E-mail newsletter based on customer base analysis

Marketing channels and omni-channel marketing analytics. Segmentation of users based on contest status. Development of recommendations for marketing strategy in E-mail channel for real company based on CRM data analysis. Identification of the problem.

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23.09.2018
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The present research is intended to cover the major issues of marketing activities in general and in e-mailing in particular. The general purpose of this academic project is to develop the recommendations for the company in terms of improvement the marketing strategy in e-mail channel and increase profit. The current research will apply statistical analysis on the base of such instruments as SPSS and Excel by processing privet CRM data provided by company.

Despite the fact that online marketing is becoming more and more powerful by creating new ways to communicate with customer, e-mail as a marketing channels remains popular and useful instrument for companies. However, a great number of research concerning the topic of emailing suggest moving the strategy from common content for whole audience to personalized messages for certain segments. Current study proposes the improvements in current communication strategy of real company based on segmentation of audience and creation an individual content for every segment in terms of personal needs.

Academic project will include a detailed research of company's product, internal processes and marketing strategy in e-mail channel. Further analysis of evaluable CRM data allows to create hypothesis in terms of business specification which will be statistically checked. The statistical results accompanied with recommendations can be used to optimize the work in e-mail channel and improve current communication strategy.

Table of contents

Introduction

Chapter 1: Marketing data

1.1 Definition

1.2 Marketing Analytics

1.3 Types of data analytics

1.3.1 Descriptive analytics

1.3.2 Inquisitive analytics (Diagnostic)

1.3.3 Predictive analytics

1.3.4 Prescriptive analytics

1.3.5 Pre-emptive analytics

1.4 Regression and dispersion analysis

Chapter 2: Marketing channels and omni-channel marketing analytics

2.1 Marketing channel. Definition

2.2 Classification of marketing channels

2.3 Measuring online and offline channels

2.3.1 Metrics for offline channels

2.3.2 Metrics for online channels

2.3.3 Emailing

2.4 Omni-channel marketing

2.5 Customer relationship management

Chapter 3: Development of recommendations for marketing strategy in E-mail channel for real company based on CRM data analysis

3.1 Description of the company

3.1.1 Analysis of company's CRM system

3.1.2 Contest status

3.1.3 Aditional options

3.2 Identification of the problem

3.3 Results of Hypothesis testing and recommendations

3.3.1 Hypothesis 1

3.3.2 Hypothesis 2

3.3.3 Hypothesis 3

3.3.4 Segmentation of users based on contest status

Conclusion

References

Attachments

Introduction

Relevance

According to statistics, every day about 6000 advertising messages via different channels are addressed to the one resident of the big city. The consequence of this fact is the fatigue of customer from the amount of advertising information. Thus, mass advertising is slowly losing its power. Companies are facing problems wasting a high amount of marketing budget on ineffective marketing instruments (Maureen & Ye, 2011). At the same time, personalized advertising messages become an essential solution for marketers to attract customers' attention and establish a contact with them (Cheklov, 2015).

One of the approaches that works at solving the problem of personalized advertising messages for customer is omnichannel marketing. This approach is based on using online and offline channels to collect different types of data concerning customers which are highly important for creating an effective marketing campaign, increasing loyalty and improving customers experience (Bezotosnaja, 2015).

Omni-channel marketing is interconnected with the data driven approach which refers to the data processing and marketing analytics (Kumar et al., 2013). Using the main tools of marketing analytics - marketing metrics - company can estimate an efficiency of the current marketing activities (Barwise & Farley, 2003).

In the current research will be provided the example of the real case - online platform where designers and companies meet each other and distribute solutions in contest design; and covered the aspects and omni-channel marketing approach and e-mail marketing in particular, and marketing analytics.

Problem Statement

The problem of online platform distributing solutions in design for business can be formulated as the following one: they have data on past purchases and would like to know which factors influence the purchasing power of their customers in order to increase earnings.

Thus, the overall aim of this research is to identify which factors influence the purchasing power of customers and based on the results - to provide the suggestions for the online platform how to optimize the work with e-mail, which is the main marketing channel for communication with customers.

Objectives

The aim of the research can be achieved through the accomplishment of the following objectives:

Literature analysis concerning the following topics: omni-channel marketing, data-driven marketing, marketing analytics, marketing metrics, regression analysis, e-mail marketing, marketing statistics, CRM;

Analysis of the current state of the online platform, logics of internal processes and contest creation, conducted marketing activities in e-mail channel and marketing data;

Identification of the hypothesis on the basis of available marketing data;

Testing the hypothesis applying a theoretical base, statistical instruments and analytical skills;

Interpretation of the results and development of the suggestion concerning focus in email activities, e-mail segmentation and email content.

