Retail Leader - May/June 2018 - S2

Retailers and their suppliers need real-time,
in-depth knowledge to attract diverse shoppers.
But the advent of distinct devices, sensors, and
machine-to-machine communications has made
data sets so large that timely manipulation,
management and analysis present significant
logistical challenges for companies using onhand data management tools or traditional
data processing applications. Other entities
have incorrect or outdated legacy data. This
is compounded by the difficulties many
companies face in recruiting the talent needed
to implement complex technology tools, analyze
the data and make effective recommendations.

or images. AI uses predictive patterns to help
understand desires, motivations and actions
across both physical and digital channels. This
lets retailers and suppliers enhance many
functions, such as executing more targeted and
personalized marketing campaigns and improve
trade promotion efforts. AI can also automate
forecasting of inventory needs, more accurately
predict out-of-stock incidences and ultimately
help optimize supply chains.

Many successful high volume retailers and
consumer packaged goods (CPG) organizations
have turned to artificial intelligence (AI) to
navigate the muddle. At the simplest level, AI
machines or systems imitate human behavior in
intelligent ways that can augment productivity
and optimize business performance. AI
applications include machine learning, natural
language processing (NLP) and robotics.
AI allows retailers and manufacturers to gather
customer insights in an automated fashion and
predict next actions based on previous patterns

Types of Artificial Intelligence

With artificial intelligence (AI), machines mimic or replace intelligent human behavior, like problem
solving or learning. They "sense," "comprehend" and "act" in accordance with the real world. In essence,
machines learn from experience and make recommendations, learning and improving over time.

AI applications fall under three key areas
Machine learning. Machines automatically analyze large amounts of data and "learn"
using rule-based algorithms that identify patterns and trends. As an example, this could
mean combining 100,000+ data points from 75 million customers regarding shopping
patterns and other habits.
Natural language processing (NLP). NLP is a machine's ability to understand, analyze
and generate human speech. A computer listens to a natural language spoken (or written)
by a person, understands its meaning and responds by generating natural language to
communicate back (as opposed to a computer language like Java or SQL). NLP can allow
retailers to request detailed information about a specific store, product, shipping method
or other topic without touching a PC.
Robotics. Involves full-scale automation of tasks traditionally performed by humans.
Warehouse picking and packing, for example, can be performed by robots.

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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
Retail Leader - May/June 2018 - 9
Retail Leader - May/June 2018 - 10
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Retail Leader - May/June 2018 - 17
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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