
Reinforcement Learning, the Right Technology in Retail

Many of us who have been in our careers for a while have probably never taken a formal course in AI. In fact, even today, less than 50% of high schools in the US even offer a course on the subject (though that is changing quickly). This means most of us have a limited understanding of what AI is, how it might be applied in our business today, and how it will affect our roles in the future. This is a perilous position given the considerable impact it’s having today, and the tremendous growth predicted over the next 10 years.
The Use of AI in the Retail Space
At the end of this decade, the global financial impact of AI is expected to reach 15-20 trillion dollars and it will fundamentally impact nearly every aspect of business. For senior retail executives, it is vital they lead their organizations through this change rather than follow from a place of unfamiliarity. To do so, their organization, and they themselves, will have to acquire a comprehensive understanding of AI and the opportunity it offers their company. The urgency in bricks and mortar (B&M) retail is especially great as this sector has historically been a laggard when it comes to technology. That said, when combined with ecommerce, it is now poised to overtake the banking industry as the top spender on AI. B&M retailers have specifically accelerated their spend, adopting AI technology for a wide range of tasks including inventory management, in store experience, and merchant planning of promotions and pricing. According to a 2021 survey by Meticulous Research, the projected growth of AI in retail will be at a pace of 35% CAGR over the next five years.
There are a several branches under the general umbrella
term of AI. However, the application that is rapidly
becoming the most important by far for merchants is
machine learning. Machine learning is the use and
development of computer systems that are able to learn and
adapt. This is done by developing and using models, which
fall in to three categories: supervised, unsupervised, and
reinforcement learning.
In merchandise
planning,
reinforcement learning (RL)
is emerging as the dominant technology. The key reason for
it's prominence is its ability to autonomously handle
massive amounts of data, countless variables, and
incredible levels of complexity without requiring much
human labour. The technology keeps humans in the loop, but
only to guide the technology to follow the strategies of
the company and to evolve the technology itself further.
What is Reinforcement Learning?
We are intuitively aware of how positive and negative
reinforcement work in our everyday lives. For example, a
parent may give their child praise after they studied or
finished a chore in the hope that this would become
regular behaviour. That is a simple everyday example of
positive reinforcement. On the other hand, an example of
everyday negative reinforcement would be being reprimanded
for arriving at work late. Leaving earlier would be an
action learned through negative reinforcement.
Moving
to the world of retail, let’s take an example of a
merchant who is brand new to her role. Her strategy of
placing hamburger patties on promotion is rewarded with
increased customer traffic to the stores, hamburger
patties flying off the cooler shelf, and boosted sales of
complementary items such as buns, ketchup, and cheese.
Clearly, the novice merchant will take note and repeat
this promotion after having received positive
reinforcement from the results. In fact, one of the key
attributes of a successful merchant is their deep
knowledge and experience of the hundreds of promotions
that work and the ones that don’t, which is learned
through positive or negative experience throughout their
career.
In principal, reinforcement learning
in AI works the same way - but any comparison on
performance quickly becomes obsolete. Unlike merchants and
their analytical team learning in real time, the AI is
continually learning autonomously at lightning speed from
billions of positive and negative reinforcements in a
virtual retail simulation of the entire business or
system. This approach explores not just the relatively
small number of promotion scenarios that a merchant and
their team is able to accomplish, it considers all
potential product combinations, potential price points,
and channels to provide fully optimized and holistic
recommendations.
The Future of Reinforcement Learning
Rapid adoption of reinforcement learning is
beginning to provide great successes. The board game Go
provides a glimpse into
the future of this technology in retail. Go is a board game invented in China more than 2,500
years ago and is considered the most complex game ever
devised by humans. DeepMind (a subsidiary of Google) has
been evolving an AI program called AlphaGo that leverages
reinforcement learning. Since the early 2010s, DeepMind
has hit the headlines for their incredible progress. In
2016, one of their first versions was able to beat the
world champion Go player 4 out of 5 times. The next
iteration, called AlphaGo Master, defeated not only the
reigning Go champion, but also 60 top professional online
players without losing a single game. Incredibly, the
latest version of the program, AlphaGo Zero, played the
previous version AlphaGo Master and defeated it 100-0.
This
very same approach is now being harnessed by retail
leaders who understand the power of AI technology. For
senior retail executives, it is vital they understand the
technology and lead their organizations through the
transformational change they must undergo. They will not
want leave their organization unprepared to compete
against the retail version of AlphaGo.