Research subject: factors which influence the total amount of earnings of online platform distributing design solutions for business.

Research object: marketing data on CRM on past purchases collected by online platform.

Methodology

The current research is mainly focused on the in-depth analysis of the literature devoted to the topic of marketing analytics. Therefore, the study consists of such theoretical methods as:

analysis of academic literature, articles, publications and electronic resources on the topic of: omni-channel marketing, data-driven marketing, marketing analytics, marketing metrics, regression analysis, e-mail marketing;

description of the current situation of the company, internal processes and collected marketing data;

synthesis and generalization of conducted information;

The number of research hypothesis developed in parallel with data analysis will be tested using the statistical instruments.

Professional significance

The current study has practical significance for online platform. The results hypothesis testing with using real data can provide a clear picture of how some contests' details influence the company's total amount of earning. This information can be used for the improvement of communication with clients via email channel and consequently can lead to the increasing customers' loyalty as well as profitability of business.

Structure

The current study is structured in the following way. The first chapter includes a theoretical basis covering the topic of marketing analytics in general and including different types of marketing analytics. The second chapter explains the concept of the omni-channel marketing in general and e-mailing in particular and provides an observation of existing marketing channels with instruments to measure them. The last part of current research explains the real case of the online platform distributing design solutions for business. The last chapter includes the observation of available data, formulation and following testing of the research hypotheses developed on the base of CRM and applying statistical instruments accompanied with theoretical background. Based on the results of hypothesis tested the recommendations for platform have been developed.

CHAPTER 1: MARKETING DATA

1.1 Definition

In order to establish the effective decision-making processes which will be focused on the constant improvement of the current state, for company it is becoming necessary to process a big volume of different sorts of data concerning customers in the effective way. Thus, for the future development of company in both long and short terms, a high level of importance has a method including the work with data and consequently interpretation of the results (Gandomi & Haider, 2015).

An importance to work with data generated a wide range of different tools and technologies which set certain frameworks for unlimited potential of data analysis. Data base can include different sorts of information which can be not interconnected to each other what makes it challenging to interpret it. However, a data analysis represents by itself a number of different tools which all aimed to identify the most essential parts of database (Al Nuaimi, Al Neyadi, Mohamed, & Al- Jaroodi, 2015).

According to the results of conducted research on the topic of data usage, in order to not only successfully compete on the market but also work on increasing a value and developing competitive advantages, a process of effective decision-making should be based on data. In terms of classification, different data analysis aimed to identify different intellects from the whole database (Labrinidis & Jagadish, 2012).

They are not comparable to each other in terms of practical significant because they focus on the different aspects and can complement each in order to use the whole potential of data. Thus, can be created a holistic view about the market and its players and be developed a number of actions to perform good in this particular environment (Davenport & Harris, 2007; Davenport & Dych ,2013)

Despite the fact that different types of analytics uses different methods and approaches, one aspect unifies all of them: the results of the analysis should be useful in terms of working outcome for a company (Davenport & Harris, 2007).

1.2 Marketing Analytics

The research concerning the marketing analytics is based on the concepts of Big data and customer behavior. The data analysis method was developed in 1930 by Hotteling who laid the foundation for the main component analysis and canonical analysis (Gabor, 2010).

Despite the fact that data emphasize a number of behavioral stimulus in context of customer's behavior, analytics has a high importance for marketers in order to identify the possible ties of dependence between different types of data which are hidden from the first view by using different marketing tools. Due to the fact that external business processes focusing on the communication with customers can include a huge amount of different types of date which company cannot afford, it is becoming an issue which information concerning the customers is important to analyze (Fayyad, Piatetsky-Shapiro, & Smyth, 1996).

1.3 Types of data analytics

1.3.1 Descriptive analytics

Descriptive analytics aimed to provide for the company an explanation of the current situation which interconnected with number of company's activity made in the past though using such approaches as data aggregation or data mining. This type of analysis is using to connect the past and the future and observe what has happened (Bihani & Patil, 2014).

According to the practical significant of data interpretation, descriptive analytics helps to describe a big volume of data collected in the past and identify the consequence of previous steps on the company's overall outcome. There are a wide range of number of these statistics which allow company to reach any operation purposes. Descriptive analytics is becoming essential for the company to emphasize such figures as total stock in inventory, average amount of money spent per customer and through years change in sales (Joseph & Johnson, 2013).

Descriptive analytics is conducting with the following number of aims:

To describe the different groups of customers, stakeholders and mediators belonging to the company

To identify a segmented structure of the market

To determine a perception of product characteristics

To determine the character of interconnections between different variables

To build a forecast concerning future outcome (Gerasimenko, 2010)

Real example of this type of analytics can be company's reports including the historical data concerning the company's production and operations, financial statements, sales and customers. Moreover, as an example can be also considered a company's dashboard, KPI - platform and Management information system (MIS) (Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V, 2017).

