Retail Leader - May/June 2018 - S4

Top AI Applications in Retail
AI is gaining an important place in retail with
growth expected to reach $40 billion by 2025,
up from an estimated $6.46 billion today. It is
being driven by an increase in customer-centric
initiatives, more social media advertising and
heightened demand for virtual assistants5.
Among retailers, 16% already use some form
of AI, while 20% plan to add it over the next
12 months; another 18% hope to implement it
more than a year from now6. Following are the
leading AI, machine learning and NLP application
areas in retail.

AI in Personalized Marketing
Retailers typically use marketing automation
software or campaign management solutions
to target customers. Applied to CRM data, tools
divide customers into groups according to
shopping behavior, demographics, preferences
or other criteria.
The problem is that rules are chosen based on
the marketer's human assumptions and leave
significant room for error. The process also
leaves out potentially useful criteria, and it can
be hard to segment customers who do not
correspond to pre-designated buckets. Since
information is historic, shopping behavior,
income and other factors are prone to change.

by analyzing individual pieces of data
simultaneously, and information can be used to
send highly personalized offers to customers.
But retailers believe personalization still
has a way to go, even though 39% say it is
extremely important7. Most retailers (54%)
gave themselves a low rating for executing
personalization strategies at an omnichannel
level, with just 4% rating themselves as high
overall. The biggest hurdle, said 69%, is lack of
appropriate technologies, followed by managing
across channels (47%).
Analyzing store point-of-sale and e-commerce
transaction history became the standard for
classifying and targeting consumer groups.
Now, advances in Big Data and AI are giving rise
to highly personalized campaigns and other
initiatives without major human intervention.
These engagement tools factor in customer
purchase history, browsing behavior, social
media activity and overall channel engagement.
The biggest difference is that today's initiatives
target people on an individualized basis, and
with AI, retailers can do this at scale.
Personalization can
grow revenue 5% to
15% and increase
efficiency of marketing
spending by up to 30%8.

Machine learning examines a full set of data,
identifies patterns and organizes it into
"clusters" of similar data. Assumptions and
stereotypes about what is important are
bypassed. Rather, information is determined
by the analysis. Trends and connections are
established that might have been overlooked

Initiatives involving AI in personalized marketing*
25%
35%

18%
28%

Currently part of
initiative
Plan to incorporate in
next 12 to 24 months

Social sentiment analysis

Elastic personalized search

* Figures are based on AI initiatives that are currently in the pipeline or are being planned in the next 12 to 24 months
5
6

ReportsnReports
mediapost.com, October 9, 2017

7
8

Source: 2018 EIQ Retail Innovation Survey

RIS News, "Closing Big Gaps in Personalization," October 2017
Harvard Business Review

4


http://www.mediapost.com

Table of Contents for the Digital Edition of Retail Leader - May/June 2018

Contents
Retail Leader - May/June 2018 - Cover1
Retail Leader - May/June 2018 - Cover2
Retail Leader - May/June 2018 - 3
Retail Leader - May/June 2018 - Contents
Retail Leader - May/June 2018 - 5
Retail Leader - May/June 2018 - 6
Retail Leader - May/June 2018 - 7
Retail Leader - May/June 2018 - 8
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Retail Leader - May/June 2018 - 18
Retail Leader - May/June 2018 - S1
Retail Leader - May/June 2018 - S2
Retail Leader - May/June 2018 - S3
Retail Leader - May/June 2018 - S4
Retail Leader - May/June 2018 - S5
Retail Leader - May/June 2018 - S6
Retail Leader - May/June 2018 - S7
Retail Leader - May/June 2018 - S8
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Retail Leader - May/June 2018 - Cover3
Retail Leader - May/June 2018 - Cover4
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