Data trends redefine leading brands

Data trends redefine leading brands
Artificial intelligence, customer journeys, and paid analytics

Executive summary
Methodology
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
Appendix 1 (respondent profiles)
Appendix 2 (additional charts)
Table of contents
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2

Executive summary
Companies that put data at the centre of their business gain better
insights and deliver more effective marketing. Data centricity at an
organisational level is the priority for larger companies, mindful of the
opportunities afforded by more scientific commercial decision-making and
data-driven marketing. A focus on data alone in the context of customer
analytics is not enough, however. Companies require insights from their
data to deliver first-class customer experiences that give them a competitive
advantage.
Our global survey of more than 1,000 business respondents shows
that companies are rightly focused on activities powered by actionable
insights as opposed to focusing on data for its own sake. More effective
segmentation and targeting (65%), and better marketing attribution (52%),
are the top data-related priorities for marketers, while ‘technologists’
(including analysts, ecommerce, and IT professionals) are primarily focused
on making their organisations more data-centric (50%).
CMOs drive omnichannel customer intelligence. With customer analytics
so vital for delivering relevant and engaging experiences in both digital and
offline environments, marketers are important stakeholders in the data
and analytics capabilities that increasingly define brands’ ability to meet
consumer expectations. The research shows that CMOs have ultimate
responsibility for customer analytics strategy at 51% of organisations
surveyed, down from 57% in 2018. The chief data officer is gaining ground,
with 13% of respondents saying that the responsibility ultimately resides with
this role, up from only 8% in 2018.
3

The CMO needs to work closely with chief digital officers, CIOs, and other
business leaders to make sure that marketers and customer-facing staff have
the insights they need to impact experiences positively. There is work to
be done. Only around a third (36%) of respondents believe that data and
analytics have been democratised within their organisations.
Companies are less reliant on free analytics as they reap benefits of
paid solutions. Companies using paid analytics exclusively outnumber
those using only free solutions. More than a third of larger companies (37%)
use a paid solution exclusively, more than twice as many as a year ago when
the figure was 18%.
Paid analytics technology continues to be a key enabler for businesses.
With customers in both a B2B and B2C context increasingly demanding
personalised digital experiences, businesses require best-of-breed software
to power them. Better at extracting more actionable customer insights, paid
analytics solutions excel at helping automate analytics-related tasks, joining
up data from internal and external sources, as well as surfacing return on
marketing investment drivers.
Companies using paid analytics exclusively are significantly more likely
than those using only free software to have democratised data within their
organisations, to have centralised customer data, and to have a complete
view of all customer interactions with their brand. The largest gap between
paid and free analytics users is the ability to map business challenges end-to-
end to their analytics technology (40% vs. 23%).
Customer journey analytics rely on real-time integrated data. Great
customer experiences depend on understanding and catering for customer
needs in the here and now. Marketers need to understand how they can
impact different moments on the customer journey, and prioritise actions
and opportunities to deliver value.
4

The continued focus on customer journey optimisation to enhance
experiences and harness data for commercial gain is evident in our
research. Customer journey analytics emerges as the most important
digital analytics capability, outranking the next most cited feature, data
visualisation, by a significant margin (25% vs. 15%).
The ability to impact customer journeys that straddle both digital and offline
channels can be limited. Only a third of organisations (35%) agree that their
digital analytics platforms allow them to aggregate and ingest online and
offline data. A lack of omnichannel customer journey data is seen as the
most significant analytics limitation for larger companies reliant on free tools.
Paid solutions help companies harness AI for more insights-driven
marketing. For larger organisations, the most exciting analytics-related
opportunity is better personalisation at scale, cited by 20% of respondents.
Such is the potential of AI for improved analytics, that technologists see
limited capabilities in this area as the biggest downside of free analytics.
Despite recognition of AI’s potential, its use for generating insights and
delivering more personalised experiences is still in its infancy. Those with
paid analytics tools are twice as likely as their peers using only free tools
to be deploying AI to generate insights, and four times as likely to be
identifying and dynamically creating segments for more targeted marketing.
5

Methodology
This report is based on a global survey of 1,037 business professionals, mainly
marketers, analysts, and IT professionals. Research participants completed
an online questionnaire in May and June 2019. The survey was publicised
through relevant LinkedIn groups and emails sent out by London Research,
its sister company Digital Doughnut, and Adobe.
More than half of survey respondents (54%) work ‘client-side’, i.e. in-house
for companies, while 46% classify themselves as ‘supply-side’, working for an
agency, consultancy, or technology company. More than half of respondents
were senior management level or above.
More than half of respondents work in the marketing function (57%). The
next best represented function is IT/tech (11%), while 9% work in analytics or
data science departments. Some sections of this report include a breakdown
of survey results for marketers and ‘technologists’. For the purposes of this
study, technologists include those working in either the analytics, data
science, IT, or ecommerce functions. Marketers include those working in
advertising and marketing roles.
Around half of those taking part in the survey are based in Europe (49%),
22% are in the Asia Pacific (APAC) region and 20% are in North America.
More detailed information about the profile of survey respondents is
included in the first appendix of this report.
Where year-on-year comparisons are included in the charts, we have used
2018 data from the Customer Analytics: The 20 Attributes that Lead to
Business Success1 report, also produced by London Research in partnership
with Adobe. For charts comparing results by size of company (‘larger’ and
‘smaller’ organisations), we have used a threshold of $50 million in US
dollars in annual revenues to classify larger companies.
1 https://www.adobe.com/offer/20-attributes-that-lead-to-business-success.html
6

