The Use of Selected Automated Tools for Creating PPC Advertising in Chosen Markets in the Czech Republic and Slovakia

Abstract

Online paid advertising is a dynamic form of online marketing that requires precision and a quick response to market changes. Automated tools are used to greatly simplify the process of creating and managing Pay-Per-Click (PPC) campaigns. The purpose of this research is to evaluate the impact of implementing an automated PPC management tool (Dotidot) on campaign performance in a travel agency compared with standard Google Ads campaigns. A structured multi-criteria procedure is first applied to select the most suitable tool for the Czech and Slovak markets. The core contribution of the paper is the observational case study of Dotidot and its performance comparison. The subject of the research is a comparative analysis of three PPC automated tools: Conviu, Dotidot, and BlueWinston. The significance of this topic lies in highlighting the relatively new possibilities of digital marketing and choosing the right tool for using automation. This research can also stimulate other researchers in this field and expand knowledge. The analysis is carried out using the method of systematic comparison based on established criteria, the result of which is a recommendation of the preferred tool and a brief discussion of its implementation options. The analyzed tools are used mainly on the Czech and Slovak markets and are oriented towards e-commerce (electronic commerce). In addition to e-commerce, a case study of the use of digital promotion in sports organizations is also presented. The research results in a systematic comparison of Conviu, Dotidot, and BlueWinston tools according to predefined criteria. The research also includes a brief discussion on the possibilities of implementing the recommended tool in practice, as well as an assessment of its benefits and limitations.

1. Introduction

Online marketing is a comprehensive set of digital strategies and tools that enable businesses to effectively reach customers through digital technologies. It continues to evolve at a rapid pace. The development of digital marketing and the growing complexity of online campaigns create the need for automated solutions that can effectively manage and optimize PPC advertising. Online marketing has several application options such as SEO (search engine optimization), content marketing, social media marketing, email marketing, and PPC marketing [1].

The main task of SEO is to modify websites so that they are easier to find in organic search engine results, for example, through Google or Bing. The goal of SEO is to ensure that websites are positioned at the top of the organic (unpaid) search results for keywords that will result in the most conversions. According to a case study, SEO optimization can bring 436% more traffic to a website [2].

Content marketing includes digital platforms and printed corporate media [3]. Content marketing is the strategy of producing and disseminating pertinent, valuable material to attract, gain, and connect to a well-defined and recognized target audience to generate consumer profit and foster customer engagement [4]. The revenue from content marketing reached a record value in 2022 in the US: USD 66 billion. Statista predicts that industry-wide content market revenues will double by the end of 2026 [5]. A systematic review of 92 scientific studies shows that content marketing has become a key tool in digital marketing, with most empirical research confirming its positive impact on customer engagement, brand metrics, and company performance [6]. Quy and Sun [7] argue that digital content marketing has become a key tool for building long-term relationships with customers and recommend that businesses invest in consistent, educational, and locally relevant content.

Social media marketing is a place where users spend a lot of their free time, and companies can communicate with these customers much more easily and sell their products or services to them. Content in social media marketing can be easily promoted, distributed among a large number of people, and easily shared, which means it can go viral very quickly. According to a study, using social media can bring 300% more interaction to campaigns. Social media interaction brings obvious benefits to the companies or e-shops (electronic shops) [8]. Swathi and Souza [9] argue that social media marketing provides a cheaper, faster, and more accurate way to reach target groups than traditional media. Importance is also attributed to authentic content that incorporates humor, emotion, and audience engagement. Phan [10] points out that the impact of social media marketing builds greater trust and credibility, and thus the likelihood of a positive impact—that the customer will purchase—is greater. Another study confirmed the hypothesis that social media marketing positively influences a customer’s tendency to repurchase a product [11]. Within social media marketing, the concept of social media marketing dimensions is often used, which includes several key elements. Entertainment represents how entertaining the content is, interaction expresses the ability of the user to communicate with the brand, trendiness denotes current “in” content reflecting current trends, customization means the personalization of content according to the needs and preferences of the user, and advertising captures the perceived advertising component of communication. Empirical findings also show that the “trendiness” dimension has the strongest correlation with the probability of purchase intention (purchase likelihood), approximately at the level of r ≈ 0.31, which points to the importance of current and trendy content within social media marketing [12].

