The American Journal of Management and Economics Innovations
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TYPE
Original Research
PAGE NO.
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10.37547/tajmei/Volume07Issue03-02
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SUBMITED
22 January 2025
ACCEPTED
18 February 2025
PUBLISHED
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VOLUME
Vol.07 Issue03 2025
CITATION
Oleksii, B. (2025). Algorithmizing B2B Sales: Can AI Create a Sales
Framework That Guarantees Predictable Results?. The American Journal of
Management and Economics Innovations, 7(03), 08
–
13.
https://doi.org/10.37547/tajmei/Volume07Issue03-02
COPYRIGHT
© 2025 Original content from this work may be used under the terms
of the creative commons attributes 4.0 License.
Algorithmizing B2B Sales:
Can AI Create a Sales
Framework That
Guarantees Predictable
Results?
Birchak Oleksii
B2B Sales Specialist at Uniqa New York, USA
Abstract:
The article examines the impact of artificial
intelligence on B2B sales processes, in particular, its role
in increasing efficiency and predicting customer
behavior. The article analyses the use of such
technologies as natural language processing [NLP],
machine learning [ML], computer vision, and chatbots.
Particular attention is paid to the practical case of the
American company Dell, which has achieved an increase
in the conversion rate and optimization of the sales
department with the help of Lattice Engines analytics.
The study demonstrates that AI integration allows
branding agencies to create more accurate customer
profiles, automate routine tasks, and increase the level
of interaction personalization. It is concluded that
further development of predictive models and
integration of AI with customer relationship
management [CRM] systems are essential for achieving
predictable
results
and
enhancing
companies'
competitiveness. The study's novelty highlights the
practical benefits of integrating AI into the B2B sales
process, particularly its role in improving sales
efficiency, personalization, and lead generation.
However, the research is limited to analyzing existing AI
applications and does not cover the potential risks
associated with data privacy and ethical concerns.
Future studies should address these challenges to
ensure the responsible use of AI. The practical
implications of this research include increased
productivity, improved customer targeting, and
enhanced
decision-making
processes.
Social
implications involve the transformation of the labor
market, as AI automates routine tasks, necessitating
workforce reskilling and adaptation to new roles. Thus,
AI not only optimizes sales processes but also drives
broader societal changes, highlighting the need for
balanced technological adoption.
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The American Journal of Management and Economics Innovations
Keywords:
B2B sales, machine learning, natural
language processing, predictive analytics, branding
agencies, automation, personalization, customer
experience, sales managers.
Introduction:
One of the most discussed topics in
modern sales management today is digitalization. It
affects the process of buying and selling things by
companies, customer interaction, and changes in B2B
sales [Mattila et al.].
After analyzing J. Hunter's research, several
conclusions were drawn:
1] the use of AI in business automates about 40% of
sales tasks [Hunter, p. 203]; 2] artificial intelligence can
be added to every stage of the sales process. There
have been changes in the value system that will
reorganize sales in the technical sector. According to J.
Hunter, this affects the usual business that salespeople
currently do [Hunter, p. 205].
However, many companies still don't know how to use
digital innovations correctly and properly integrate
into sales process. So, the question arises: how can AI
help boost sales in B2B commerce? A thorough
analysis of B2B sales research in this area was
conducted to answer this question.
This research is aimed at the B2B trade community and
brand agencies. It is hypothesized that AI integration
increases conversion rates, improves customer
profiling, and boosts sales team productivity. By
analyzing big data and predicting customer behavior,
the use of AI will normalize and optimize the customer
acquisition process by increasing conversion rates.
Branding agencies can become more competitive,
create distinctive offers, and establish long-term
customer partnerships.
Intelligent operational information systems based on
collected data are called artificial intelligence. Its
purpose is to identify the best or most expected
solution. The behavior of artificial intelligence does not
necessarily mimic human intelligence; instead, it
achieves ideal efficiency, which is called rationality
[Paschen et al., p. 1413].
There are two types of definitions of AI:
1] narrow intelligence
–
the ability to process and
analyze data and perform specific tasks; 2] general
intelligence, the potential of which can be compared to
the human brain. Today, AI is used in the field of
narrow intelligence, in particular, by applying machine
learning technologies, human speech processing, and
educational purposes [Jarek and Mazurek, p. 48].
According to the AI Index 2021, artificial intelligence
research is increasingly gaining attention. Between 2010
and 2022, the total number of such publications almost
tripled, rising from around 88,000 in 2010 to over
240,000 in 2022.
In terms of geographical distribution, in 2022, China was
the leader in the number of patents granted in the field
of artificial intelligence, accounting for 61.1% of the
total, while the US accounted for 20.9% [The AI index
report].
In business, the main impact of AI is to improve process
efficiency, generate analytical insights, and transform
business processes. Artificial intelligence is used to
automate routine tasks and enhance cognitive
capabilities by integrating it with human experience
[Enholm et al.].