About London Research
London Research, set up by former Econsultancy research director Linus
Gregoriadis, is focused on producing research-based content for B2B audiences.
The company is based in London, but its approach and outlook are very
much international.
London Research works predominantly with marketing technology vendors
and agencies seeking to tell a compelling story based on robust research
and insightful data points. As part of Communitize Ltd, the company works
closely with its sister companies Digital Doughnut (a global community of
more than 1.5 million marketers) and Demand Exchange (a lead generation
platform), both to syndicate research and generate high-quality leads.
For more information, visit https://londonresearch.com.
About Adobe Analytics
Customer experience is driving the next wave of competitive advantage.
To deliver standout experiences, you need clear, fast, and actionable
insights. This means moving beyond simple data collection and web
analytics to true customer intelligence. With Adobe Analytics, driven by
AI and machine learning, anyone in the enterprise can understand and
improve how customers interact with their brand across all channels simply,
instantaneously, and at massive scale. Customers using Adobe’s industry-
leading analytics solution yield an average 224% ROI and 14% increase in
conversion rates.
For more information, please visit https://www.adobe.com/uk/
analytics/adobe-analytics.html.
77

Commercial success depends on good
decision-making, whether you’re a
techstart-up bootstrapping your way to
success or a global business with thousands
of employees and millions of customers.
Companies such as Amazon, Apple, Facebook, and Google have built their
brands on effective use of data to generate insights, drive decision-making,
and provide stellar customer experiences at scale.
When respondents to our global business survey were asked to name their
data-related priorities for the year ahead, data centricity (59%) emerged as
the main area of focus for larger organisations, defined as those with annual
revenues of at least $50m (Figure 1).
The need to be data-centric is particularly recognised among the ‘technologist’
business respondents (Figure 2), defined as those working in analytics, data
science, ecommerce or IT functions, rather than in marketing or advertising.
These research participants rank data centricity as their top priority (50%),
and they are also more likely than their marketing colleagues to include data
hygiene (29% vs. 16%), data privacy (16% vs. 11%), and cybersecurity (19% vs.
6%) among their top priorities for the year ahead.
While the quest to be data-centric at an organisational level is still a top-
three priority for almost half of marketers (47%), this group of respondents
collectively regard more effective segmentation and targeting (65% vs. 42%),
and better attribution (52% vs. 36%) as higher priorities. Marketers recognise
the importance of data centricity, but they are more switched on to the
tangible benefits of data-driven insights in the form of more relevant and
personalised customer experiences (through better targeting), and a greater
understanding of which channels are truly delivering.
Marketers are also more focused on the ability to access real-time customer
intelligence (41% vs. 32% for technologists). This is increasingly the lifeblood
of organisations wishing to succeed in a customer-centric world, and a
priceless asset for marketers seeking to champion the customer.
Quest to be more data-centric
and insights-driven
Making their organisations
more data-centric is the top-
ranking data-related priority for
respondents at larger companies.
8

Methodology note:
Respondents could select up to three options.
15 %
More effective
segmentation and targeting
Greater use of AI
Privacy
Cybersecurity
Better marketing attribution
Greater access to real-time
customer intelligence
Unification of customer data
Data hygiene
Making our organisation
more data-centric
42 %
30 %
59 %
56 %
56 %
49 %
47 %
37 %
37 %
35 %
36 %
19 %
16 %
10 %
16 %
13 %
6 %
= Organisations with annual revenues of less than $50m
= Organisations with annual revenues of at least $50m
Real-time customer intelligence is increasingly the
lifeblood of organisations wishing to succeed in a
customer-centric world, and a priceless asset for
marketers seeking to champion the customer.
Figure 1: Top data-related priorities for the next year (larger vs. smaller organisations)
9

16 %
Greater use of AI
Privacy
Cybersecurity
Making our organisation more
data-centric
Greater access to real-time
Unification of customer data
Data hygiene
More effective segmentation
and targeting
65 %
29 %
42 %
52 %
47 %
50 %
41 %
32 %
35 %
38 %
23 %
11 %
15 %
6 %
36 %
Better marketing attribution
16 %
= Marketers
= Technologists
Methodology note:
Respondents could select up to three options.19 %
Figure 2: Top data-related priorities for the next year (marketers vs. technologists)
So how is this evidently widespread desire to be more rooted in data
going for businesses? The research shows some notable regional
differences. APAC respondents are most likely to regard themselves as
data-centric, with two-thirds (66%) of respondents in this region agreeing
that this is the case, compared to 47% in Europe and 49% in North
America (see appendix, Figure 25). At an overall level, more than half
of larger companies (55%) agree that their organisation is data-centric,
defined as those who are ‘organised around data with data science at the
core’ (see appendix, Figure 26). For smaller organisations, the equivalent
figure is 48%, suggesting that organisations typically seek to become more
data-conscious the larger they grow.
10

Learning from direct-to-consumer
US-based retailer Rent the Runway, valued at $1bn in April 2019, is a
great example of a brand that has built its business around data, turning
its strength at using insights for a competitive advantage. The direct-to-
consumer (D2C) business, which made its name allowing consumers to rent
designer dresses and accessories, uses data to drive all its operations, from
personalisation of recommendations to highly efficient dry cleaning and
shipping of products.
The success of D2C brands such as Rent the Runway, Dollar Shave Club, and
Allbirds is a reminder to companies large and small alike that an obsessive
focus on data-driven marketing and customer analytics is often instrumental
to success.
Figure 3 shows the level of customer analytics maturity, compared with
2018. Disappointingly, there has been a lack of progress in terms of the
proportion of companies describing themselves as ‘established’ or ‘advanced’
(19%, compared to 22% last year), defined as having digital analytics that are
integral to delivering personalised customer experiences, and – for the most
sophisticated end of the spectrum – customer insights that systematically
lead to automated marketing actions.
Half of companies describe their customer
analytics capabilities as ‘developing’, which
at least shows that progress is being made.
The onus is on marketers to help take their
organisations to the next level with a more
integrated approach to customer intelligence.
11