Email marketing is also one of the most important digital marketing tools nowadays. Email marketing is effective in terms of ROI (return on investment). Studies prove the importance of email in for-profit marketing: 76% of Swiss and Austrian online retailers consider email marketing to be at least “somewhat relevant” as a marketing tool for their online stores, and for 83% of Swiss companies, email marketing is a “rather relevant” or “relevant” marketing technology [13]. Kumar [14] empirically analyzes newsletter design elements (subject line, main image, text, other communication elements) and their impact on three types of response—open, click, and reopen and subsequent purchase. The study shows that while all three metrics are related to purchase, click has the strongest impact on purchase. A study by Yoganada [15] on email marketing in Indian e-commerce (66 respondents) examined which specific factors most influence whether a recipient opens an email at all. The results show that 37.88% of respondents consider the subject line to be decisive, confirming that the subject of the email is the most critical element in initially gaining attention. Another 27.27% cited the sender’s name as the main reason for opening, reflecting the importance of trust and brand recognition. Approximately 10.61% respond mainly to preview text, i.e., the first few lines of content displayed before opening. The study also analyzed the impact of content on recipients. Approximately 59.09% of participants responded positively to the statement that exclusive offers in emails increase their enjoyment of receiving these messages, indicating that such offers can actually increase engagement. Finally, 30.31% of respondents said that they would subscribe to the newsletter precisely because of exclusive offers, while 31.82% are rather negative. Aji [16] found that 70% of respondents (in Generation Z) consider email newsletters to be a factor that increases their interest in reading news, while 30% do not experience this effect.

Pay-Per-Click (PPC) advertising systems are used to create and optimize PPC ads. These systems can display ads in the search network (paid ads in search engines) as well as in the content network (paid or banner ads on related or third-party websites). PPC advertising has become one of the key instruments for driving measurable traffic and sales, as it allows advertisers to target specific audiences and pay only for actual clicks on their ads [17]. According to the study, this form of online advertising is considered effective, accounting for up to 41% of total global ad spending in 2020 [18].

As PPC ecosystems grow more complex, advertisers increasingly manage hundreds or thousands of campaigns and ad groups across multiple product categories, markets, and devices. Manually setting up, monitoring, and optimizing such campaigns is time-consuming and prone to error. These challenges are particularly relevant for e-commerce businesses and travel agencies in the Czech and Slovak markets [19].

Davenport [20] highlighted the importance of marketing automation alongside AI and their findings highlight the competitive advantages, but require that executives have the expertise and skills to work with these advancements. Automation and forecasting in marketing are a significant achievement, which, according to the study, brings an increase in efficiency in operational processes by 20–30% and an improvement in ROI of 75% [21]. Kevasan [22] reports that using Salesforce Engine’s AI (artificial intelligence)-driven automation in marketing has increased user satisfaction by more than 30% and reduced manual task completion time by about 25%. These arguments are one of the few reasons why automation in marketing is a topic discussed today.

According to the survey, the most important metrics for marketing automation are Conversion Rate (58%) and Revenue Generated (58%). These metrics are essential when selling goods and services to customers through PPC marketing, and automation plays a significant role [23].

Dalsanya [24] mentioned in his scientific article that RPA (robotic process automation) is a revolutionary technology from the perspective of digital marketing as it helps automate many tedious tasks and increases efficiency. RPA has significantly facilitated the daily functioning of several digital campaigns by automating important procedures. With the help of RPA, tools can create and launch marketing campaigns that are fully automated and can target selected segments.

The primary objective of this paper is to empirically evaluate the performance of an automated PPC management tool (Dotidot) implemented in a travel agency, compared with standard manually managed Google Ads campaigns. As a preparatory step, a structured multi-criteria procedure is used to select the most appropriate tool for the Czech and Slovak markets. The main contribution of the paper lies in the observational case study of Dotidot and its performance evaluation.

