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Abstract and Figures

This paper gives an outline of the role of artificial intelligence in Sales and its impact on the Sales Processes. The paper discusses four case studies about companies like Coca-cola, eBay, Amazon (AWS), and Deutsche Telekom (eLiza). A conclusion is made that many small, medium and large-scale companies are now dependent on AI and cannot survive without it.
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I
How AI Is Changing Sales
Term Paper in Sales Management
within the framework of the study course “General Management"
with the degree "MSc. in General Management" at the
PFH - Private University Göttingen
submitted on: 2nd July, 2020
of: Parate, Akshata
from: Göttingen
First reviewer: Prof. Dr. Julian Voss
II
Table of Contents
I. Index of Figures……………………………………….….…………………...…III
II. List of Abbreviations………………………………..…………………………..IV
1 Introduction……………………………………………………………..……………...1
1.1 Relevance of Research………………………………………………………..……..1
1.2 Research Questions……………………………………………………………..……...1
1.3 Methodology and Scope of Work………………………………………………..….....2
2 Artificial Intelligence in Sales……………………………………………..…………....3
2.1 What is Artificial Intelligence?...........................……………………………..……….3
2.2 How is Artificial Intelligence used in Sales?...........................................................3
2.3 How will AI revolutionize Sales?............................................................................4
3 Literature Review…………………………………………………………………….....5
3.1 AI in Customer Relationship Management (CRM) and Sales Process…………...5
3.1.1 AI in CRM…………………………………..…………………………………..5
3.1.2 Hyperpersonalization Pattern in AI…………………………………………..5
3.1.3 Sales Forecasting …………………………………………………………… .6
3.1.4 Building Sales Pipeline Analysis……………………………………………..6
3.1.5 Lead Generation…………………………………………………………..…..6
3.1.6 AI for enhancing customer experience………………………………..…….7
3.1.7 Chatbot……………………………………………………………....……....7
3.2 Cloud Platform Solutions…………………………………………………….…………8
3.3 Impact on Employment because of AI enabled Sales process………………..…...8
4 Case Studies: Artificial Intelligence used by companies…………………………..10
4.1 Case Study 1 AWS for Arena Flowers………………………………………….…10
4.2 Case Study 2 eBay Using AI for Customer Retention and Business Success..10
4.3 Case Study 3 Coca Cola using AI for Commercial System …………………….11
4.4 Case Study 4 Deutsche Telekom DT………………………………………….…..12
5 Conclusion………………………………………………………………………………13
6 Bibliography……………………………………………………………………………..14
Statement
III
I. Index of Figures
Fig. 1: Sector-wise difference of technologies most impactful for business operations .. 4
Fig. 2: Cost decrease and revenue increase After adopting AI in business by function..4
Fig. 3: Brands using Hyperpersonalization with AI………………………………………....5
Fig. 4: Sales Pipeline Analysis………………………………………………………………..6
Fig. 5: Chatbots used by Sales Teams ......................................................................... 7
Fig. 6 Chatbots used for various purposes by a company…………………………………8
Fig. 7: Retail Process on AWS……………………………………………………………..9
IV
II. List of Abbreviations
AI
-
Artificial Intelligence
API
-
Application Programming Interface
Amazon EC2
-
Amazon Elastic Compute Cloud
Amazon RDS
-
Amazon Relational Database Service
Amazon SES
-
Amazon Simple Email Service
Amazon S3
-
Amazon Simple Storage Service
CRM
-
Customer Relationship Management
CTO
-
Chief Technology Officer
DT
-
Deutsche Telekom
HTML
-
Hypertext Mark-up Language
ROI
-
Return on Investment
SQL
-
Structured Query Language
1
1 Introduction
The goal of this paper is to provide an outline of artificial intelligence and its impact on
sales. It will provide a conclusion of the research based on relative case studies which
are discussed in this paper.
1.1 Relevance of Research
The inspiration for the paper is derived from some recent findings by the McKinsey
Global Institute, which states that even though AI is at an experimental stage for most
businesses around the world, it is catching up speed rapidly. A survey by Statista (2018)
states that the global AI software market will grow by 154 percent every year. The
relevance of this topic is backed by a McKinsey (2020) report, which estimates cost
savings of up to 20 percent in 12 to 18 months for various businesses.
AI is affecting many aspects of our life, including our day-to-day activities, learning,
working, and shopping experiences. Just as the laptop, smartphone and other digital
devices have increased the efficiency and productivity of the salespeople around us, AI
will continue the trend.