As early as 2020, artificial intelligence became the
second largest area in sales after automated customer
service agents [Mehta], [Senn-Kalb].
In the United States, artificial intelligence is actively
transforming busineses in the areas of branding and
marketing. The integration of artificial intelligence in
partner relations [AI-PRM] increases the efficiency of
operations and trade values through better partner
coverage and personalized services [Cotter et al.
Artificial intelligence provides for sales features by
analyzing customer behavior data and dynamic pricing
[Fischer et al.]
In marketing, AI provides personalization through
recommended agents and chatbots that provide round-
the-clock support [Paschen et al., p. 1417]. Despite the
benefits, successful AI implementation requires data
security and staff training. Human and artificial
intelligence are constantly interacting to maintain long-
term customer relationships.
MATERIALS AND METHODS.
This study employed a qualitative research design,
focusing on analyzing secondary data from reputable
sources, including academic journals, industry reports,
and case studies. The research explored the practical
applications of artificial intelligence [AI] in B2B sales,
particularly its role in increasing efficiency, enhancing
customer profiling, and improving conversion rates. The
case study of Dell’s use of Lattice Engines analytics was
selected to illustrate the real-world impact of AI on sales
processes.
The research was conducted over a six-month period,
during which relevant literature and industry reports
were systematically reviewed to identify key AI
technologies, such as natural language processing (NLP),
machine learning (ML), computer vision, and chatbots.
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Inclusion criteria focused on studies and reports
published within the last decade that provided
empirical evidence of AI's impact on B2B sales. Sources
that lacked clear methodologies or measurable
outcomes were excluded to maintain the reliability of
the findings.
Data analysis involved synthesizing information from
multiple sources to identify common patterns and
trends in AI adoption. The primary focus was on
understanding how AI influences different stages of
the sales cycle, from prospecting to after-sales service.
Additionally, the study examined the integration of AI
with customer relationship management (CRM)
systems and its role in predictive analytics.
While quantitative data from Dell's case study, such as
conversion rates and return on assets, provided
measurable evidence of AI's effectiveness, qualitative
insights from literature reviews helped contextualize
these findings within broader industry trends. Ethical
considerations were acknowledged, particularly
regarding data privacy and the responsible use of AI in
sales.
RESULTS.
Artificial intelligence turns large amounts of data into
information to improve customer understanding and
knowledge management during sales process.
Branding agencies are participating in digital change as
they use artificial intelligence to optimize sales cycles
and increase engagement with potential customers
[Paschen et al., p. 1416]. Branding agencies use the
following AI technologies to improve their sales:
Natural language processing [NLP]. NLP analyses
textual data: emails, social media posts, and customer
reviews. In this way, NLP identifies keywords and
customer sentiment, creates accurate profiles of
potential customers, and predicts their needs [Syam &
Sharma, p. 137].
- Machine learning [ML]. ML is used to analyze the
behavioral data of potential customers and identify the
most promising leads. Algorithms are able to detect
patterns and predict the likelihood of a purchase based
on historical data [Syam & Sharma, p. 139].
- Computer vision. Computer vision technology allows
analysing visual content, such as images and videos.
This is especially important for branding agencies,
which use this tool to analyze customers' visual
preferences and improve the design of branded
materials [Forsyth & Ponce].
- Chatbots and digital agents. Chatbots are capable of
performing routine tasks, including answering
common questions and making the first contact with a
customer [Paschen et al., p. 408]. - Predictive analytics.
Predictive analytics predicts customer behavior based
on the analysis of their past actions. It allows branding
agencies to determine the best time to interact with a
customer and adjust their marketing campaigns to
increase conversion [Syam & Sharma, p. 1410]. Artificial
intelligence at different stages of the sales cycle
increases the efficiency of the process and minimizes
the human factor:
1. Prospecting. AI is able to analyze large amounts of
data to create a list of potential customers and assess
their likelihood of making a purchase.
2. Customer approach. AI personalizes communication
with customers based on the analysis of their behavior
and interests. ServiceMax uses ML to recommend web
pages to its website visitors, which has reduced the
bounce rate by 70% and doubled the time spent on the
site.
3. Presentation. At the presentation stage, AI helps to
create product prototypes and adapt presentation
materials. Airbnb uses algorithms to transform design
sketches into program code, which significantly speeds
up the prototyping process.
4. Dealing with objections and closing the deal. AI
provides up-to-date information about competitors
using Klue's AI-enabled battlecards, which helps sales
managers answer customers' questions effectively.
Dynamic pricing algorithms offer individualized prices
depending on customer characteristics.
5. After-sales service. AI automates order processing
and communication with customers using chatbots.
Analysis of purchase data reveals opportunities for
cross-selling and upselling. For example, Hyatt Hotels
Group increased upselling revenue by 60% by using ML
to predict guest needs [Forsyth, Ponce], [Paschen].