Basic – we sometimes use
analytics for rear-view-
mirror reporting based on
aggregated data, but not for
Developing – digital
analytics feed into customer
intelligence activities, but this
happens only in silos and is
ad hoc
Established – digital analytics
are an integral part of
delivering personalised
customer experiences
Advanced – digital analytics
produce customer insights
that systematically lead
to automated marketing
actions
33 %
31 %
45 %
50 %
15 % 14 %
7 % 5 %2018 =
2019 =
More encouragingly, fewer organisations than last year
describe their organisations as ‘basic’, defined as only
‘sometimes using analytics for rear-view-mirror reporting
based on aggregated data, but not for customer intelligence’
(31%, compared to 33% in 2018). Half of companies describe
their customer analytics capabilities as ‘developing’, which
at least shows that progress is being made. As we shall
explore in this report, the onus is on marketers to help take
their organisations to the next level with a more integrated
approach to customer intelligence.
Figure 3: How would you describe the maturity of your organisation’s
customer analytics capabilities?
More effective segmentation and
targeting, and better marketing
attribution are the top priorities for
marketers. Technologists are more
likely to prioritise data centricity at
an organisational level.
12

Chief Data Officer
CIO
Chief Revenue Officer
Chief Customer Officer
Other
CMO / Head of Marketing
55 %
19 %
46 %
21 %
9 %
7 %
1 %
5 %
3 %
22 %
6 %
6 %
Data-driven CMOs drive omnichannel
Experiences are built on interactions, and interactions are built on data.
With customer analytics so fundamental to the overall experience, marketers
seeking to steer the brand’s relationship with the customer need to take
more ownership of the data that underpins this. In the
experience era, companies require experience-related data
to power real-time personalised experiences. We are rapidly
moving past the era of digital analytics in a silo, towards a
world of unified customer intelligence.
Nike is a great example of a brand that has successfully used its customer
data to change its marketing strategy. More than a decade ago the
company decided to segment its audience by sports played, rather than by
location, as part of its ‘category offense’ strategy2. The move was driven by
insights that suggested that people who were keen runners, for example,
had more in common with other runners than those in their own country
with different interests.
CMOs have ultimate
responsibility for customer
analytics strategy at 51% of
organisations surveyed.
2 https://news.nike.com/news/nike-consumer-direct-offense
Figure 4: Ultimate responsibility for customer analytics strategy (larger vs. smaller organisations)
13

As a result of changing its marketing strategy and becoming an organisation
where data is more effectively utilised in key decision-making, Nike´s sales
and share price have rocketed, helped more recently by a data-fuelled
campaign to focus on 12 key cities across ten countries. At the start of 2018
Nike promoted Dirk-Jan van Hameren to CMO, tasked with driving deeper
consumer relationships. According to our research, the CMO or head
of marketing is most likely to have ultimate C-suite responsibility for an
organisation’s customer analytics strategy (Figure 4).
Chief data officers hold sway at around a fifth (21%) of larger organisations,
but even at $50m+ companies the CMO is more than twice as likely
to have final responsibility (46% vs. 21%). Even when looking from the
perspective of technologists (see appendix, Figure 27),
the CMO is regarded as most likely to have ownership
of customer analytics, ahead of chief data officers and
CIOs. However, it is likely that the chief data officer will
continue to become a more visible presence within
many organisations – the proportion of respondents
saying that this is the C-suite executive with ultimate
responsibility has jumped from 8% last year to 13% in
2019 (see appendix, Figure 28). Irrespective of who is
ultimately responsible for customer analytics, CMOs must work with other
stakeholders such as chief data officers and CIOs to ensure that marketers
and customer-facing employees are getting the insights from data that
they need.
Only around a third (35%) of
marketers surveyed believe
that data and analytics have
been democratised within their
organisations, compared to almost
half (47%) of technologists who say
this is the case.
14

The creation of digital analytics
insights is very much an
automated process
We are able to map our business
challenges end-to-end to our
analytics technology
Data and analytics have
been ’democratised’ within
our organisation
Data is immediately available
and actionable for our analysts
and data scientists
We get ’actionable insights’ from
our digital analytics solution
Digital analytics provide a strong
foundation for our customer
experience initiatives
54 %
47 %
33 %
62 %
49 %
26 %
27 %
35 %
61 %
38 %
38 %
50 %
Methodology note:
Sample includes respondents that selected
either ’strongly agree’ or ’somewhat
agree’ options.
= Marketers
= Technologists
Democratisation of data and analytics
The importance of data-driven insights for improving customer experience
in both a digital and physical environment should not be underestimated.
According to research by McKinsey & Company3, 44% of CMOs say that
frontline employees will rely on insights from advanced analytics to provide
a personalised experience. In retail, for example, this might mean customer-
facing employees accessing AI-powered customer insights via their own
personal devices when they are in conversation with shoppers.
3 https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/
the-future-of-personalization-and-how-to-get-ready-for-it
In recent years, the democratisation of
data and analytics has been an important
trend, empowering marketing and other
functions outside analytics and data science
departments to harness insights for better
customer experiences and improved
commercial performance.
Figure 5: Proportion of respondents agreeing with statements related to customer
analytics capabilities (marketers vs. technologists)
15