Being an important asset for us, the use of AI will be defined thoroughly by its effect on
companies, their sales processes and CRM. Additionally, the concepts like
Hyperpersonalization, sales forecasting, lead generation, sales pipeline analysis,
chatbots and cloud platform solutions as well as its impact on sales will be discussed.
How has AI changed sales will be studied with the help of references and case studies,
which will be reflected in this report conclusion.
1.2 Research Questions
The paper will answer the following research questions:
What is Artificial Intelligence, the fundamentals of AI and the different parameters
involved?
How is the AI technology used in Sales?
How will AI revolutionize sales and what are the different examples of this?
How has AI transformed the sales process and CRM?
What is predictive scoring, customer experience and personalization, chatbots,
cloud platforms and solutions?
2
1.3 Methodology and Scope of Work
The research methodology of this paper is three-fold. First, an overview of the current
situation in sales is discussed, with a focus on relation between sales and the AI
technology. Second, the basic terminologies of AI related processes in sales were
researched with the help of scholarly articles by McKinsey and Company, news articles
by Harvard Business Review and Forbes, and research papers by PhD students and
professors. Finally, four case studies were studied thoroughly, and a synopsis of each
case study was made to explain the research question of how AI is changing sales.
AI has been helping companies worldwide in reducing costs and time in the sales
departments. The work of the salespeople has decreased significantly. The scope of the
work is by evaluating the different ways in which AI has been changing the sales scenario
and how it benefitted the organizations. Based on the structured review, the study
recognizes different concepts of AI in sales and the technologies used by various
businesses. The paper later focuses on four case studies, all of whom have used AI for
sales in a different way and concludes the research at the end.
3
2 Artificial Intelligence in Sales
2.1 What is Artificial Intelligence?
John McCarthy coined the term Artificial Intelligence or AI in 1956, stating that it is the
“science and engineering of making intelligent machines”, but AI’s definition today is
beyond this simple definition. It is the simulation of machines that are programmed to
think and act like humans, so that they can perform tasks like decision-making, speech
recognition and applying human intelligence. The AI technology can be applied to various
sectors and industries. In this paper, we will focus on how AI is changing sales in the
business sector.
2.2 How is AI used in sales?
AI is the ultimate mantra for business tycoons today. According to recent market
researchers, the potential for AI to drive profit and increase growth margin for businesses
is enormous. Adoption of AI is currently generating heavy returns, with approximately 25
percent year-on-year usage of AI in standard business processes and up to 63 percent
increase in revenue generation (McKinsey, 2017).
The graph in Fig.1 shows how the finance industry is using AI the most among all the
other industries. The technology can be applied to fraud detection, unusual debit card
usage, financial risk management, for transactional transparency, digital wealth
management, blockchain, lending, etc. Application of AI in the financial department of an
organization can impact the business in a positive manner.
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Fig. 1 Sector-wise difference of technologies most impactful for business
operations
Source: Statista, 2020
2.3 How will AI revolutionize sales?
Salespeople should adopt AI or machine learning as a part of their sales process
because it will be able to assist in the growth of the company. It can increase the lead
volume of the products, quality, and the close rate. According to the Harvard Business
Review (2016), AI was able to increase sales leads by 50 percent and reduce cost and
time up to 60 percent for most companies.
According to a 2019 survey report by McKinsey in Fig. 2, revenues of companies have
increased after incorporating AI in marketing and sales division and the costs have
decreased most in the manufacturing division. The functions of AI applied in the sales
division are customer-service analytics, buying forecast, next product to buy and channel
management.
Fig 2. Cost decrease and revenue increase After adopting AI in business by
function
Source: McKinsey and Company, 2019
Another value addition of AI is that it crunch large volumes of data to make informed,
quicker and better decisions, which will answer the who, what, when and where in a B2B
business scenario as it can help increase revenue in a short span of time.
5
3 Literature Review
3.1 AI in Customer Relationship Management and Sales Process:
3.1.1 AI in CRM
CRM is a strategic tool to manage a company’s interaction with the existing and potential
customers. When AI is used in CRM, the customer data can be analyzed at every second
and from anywhere on the globe, by gathering data from Salesforce, external sources
and IoT to visualize a complete profile of the customer. It enables a company to forecast
quality sales, marketing and service interaction with each customer and automate the
routine interaction and special customer engagement possibilities.
3.1.2 Hyperpersonalization Pattern in AI
Hyperpersonalization is critical for companies in today’s fast-moving world. It is important
to grab your customers’ attention within a few seconds so that your product or service
gets noticed by them and stands out from its competitors. According to an article by
Gonfalonieri in Good Audience, Hyperpersonalization is an incorporation that “utilizes
behavioral and real-time data to create highly contextual communication that is relevant
to the user.”