1.3.2 Inquisitive analytics (Diagnostic)

Whereas descriptive analytics is used for data description and exploration in terms of current state of the company, inquisitive analytics aimed to provide the explanatory reasons of it by answering on the more detailed questions such as what, why, how and what if (Bihani & Patil, 2014).

Inquisitive analytics not only estimates the main points under the control but provides deeper analysis of data in order to identify new trends and patterns. In the majority of real cases this type of analytics is used to test the hypotheses or to accept or reject the business prepositions which were identified conducting descriptive analytics (Stimmel, 2014). Thus, inquisitive analytics coupled with a descriptive one is becoming more difficult in terms of providing an important information.

Hwaiyu Geng in his book Internet of Things and Data Analytics Handbook mentioned the following example: Marketers can sometimes interpret correlation based on the business logic and take marketing actions accordingly. However, correlation cannot conclusively prove causality by machine learning algorithms, as the correlation is only true based on the limited learning data set used (p.332).

As an example of inquisitive analytics can be considered the following statistical models: analytical drill downs and drill up into data which relate to the data discovery instrument, statistical analysis, factor analysis etc.

1.3.3 Predictive analytics

Predictive analytics aimed to provide a forecast concerning the opportunities for company which can become open in the future, estimation of the future outcome in the different business processes trends as well as negative perspectives and possible risks and challenges (Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V, 2017).

Nowadays a practical significance of using predictive analytics for a company cannot be underestimated taking into consideration a highly competitive environment on the majority of markets. This analytics allows company to work on the operations constantly and increase competitiveness through targeting customers more efficiently (Finlay, 2014).

Regression models are the basis of predictive analytics. In order to conduct it can be used such instruments as data mining referring to the identification of interconnections within data base through statistical analysis, machine learning algorithms and modelling. There three advantages of these type of analytics which has high importance for the company (Appelbaum, Kogan, Vasarhelyi & Yan, 2017).

Three advantages of predictive analytics:

Vision referring to the opportunity to have a look at patterns which are hidden from the first view on data. In terms of marketing activities, it opens opportunity to differentiate the customers in terms of their purchasing power, make a forecast about their needs and activities for a future.

Decisions that provide predictive analytics are built on usage of mathematical bias what exclude the issue of human factor such as emotions for example.

Precision referring to the fact that predictive analytics uses automated tools what significantly decrease a possibility of making errors working with data.

Nevertheless, predictive analytics has a strong interconnection with the previous type - descriptive one, despite the fact that they are focusing on the different time frames. For company in order to build a trustful forecast for a future, to perform in terms of process optimization and improve decision-making process is becoming very important to be aware of all results and steps taken in the past. Predictive analytics can also be considered as a certain continuation of the descriptive one due to the fact that it can take into the account the same number of issues already solved in the past and analyze them in terms of future perspectives well as provide possible recommendation for the company learning on its past experience (Stimmel, 2014).

1.3.4 Prescriptive analytics

Prescriptive analytics aimed to prescribe a set of actions which company can take in the future and possible consequences and outcomes during the process of mentoring these actions. This type of analytics is about providing advices for a company concerning future steps (Bihani & Patil, 2014).

In general, prescriptive analytics works with company's data in order to estimate the effect of possible future activities and its effect on overall performance in advance, before taking any actions. Ideally, it can also ensure some suggestions in terms of company's future steps which can help to increase overall outcome from predictions (Joseph & Johnson, 2013).

The distinctive feature of this analytics refers to the fact that its instruments do not work with standard data such as historical or transactional data ones. Prescriptive analytics uses a combination of techniques and tools such as business rules, algorithms, machine learning and computational modelling procedures (Geng, 2016).

One of the analytical instruments that can be implemented in the prescriptive analysis is an optimization modeling the main idea of which is searching for the most positive action scenarios for the company or action planning taking into consideration an influence of different factors and possible limitations. Another example of prescriptive analytics' instrument is a simulation of action scenarios which are possible for company in the future. Their aim is to identify the possible consequences of different variants of actions taking into account a great number of both external and internal factors. Comparing with two previous types of analytics - descriptive and predictive ones described before, prescriptive analytics goes feather and provides to the company the multiple ways how to act in the future. However, despite the fact that this type of analytics can bring extremally high value to the company, it difficult to conduct this analytics because of its complexity (Stimmel, 2014).

1.3.5 Pre-emptive analytics

Pre-emptive analytics is not wildly implemented comparing with previous types. An advantage of using pre-emptive analytics for company is a capacity to take certain actions in order to avoid cases which can negatively affect the company's performance. As an example, can be mentioned an identification of possible challenges and dangers and recommendations to mitigate them in advance. (Szongott, Henne, & von Voigt, 2012).