2. Materials and Methods

This section presents the methodological procedures employed in the research aimed at identifying the most appropriate PPC tool for online advertising automation. To contextualize the methodological design and ensure its relevance to regional market conditions, the section initially outlines a theoretical framework of digital marketing applicable to the Czech and Slovak markets. Within the domain of multi-criteria decision-making, a wide range of analytical approaches can be applied to problems of this nature. Broniewicz and Ogrodnik [25] classify such methods as AHP (Analytic Hierarchy Process), TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution), DEMATEL (Decision-Making Trial and Evaluation Laboratory), and VIKOR (VIseKriterijumska Optimizacija i Kompromisno Resenje), among others. For the purposes of this research, however, the SMART method (Simple Multi-Attribute Rating Technique), as conceptualized by Taherdoost and Mohebi [26], was selected due to its suitability for evaluation.

The theoretical framework applied in this study follows a process-oriented conceptualization of digital marketing as presented in recent Czech and Slovak literature. According to Koman [27], digital marketing can be understood as a structured sequence of activities through which an organization systematically implements its marketing strategy in the online environment. In their model, digital marketing proceeds from (1) establishing an online presence to (2) acquiring traffic, (3) converting users, and (4) sustaining customer relationships. Each stage is associated with specific tools and measurable performance indicators, forming a logically ordered process applicable to both the Czech and Slovak markets.

First, the establishment of online presence involves creating and optimizing the company’s website and landing pages, which serve as the central hub of all digital communication. This corresponds to the initial step in which the organization ensures the visibility, accessibility, and usability of its online environment [27].

Second, traffic acquisition is achieved through channels such as PPC advertising, SEO, social media communication, email marketing, or product comparison engines. Burešová [28] highlight that this phase is characterized by the customer’s active search for information, where digital marketing tools must generate contact points and initiate interaction.

Third, the framework proceeds to user conversion, where the objective is to transform website visitors into leads or customers. Burešová [28] notes that Czech digital marketing practice commonly applies structured decision-making models such as See–Think–Do–Care, which classify users according to stages of awareness, consideration, and purchase, assigning appropriate tools and communication techniques to each step. In this stage, PPC advertising, targeted email communication, and remarketing play a central role in shaping purchase motivation and reducing friction in the decision process.

Fourth, the relationship sustainability phase focuses on post-purchase care, retention, and loyalty building. Both Slovak and Czech literature emphasize that digital marketing is not limited to acquisition, but includes long-term relationship management through personalized email communication, social media interactions, loyalty mechanisms, and continuous content delivery [27,28]. This stage aims to increase customer lifetime value and reinforce repeated engagement.

Recent Czech research further emphasizes that the effectiveness of the entire process depends on the organization’s ability to adopt, professionalize, and integrate digital activities. Knihová and Hes [29] argue that Czech companies face a structural need to reduce the gap in digital marketing adoption and competence compared to other European markets, positioning digital marketing not only as a set of tools, but as a strategic capability that influences overall firm performance.

Taherdoost and Mohebi [26] constructed this procedure in the SMART method:

• Identify the problem.
• Identify alternatives for solving the problem.
• Normalize the weights and position each alternative along each criterion.
• Rank the criteria using a scale (standard: 1—minimum, 5—maximum).
• Calculate the results.
• Make the decision.

The basis of an effective decision-making process using SMART is the correct selection and weighting of criteria, ensuring that decisions reflect the real priorities and complexity of the problem. This process begins with a thorough analysis of all relevant factors that may affect the outcome, and based on this analysis, the most appropriate criteria are identified and selected in accordance with the decision-making context. Weights are determined using a direct evaluation or ranking method that expresses the relative importance of each criterion. The alternatives are then rated on a uniform scale, which allows them to be quantitatively compared according to how well they meet each criterion. The weighted ratings are then aggregated, thereby synthesizing them into a final decision. Thus, the SMART method allows for a systematic, quantitative, and differentiated assessment of alternatives, leading to the selection of the most appropriate solution. According to the study, it was found that this method is useful not only in decision sciences, but also in computer science or mathematics [26].

