=Paper=
{{Paper
|id=Vol-3688/paper20
|storemode=property
|title=Analysis and Formation of Sales Forecasts in CRM Systems
|pdfUrl=https://ceur-ws.org/Vol-3688/paper20.pdf
|volume=Vol-3688
|authors=Andrii Berko,Iryna Pelekh,Pavlo Hlova
|dblpUrl=https://dblp.org/rec/conf/colins/BerkoPH24
}}
==Analysis and Formation of Sales Forecasts in CRM Systems==
https://ceur-ws.org/Vol-3688/paper20.pdf
Analysis and formation of sales forecasts in CRM systems
Andrii Berko1, Iryna Pelekh1 and Pavlo Hlova1
1 Lviv Politechnic National University, 12 Stepana Bandera st., Lviv, 79013, Ukraine
Abstract
Creation of system of analysis and formation of sales forecasts in CRM systems has been described that
allows you to forecast sales in customer relationship management systems and is a powerful tool for
increasing the company's competitiveness and improving cooperation with customers. The Information
processes in CRM systems have been analyzed. The sequence of events in the process of forecasting sales
in system of analysis and formation of sales forecasts in CRM systems has been highlighted. The problem
of forecasting sales as a regression problem have been defined. The main stages that need to be
performed to test the model of the sales analysis and forecast system in customer relationship
management systems in Azure Machine Learning are shown.
Keywords
Sales analysis, sales forecast, CRM system, machine learning, machine learning algorithms, regression
analysis. 1
1. Introduction
Sooner or later, every company faces the task of optimizing the interaction with customers
process. The choice of the method of organizing this interaction depends on the company's
activity field, its internal structuring and approaches to cooperation with clients. Often, this
process includes solving additional tasks, such as storing client information, ensuring its security,
optimizing internal communication in the company, and convenient access to the developed
methodology for working with clients.
To solve these problems CRM (Customer Relationship Management) systems have been
developed. Unlike Excel spreadsheets (or Google spreadsheets), CRM systems allow you to
automate business processes and ensure competitiveness [1]. They help increase the speed of
processing applications, reduce costs and predict sales volumes.
Own sales forecasting for CRM systems is a reasonable scope of research, as it allows you to
reduce costs, predict a decline in demand, effectively manage stocks, set the right performance
indicators, plan the next purchases, launch advertising campaigns, rent warehouses, etc.
2. Analysis of the last research and problem statement
Modern customer relationship management (CRM) systems are aimed at studying the market and
individual needs of customers. Based on this knowledge, new products and services are
developed, which allows the company to achieve its goals and improve its financial indicators [2].
Implementation of the CRM system can be carried out in different ways, depending on the
needs and circumstances of a particular company. Below are three general approaches to CRM
implementation [3]:
Strategic approach: this approach involves the implementation of CRM as part of the
company's overall strategy. Before choosing a specific CRM system, a company must define its
strategic and CRM goals. This includes studying customer needs, identifying opportunities to
COLINS-2024: 8th International Conference on Computational Linguistics and Intelligent Systems, April 12–13, 2024,
Lviv, Ukraine
Andrii.Y.Berko@lpnu.ua (A.Berko) Iryna.I.Kushniretska@lpnu.ua (I.Pelekh);
Pavlo.Hlova.mSAAD.2022@edu.lpnu.ua (P.Hlova)
0000-0001-6756-5661 (A.Berko); 0000-0002-3769-6844 (I.Pelekh)
© 2024 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
attract and retain customers, and developing a strategy accordingly. After defining the
strategic goals, choose the CRM system that best meets the needs of the company;
Functional approach: in this approach, CRM implementation begins with specific
functional tasks. For example, a company may determine that it needs to improve contact
management or improve the efficiency of deal management. After that, choose the CRM system
that best meets specific functional needs;
Distribution in stages: this approach involves the implementation of CRM in stages or in
different divisions of the company. A company can initially implement CRM in only one
department or part of the business, and then gradually expand its implementation to other
divisions. This approach can allow a company implement CRM to effectively, gradually
adapting it to the needs of different departments and reducing risks.
Each of these approaches has its advantages and disadvantages, and the choice of approach
will depend on the specific circumstances and purpose of implementing CRM for the enterprise.
CRM platforms are different, depending on the functions they perform:
Lead management. These customer relationship management (CRM) systems collect
information about potential customers from various sources, evaluate their potential, and
connect new leads with appropriate sales representatives.
Sales funnel management. Such CRM systems map the sales funnel within the CRM
program to better understand the customer journey to purchase and increase conversion.