1.4 Regression and dispersion analysis

Regression analysis is a statistical method of data analysis which allows researchers to determine the interdependence between different variables. It is a technic of predictive analytics. According to the academic literature there are two different types of regression analysis: linear regression aimed to test the dependence of two variables and multiple one which determine the influence of independent variables on one dependent (Montgomery, Peck & Vining, 2015).

Regression analysis is used in the situations when it becomes necessary for researcher to determine whether any connections between different variables in the collected data, identify a tightness in the relationships between dependent and independent variables or to make a forecast concerning the meaning of dependent variable with an extent to what independent variables influence dependent one. However, regression analysis by itself represents a selection and consequently solution of mathematical equations which describes the investigated dependences. Due to the fact that the different aspects of market depend on different factors, regression analysis starts from the creation of graph showing this dependence. Relying on its basis can be selected a mathematical equation and then through solving the system of normal equations can be determined a set of suitable parameters (Montgomery, Peck & Vining, 2015).

In terms of marketing, multiple regression becomes very useful tool for researchers because it can help in drawing parallels between the product demand and some independent variables such as prices, number of competitors representing the market and mediators. In the same way can be analyzed how a market share depends on conducted PR activities, advertising and the budget of promotion campaign. However, company can also analyze the dependence of demand on goods and services from price policy conducted by competitors (Malhotra, Baalbaki & Bechwati, 2013). Coming back to the marketing, the main advantage of the regression model is the fact that it serves as a predictive tool which company can use in order to bring some possible changes in the operation processes to increase performance in the future. Thus, regression analysis is a predictor of future state for the company. In some literature, independent variables also mentioned as predictors which can affect some changes in the future and influence the dynamic of development. In order to conduct the regression analysis can be used different tools such as Excel, SPSS, STATISTICA and others (Armstrong, Green & Graefe, 2015).

The main goal of dispersion analysis is to research a significance of difference between average values of different groups in the data base. A common dispersion of research characteristics becomes divides into separate components influenced by certain factors. Afterword the hypothesis about influence of these factors on the research characteristics need to be checked.

Comparing dispersion's components with each other by using Fisher Criterion can be identified a part of total characteristic variability resulted from the influence of regulated factors (Diez, Barr, C. D., & Cetinkaya-Rundel).

CHAPTER 2: MARKETING CHANNELS AND OMNI-CHANNEL MARKETING ANALYTICS

2.1 Marketing channel. Definition

A term Marketing channels refers to the channels through which consumers access product, experience, purchase, and post-purchase information (Bradlowa, Gangwar, Kopalle & Voleti 2017, p.81).

Marketing channel acts as a link between companies and customers. Through using these channels company can inform customers about its products and services and influence their decisions concerning purchasing. According to the AIDA model aimed to describe customers' behavior, there are steps which customer attending on the way to finally by goods. These steps including phases of attention, interest, desire, action. In case of the model marketers can determine which channels can be used to make customers move from one stage of AIDA model to the following ones. Thus, it becomes very important for company to identify which channels should be implemented in the dialogue with customers (Hanlon, 2013).

2.2 Classification of marketing channels

Every channel collects a certain set of date concerning customers which company can consequently use in terms of its own purposes. Thus, channels' data allow company to better understand customer journey and determine which channels are the most effective for attracting customers' attention. According to the theory which Mares and Weinberg provided in their book Traction: A Startup Guide to Getting Customers marketing channels can be divided into two parts: one is covering online sphere, and another - offline (Mares & Weinberg, 2014).

Table 1

Types of marketing channels

Offline marketing channels

Online marketing channels

Direct sales

Search Engine Optimization (SEO)

Event marketing

Search Engine Marketing (SEM)

Offline ads (TV, radio, newspaper outdoor ads)

Search Engine Advertising (SEA)

Public Relations/PR

E-mail marketing

Guerrilla marketing

Content marketing

Affiliate programs

Existing online platforms

Trade Shows

Targeting blogs

Speaking Engagements

Social media marketing

Unconventional PR

Nevertheless, the number of channels which company want to choose for communication with its customers depends on the characteristics of business sphere in which company operates. Also, choosing marketing channels it is very important to understand the target audience and weather chosen number of channels will reach it. Otherwise, company could not build an effective communication with its customers wasting the marketing budget on non-working instruments. However, online and offline channels have both advantages and disadvantages. That is why some authors emphasize an importance of using different channels from both spheres. Due to the fact that digital became an integral part of human daily activity, marketing campaign should cover traditional and online channels (Bolotov, 2015).