The SMART method has been applied in various industries such as technology, healthcare, logistics, and HR. Mohammed [30] reports that the SMART method helped in selecting a 5G provider in Egypt. She evaluated several vendors and the final selection was Ericsson (Stockholm, Sweden). The SMART method helped manage uncertainty and vagueness, especially when evaluating criteria. In the healthcare sector, it has been shown that the SMART method can be applied even in the area of very sensitive data. SMART was able to help evaluate areas (hospital facilities) within the risk where capacity additions within the healthcare staff are needed. This analysis was carried out in Romania [31]. Dewi [32] demonstrated that the SMART method helped in selecting a logistics location from a selection of multiple alternatives. Alternative A1 was shown to be the best option with a weighted score of 64. Mihuandayani [33] applied the SMART method in the field of HR. Based on calculations in the decision making for the recruitment and selection of prospective employees at a company, the calculations in the 2019 and 2020 selections using the SMART method consisted of five criteria, namely, written tests, interviews, education, award certificates, and working experience; one person had the highest score in each selection with a value of 1.00 in 2019. Furthermore, one person had the highest score with a value of 0.93 in 2020.

2.1. Choosing Alternatives to Compare in SMART

The problem in this type of scientific research is the difficulty of choosing a PPC marketing automation tool. It is necessary to choose a research area and selection alternatives.

The subject of the investigation are tools that are intended for use in the digital marketing environment, especially in PPC advertising intended for the Google Ads (GADS) advertising system. The chosen focus is limited to this area, as digital marketing is a broad field, and the topic is specifically centered on the Czech and Slovak markets for online automation tools. In Slovakia, a significant increase in spending on digital advertising was recorded in 2021 in the amount of 32%, when it reached a value of EUR 184 million. Of the total amount of expenses, 54% is related to mobile advertising (EUR 99 million) and 46% to desktop advertising (EUR 85 million). According to estimates, when compared to spending on other media types, online has become the strongest advertising medium for the first time. This industry has high potential for growth in the coming years, which is also a reason for research [34].

The selected tools are oriented toward the Czech and Slovak PPC market. However, not all tools can be the subject of research. Several selection conditions need to be met to make the tool applicable for automation purposes. The evaluation of the conditions is presented in

Table 1. Choosing alternatives for comparison in the Simple Multi-Attribute Rating Technique method.

Name of Tool	Automation Tool for GADS	Works with Data Feed for Every Campaign	Choose Tool for Comparison
Dotidot	Yes	Yes	Yes
Conviu	Yes	Yes	Yes
BlueWinston	Yes	Yes	Yes
Optmyzr	Yes	No	No
Adalysis	No	No	No
Marketing Miner	No	No	No
Madgicx	No	No	No

and data were obtained from the official websites of the tools [35,36,37,38,39,40,41].

The list of PPC automation tools considered in Table 1 was compiled through a structured search focused on the Czech and Slovak markets. First, an online search was conducted in Google using combinations of Czech, Slovak, and English keywords such as “PPC automatizace”, “PPC automation tool”, “Google Ads automation”, “product feed PPC tool”, and “PPC nástroj pre e-shopy”. Second, trade blogs, PPC agency websites, and conference presentations were screened for references to commonly used tools in these markets. This procedure resulted in an initial pool of 7 tools.

To be eligible for further comparison, the tools had to meet three inclusion criteria: (1) provide automation for Google Ads campaigns, (2) support work with product data feeds at the campaign level, and (3) be available and actively supported in the Czech and Slovak markets. Only three tools, Dotidot, Conviu, and BlueWinston, fulfilled all criteria and were therefore included in the subsequent SMART multi-criteria evaluation.

