to your average competitor". "Above competition"
defined as 6 to 7 on a 7-point scale: 1 = Well below competition, 7 = Well above competition.
2 Based on "Please indicate how much you agree or disagree with the following statement: 'We use customer analytics extensively in our firm/business
unit'." Scale of 1 to 7: 1 = Strongly disagree, 7 = Strongly agree. Comparison of items assigned 1 or 2 vs. 6 or 7.
22
43
15
20
22
45
50
49
+132%
+186%
+131%
+126%
ROI
Sales growth
Sales
Profit
Extensive use of customer analytics
No extensive use of customer analytics2
Contacts
Exhibit V
20
21. Methodology and sample structure
Detailed survey results:
Objectives and value contribution
Documentation
Successful companies are more likely to
outperform competitors across the full customer
lifecycle (Exhibit VI).
The more mature the customer analytics
approach, the stronger its contribution is likely to
be to corporate performance (Exhibit VII).
Exhibit VI Exhibit VII
SOURCE: McKinsey, DataMatics 2013
1 Based on "Please indicate how much you agree or disagree with the following statement about the contribution of customer analytics to your
firm's/business unit's performance: 'The use of customer analytics contributes significantly to our firm's/business unit's performance'." Scale of 1 to 7:
1 = Strongly disagree, 7 = Strongly agree.
2 Based on "Please indicate which of the following best describes the level of analytic development of your firm/business unit."
43
24
13
11
5
18
+37
percentage points
High
…
Low
The use of customer analytics contributes significantly
to our firm's/business unit's performance1
Percentage of respondents who strongly agree
Analytics
maturity2
The highest absolute
increase is at the last step
(+19%); excellence in
customer analytics drives
disproportionate value
contribution
High performance
on all dimensions;
goals need to be
set for both
strategic and
tactical indicators
1 Based on "Please describe the performance of your firm's/business unit's marketing group in the following areas relative to your average competitor
(consider the immediate past year in responding to these items)". Above competition defined as 6 to 7 on a 7-point scale: 1 = Well below competition,
7 = Well above competition.
2 Based on "Please describe the performance of your firm/business unit in the following areas relative to your average competitor". Aggregate index
derived from the dimensions Sales, Sales Growth, Profit, ROI. Comparison of bottom vs. top quartile.
Tactical KPIs
9
3
14
x 5.8
x 9
11
x 6.5
x 23
Customer
satisfaction 81
Customer
loyalty 80
Customers
retained 71
Customers
acquired 69
High performer
Low performer2
3
5
4
x 21
x 18.8
10
x 15
x 7.4
Migration to
profitable
segments 63
Value
delivered to
customers 76
Customer
profitability 75
Sales to
existing
customers 74
Strategic KPIs
SOURCE: McKinsey, DataMatics 2013
Performance index1
Contacts 21
22. Methodology and sample structure
Detailed survey results:
Objectives and value contribution
Documentation
Also, companies that actively measure their
campaign performance with quantitative metrics
are more profitable (Exhibit VIII).
The competitive and market context is a hugely
important driver: the stronger the related external
factors are perceived to be, the more intensively
companies apply customer analytics (Exhibit IX).
Exhibit VIII Exhibit IX
Extensive use of
customer analytics
is primarily driven by
the competitive and
market context
Companies stating Top2Box
Percent1
SOURCE: McKinsey, DataMatics 2013
1 Based on "Please indicate how much you agree or disagree with the following statements: 'Our customer base is very diverse'; 'Our customers' needs
and wants change frequently'; 'Customer analytics is used extensively in our industry'; 'Our firm faces intense competition'."
2 Based on "We use customer analytics extensively in our firm/business unit". Scale of 1 to 7: 1 = Strongly disagree, 7 = Strongly agree. Definition of
extensive use of analytics: comparison of items assigned 1 or 2 vs. 6 or 7.
47
0
3
35
70
87
100
52
Competition
Diverse customer
base
Used extensively
in industry
Change of needs
and wants
No extensive use of customer analytics
Extensive2 use of customer analytics
Performance of companies using different metrics
Share of highly profitable companies, percent1
1 Based on "Please describe the performance of your firm's/business unit's marketing group in the following areas relative to your average competitor
(consider the immediate past year in responding to these items)". Scale of 1 to 7: 1 = Well below our competition; 4 = About equal; 7 = Well above our
competition. Values 6 and 7 are classified as "highly profitable".
2 Based on "Which campaign performance metrics does your firm/business unit use?".
Companies applying
quantitative marketing
campaign metrics are
more profitable
SOURCE: McKinsey, DataMatics 2013
28
29
31
23
25
39
40
42
42
41
Profit uplift
Reach
Response rate
Campaign ROI
Revenue uplift
Using metric2
Not using metric2
Contacts 22
23. Methodology and sample structure
Detailed survey results:
Objectives and value contribution
Documentation
Customer analytics is most often focused on
customer retention and loyalty. This leaves a great
deal of as yet untapped potential in fields relating
to sales opportunities and customer understanding
(Exhibit X).