2.3 Measuring online and offline channels

In the different spheres, there is a common practice to use additional instruments with aim to achieve a good performance. However, an implementation of using marketing metrics becomes a basis of productivity measurement and management system. Marketing metric is determined as measuring system which quantifies the trend, dynamics or other characteristics (Harvey & Green, 1993).

2.3.1 Metrics for offline channels

Mark Jeffery in his book Data-Driven Marketing: The 15 Metrics Everyone in Marketing Should Know emphasizes the importance for companies to use marketing metrics in assessing the marketing performance and efficiency of distribution the marketing budgets through channels. Due to the fact that most of the companies use both online and offline channels, he mentions two categories of marketing metrics: traditional metrics and new age marketing metrics (Jeffery 2010, p.27). The first category refers to the traditional offline marketing metrics and includes non-financial and financial ones. The first group is represented by brand awareness, test drive, churn, customer satisfaction, take rate and refer to the measurement marketing campaign outcome. The group of financial metrics incudes profitability, net present value (NPV), internal rate of return (IRR), payback and customer lifetime value (CLTV) (Jeffery 2010, p.25).

2.3.2 Metrics for online channels

Online marketing includes different groups of metrics covering different aspects of measuring the online activities. The first type - coverage metrics identify the customers' activity and determine the efficiency of marketing campaign in quantitative terms of customers. However, the reasons of customers' behavior are not taken into the account. The second type - sales efficiency metrics emphasize the efficiency of every single marketing channel. The third type- social media metrics refers to the information exchange about the company or its products in the social media. Finally, e-mail metrics should be taken into consideration if company uses this channel as source for communication with customers (Jahneeva & Podoljak, 2009).

Mark Jeffery emphasize a high importance of using a set of online metrics in context of new marketing realities where online channel becomes very powerful. In his book, a category of new age marketing metrics is connected to sphere of online activity and represented by the following metrics: Cost per click (CPC), transaction conversion rate (TCR), Return on ad dollars spent (ROA), Bounce rate, Word of mouth (WOM) (Jeffery 2010, p.26).

2.3.3 Emailing

Despite the fact that online marketing is very dynamic sphere and marketers creates more and more new digital channels to reach the customer, e-mail marketing remains one of the most powerful marketing one. According to statistics, e-mail marketing has the highest ROI among all channels and 18% of respondents proved the fact that this marketing channel pays off effectively while social networks took the second place with 17%. Moreover, in 2017 approximately 58% of marketers decided to increase the budget for e-mail marketing and 7% only decided to reduce it (Pfanshtil', 2017).

Due to the fact that the current research is based on the case of real company which uses e-mail as the main channel to communicate with customers, to cover the aspect of marketing metrics for e-mail become very important for the further research.

According to the classification of marketing metrics provided by Jahneeva and Podoljak (2009), in order to measure e-mail channel, company need to use a following set of marketing metrics:

Sending and returning e-mails

Company should focus on the decreasing a number of returning e-mails.

Delivery rate of e-mails

This metrics shows the difference between the number of sending and returned e-mails. Delivery rate also takes into account the issue of spam filters.

E-mails' open rate

This metrics estimate the number of open e-mails. The high rate can indicate that the target was properly chosen and an email has useful and interesting context.

Click through Rate (CTR)

This metrics show the number of people who went to the web-site of the company through the link included in e-mail.

Unsubscribe Rate

Number of people who unsubscribed from getting new e-mail. This metrics shows the extent to which the audience appreciates the context of email.

Conversion

This metrics is calculated as percentage of visitors who made the target action.

Number of new followers

By means of different tools of web analytics it becomes possible to assess the number of new followers joining web-site taking into consideration certain period of time.

Return on investment (ROI)

This metrics shows the efficiency of investment in e-mail channel and means the ratio of the received profit to the cost on the activates provided to attract customers' attention via email.

An implementation of these metrics allows company to measure marketing activities via e-mail channel.

According to the recent research, personalized approach in email channel is more effective in comparison with mass marketing having better conversion, 29% higher Open Rate and 41% higher CTR. As a result, companies using personalized marketing approach increases their profit by 20%. Moreover, personalized approach lead to creation emotional links between brand and customers what lead to increasing brand awareness and loyalty due to the fact that personalized emails have an analytical and statistical research behind and focus on targeting content which clients are interested in (Gelatto, 2018).

2.4 Omni-channel marketing

Omni-channel approach in marketing refers to using of all offline and online communicative channels which in turn aimed to collect all customers data. In this case, the picture of customer journey could be fully completed for the company. The overall aim of omni-channel marketing is to create a flourished infrastructure for interaction with customer and make it real for him to make a purchase through all available ways. Thus, the term omni-channel can be identified as an individual approach to every customer which lead to increasing customers' loyalty toward the company as well as number of sales and efficiency of business processes (Bezotosnaja, 2015, p.16).