23 %
Lack of investment in
data capabilities
Too much data – we don‘t know
where to start
Walled gardens (closed platforms
blocking off access to data)
No boardroom buy-in
Data regulation / compliance
None of the above
Culture of organisation
Lack of unified technology
Data silos
Access to real-time
or near real-time data
Lack of skills
48 %
26 %
36 %
47 %
46 %
41 %
42 %
44 %
32 %
29 %
17 %
36 %
18 %
26 %
16 %
29 %
24 %
= Marketers = Technologists
14 %
15 %
2 %
4 %
Technologists regard lack of
unified technology (44%) as
the most significant challenge
preventing organisations from
harnessing customer data
for better engagement and
experiences.
According to our own research, only
around a third (35%) of marketers
surveyed believe that data and analytics
have been democratised within their
organisations, compared to almost half
(47%) of technologists who say this is the
case (Figure 5). It appears that those in
more technical roles may in some cases be
unaware of the frustrations that marketers
are experiencing when trying to access
analytics. Marketers are also significantly
less likely than technologists to agree that
they get actionable insights from their
digital analytics solution (50% vs. 62%),
or that digital analytics provide a strong
foundation for their customer experience
initiatives (49% vs. 61%).
Figure 6 shows the challenges facing
companies as they seek to harness
customer data for better engagement and
experiences, again from the perspective of
both marketers and technologists. While
marketers are most likely to highlight lack of
skills, lack of investment, and the culture of
the organisation as significant challenges,
technologists are most likely to flag up the
disjointed nature of technology (44%).
Figure 6: Most significant challenges preventing organisations from harnessing
customer data for better engagement and experiences (marketers vs. technologists)
16

Our platform enables us to keep knowledge about
our customers within our own environment
Our platform allows us to aggregate and ingest
online and offline data
Artificial intelligence and machine learning are
embedded into our technology platform
We have an open technology platform that enables
a seamless flow of behavioural, transactional, and
operational customer data in real time
Our platform enables us to stitch together known and
anonymous data to activate real-time customer profiles
across channels throughout the customer journey
Our platform enables us to respect customer data
requests and comply with data privacy laws
67 %
83 %
62 %
45 %
33 %
71 %
29 %
33 %
33 %
20 %
25 %
27 %
= Organisations with annual
revenues of less than $50m
revenues of at least $50m
Methodology note:
Sample includes respondents that
selected either ’strongly agree’ or
’somewhat agree’ options.
A unified technology platform for marketing and customer experience
activities is essential for success. Figure 7 shows the extent to which survey
respondents agree with statements specifically relating to their technology
stack. Companies (both large and small) are generally in agreement that their
platform enables them to respect customer data requests and comply with
data privacy laws, and that their platform enables them to keep knowledge
about customers within their own environment.
But they are more likely to disagree than agree that they have an open
technology platform that enables seamless flow of behavioural, transactional,
and operational customer data, a capability which is essential for powering
effective real-time marketing. Companies are also more likely to disagree
that they have the ability to stitch together known and anonymous data
to activate real-time customer profiles across channels throughout the
customer journey, another important capability for building a 360-degree
view of a prospect or customer, and for bridging the gap between adtech
and martech. Companies that can aggregate and ingest online and offline
data are also in the minority, as are those who say that artificial intelligence
(AI) and machine learning (ML) are embedded in their tech platform. These
themes are explored in more detail later in the report.
Figure 7: Proportion of company respondents agreeing with statements relating to
overall data capabilities (larger vs. smaller organisations)
17

Analytics tools have played an important role in the digital revolution,
helping tens of millions of businesses, both large and small. Much has
changed since the most high-profile of these services – Google Analytics –
went live back in 2005, however.
While free analytics applications have allowed companies to grasp low-
hanging fruit when seeking to understand what is happening on their digital
properties, the game has changed markedly over the last decade and a
half. Marketers increasingly need insights on the ‘why’ and not just data
that reports on the ‘what’. With customers in both a B2B and B2C context
increasingly demanding personalised digital experiences, businesses require
best-of-breed technology to provide the insights that power them.
The introduction of the EU General Data Protection Regulation in 2018,
and spread of privacy consciousness more generally, has thrust the issue of
data control into the spotlight. The onus is on businesses to tighten up how
information flows within their own organisations, and out to cloud-based
services where control of data may be lost.
The business case for paid analytics
The business case for investment in paid analytics solutions to fuel customer
understanding and engagement has become more compelling. Over the
last few years many organisations have used free tools and paid software
in combination, to provide backup and validation of data, or simply out of
habit. This approach appears to be changing, a trend accelerated by concerns
within organisations about how their data is being used by third-party
companies including large digital platforms such as Facebook, LinkedIn, and
Google. As shown in Figure 8, the proportion of companies that depend
solely on paid analytics software to meet their digital analytics needs has
risen by seven percentage points, from 15% to 22%, over the past year.
Companies using paid analytics exclusively now outnumber those using only
free solutions (21%).
Companies turn to paid analytics
for enhanced capabilities
The proportion of
companies that depend
solely on paid analytics
software to meet their
digital analytics needs
has risen by seven
percentage points, from
15% to 22%, over the past
year. Companies using
paid analytics exclusively
now outnumber those
using only free solutions
(21%).
18

2018 =
2019 =
A mixture of paid and free
software
A paid solution Only free software
54 %
43 %
15 %
22 %
19 %
21 %
8 % 9 %
None of the above
4 % 5 %
A home-grown solution
Figure 8: What type of technology are you using for digital analytics?
The dual approach remains the most common way of doing things, though the popularity of
this model has fallen significantly since 2018 (from 54% to 43%). Businessess increasingly see
the running of a secondary, free application as unnecessary or even detrimental.
The tendency towards paid solutions is particularly
prevalent among larger organisations. The proportion
of larger companies (37%) using a paid digital analytics
solution exclusively is now more than double the 18%
figure reported in 2018 (see appendix, Figure 30).
Proponents of paid analytics cite numerous advantages,
including various areas of functionality that free alternatives are lacking. Paid analytics tools
typically do a better job of extracting more actionable insight for customer experience and
marketing programmes, helping to automate analytics-related tasks, join up data from
internal and external sources, and to understand more fully the drivers of marketing return
on investment.
Hostelworld Group is just one organisation that has reported significant gain from migrating
to paid analytics. The booking platform adopted Adobe Analytics4 to help gain deeper
insight into the behaviour of its digital-savvy target segment of younger travellers, so it could
optimise customer journeys on its websites and apps. In 2017, the company reported a
500% increase in engagement across digital channels over two years, while also generating
automation-driven efficiency benefits, such as reduced cost-per-booking.
4 https://www.adobe.com/customershowcase/story/hostelworld.html
After migrating to paid analytics, Hostelworld
reported a 500% increase in engagement
across digital channels over two years, while
also generating automation-driven efficiency
benefits, such as reduced cost-per-booking.
19