In a case study of the impact of AI on B2B sales
algorithmisation, the American representative office of
Dell was identified. The company's use of artificial
intelligence demonstrates a significant impact on
conversion rates, lead quality, and overall sales
performance. To increase the productivity of its sales
team, Dell turned to cloud analytics from Lattice
Engines. This software is specially designed to help sales
managers close more deals and identify potential
customers who are most likely to buy Dell products.
The program works by analyzing the behavioral patterns
of companies that already buy Dell products. It tracks
events and behavior of potential customers, such as the
opening of a new office, which is usually accompanied
by the need for new computers and equipment. Using
predictive
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analytics algorithms, Lattice Engines matches these
trigger events with external data, such as LinkedIn ads,
information on websites, and public statements by
potential candidates. Additionally, the company's
internal data and purchase history are taken into
account. Customers who have bought Dell products
before receive a higher priority when generating a list
of potential leads [King].
Integration of social networks, including LinkedIn,
creates a complete portrait of the client. In today's
market, where buyers are better informed and more
demanding of a personalized approach, the ability to
understand customer needs in advance significantly
increases the chances of a successful sale. Lattice
Engines helps managers use this information more
effectively to target potential customers, reducing the
time spent searching for data on the Internet and
increasing the number of productive contacts.
The impact on conversion rates has been significant. For
example, Dell's marketing department in Europe
reduced the number of leads coming into the sales
department by 50% by focusing on only the most
promising candidates. This allowed them to almost
double their productivity, efficiency, and revenue. This
approach allows sales managers to spend time only on
those customers who really need Dell products at a
particular time [King].
Figure 1. The impact of AI implementation on the number of leads in the Dell sales department [King]
In general, using Lattice Engines analytics allowed Dell
to increase the efficiency of its resources and improve
profitability. Despite the general decline in demand for
personal computers and an 11% decline in quarterly
profits, the company achieved a return on assets of 6%,
which is significantly higher than the average
competitor's rate of 3.2% [King].
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The American Journal of Management and Economics Innovations
Figure 2. Comparison of return on assets of Dell and its competitors [King]
Thus, the study showed that Dell was able to use its
assets more efficiently due to the introduction of AI in
the sales process.
Discussion. The results highlight the practical benefits
of AI in B2B sales, supporting the hypothesis that AI
integration increases conversion rates, improves
customer profiling, and enhances sales team
productivity. Dell’s success demonstrates how
predictive analytics can help companies identify high-
potential leads and focus their efforts more effectively.
By analyzing behavioral patterns and external data, AI
enables sales managers to anticipate customer needs
and offer personalized solutions, reducing the time
spent on unqualified leads.
The findings align with existing research, which
emphasizes the role of AI in automating routine tasks
and enhancing decision-making processes. For
example, previous studies have shown that AI can
automate up to 40% of sales tasks, allowing sales
teams to allocate more time to strategic activities. The
use of NLP and ML further enhances customer
engagement by providing insights into customer
behavior and preferences. However, the study is
limited to the analysis of existing AI applications and
does not address potential challenges related to data
privacy and ethical considerations.
Overall, the study demonstrates that AI can
significantly improve B2B sales performance by
enhancing customer profiling, optimizing resource
allocation, and automating routine tasks. Future
research should explore the integration of AI with
customer relationship management [CRM] systems and
examine the long-term impact of AI on customer
satisfaction and sales performance. Conclusion. The
introduction of artificial intelligence in sales processes
between companies demonstrates the potential of this
technology to optimize and normalize commercial
processes. According to research, AI automates about
40% of sales tasks, which significantly increases business
efficiency.
Brand agencies predict customer behavior, create
personalized incentives, and analyze large amounts of
data using AI. As customers receive more relevant and
timely offers, it increases conversion rates. Integrating
AI into partner relationship management [AI-PRM]
helps to improve performance by understanding more
about customer needs and the ability to set dynamic,
ready-to-buy pricing.
Dell is a great example of using AI to improve sales
efficiency. The company used Lattice Engines' cloud-
based analytics to reduce the number of sales leads by
focusing on only the best candidates. The company's
return on assets reached 6%, which is significantly
higher than its competitors' 3.2%.
The benefits of AI for branding agencies include the
ability to create more accurate profiles of potential
clients with natural language processing services,
predict the likelihood of closing deals with machine
learning, and automate routine tasks with chatbots.
Future research and development in this area should
focus on further improving predictive models and
increasing the accuracy of customer behavioral data
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analysis. Particular attention should be paid to the
integration of AI with customer relationship
management [CRM] tools, which will create better-
personalised offers and increase customer satisfaction.
In addition, an important area of research is ensuring
transparency and ethical use of AI in sales in the
context of personal data processing.
Thus, the introduction of artificial intelligence in B2B
sales in branding agencies opens up new opportunities
to increase the efficiency of business processes,
improve customer interaction, and achieve predictable
results. Combined with human expertise, AI creates a
significant competitive advantage in the current US
market and ensures sustainable growth for companies
in the long term.
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