In Fig. 3 below, the graph indicates to what level some big brands like Amazon, Spotify
and Starbucks apply this technique of Hyperpersonalization, as compared to 90 percent
of other companies. Amazon creates customer profile according to their buying history,
search query, brand affinity average time spent per search and average spend amount.
Fig 3. Brands using Hyperpersonalization with AI
Source: Gonfalonieri, Good Audience, 2018
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3.1.3 Sales Forecasting
With the help of AI, an analysis of a large data set is possible, which can help
programmers forecast conditions, help salespeople to meet targets, and allocate
budgets. Before AI integration in sales was possible, salespeople would spend months
trying to get leads. With the help of predictive analytics and algorithms, a lead score can
be obtained, which gives each sales lead a score. This score represents the likelihood
of the lead turning into an opportunity for a successful sale. The use of historical data
and current data like demographic, firmographic, geographic, and activity data helps in
analyzing the customer’s readiness to buy. Now, AI enables elimination of that time and
effort.
3.1.4 Building Sales Pipeline Analysis
A sales pipeline, in Fig. 4 below, is a visual description of a company’s prospects in the
sales process. It gives a clear overview of the sales leads in a week, month, or year and
how soon a deal is expected to close. Here, AI can be used to understand what is
required for the pipeline analysis and what must be removed. For example, if a sales rep
in a company has 20 opportunities working at once, with an average of 2 decision makers
per opportunity, they have 40 perspectives or data points that need to be managed. AI
can be used to analyze these opportunities as “risky” or “accelerators” and give an
opportunity health score at the end.
Fig. 4 Sales Pipeline Analysis
Source: Perkins, 2019
3.1.5 Lead Generation:
Now-a-days “Data Scraping” or “Web Scraping” is a known method to extract required
information from human-readable output which is derived from another program by
working on the HTML files of websites. An emerging business or a small-scale company
can find new leads by this method, to win prospective clients and get quality sales leads.
7
For most salespeople, this is one of the most difficult tasks. With the help of AI, you can
understand your client’s behavior and forecast before approaching them with a proposal.
3.1.6 AI for enhancing customer experience
The goal of using AI for any company is sales maximization and improving customer
satisfaction. One of the best strategies to maintain your client relationship is to keep them
engaged and connected with your company and the products you are offering. AI helps
in organizing and improving client-oriented tasks like personalized emails, content
personalization, offers, chatbots, behavior profiling, and recommendation systems.
Through Big Data Analytics and AI, companies can understand the exact needs,
expectations, and desires of the client. One such tool is “chatbot” and it is thoroughly
explained below.
3.1.7 Chatbot
A chatbot is a computer program and a “class of intelligent, conversational software
agents activated by natural language input.” (Radziwill, et al.) In the pie chart analysis
below (Fig. 5), it is stated that sales teams make use of chatbots the most in comparison
to the marketing, support and other divisions within a company. In 2019, businesses
saved up to EUR 265,447 by using chatbots with the greatest effect on sales and support
systems for their customers. Approximately 87% of customers prefer interacting with
chatbots than with humans for quick answers. (Yin, 2019)
Fig. 5 Chatbots used by Sales Teams
Source: Intercom, 2019
The graph in Fig. 6 shows how chatbots are used to route the website of a company,
collect information and to qualify leads. The ROI of a company has increased significantly
because increase in qualified leads (18%) and sales (67%). The technology, retail,
8
manufacturing, and healthcare industries have been most benefitted by chatbots. (Yin,
2019)
Fig. 6 Chatbots used for various purposes by a company
Source: Intercom, 2019
3.2 Cloud Platforms and Solutions:
Cloud computing platforms provide solutions to enterprises for heavy computing power
and it can be used for developing, managing, and deploying applications. There are
various cloud platforms and solutions available, for example, Amazon Web Service
(AWS), Microsoft Azure002, Alibaba Cloud, VMWare, SoftLayer by IBM, and Google
Cloud Platform.
Here, three of these platforms will be explained. AWS by Amazon is in the lead, with on-
demand cloud computation platforms for storing and analyzing ever-increasing data of a
company. Governments, businesses, and personal users make use of AWS solutions.
Microsoft Azure provides services like AWS, but offers more web development support
for Node.js, PHP, or ASP.net. The Alibaba Cloud is used by the Alibaba Group of
companies in China and is a competitor of AWS. Currently, it only supports its company’s
internal infrastructure, but will soon be available for public use.