2.2. Process of Normalizing Weights and Assigning Points to Criteria

The assignment of weights to individual criteria was based on subjective evaluation (expert judgment), as recommended by Lakmayer and Danielson [42]. According to Kizielewicz [43], “Subjective weight determination is a critical step in MCDM (Multi-Criteria Decision-Making) processes, and subjective methods play a significant role in this area.” Thus, weights can be derived using scoring or ordinal approaches. The weight normalization is conditional on being equal to 1.

After assigning weights and points based on the scale (1—minimum; 5—maximum), it is necessary to calculate the total score of the system (alternatives) according to the standard weighted sum described by Khanal [44]:

$$V_j=\sum _{i=1}^n{S}_{ij}\cdot W_i$$

(1)

where the total score V_j for alternative j represents the sum of the tools of the scores of individual criteria and their weights, and S_ij denotes the score of alternative j on criterion i and W_i represents the weight of criterion i.

2.3. Categories of Criteria in Decision-Making

The selection criteria were divided into two categories and were chosen based on Kanumuri’s [45] recommendations in the form of Cloud Cost Management. The first category is the functional criteria in the area of optimization, automation, and flexibility of the given solution. They are necessary for fulfilling the goal of the given research and for the correct choice of the tool. Automation is inevitable in this case and is very closely related to the data feed. The second category is in the area of pricing and product settings (budgeting) that the tool can manage.

2.3.1. Functional Selection Criteria

Cavalcanti [46] perceived usefulness (for example, whether the system supports required functions) is a primary driver of adoption and this gives a reason to set higher weights compared to the budgeting criteria.

“Compatibility of advertising systems” was assigned a weight of 0.25, because, according to Yoon [47], compatibility is the extent to which technology is consistent with existing organizational practices and procedures. This argument was also confirmed by statistical hypothesis testing, and it was shown that system compatibility is statistically significant.

“Number of functionalities in the feed settings” was assigned a weight of 0.21, “Number of functionalities in the web application interface” was assigned a weight of 0.18, and “Number of feed import options” was assigned a weight of 0.14. Fürst [48] showed that increasing the number of product features increases expected capability, which, in turn, increases purchase intentions. The effects are even stronger when features are well integrated (high interrelatedness). Number of features has a direct positive association with adoption via capability.

2.3.2. Budgeting Selection Criteria

“Price of the cheapest package for using automation” was assigned a weight of 0.11. According to a study by Somasundaram [49], price sensitivity has been shown to be a factor in choosing automated services.

“Maximum number of products in the cheapest package” was given a weight of 0.07. The analytical results show that an entry allowance (limited free use) is optimal and powerful over a wide set of market conditions. The size of that allowance is pivotal for attracting heterogeneous users and stimulating consumption before monetization [50].

“Number of days in free access” was assigned a weight of 0.04. The length of free access works especially well in the top-funnel (attraction) and later stages, but may not be a critical predictor of intermediate conversion or product performance itself [51].

Table 2. Factors in Simple Multi-Attribute Rating Technique.

Automation PPC Tools		Dotidot			Conviu			BlueWinston
Factor	Weight	Points		Score	Points		Score	Points		Score
CAS-F	0.25	4		1	2		0.5	2		0.5
NFFS-F	0.21	5		1.05	1		0.21	2		0.42
NFWAI-F	0.18	5		0.9	4		0.72	3		0.54
NFIO-F	0.14	5		0.7	2		0.28	1		0.14
PCPA-B	0.11	1		0.11	5		0.55	4		0.44
MNPCP-B	0.07	5		0.35	5		0.35	1		0.07
NDFA-B	0.04	2		0.08	4		0.16	4		0.16
Summary	1			4.19			2.77			2.27

Note: F: functional; CAS: Compatibility of advertising systems; NFFS: Number of functionalities in the feed settings; NFWAI: Number of functionalities in the web application interface; NFIO: Number of feed import options; PCPA: Price of the cheapest package for using automation; MNPCP: Maximum number of products in the cheapest package; NDFA: Number of days in free access.

shows the selected factors of the tools and their comparison and

Table 3. Scales used to compare PPC tools.