Analytics of omni-channel marketing works with big volumes of data collected through different channels stored on CRM. However, there are some challenges during the implementation of omni-channel marketing campaign and most of them relate to the issues of information processing, usage of communication channels and subsequent work with customers' data (Pashhenko, 2016).

2.5 Customer relationship management

marketing channel company segmentation

The term Customer relationship management (CRM) refers to the marketing optimization based on statistical data which company collects from the communications with customers via different marketing channels. This data has a high value for the company due to the fact that it allows to identify any possible problems in the current state and build forecasts for the future (Chernov 2008, p.83).

From the strategic perspectives CRM is considered as the process that identifies customers, creates customer knowledge, builds customer relationships, and shapes customers' perceptions of the firm and its products/ solutions (The Sales Educators 2006, p. 93).

According to the classification provided by Davey Chaffey etc. there are three different types of data collected by CRM including: profile data of customer, transaction data concerning goods and services were bought and communications data including customers' response on the conducted marketing campaign (Chaffey, Ellis-Chadwick, Mayer & Johnston, 2009).

Based on the recent academic research in sphere of Customer Relationship Management, Richards and Jones identified the main benefits of CRM (Richards & Jones, 2008, p.123).

improved ability to target profitable customers;

integrated offerings across channels;

improved sales force efficiency and effectiveness;

individualized marketing messages;

customized products and services;

improved customer service efficiency and effectiveness; and

improved pricing.

Thus, an implementation of CRM system becomes essential for following omni-channel marketing approach.

CHAPTER 3. DEVELOPMENT OF RECOMMENDATIONS FOR MARKETING STRATEGY IN E-MAIL CHANNEL FOR REAL COMPANY BASED ON CRM DATA ANALYSIS

3.1 Description of the company

A company taking part in the current research is online platform which aimed to connect clients who are interested in new items from different design categories and experienced designers who can bring these ideas into reality. Online platform has several categories connected with the type of design, namely: corporate identity, web design, illustrations and contextual design, product design, advertising materials, clothing and apparel design, application design and graphic design.

Since user joins web-platform, his/her customer journey can be described in the following way. The first users' step on the web platform is launching a new contest choosing type of design which they are interested in. At this stage, company establishes statement of work describing certain characteristics of design which they would like to get in the end. Then user can choose a package the most appropriate for him in terms of price, additional contest options and identify the amount of reward for designer. Finally, before launch user inserts payment details and completes personnel information.

At the second stage, it becomes possible for customers to see different variants of design described on before in the statement of work and evaluate them by leaving some comments or feedbacks and remove variants which did not meet expectations. Right after the satisfied design will be found, customer can finish and close the contest. The third step refers to the project completeness formalities. Finally, customers get the source files and designers sign a design's copyrights transfer document. Once a copyrights transfer document is signed - company becomes an owner of new design and designer gets his/her reward for completed work.

3.1.1 Analysis of company's CRM system

Online platform uses a CRM system to collect information about its customers including the following data:

1. Date of launching a competition

2. Title of contest

3. Contest status

Expired

Draft

Closed

Open

Finalization

Refunded

Abandon

4. Type of contest

o Corporate identity

logo design

business card design

business stationery design

letterhead design

logo and business card design

o Web design

website design

mobile website design

theme design

landing page design

fans page design

o Illustrations and contextual design

icon design

illustration design

character design

infographic design

PowerPoint design

o Product design

packaging design

label design

ticket design

book cover design

magazine cover design

cd cover design

other product design

o Advertising materials

poster design

flyer design

billboard design

car wrap design

banner design

trade show swag design

o Clothing and apparel design

t-shirt design

team clothing design

other clothing design

o Application design

mobile app design

application icon design

desktop software design

o Other design

graphic design

5. System of payment

o Pay Pal

o Credit Card

6. User ID

7. Full name

8. Country

9. Prize Deposit

10. Platform's fee

11. Package

12. Additional options

Extend

Bold

Highlight

Featured

Hidden

Promoted

Other

3.2 Total Amount

3.2.1 Contest status

In CRM system every contest is assigned with contest status which shows the progress on the platform. Such statuses as Draft, Open, Finalization and Expired are temporary and changing over time according to the contest stage.

Status Draft means that the contest is on the pre- launching stage, user completes an online form putting there all recommendations for designers concerning style, color, features, size and etc. Then contest becomes Open and visible on the platform. At this stage designers can start working on statement of work completed by client and applied their works. At the Finalization stage contest is closed for new designers. A client makes a decision about the work which meet his/her expectations and winner gets a prize. After this stage contest becomes Closed.