3.3 Impact on Employment because of AI enabled Sales process
The employment of the salespeople in businesses has been and will be affected because
of AI enabled sales processes, mostly in a negative manner. According to the Salesforce
Research, 61% of employees predict a heavy impact of AI on their work life; it will be
used for automation and assistance in work-related activities. Companies are investing
in AI and robots and think twice to pay a salesperson a six or eight figure salary which
he previously earned. If a salesperson wants to prevent being replaced by AI technology,
he is expected to have better skills and technical knowledge to influence a company to
be retained as an employee. Since AI is getting human-intensive day-by-day, it is getting
easier to implement a sales process without a human being present.
9
4 Case Studies: How AI changed sales scenarios of companies
Let us take a look at how AI helped ease the problems of some companies and changed
their sales scenario with the help of synopses of 4 interesting case studies.
4.1 Case Study 1 - AWS for Arena Flowers
This is a case study of a UK-based online retailer company called Arena Flowers, which
although rose to success soon after its launch in 2006, began outgrowing its small
infrastructure and servers after building an API and working with multiple partners. “We
couldn’t scale the way we needed to.” “We literally have to move to a bigger warehouse
and hire hundreds of people to fulfill demand,” says Sam Barton, CTO. The company
experienced a booming sale during Valentine’s and Mother’s Day. “We’ll go from
processing 2,500 orders in a day to 50,000 orders.” Scalability, flexibility, and support
was important for this company as it had high sales of up to 2000 percent each year.
Arena Flowers decided to try AWS, an AI-based solution, to solve their problem. A typical
AWS retail process looks like the Figure 5 below. The company used Amazon RDS for
MySQL to handle operations related to web content and transactional processes. This
improved their overall sales performance, by processing orders quickly and reliably.
Soon the company opted many AWS solutions like Amazon EC2 to ensure speed of
response for customers across all zones and for order management and tracking,
Amazon SES for email services, AWS IAM for securing data with multi-factor
authentication and Amazon S3 for storage.
Fig. 7 Retail Process on AWS (Source: AWS, Amazon)
So, Arena Flowers was not only able to solve its previous problem by improving its sales
and completing orders, but it was also able to expand its business with more features
because of AWS. Additionally, it was able to save 30 percent every year on hosting costs.
10
4.2 Case Study 2 eBay Using AI for Customer Retention and Business Success
eBay is an American multinational e-commerce website that was first launched by Pierre
Omidyar in 1995. As per a business article, eBay began using AI since a decade ago
(Marr, Forbes, 2019).
The company began using AI and machine learning tools for customer search,
personalization, and insights. Subsequently, there was an improvement in buyer
experience because the shopping experience was customized according to the
behaviour, style and need of the customer. The website could recommend products as
per search and the buyers would usually buy those items. Another aspect that improved
was the product search. “The computer vision algorithm sifts through more than half a
billion images and eBay’s listings” to help the buyer find what they he is searching for
(Katariya, Ramani, 2019). So, eBay started making “visual shopping more intuitive”
(Joita, 2019). Later on, they enabled the website to be a platform as a service for the
sellers. The data management systems and analytics were available for them on the
eBay platform so that they do not have to do it themselves, hence making it an efficient
and easy platform to sell on. The sellers were able to optimize their product price levels
because of machine learning tools and would know when to sell the right product. AI also
greatly reduced time to process orders for international clients. AI algorithms improved
a customer’s buying journey till he received his order.
How did AI help the business? Because of the above-mentioned technologies and
improvements, eBay’s net income became 763 Million USD in the fourth quarter of 2018.
This is how AI helped eBay renew their online shopping website and application and
increased their sales.
4.3 Case Study 3 Coca Cola using AI
Coca cola is an American multinational beverage company which was founded in 1892.
Despite being a business entity with many competitors, Coca cola is at the top in its
industry with a brand value of approximately 73 Billion USD. Usage of innovative
technologies like the Big Data technology helped the company reach this position in the
market. Coca cola uses AI and machine learning to analyse large amounts of data,
including production, sales blocks, retailing counters and customer feedbacks (Vyas, et
al., 2019).
Technologies like computer vision helps Coca cola in identifying and verifying customers’
feedbacks and buying behaviour by using AI algorithms. The algorithm also enables
them to separate base or loyal customers from irregular customers, so that they maintain
client relations. For CRM, the salesforce used the AI application called “Einstein” to
manage the inventory of coolers; if some bottles are missing, they are automatically
added according to the algorithm in the application.
11
To understand the buying behaviour and product response, Coca cola has set up 37
“social centres” to collect data and conduct an analysis by using the “Salesforce
platform. The company also used Google’s TensorFlow technology for purchase
verification by machine recognition of codes.