Work processes automation and marketing. These CRM systems automate routine tasks
such as prospecting, lead segmentation and customer interaction to streamline workflows and
provide a personalized approach to potential customers.
Reporting and forecasting. These customer relationship management (CRM) systems use
dashboards, CRM analytics, and reporting tools to identify customer needs and measure sales
performance.
CRM programs are widely used in various industries, including SaaS, real estate,
manufacturing, and marketing. The CRM platform is suitable for businesses of all sizes looking to
improve customer satisfaction, increase productivity and increase sales [1, 4].
Within an organization, sales, marketing, customer support, and also finance and operations
departments can use a CRM program to solve the following tasks:
Optimization of the sales funnel to close more deals;
Generating more promising leads to increase income;
Building strong relationships to increase customer loyalty;
Optimization of inter-team cooperation to achieve greater goals;
Load reduction to increase productivity.
Depending on the needs, the customer relationship management system can be installed on a
computer or use a cloud service. Most modern CRM systems also have a mobile application that
allows you to work with your customer base from anywhere.
Choosing the right sales platform and CRM is fundamental to the success of any business.
Consider the most popular CRM platforms: HubSpot, Salesforce, SalesDrive.
HubSpot [5] is a cloud-based customer relationship management (CRM) platform that helps
businesses scale better with sales, service, marketing and content management software.
Salesforce [6] is a cloud-based customer relationship management (CRM) platform with
applications for sales, service, marketing, and more that help connect customers and businesses.
Salesforce was built together through acquisition, which means your experience and connections
may differ depending on the products you use.
SalesDrive [7] is a Ukrainian CRM system aimed at online stores that automates the processes
of processing orders, keeping records of goods in the warehouse and tracking delivery statuses.
In addition, SalesDrive records all communication with customers, including calls, SMS, messages
in messengers and e-mail, and provides the ability to effectively monitoring the work of
managers.
These systems are suitable for both small and medium-sized companies. Each of them has its
advantages and disadvantages. When choosing a system, it is recommended that you evaluate
your company's core CRM requirements and determine which features are best implemented in
each system and which features can be sacrificed because they are not business critical.
Sales analysis and forecasting are key to developing a sales strategy and optimizing business
processes. They allow companies to make decisions taking into account demand, the structure of
the customer base and other factors.
To date, the problem of forecasting sales in CRM systems remains relevant due to the need for
enterprises to obtain accurate estimates of their costs and revenues, which enables them to
forecast their short-term and long-term effectiveness. Currently, sales forecasting is a
requirement of the competitive race of modern business, enabling the enterprise to stay afloat.
3. Analysis and sales forecast formation principles in CRM systems
Among the main capabilities of the CRM system, can be distinguished the following:
1. Customer information management. The CRM system consolidates the customer base and
provides the organization with complete information about its customers, their preferences,
on the basis of which an interaction strategy is built.
2. Sales management: the system saves the history of interaction with customers, helping
the sales department to analyze customer behavior, form appropriate offers and win their
loyalty.
3. Marketing automation: the CRM system allows you to optimize the company's marketing
management, organize marketing activities, manage marketing resources and budgets, and
coordinate marketing activities.
4. Automation of document flow: the system provides all the necessary tools for managing
the company's external and internal document flow, including automatic document
generation, preparation of printed forms of documents, maintenance of current versions of
documents and quick search of documents in the system.
5. Business process management: dividing work processes into stages and formalizing their
structure help reduce the number of errors and speed up the company's work, making results
more predictable.
6. The CRM system analytical capabilities: the system provides companies with the
opportunity to obtain static information and conduct deep data analysis necessary for
strategic business processes.
Information processes in CRM are presented in Fig. 1 [1].
Figure 1: Information processes in CRM system
Since the CRM system is a web application, the following components are necessary for its
correct operation: a web server and a database management system (DBMS). No additional
software needs to be installed on the user's workstation other than a standard web browser.
Implementation in the form of a web application allows you to work with the system on any
platform, including mobile devices. This approach does not require significant computing
resources, so the power of an ordinary computer or laptop is sufficient for the stable operation of
the information system.
3.1. The analysis and formation of the sales forecast system main processes
We identified main processes of the analysis and formation of the sales forecast system such as:
Strategic planning:
- strategic goals determination in the field of sales and profitability;
- strategies and tactics development to achieve these goals through sales forecasting.
Data collection and integration:
- identification of data sources, such as historical sales data, customer data,
demographic data, etc.;
- integration of these data into a single system for further analysis.