Exhibit X
Why does your firm/business unit deploy customer analytics?
Percentage who highly agree (6 or 7 on a scale of 1 - 7)
36
39
42
49
49
50
50
51
53
55
61
To reduce customer acquisition and servicing costs
To migrate customers to more profitable segments
For cross-selling purposes
To improve the value delivered to customers
To increase customer profitability
To increase customer satisfaction
To acquire new customers
To better understand the needs and wants of customers
To increase the number of customers retained
To increase the proportion of loyal customers
To increase sales to existing customers
SOURCE: McKinsey, DataMatics 2013
Focus on retention
and loyalty
Sales opportunities
(acquisition, profit-
ability, cross-selling)
and customer under-
standing (needs,
satisfaction, value
delivered)
Contacts 23
24. Methodology and sample structure
Detailed survey results:
Capabilities and challenges
Documentation
Exhibit XI Exhibit XII
SOURCE: McKinsey, DataMatics 2013
1 Based on "What do you see as the greatest challenges that your firm/business unit will face within the next 5 years in the area of customer analytics?".
Challenges seen in customer analytics over the next 5 years1
The biggest challenge is to build analytic capabilities and
integrate them into the demand generation process.
Capabilities
Access to reliable data in real time and ensuring data
protection and privacy concerns are key to success.
Data
It is vital to embed a culture of customer analytics across
the organization and enable users to easily access
customer analytics relevant to their functions.
Company culture
The issue we are facing is to embed customer analytics
into everyday frontline operations.
Implementation
The whole market faces intense competition in attracting
customers. It is getting harder to stay ahead.
Cost/markets
59
72
73
77
77
81
82
95
95
100
Broad use of customer analytics 91
Actionability of insights 92
Use of appropriate techniques 93
Management expectations
Fast translation to action
Fact-based decisions
Interlinked IT systems
Software accessibility
360°perspective
Automated data processing
Speed of model development
Automated analytics
Quality management of analytics
In-house expertise 84
Management attitude 91
Analytics valued by the front line 91
SOURCE: McKinsey, DataMatics 2013
Execution and organization
1 Pearson correlation of respective capability with value contribution of customer analytics (based on agreement with the statement "The use of customer
analytics contributes significantly to our firm's/business unit's performance", with the highest scaled to 100%).
Analytics IT
Capability
Importance for value contribution
through analytics1
Percent
Objective
Understand the most
important capabilities that
enable an organization to
gain value from customer
analytics
Method
Correlate level of capability
to perceived value
contribution of customer
analytics and index highest
correlation of 65 to 100%
94
86
72
Average for category
Capabilities and challenges
Having a culture that appreciates and acts on
customer analytics is critical for value creation
(Exhibit XI).
Key challenges can be grouped into five
areas: costs and markets, data, capabilities,
implementation, and company culture (Exhibit XII).
Contacts 24
25. Methodology and sample structure
Detailed survey results:
Capabilities and challenges
Documentation
Exhibit XIII
SOURCE: McKinsey, DataMatics 2013
1 Based on "What do you see as the greatest opportunities ahead for your firm/business unit within the next 5 years in the area of customer analytics?".
We definitely have opportunities to increase our customer expe-
rience with a focus on the long-term value of individual customers.
Better customer insights could help us anticipate customer needs
and improve the conversation with our customers.
Granular customer insights allow the customization of products,
prices, and user experience along all touch points.
Opportunities perceived in customer analytics over the next 5 years1
Opportunities are mainly seen in improving
customer experience and multichannel integration
(Exhibit XIII).
Contacts 25
26. Methodology and sample structure
Detailed survey results:
Trends and future investments
Documentation
Exhibit XIV Exhibit XV
11
11
13
15
16
IT (software, hardware,
licenses for software tools)
Overall change in customer
analytics spending
Use of external consultants
Human resources
(customer analytics staff)
Data acquisition (fees for
acquisition of third-party data
and data collection)
10
13
13
16
18
SOURCE: McKinsey, DataMatics 2013
1 Based on "Compared with the past 12 months, how will your company's/business unit's customer analytics spending change (as a percentage) over
the coming 12 months?".
Expected increase in spending over the next 12 months
Percent1
One-off expenses Ongoing expenses
Respondents perceiving the trend as "extremely important"1
Percent
14
17
19
21
21
24
27
28
29
Frontline embedding of analytics
Enabling integrated multichannel marketing
Sharing data in strategic alliances
Including third-party/external data
Analyzing unstructured data
Automating of customer analytics
Generating automated commercial actions
Processing real-time data
Expanding customer analytics
across the value chain
SOURCE: McKinsey, DataMatics 2013
1 Based on "What trends do you consider most important for customer analytics within the next 5 years?".
Integration of customer
analytics across func-
tions and channels is
seen as extremely
important
Acquisition of additional
data sources is viewed
as less important
Trends and future investments
Integration of customer analytics across
functions and channels is the top trend
(Exhibit XIV).