We have centralised all our customer data
Data analytics have been ’democratised’ within
our organisation
We are utilising our digital analytics thechnology
to its full potential
We have a complete view of all customer interactions
with our brand
We are able to map our business challenges
end-to-end to our analytics technology
Digital analytics provide a strong foundation for
our customer experience initiatives
63 %
32 %
46 %
43 %
54 %
63 %
33 %
23 %
34 %
40 %
23 %
Methodology note: Sample includes respondents that selected either
’strongly agree’ or ’somewhat agree’ options.
45 %
= Paid analytics = Free analytics
Figure 9: Proportion of company respondents agreeing with statements relating to
customer analytics capabilities (paid vs. free software)
Moving beyond the Hostelworld case study, Figure 9 shows how paid
analytics are a key enabler for businesses. For example, companies that use
paid analytics exclusively are significantly more likely than those using only
free software to have democratised data within their organisations (54% vs.
32%), and to have centralised customer data (63% vs. 43%).
These companies are also more likely to have a ‘complete
view of all customer interactions’ with their brand (46% vs.
33%), an attribute which was shown in last year’s equivalent
research to be the attribute most strongly correlated with
customer analytics maturity.
More than a third of larger
companies (37%) are now using
a paid digital analytics solution
exclusively, more than double the
percentage reported in 2018 (18%).
20

Mapping business challenges
The largest gap between paid and free analytics users is the ability to map
business challenges end-to-end to their analytics technology (40% vs. 23%).
Companies need visibility on KPIs that relate to meaningful commercial
objectives, such as revenue, costs, customer loyalty, and customer lifetime
value. Integration of digital analytics with business intelligence tools can
provide a real-time view of how their organisations are performing so they
can be agile in capitalising on opportunities and responding to negative
signals.
Paid solutions provide a stronger feature set for businesses to work with.
Some important features and capabilities, such as segment analysis, data
visualisation, and customer journey analytics, are available to more than half
of paid software users, but only a minority of those using free tools (Figure
10). For various other features, a significant percentage of paid analytics
users have access, but they remain out of reach of all but a few of those
that have deployed free software only. These include audience clustering
to automatically discover statistically valid segments, anomaly detection, AI
capabilities, uncapped calculated metrics, and contribution analysis to unlock
root cause of anomalies.
Paid analytics are a key enabler for businesses.
Companies that use paid analytics exclusively
are significantly more likely than those using
only free software to have democratised data
within their organisations (54% vs. 32%), and to
have centralised customer data (63% vs. 43%).
21

8 %
Data visualisation
Anomaly detection
Artificial intelligence
Uncapped calculated metrics
(from variables and operators)
Customer journey analytics
Flexible user interface for quick
ad hoc reports
Ability to generate custom reports
without data sampling
Audience clustering to
automatically discover
statistically valid segments
Segment analysis
67 %
11 %
48 %
64 %
53 %
39 %
38 %
23 %
25 %
33 %
30 %
20 %
15 %
29 %
5 %
7 %
11 %
36 %
38 %
27 %
20 %
26 %
Ability to change metadata
retroactively during campaigns
Full omnichannel data sources
out of the box
None of the above
Contribution analysis to unlock
root cause of anomalies
Native activation of insights into
customer engagement workflows
2 %
18 %
17 %
10 %
9 %
= Paid analytics
= Free analytics
Figure 10: Does your digital analytics solution offer the following capabilities? (paid
vs. free software)
22

Great customer experiences depend on understanding and
catering for customer needs in the here and now. A lack of
data cohesion has historically stifled marketing efforts to
map intricate cross-channel journeys accurately, let alone
pinpoint the key moments that lead to conversion. Fortunately,
the best analytics packages now enable organisations to
identify and prioritise opportunities to deliver value across
the customer journey that they’d otherwise overlook.
Research by Tata Consultancy Services5, published in April 2019, found that
the best-performing marketers are introducing personalisation earlier in the
customer journey than their under-performing peers. Eric Matisoff, analytics
and data science evangelist at Adobe, has summarised the importance
of employing analytics to uncover customer journey insights6 as follows:
“Analysis of all of the data that supports the customer journey is the direction
every brand should head in.”
Encouragingly, the continued focus on customer journey optimisation to
elevate experiences and harness data for commercial gain is evident in
our research. Customer journey analytics emerges as the most important
digital analytics capability, outranking the next most cited feature, data
visualisation, by a significant margin (25% vs. 15%). As Figure 11 shows, it is
a highly valued capability among small and large organisations alike (27%
and 22%, respectively).
The feature rankings by business size reflect the challenges faced. While
smaller organisations typically struggle to disseminate data efficiently and
make it actionable, their enterprise counterparts are more concerned with
higher-order goals, such as joining up the dots across channels.
The power of now: customer journey
analytics rely on integrated data
emerges as the most important
digital analytics capability,
outranking the next most cited
feature, data visualisation, by a
significant margin (25% vs. 15%).
5 https://sites.tcs.com/bts/wp-content/uploads/TCS_2019_CMO_Study-Initial-Findings.pdf
6 https://theblog.adobe.com/paving-a-consumers-journey-from-research-to-retrieval-and-datas-role/
23