Data analysis:
- analytical methods usage for processing and analysis of historical sales data;
- correlations and connections identification between various factors and sales
volumes.
Forecasts development:
- prognostic models construction based on data analysis and taking into account
historical trends and variable factors;
- generation of sales forecasts based on these models for different time periods.
Validation and adjustment:
- comparison of forecasts with actual sales results to validate accuracy;
- corrections making to forecasts based on deviations from actual data.
Planning and resources:
- determination of resource needs based on sales forecasts;
- planning of work processes and resources to meet demand.
Monitoring and reporting:
- constant monitoring of strategy implementation and forecast results;
- creation of reports and analytical data to inform management and decision-making.
The top level of business processes of the subject area:
Figure 2: The main processes of the analysis and formation of the sales forecast system
Brief description of the TO BE target process:
Data collection and processing: collection, processing and integration of data from
various sources, such as historical sales data, customer data and other factors affecting sales;
Analysis and modeling: use of analytical methods and models to determine trends,
correlations and forecast sales based on collected data;
Interaction with customers improving: improving methods of communication and
interaction with potential and existing customers based on analytics and sales forecasts;
Process automation: introduction of automated tools to optimize routine operations
related to sales and customer service;
Monitoring and evaluation: constant monitoring of sales results, comparing them with
forecasts and improving the system based on acquired knowledge;
Reporting and analysis: creating reports and analytical data for management and the
sales team to make strategic decisions.
As a result of this targeted process predictability has been improved and sales efficiency has
been achieved, which contributes to increased profitability and customer satisfaction in the CRM
system.
3.2. The analysis and sales forecast methods
The goal of the work is the development of a system that provides optimization of customer
relationship management (CRM) through sales analysis and forecasting.
To achieve the set goal, the task of the sales analysis and forecast system in customer
relationship management (CRM) systems is as follows:
1. Data collection and storage: ensure the collection and storage of information about
customers, their purchases, interaction history, and other important data necessary for sales
analysis and forecasting.
2. Data analysis: develop algorithms and models to analyze historical data, including
customer segmentation, determining correlations between factors and sales, analyzing the
effectiveness of marketing campaigns and other aspects.
3. Sales Forecasting: Develop sales forecasting models based on historical data and other
factors that may affect demand.
4. Sales planning and strategy: based on analysis and forecasts, develop sales strategies,
including optimization of pricing policy, marketing activities, as well as inventory and
production planning.
5. Monitoring and evaluation of results: constantly monitor the results of the
implementation of strategies and adjust them if necessary to achieve better results.
Building models for analyzing and forecasting sales in the CRM system helps the company to
effectively manage relationships with customers, maximize profitability and improve the quality
of customer service.
A sales forecasting problem is usually a regression problem because it involves predicting
numerical values (sales volumes). The task can be divided into several subtasks, such as
forecasting sales by product categories, geographic regions, etc.
Thorough analysis and understanding of sales data is an important step. This analysis will help
determine which algorithms and methods can be the most effective for this task.
Purpose of regression analysis [9]:
1. Determination of the degree of determinism of the variation of the criterion (dependent)
variable by predictors (independent variables).
2. Predicting the value of the dependent variable using the independent one.
3. Determination of the contribution of individual independent variables to the variation of
the dependent variable.
Regression analysis can’t be used to determine the presence of a relationship between
variables, since the presence of such a relationship is a prerequisite for applying the analysis.
The regression analysis algorithm has the following form: let the measurements of Yn be
obtained at the points xn of the independent variable x. It is necessary to find the dependence of
the average value of the value on the value of x,
𝑌 ∗ (𝑥) = 𝑓(𝑥|𝑎), (1)
Where a is a vector of unknown parameters ai. The function f(x|a) is called the regression
function. It is assumed that f(x|a) is a linear function of parameters a, that is, it has the form:
𝑛
(2)
𝑓(𝑥|𝑎) = ∑ 𝑎𝑖 𝜇𝑖 (𝑥),
𝑖=0
Where fi(x) are given functions.