Coca cola began to see advancements in their sales scenario after studying the analysis
gathered by the various AI systems. Data from vending machines collected by AI
algorithms enabled accurate understanding of buying habits of billions of customers in
the world. The computer vision analytics and deep learning techniques enabled Coca
cola to create resonance with customers on social media and drive sales.
4.4 Case Study 4 Deutsche Telekom: eLIZA
A German telecommunications company, Deutsche Telekom was founded in 1995 and
is currently headquartered in Bonn. In 2019, it had a revenue of EUR 80 Billion with 168
million customers and in 213, 000 employees. The number of employees started
decreasing steadily since 2010.
Given the large number of mobile phone customers, DT had issues with “massive
volumes of customer calls” and “suboptimal customer care experience”, due to which the
customers had to wait or connect multiple times to get answers. The customer care
department also faced problems because of repetitive questions from many customers.
So, DT decided to try using AI to handle these large amounts of repetitive queries so that
the employees could work with other high value tasks.
In 2015, the new AI innovation project called “eLIZA” was launched as a large distributed
team spread across 5 sites and 3 countries. The focus of the project was on quick user
feedback and agile development. The goal was to create a virtual assistant that helps
the customers by having a human-like dialogue. This created new opportunities for the
company like cross-sell and up-sell apart from solving customer problems and
processing feedbacks. “Tinka” was an avatar generated by the team to be the figure of
the virtual assistant ELIZA. There were connections made across various touchpoints so
that the customers have a one-stop for assistance wherever they are. Tinka remained
not only on DT’s website or application but was also integrated with Facebook
Messenger. Tinka chatted with more than 250 customers daily and processed 120,000
questions per month. The unsolvable questions would be forwarded to a human agent.
(Siddique, 2018)
12
So, DT kept customer service as the highest priority by launching ELIZA. The project
helped the company in CRM and it also received positive feedback from the customers
after the project was successful.
13
5 Conclusion
We have looked at the key concepts of AI in sales and read case studies with different
applications of AI for sales generation. It is evident that AI has proved to be useful for
companies more than harmful. The only drawback of AI in sales could be the loss of
employment of salespeople, but it has greatly solved many problems that salespeople
face.
Today, it is more important to be smart and efficient than hard working. AI is therefore a
boon to companies so that the salespeople of companies can focus more on tasks that
require more of their attention and their leaders can optimize their talents to full potential.
The four case studies discussed in the paper before gave an insight about the different
ways with which AI is affecting sales and all cases were positive scenarios. It is hence
the perfect tool to fuel a company’s sales efforts and give them an opportunity to sell
more.
Finally, the paper concludes that AI has changed sales to such an extent, that many
small, medium, and large-scale companies are now dependent on AI and cannot survive
without it.
14
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17
STATUTORY DECLARATION
I, Akshata Satyawan Parate, hereby declare that this paper titled “How AI Is Changing
Sales” has been authored in my own words. I have not used any sources other than the
declared sources and have explicitly marked the material which has been quoted either
literally or by content from used sources.
Place: GÖTTINGEN Name: AKSHATA SATYAWAN PARATE
Date: 02.07.2020 Signature: ___
ResearchGate has not been able to resolve any citations for this publication.
Thesis
Full-text available
Increases in the volume of data and the availability of compute power have driven a number of advancements in the field of Artificial Intelligence (AI), and AI technologies and applications are getting a flood of publicity in the media. While four in five executives agree that AI is a strategic opportunity for their organization, only about one in five has incorporated AI in some offerings or processes, and only one in 20 has extensively incorporated AI in their offerings or processes. There is a gap between expectation and action, and we are still in the early days of enterprise AI adoption. This thesis explores the path enterprises need to take to close this gap and to build an enterprise AI capability, thereby realizing the full value of this disruptive technology. Through a literature review it proposes a seven component holistic framework that can guide enterprises through this journey. The framework is more ‘wide than deep’, and it is supplemented with five case studies that take deep dives into the real life journeys of enterprises from different industries. These stories provide a vivid illustration of best practices and challenges. The case studies cover Danske Bank fighting financial fraud with deep learning, Deutsche Telekom improving customer service with an intelligent digital assistant, General Electric deploying machine learning applications for monitoring workflows in the Industrial Internet of Things, General Mills automating insights for marketers, and Kaiser Permanente using state of the art Natural Language Processing techniques on unstructured triage notes to improve patient flow forecasting. Learnings from the case studies are synthesized into recommendations to aid practitioners on the road to enterprise Artificial Intelligence.
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