4 %
Segment analysis
Audience clustering to automatically
discover statistically valid segments
Data visualisation
out of the box
ad hoc reports
13 %
12 %
4 %
4 %
8 %
5 %
3 %
3 %
7 %
7 %
11 %
22 %
6 %
6 %
27 %
Anomaly detection
(form variables and operators)
Other
1 %
1 %
10 %
10 %
2 %
3 %
3 %
2 %
17 %
0 %
0 %
9 %
Smaller organisations place more weight
on data visualisation (17% vs. 12% for their
larger peers), which can support data
democratisation initiatives and increase the
likelihood of the entire business putting it
to good use. Larger organisations are more
likely than smaller ones to value access to
‘full omnichannel data sources out of the
box’ (10% vs. 6%). Retail is one of the sectors
where joining up digital and offline channels
is particularly advantageous as companies
need to reconcile data for those buying
online and returning in-store, and for those
starting their journey online and continuing
it in-store (or vice versa).
Technologists are almost twice as likely
as their marketing peers (23% vs. 13%) to
rate data visualisation as the most valuable
feature, giving it equal importance as
customer journey analytics (see appendix,
Figure 31). Segment analysis, which is
particularly valued by marketers (16% vs. 6%
of technologists) is especially appreciated
in the US, where it is seen as even more
important than customer journey analytics
(25% vs. 17%, appendix, Figure 32).
Figure 11: Which feature/capability do you value the most from
digital analytics? (larger vs. smaller organisations)
There has been a sharp drop in
companies using identity-related
data (from 33% to 12% this year),
which can be attributed to increased
awareness of data privacy legislation,
and an appropriate degree of
nervousness around using
personal data.
24

Limitations of free analytics
Only a third of organisations (35%) agree that their digital analytics platforms
allow them to aggregate and ingest online and offline data (see appendix,
Figure 29). This is the most pressing issue for larger companies using free
software, with the ‘lack of omnichannel customer journey data / cross-
channel analysis’ the most significant analytics limitation (Figure 12).
Integrations limited to a particular ecosystem are another prominent
restriction, irrespective of business size, as is difficulty customising reports.
For smaller organisations, which are more likely to use a free solution
exclusively, limitation of metrics (19%) is the second most significant
obstacle to success.
Figure 12: What is the main limitation of your free digital analytics solution? (larger
vs. smaller organisations)
Integrations limited to particular ecosystem
(e.g. Google)
Difficulty customising reports
Segmentation limitations
Limited AI / machine learning capabilities
Limitation of metrics
Lack of omnichannel customer journey data /
cross-channel analysis
16 %
10 %
17 %
15 %
15 %
20 %
14 %
8 %
5 %
19 %
8 %
Other
Inability to action insights into other
martech / adtech solutions
Data sampling
9 %
3 %
2 %
4 %
2 %
2 %
31 %
Methodology note:
Respondents used free software, either
exclusively or in combination with
paid technology.
25

The breadth of data types and sources employed is a good indicator of the
level of sophistication of a company’s customer intelligence strategy. Compared
to last year, fewer organisations are using a range of data types as part of their
customer intelligence activities, with behavioural, social, and transactional
data still the most widely used (Figure 13). There has been a sharp drop in
companies using identity-related data (from 33% to 12% this year), which
can be attributed to increased awareness of data privacy legislation, and an
appropriate degree of nervousness around using personal data.
Integrating digital analytics solutions with other layers of the marketing
stack can unlock additional value and overcome some of data availability
limitations. However, the level of integration varies greatly by business
size (Figure 14). Smaller companies are more likely than larger ones to focus
on integrating their digital analytics solutions with email (or marketing
automation) and social platforms, while larger organisations prioritise
integration with a vast array of technologies, such as CRM, paid search,
tag management, personalisation, data management platforms, business
intelligence solutions, and customer data platforms.
Ultimately, organisations need to tap into data from an array of marketing
and business tools in real time, if they are to succeed in serving prospects
and customers as effectively as possible.
Integrating digital analytics solutions with
other layers of the marketing stack can unlock
additional value and overcome some of data
availability limitations.
26

12 %
Social
Descriptive
Identity-related (e.g. PII)
Attitudinal
Firmagraphic
None of the above
Transactional
Socio-demographic
Third-party
Second-party (from partners)
Behavioural (i.e. browsing history)
55 %
20 %
50 %
56 %
46 %
48 %
33 %
50 %
47 %
33 %
23 %
27 %
18 %
16 %
29 %
8 %
17 %
= 2018 = 2019
11 %
8 %
6 %
9 %
Figure 13: In addition to first-party data, what types of data do you use as part of
your customer intelligence activities?
272727

24 %
Email / marketing automation
Data management platform (DMP)
Business intelligence solution
Customer data platform (CDP)
Social media channels
Paid search
Tag management
Personalisation / recommendation technology
Customer relationship management (CRM)
54 %
35 %
64 %
69 %
58 %
40 %
54 %
63 %
59 %
11 %
38 %
16 %
33 %
18 %
21 %
19 %
18 %
Figure 14: What other sources of data are integrated with your digital analytics
solution? (larger vs. smaller organisations)
28

Paid analytics =
Free analytics =
Generation of
insights
Targeting the right content
and messaging at the
right moment
Identification and
creation of segments
Creating and
adapting content for
personalisation at scale
39 %
17 %
25 % 24 %
30 %
7 %
32 %
20 %
Harnessing AI for more insight- dri-
ven marketing and better customer
experiences
According to the 2019 Gartner CIO Agenda Survey, AI was ranked as the most disruptive
technology, ahead of data and analytics in second place. When these two categories of
technology are harnessed in unison, the results can be immensely powerful, allowing
marketers to access insights and then execute more effective marketing campaigns without
the need for data scientists.
Figure 15 shows that those with paid tools are twice as likely
as their peers using only free tools to be deploying AI to
generate insights, and four times as likely to be identifying
and dynamically creating segments for more targeted
marketing. They are also more than one-and-a-half times
more likely to be using AI to create and adapt content for
personalisation at scale, an area of huge potential seen
by larger organisations surveyed as the most exciting
opportunity in the context of customer analytics.
Those with paid analytics tools
are twice as likely as their peers
using only free tools to be
deploying AI to generate insights,
and four times as likely to be
identifying and dynamically
creating segments for more
targeted marketing.
Figure 15: Proportion of company respondents saying they are deploying AI to improve the customer
experience and effectiveness of marketing activities (paid vs. free software)
29