In this case, the matrix Am=fi(xn) is called a regression matrix. To determine the parameters,
the method of least squares is used, that is, the estimates ai are determined from the minimum
conditions of the functional:
𝑁 (3)
𝑌𝑛 − ∑𝑖(𝐴𝑛𝑖 𝑎𝑛𝑖 )2
𝛾=∑
𝜎𝑛2
𝑛=0
And from the functional minimum:
(4)
𝛾 = ∑(𝑌𝑛 − ∑ 𝐴𝑛𝑖 𝑎𝑛𝑖 )(𝑅 −1 )𝑛𝑚 (𝑌𝑚 − ∑ 𝐴𝑚𝑖 𝑎𝑚𝑖 ),
𝑖 𝑖
𝑛,𝑚
For correlated measurements with the correlated matrix R. Power functions fi(x) = x2 serve as
functions fi(x). Orthogonal and normalized polynomials on the set fn are often used. In this case,
it is easy to find the estimate 𝑎̃𝑙 :
𝑎̃ = ∑ 𝜇 (𝑥 )𝑌 ) (5)
𝑙 𝑖 𝑛 𝑛
𝑛
It follows that the calculation 𝑎̃𝑙 does not depend on calculation of others estimates 𝑎̃𝑗 .
Usage the regression method for forecasting sales in the CRM system:
Rationale: regression allows you to model the relationship between a dependent variable
(such as sales) and independent sales variables, which is critical for accurate forecasting.
Application: regression can be used to develop a model that will predict sales based on
historical data and other factors. Factors such as product price, number of customers,
advertising expenditure, etc. can be included as independent variables.
Effectiveness: the regression method is effective for modeling linear and non-linear
relationships between variables. It allows you to analyze the impact of various factors on sales
and develop sales strategies based on these analyses.
When considering the problem of forecasting, it is always necessary to take into account the
presence of uncertainty and as a result of incomplete information [10]. Considering the presence
of the uncertainty factor and heterogeneous features of information systems for which a forecast
is formed, one of the general approaches to the analysis and forecasting of the properties of such
processes is the consideration of their time series [10].
Effective planning and management of many processes in CRM systems is impossible without
operational forecasting. Often, economic processes in enterprise management have a complex
behavior that is similar to "chaotic". There are models of chaotic dynamics that are increasingly
used in the management of economic systems [10, 11].
The best solution for forecasting the values of time series levels is considered to be performed
on the basis of the generalized logistic mapping model, given by the following recurrent
relationship [2]:
𝛽 (6)
𝑦
𝑛+1 = 𝜑𝑦 𝛼 (𝑁 − 𝑦 ),
𝑛 𝑛
Where 𝜑, α and β are model parameters calculated by the method of least squares; N is the
maximum value of the levels of the series.
The mapping 𝑦𝑛+1 is one-dimensional nonlinear. The previous level of the series is used to
form the result. According to the conducted studies, even simple nonlinear models, for some
parameter values, have a chaotic behavior that seems random with a sufficient number of levels
of the series. However, in deterministic nonlinear models, such chaotic behavior is generated
precisely by nonlinearity [10, 11]. For our tasks, in practice, it is not possible to establish the
length of the series model 𝑦𝑛+1 , from which "chaotic behavior" will begin. Therefore, forecasting
is performed on the basis of the generalization of the results of calculations for several models of
the type 𝑦𝑛+1 , the parameters of each of which are calculated by the method of least squares for
fragments of time series of different lengths (and are significantly different). The construction of
the forecast, in our study the operative one, occurs recurrently.
Usage the time series method for forecasting sales in the CRM system:
Rationale: the time series method is very important for the analysis and forecasting of
temporal data such as sales volume in different periods. Taking into account temporal factors,
such as seasonality and trends, helps to obtain more accurate forecasts.
Application: the time series method can be used to analyze and forecast daily, weekly,
monthly, or quarterly sales. This method allows you to take into account seasonal fluctuations
and identify trends in sales changes.
Efficiency: the time series method is well suited to situations where time is an important
factor in changing sales volumes. It allows you to analyze the dependence between today's and
past sales, which is critical in CRM systems.
4. CRM-system sales forecast system description
We will describe the sequence of events in the process of forecasting sales in CRM using a UML
sequence diagram (Fig.3) [12-14]. In this case, we have three main objects: the CRM user, the CRM
system, and the forecasting process. A sequence diagram shows the sequence of messages and
interactions between them:
1. CRM user sends forecast request;
2. The CRM system receives this request and starts the process of downloading data for
forecasting;
3. After the download is complete, the CRM system sends a forecast request to the forecasting
process;
4. The forecasting process processes this request and starts the forecast generation process;
5. After completion of the forecast generation, the forecasting process sends a response with
the forecast to the CRM system;
The CRM system transmits this forecast to the CRM user.
Figure 3: CRM system sales forecast sequence diagram
This diagram allows you to visualize the sequence of events in the process of forecasting sales
in CRM and shows the interaction between objects.
To visualize the architectural structure of the software system, we will use the component
diagram. This diagram allows you to represent the system as interconnected components and
show how they interact with each other.