However, there are situations when client decides to prolong a contest for some reasons - in this case contest becomes Extend. This is an additional option and to take it clients need to pay. In some cases, when client is highly unsatisfied with results applied by designers, platform can offers extending for free but, according to feedback of manager of platform - it is an extreme situation. Nevertheless Refunded status is taking place when client and platform cannot find a compromise and client gets his money back. In contrast, some clients do not finalize their contest leaving them opened and do not choose a winner. In this case a contest gets status Abandon.

3.2.2 Additional options

Additional options extend the offer and brings extra value for platform. Promoted contest is announced in platform's SMM channels (Twitter, Facebook, Google + and LinkedIn) with coverage in 250 000 users and engaging the most talented designers. Featured contest is published on both platform's home page and on the top of announced contests page. According to statistics, this option increases the number of designer joining a contest up to 40%. Highlighted and Bold contests also attract designers' attention and offer 20% more participants. Hidden contests make it impossible to see which designers joined the contest. Other options can include additional prize for designers taking 2 and 3 places or some individual options asked by client which can be realized technically.

Optionally user can work with only one particular designer. It happens due to the different facts, for example, company has already worked with this designer and was fully satisfied with the results or he/she is interested in the particular style or the manner which the designer is working in. This type of contest is called one on one. In this case platform acts as a guarantor that both sides - designer and customer - will be satisfied in the end.

By the term Package means the amount of reward which customer pays for launching a contest. However, platform also provides some supplemented payments for additional options which customer can use in terms of his/her personal preferences concerning contest. The final cost of launching a contest is composed of several factors, namely prize amount (which user choose by himself), additional options which add features to the contest and contest duration. Commission is calculated depending on choosing the contest type and includes two positions: fixed fee according to contest type and 14.9% taken from the prize amount. All these payments including package and taken supplemented options complete the total amount.

3.3 Identification of the problem

Due to the fact that the main channel which company uses to stay in contact with its customers is e-mail, it is becoming important to optimize the work of this channel in order to increase company's profit.

According to the information provided by manager, the platform uses mass emailing approach. Emailing strategy includes:

Welcome email with log in details and guide how to use a platform for new users;

Transaction e-mails with information concerning launched contests;

Regular emails with useful content. They can include entertainment materials about branding and design, short learning materials how to complete the statement of work and get desirable results in the end, how to choose the best design among several options etc.;

News about updates on the platform;

Promo E-mails before national holidays or famous events with exclusive offers;

Targeted email for those users who already launched contest offer a discount on the supplement design (one -time activity).

Based on the provided information were identified that the platform uses one communication approach treating all their clients. The platform follows a mass strategy in e-mail channel, sharing the information more or less relevant for every client while personalized e-mails are more effective in terms of marketing activity what also can be approved by marketing metrics. In the current research was mad a short test of mailing strategy for new users.

First of all, learning the processes on platform was identified that the button for signing up for getting messages is invisible for users and was put at the bottom of web-page. To reach it user need to scroll down the whole web-site. Another way to become subscribed is to create profile on the platform. Once user signed up, he/she gets an email to approve a subscribing process and after that user gets a first invitation letter. As was identified in the current research, platform has an issue with online resource of mass-mailing due to the fact that an email with an invitation to subscribe a new user went to spam folder. It means that for platform there is a threat of losing new subscribers as a conformation message between signing up process and first email can get into to spam foulder.

A first email proves the fact that platform uses mass-emailing approach as the first letter is not personalized (Attachment 3). First of all, the first letter has non-personal call due to the phrase - Every subscriber (by default) has a valuable advantage - he is the first to receive news about special discounts and proposals from. According to statistics, users are more willing to react on personalized e-mails, even if they contain very basic information. Putting the personnel name in and using person- to-person call improve the efficiency of e-mail channel (Alsmadi & Alhami).

As was identified during the research, the key issue for company nowadays is to identify the target to become focused on and the content of message for different target audience. Targeted e-mails can attract customers' attention and make them back on the platform with its subsequent usage. In return, for platform it can lead to increasing a total amount of earnings.

The overall question which stayed behind this research is the following one: How online platform can increase its total amount of earning and customer service and which factors influence it using its e-mail channel?. Thus, all four hypnotizes relates to the overall research questions. After checking the hypothesis can be identified a set of variables which influence the total amount. This knowledge can be implemented in daily operation activities which online platform conducts via e-mail channel.

In terms of current research were formulated hypothesis which will be tested by implementation of different statistical and analytical tools that allows to identify the relationship between variables and make a conclusion on the base of conclusive results.

Hypothesis 1: Purchase of additional options for contests influence the probability of following purchase.