5 %
Integrations limited to
particular ecosystem (e.g. Google)
Segmentation
limitations
Data
sampling
Other
Limitation
of metrics
Difficulty
customising reports
Inability to action insights into
other martech / adtech solutions
Limited AI / machine
learning capabilities
Lack of omnichannel customer
journey data / cross-channel analysis
24 %
24 %
16 %
16 %
20 %
17 %
10 %
14 %
18 %
9 %
3 %
6 %
4 %
4 %
8 %
= Marketers = Technologists
2 %
0 %
The growing importance of AI capabilities for turbo-charging customer
analytics is also underscored by Figure 16 which shows both the marketer
and technologist view on the main limitation of free analytics tools. It is the
data and IT professionals who see most clearly how free tools can restrict
their endeavours in this area. Just under a quarter of technologists (24%)
report that this is the main limitation of free software, compared to only
6% of marketers surveyed.
For larger organisations the most exciting opportunity to improve analytics
is ‘better personalisation at scale’, cited by 20% of respondents (Figure 17), an
increasingly important trend where AI offers huge potential for improvement.
Figure 16: What is the main limitation of your free digital analytics solution?
(marketers vs. technologists)
30

AI in action
Adobe Sensei AI and machine learning allows companies to analyse data and
identify patterns and anomalies, producing insights that can then be used
to make decisions about the type of content to deliver, and the right time to
deliver it. This ensures that digital experiences are personalised and optimised
to make a tangible difference to revenue streams and maximise ROI.
Larger organisations are also enthusiastic about the ability
to further understand and predict customer needs, with 18%
reporting this to be the single most exciting opportunity. AI
also has a role to play here. Sensei technology, for example,
uses AI to personalise content according to customer profiles,
and tailors the content taking into account factors such as
geographical location, the season, and the time of day.
With 72% of its UK customers interacting across three or more channels, Sky
is just one of the many brands taking advantage of Sensei’s features, such as
automated personalisation7, to deliver more impactful experiences and scale
personalised recommendations. AI enables Sky to analyse the vast volume of
customer information in real time to uncover the recommendations, services,
and experiences that can best resonate with each customer, at scale. The
company also uses AI to provide better customer care through its call centre
by analysing a variety of customer attributes and connecting customers with
the customer service representative best suited to handle their issue.
Further down the scale, the ability to drive customer loyalty and increase
customer lifetime value is deemed to be the single most exciting opportunity
by 15% of larger organisations surveyed.
For smaller organisations, the most exciting opportunity is deemed to be
the ability to drive customer lifetime value, cited by 18% of respondents,
just ahead of the ability to understand and predict customer needs and
optimisation of the customer journey (both 17%).
7 https://www.adobe.com/content/dam/acom/en/customer-success/pdfs/sky-uk-ai-case-study.pdf
For larger organisations the most
exciting opportunity to improve
analytics is better personalisation
at scale, cited by 20% of
respondents, an increasingly
important area where AI offers
huge potential for improvement.
31

Chapter 2 Page 4
Chapter 4 Page 4
Appendix 2
5 %
Understanding and predicting
customer needs
Ability to enrich customer
profiles and segments
Ability to utilise
more sources of data
Driving customer loyalty and
increasing customer lifetime value
Automated marketing actions
based on insights
Optimisation of
customer journey
Identifying new
and valuable customers
Better personalisation
at scale
5 %
7 %
20 %
17 %
17 %
15 %
13 %
18 %
18 %
9 %
11 %
12 %
4 %
3 %
More efficient allocation of
media budget (i.e. better attribution)
3 %
3 %
Ability to create
dynamic segments
0 %
None of
the above
2 %
6 %
6 %
6 %
Adobe Sensei can help to optimise the customer journey and understand
what works. Part of the technology includes a virtual analyst who sifts
through large quantities of data and alerts marketers of statistically significant
events.
AI technology can be used effectively for deeper personalisation, but most
marketers are not yet actively using this to personalise and adapt their
content. As can be seen in Figure 33 (see appendix), there is little year-on-year
difference in the proportion of companies harnessing AI for better marketing
and customer experience activities. Many companies are missing a huge
opportunity, particularly when considering the benefits AI can bring.
Figure 17: What do you see as the single most exciting opportunity for your business
in the context of improved customer analytics capabilities? (larger vs. smaller
organisations)
32

49 %
Client-side
(working in-house
for a company)
Supply-side
(agency, consultancy,
or technology company)
54 %
46 %
Appendix 1
(respondent profiles)
Figure 18: What best describes your role?
333333

Europe Asia Pacific
49 %
Client-side
(working in-house
for a company)
Supply-side
(agency, consultancy,
or technology company)
22 %
North America
20 %
Other
9 %
Marketing
IT / tech
Analytics
Ecommerce
Advertising
Other
Operations + Procurement
Data science
57 %
11 %
11 %
8 %
8 %
12 %
1 %
2 %
4 %
5 %
Manager
Director
Head of department
C-level
Executive
Administrator
Senior management
Managing director
21 %
13 %
13 %
6 %
7 %
10 %
Supervisor 3 %
Figure 19: In which region are you based?
Figure 20: In which business function do you work?
3434