Let's look at the main elements that can be found in a UML component diagram [12-14]:
1. Component: the main element of the diagram, which represents a separate part of the
system. It can be a module, library, subsystem or other logical block of the program;
2. Interface: specifies the method of interaction between components. Interfaces show which
services or methods can be used by other components;
3. Dependencies: indicators of the relationship between components. Dependencies show
which component uses or refers to another component;
4. Conditional areas (frames): used for grouping components and specifying subsystems or
logical blocks in the system;
5. Private components: these are components that are hidden from others and interact only
through defined interfaces;
6. Recommendations (stereotypes): Additional marks that can be used to detail components
or show specific characteristics.
Figure 4: The structure model of the CRM system
A component diagram helps model the architecture of a software system and can be used for
analysis, documentation, and communication with the development team.
Microsoft Machine Learning Studio as one of the main tools for solving the problem of analysis
and forecasting in CRM systems has been chosen. Microsoft Machine Learning Studio (formerly
known as Azure Machine Learning Studio) is an integrated platform for developing and deploying
machine learning models [8]. It is developed by Microsoft and provides advanced tools for any
stage of the model lifecycle, including data preparation, model building, training, evaluation, and
deployment.
The development of a project to solve the problem of forecasting sales in CRM systems using
Azure Machine Learning included the following steps:
1. Data preparation: collection of necessary data for forecasting sales from the CRM system
and other sources. Checking and cleaning data from missing values, anomalies and extraneous
factors;
1. Creation of an Azure Machine Learning workspace: a workspace in the Azure Machine
Learning service, for project development and execution;
2. Creating an experiment: an experiment in Azure Machine Learning, which includes the
steps of data preparation, model selection, training and model evaluation;
3. Data preparation and visualization: Azure Machine Learning tools, such as Azure Data
Studio, for data preparation and visualization;
4. Model selection and training: machine learning libraries in Azure Machine Learning to
select and train sales forecasting models. Various models can be used, such as linear
regression, decision trees, random forest, etc. Setting model parameters and evaluating their
effectiveness using quality metrics;
5. Model validation and tuning: validation techniques, such as cross-validation, to assess
model accuracy and reliability. Adjusting model parameters to improve its results;
6. Model deployment: after training and validating the model, it was deployed in an Azure
Machine Learning environment using a prediction web service that can be integrated with a
CRM system;
7. Provision of a mechanism for updating the model with new data.
To obtain a forecast based on the developed forecasting model, functions (Azure Functions)
have been developed, the result of which can be obtained using an http request.
Figure 4: Functions for working with forecasts
For the possibility of integration with the CRM system, Vue.js components has been developed,
with the help of which you can start the training of the forecasting model and view the sales
forecast for a certain period.
As a result of the conducted research, a web component was developed that can be integrated
into the CRM system. This program allows you to forecast sales in customer relationship
management (CRM systems) and is a powerful tool for increasing the company's competitiveness
and improving cooperation with customers.
After training machine learning models, we need to deploy them in production so that others
can use them to make predictions. In Azure Machine Learning, we can do this with Endpoints and
Deployments. Endpoints and Deployments allow you to separate the workload interface from the
implementation that serves it.
To deploy machine learning models in Azure Machine Learning, you can use Web Service
deployment to access the model online. Here is a general overview of the process of deploying a
web service to Azure ML: Once deployed, the web service will be available at a specific URL. You
can use this URL to interact with the resulting machine learning model via HTTP requests.
Acknowledgements
In this work, a detailed analysis of the systems of interaction between consumers and the
company was carried out, the business processes of the CRM system were described using a
structural approach, various sales forecast models were developed in customer relationship
management systems (CRM) systems, and the best of them was chosen. Creation of system of
analysis and formation of sales forecasts in CRM systems allows you to forecast sales in customer
relationship management systems and is a powerful tool for increasing the company's
competitiveness and improving cooperation with customers.
Building models for analyzing and forecasting sales in the CRM system helps the company to
effectively manage relationships with customers, maximize profitability and improve the quality
of customer service.
The problem of forecasting sales as a regression problem have been defined. The main stages
that need to be performed to test the model of the sales analysis and forecast system in customer
relationship management systems in Azure Machine Learning are shown.
The developed system is effective and provides high accuracy, which allows enterprises to
rationally use their resources for future growth and monitor cash flows.
The innovativeness of the work consists in an automated approach to forecasting sales of CRM
systems, based on the use of large volumes of data and machine learning algorithms.
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