Variables for regression analysis:

Dependent variable: Number of launched contests.

Independent variable: Sum of additional options.

Hypothesis 2: The longer the interval between registration and launch of the first contest - the less probability that customer will launch a new one.

Variables for analysis

Dependent variable: number of contest launched by user.

Independent variable: length of the period between registration date and first contest date.

Hypothesis 3: Purchasing of additional options directly depends on the type of contest. Certain types of contests contribute to the fact that the customer takes more particular additional options or additional options in general.

Variables for regression analysis:

Dependent variable: additional options

Independent variable: type of contest

3.4 Results of Hypothesis testing and recommendations

Ones company provided data from their CRM system, work on testing had been started. For testing the hypothesis had been using two main data bases - the first one consolidated the main data concerning clients `information (Attachments 1), and the second one which includes specific details of every launched contest on the platform (Attachments 2). In order to check Hypothesis, platform provided private CRM data. As an analytical and statistical instrument to work on Hypothesis were chosen as Excel 2018 and SPSS16.0.1.

3.4.1 Hypothesis 1

For checking the Hypotesis 1, some transformations of the original table which includes specific details of every launched contest on the platform (Attachments 2) have been done. First of all, on the base of provided information, pivot table has been built - Table 3.

New table includes following information:

Exclusive User ID of every customer;

Number of launched contest by every User; has been calculated through the function COUNT applied in pivot table;

Sum of every particular taken additional option, namely Bold, Highlight, Featured, Hidden, Promoted and Other; calculated through the function SUM applied in pivot table;

Table 3

Part of the Pivot table with Additional Contest options

User Id

Contest Number

Sum Promoted

Sum Hidden

Sum Featured

Sum Extend

Sum Bold

Sum Highlight

Sum Total

7180

3

0

0

0

57

0

0

57

12121

4

79

39

0

0

0

0

118

14074

1

0

0

0

0

0

0

0

15107

2

0

0

0

0

0

36

36

15602

2

0

0

49

0

0

0

49

15809

1

0

0

0

0

0

0

0

16087

1

0

0

0

19

0

0

19

16569

1

0

0

0

0

0

0

0

18043

1

0

0

0

0

0

0

0

19906

1

0

0

0

0

0

0

0

20835

3

0

0

0

19

0

0

19

24663

3

79

0

0

38

0

0

117

27603

8

0

0

0

0

0

0

0

28064

1

0

0

0

0

0

0

0

28139

1

0

0

0

0

0

0

0

29120

2

0

0

0

19

0

0

19

31624

2

0

0

0

0

0

0

0

The second step was to build a multiple regression model based on the information provided in the Table 3. The main aim of building this model is to identify the influence of additional options which customer can choose on the number of contests. In this case, Contest Number (number of contest has been launched by users) becomes dependent variables and Sum Promoted, Sum Hidden, Sum Featured, Sum Extend, Sum Bold and Sum Highlight are independent ones.

According to the statistics provided in multiple regression model (Table 4), the following conclusion can be done.

Table 4

Multiple Regression Model for Hypothesis 1

Regression statistics

Multiple R

0,421252167

R-square

0,177453388

Adjusted R-Square

0,175213134

Standart Error

1,532599125

Observation

2210

ANOVA

df

SS

MS

F

Sugnificance F

Regression

6

1116,337263

186,0562105

79,21127876

6,84308E-90

Residual

2203

5174,538755

2,348860079

Total

2209

6290,876018

Coef.

Standard Error

t-Stat

P-Value

Lover 95%

Upper 95%

Lower 95,0%

Upper 95,0%

Intercept

1,4150

0,0380

37,2496

0,0000

1,3405

1,4895

1,3405

1,4895

SUM Promoted

0,0086

0,0014

6,1701

0,0000

0,0059

0,0113

0,0059

0,0113

SUM Hidden

0,0215

0,0016

13,1772

0,0000

0,0183

0,0247

0,0183

0,0247

SUM Featured

0,0073

0,0015

5,0223

0,0000

0,0045

0,0102

0,0045

0,0102

SUM Extend

0,0089

0,0009

9,6254

0,0000

0,0071

0,0107

0,0071

0,0107

SUM Bold

0,0008

0,0043

0,1760

0,8603

-0,0077

0,0092

-0,0077

0,0092

SUM Highlight

0,0064

0,0036

1,7652

0,0777

-0,0007

0,0134

-0,0007

0,0134

Multiple regression analysis shows that P-value of Sum Bold and Sum Highlight variables can be removed as the value is higher than 5 % significance level (95 per cent confidence). After removing of nonsignificant variable regression analysis was repeated. The results are represented in the Table 5.

Table 5

New Multiple Regression Model for Hypothesis 1

Regression statistics


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