Other
Operations + Procurement
Data science
11 %
8 %
8 %
12 %
1 %
2 %
Manager
Director
Head of department
C-level
Executive
Administrator
Senior management
Managing director
21 %
13 %
13 %
6 %
7 %
10 %
Supervisor
Other
3 %
Figure 21: What is your level of seniority within the business?
Manufacturing
Financial services and insurance
IT consultancy
Education
Professional services
Retail
Consumer goods
Charity / Non-profit
11 %
9 %
7 %
7 %
6 %
6 %
6 %
6 %
Media and entertainment 5 %
3 %
3 %
2 %
2 %
Travel, leisure and hospitality
Healthcare / Medical / Pharmaceutical
Accountancy / Business services / Law
Telecommunications
Food and beverage
Environmental
Government and local authority
Property
4 %
4 %
4 %
1 %
Other 14 %
38 %
29 %
33 %
Figure 22: In which business sector does your company primarily operate?
353535

38 %
2 %
Environmental
Property
1 %
Other 14 %
Business-to-business (B2B) Business-to-consumer (B2C)
38 %
Even mix of B2B and B2C
29 %
33 %
Less than
$5 million
$5 million -
$49 million
$50 million -
$99 million
$100 million -
$249 million
$250 million -
$499 million
$500 million -
$999 million
$1 billion +
39 %
6 %
27 %
8 %
3 %
5 %
12 %
Figure 23: Is your organisation focused mainly on B2B or B2C?
Figure 24: What were your company's annual revenues in the last financial year?
3636

10 % 37 % 17 % 24 % 12 %
17 % 32 % 18 % 22 % 11 %
25 % 41 % 16 % 14 %
4 %
= Strongly agree
= Strongly disagree= Somewhat disagree
= Somewhat agree
= Neither agree nor disagree
Europe
North America
APAC
Figure 25: 'Our organisation is data-centric… we are organised around our data with
data science at the core' – agree or disagree (regional breakdown)
Appendix 2
(additional charts)
3737

11 %
37 %
21 %
18 %
13 %
15 %
40 %
14 %
26 %
5 %
= Strongly agree
= Somewhat agree
= Neither agree nor disagree
Organisations with annual revenues
of less than $50m
Organisations with annual revenues
of at least $50m
Figure 26: 'Our organisation is data-centric… we are organised around our data with
data science at the core' – agree or disagree (larger vs. smaller organisations)
3838

Chief Data Officer
CIO
57 %
51 %
8 %
7 %
6 %
4 %
2 %
13 %
8 %
3 %
= 2018
= 2019
Other
19 %
22 %
Chief Data Officer
CIO
58 %
38 %
11 %
15 %
3 %
5 %
2 %
21 %
3 %
4 %
= Marketers
= Technologists
Other
22 %
18 %
Figure 28: Who within your organisation is ultimately responsible for your customer
analytics strategy?
Figure 27: Who within your organisation is ultimately responsible for your customer
analytics strategy? (marketers vs. technologists)
3939

A paid solution
Only free software
A home-grown solution
None of the above
A mixture of paid and free software
43 %
18 %
12 %
8 %
9 %
2 %
2 %
37 %
9 %
60 %
= 2018
= 2019
Our platform enables us to keep knowledge about
our customers within our own environment
Our platform allows us to aggregate and ingest
online and offline data
Artificial intelligence and machine learning are
embedded into our technology platform
We have an open technology platform that enables
a seamless flow of behavioural, transactional, and
operational customer data in real time
Our platform enables us to stitch together known and
anonymous data to activate real-time customer profiles
across channels throughout the customer journey
Our platform enables us to respect customer data
requests and comply with data privacy laws
37 % 38 % 14 % 8 %
3 %
23 % 43 % 18 % 9 % 7 %
10 % 25 % 21 % 22 % 22 %
8 % 25 % 17 % 28 % 22 %
22 % 19 % 23 % 28 %
7 % 15 % 17 % 19 % 42 %
8 %
= Strongly agree
= Somewhat agree = Neither agree nor disagree
Figure 30: What type of technology are you using for digital analytics? (larger
organisations)
Figure 29: Thinking about your organisation’s overall data capabilities, please indicate
whether you agree or disagree with the following statements.
4040

Segment analysis
Audience clustering to automatically discover
statistically valid segments
Data visualisation
ad hoc reports
out of the box
26 %
6 %
16 %
13 %
9 %
8 %
4 %
7 %
2 %
5 %
8 %
6 %
4 %
4 %
23 %
23 %
9 %
5 %
2 %
4 %
2 %
3 %
7 %
Anomaly detection
(from variables and operators)
1 %
2 %
1 %
= Marketers
= Technologists
Figure 31: Which feature/capability do you value the most from digital analytics? (marketers vs.
technologists)
4141

= Europe
= North America
= APAC
Data visualisation
Audience clustering to automatically
discover statistically valid segments
Anomaly detection
ad hoc reports
Segment analysis
out of the box
(form variables and operators)
Other
17 %
29 %
26 %
13 %
19 %
11 %
5 %
10 %
6 %
25 %
9 %
6 %
6 %
6 %
8 %
8 %
8 %
5 %
5 %
5 %
5 %
3 %
0 %
3 %
0 %
0 %
3 %
3 %
3 %
7 %
12 %
9 %
2 %
2 %
5 %
5 %
1 %
1 %
2 %
5 %
1 %
1 %
Figure 32: Which feature/capability do you value the most from digital analytics?
(regional breakdown)
4242

Generation of
insights
right moment
Creating and adapting content
for personalisation at scale
Identification and dynamic
24 %
27 % 26 %
23 %
19 % 19 % 20 % 19 %
= 20192018 =
Figure 33: Proportion of company respondents saying they are deploying AI in these
ways to improve the customer experience and effectiveness of marketing activities
Generation of
insights
right moment
Identification and dynamic
Creating and adapting content
for personalisation at scale
24 %
41 %
23 %
25 %
19 %
23 %
16 %
28 %
= TechnologistsMarketers =
Figure 34: Proportion of company respondents saying they are deploying AI in these ways to improve
the customer experience and effectiveness of marketing activities (marketers vs. technologists)
434343

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