Spending on customer analytics is expected
to increase across all dimensions in the near
future, with a focus on IT and data acquisition
(Exhibit XV).
Contacts 26
27. Methodology and sample structure
Detailed survey results:
Trends and future investments
Documentation
Exhibit XVI
26
19
9
21
Customers acquired2
25
18
13
21
Sales to existing
customers2
25
19
11
21
Customer profitability2
23
19
13
21
Customer satisfaction2
25
18
21
21
Value delivered to
customers2
24
18
22
21
Proportion of loyal
customers2
24
19
11
21
Customers retained2
28
18
9
21
Customer migration to
more profitable segments2
Share of marketing budget spent on customer analytics
Percent1
Top tier
Middle tier
Low tier
1 Based on "Considering the immediate past year, what percentage of your firm's/business unit's overall marketing budget (excluding sales force
expenditure) was spent on customer analytics?".
2 Based on "Please describe the performance of your firm's/business unit's marketing group in the following areas relative to your average competitor
(consider the immediate past year in responding to these items)". Scale of 1 to 7: 1 = Well below our competition; 4 = About equal; 7 = Well above our
competition. Values 6 and 7 are classified as "top tier"; 3, 4, and 5 as "middle tier"; and 1 and 2 as "low tier".
SOURCE: McKinsey, DataMatics 2013
Companies with high expenditure on customer
analytics perform better in most areas
(Exhibit XVI).
Contacts 27
28. Methodology and sample structure
Detailed survey results:
Organization and governance
Documentation
Exhibit XVII Exhibit XVIII
SOURCE: McKinsey, DataMatics 2013
1 Based on "The use of customer analytics contributes significantly to our firm's/business unit's performance". "Significant performance contribution
through analytics" defined as 6 to 7 on a 7-point scale: 1 = Strongly disagree, 7 = Strongly agree.
2 Based on "To what extent is senior management involved in customer analytics initiatives?". "Extensive involvement of senior management" defined as
6 to 7 on a 7-point scale: 1 = Not at all, 7 = To a great extent.
Significant performance contribution of analytics1
Percentage of companies that strongly agree
76
29
+162%
Extensive
senior
management
involvement
No extensive
senior
management
involvement2
1 Based on "What level of senior management is involved in customer analytics?". Answer categories "CEO" and "Other C-level executives".
2 Based on "Please describe the performance of your firm/business unit in the following areas relative to your average competitor". Aggregate index
derived from the dimensions Sales, Sales Growth, Profit, ROI. Comparison of bottom vs. top quartile.
76
45
+69%
High
performers
Low
performers2
C-level involvement
Companies with C-level executives involved in customer analytics, percent1
SOURCE: McKinsey, DataMatics 2013
High-performing companies
are led by data-savvy C-level
executives who understand
the importance of customer
analytics and take a hands-
on approach to the topic
Organization and governance
Having data-savvy C-level executives at the
forefront of their customer analytics activities is
perceived as key to company performance
(Exhibit XVII).
Companies that involve their senior management
extensively in customer analytics are almost
three times as likely to find it makes a significant
performance contribution (Exhibit XVIII).
Contacts 28
29. Methodology and sample structure Detailed survey results
Documentation
Contacts
Jesko Perrey
is a Director in
McKinsey’s Düsseldorf office.
jesko_perrey@mckinsey.com
Andrew Pickersgill
is a Director in
McKinsey’s Toronto office.
andrew_pickersgill@mckinsey.com
Contacts
Lars Fiedler
is an Associate Principal in
McKinsey’s Hamburg office.
lars_fiedler@mckinsey.com
Marcus Roth
is a Senior Expert in
McKinsey’s Chicago office.
marcus_roth@mckinsey.com
Matthias Kraus
is a Practice Specialist
in McKinsey’s Munich office.
matthias_kraus@mckinsey.com
Alec Bokmann
is an Expert Associate Principal
in McKinsey’s New York office.
alec_bokmann@mckinsey.com
Julie Hayes
is a Practice Manager
in McKinsey’s Chicago office.
julie_f_hayes@mckinsey.com
29
30. Academic advisors
Documentation
Methodology and sample structure Detailed survey results Contacts/Academic advisors
Gary L. Lilien
Distinguished Research Professor of
Management Science at Penn State
and Research Director at the Institute
for the Study of Business Markets
(ISBM)
Frank Germann
Assistant Professor of Marketing
at the University of Notre Dame
30