Points	1	2	3	4	5
NDFA-B	7 days and less	8–15 days	16–23 days	24–31 days	32 days and more
MNPCP-B	1999 products and less	2000–4000 products	4001–6000 products	6001–8000 products	8001 products and more
PCPA-B	EUR 160 monthly and more	EUR 110–15,999 monthly	EUR 60–10,999 monthly	EUR 10–5999 monthly	EUR 999 monthly and less
NFIO-F	1–2 options	3–4 options	5–6 options	7–8 options	9 and more options
NFWAI-F	3 and less	4–7 functions	7–10 functions	11–14 functions	15 and more functions
NFFS-F	1–2 functions	3–4 functions	5–6 functions	7–8 functions	9 and more functions
CAS-F	1 system	2 systems	3 systems	4 systems	5 and more systems

Note: F: functional; CAS: Compatibility of advertising systems; NFFS: Number of functionalities in the feed settings; NFWAI: Number of functionalities in the web application interface; NFIO: Number of feed import options; PCPA: Price of the cheapest package for using automation; MNPCP: Maximum number of products in the cheapest package; NDFA: Number of days in free access.

shows the scales used to compare PPC tools. The scale used was from 1 to 5, with 1 being the worst rating and 5 being the best rating.

Using the SMART (Simple Multi-Attribute Rating Technique) method and relevant factors, the Dotidot tool was selected with a final rating of 4.19. Conviu came in second place with a final score of 2.77, and BlueWinston received an overall score of 2.27.

Dotidot offers continuous marketing automation, product feed management, and data-driven decisions. In terms of compatibility of advertising systems, it was found that this tool is compatible with Google Ads, Facebook, Microsoft Ads, Sklik.cz. Dotidot offers 17 functionalities within the website and 9 functionalities within the feed settings. There are several options for purchasing a price package. In terms of this study, the Essentials package was selected (EUR 210/month with feed content up to 10,000 products). The free version of Dotidot is set for 14 days, which is half as long as competing tools. There are up to 13 options for importing a feed, namely, using Data feed file, Google Merchant Center, Google Sheets, Scraper, Scraper Lite, Scraper Wizard, GA4 (Google Analytics 4), Google Ads, Facebook, Heureka feed, Weather feed, CSS (Cascading Style Sheets) Selector, and merging feeds [35].

BlueWinston offers campaign automation, dynamic ad options, and keyword creation. It is fully compatible with advertising systems such as Google Ads and Microsoft Ads. It offers 9 functionalities in website settings and 4 functionalities in feed settings. If the user chooses the quarterly option, the cheapest package will cost EUR 39/month with an available number of products up to 1000 (the most expensive quarterly package costs EUR 189/month with the option of up to 50,000 products). If the user chooses the monthly option, the cheapest package costs EUR 59/month with the option of up to 1000 products and the most expensive at EUR 309/month for the number of products up to 50,000. The free trial period is set at 30 days, just like the Conviu tool. Importing the feed is only possible via an XML (eXtensible Markup Language) file [36].

Conviu offers a set of multiple options such as convenient e-shop management in one place, bulk product editing, intuitive feed optimization without using a programmer, automatic translator without the need for a translator, rule timing, and full automation. As for the compatibility of advertising systems, its optimization can be set up on Google Ads and Sklik.cz. On the web interface, it has slightly fewer functionalities than Dotidot, namely, 13 options. The feed is set up in 2 options: by editing variables and by editing the basic settings of the imported feed. In terms of price, Conviu offers 2 sections for automation, namely, “Automatic PPC advertising” and “Data feed editor”. The cheapest package is EUR 46 per month (Automatic PPC advertising = EUR 37/month + EUR 9/month “Data feed editor”). The most expensive package is EUR 280/month (Automatic PPC advertising = EUR 196/month + EUR 84/month “Data feed editor”). The free trial version is set in Conviu for 30 days. The range of feed import options is set at 4, namely, via Data feed file, Google Sheets, URL (Uniform Resource Locator) address, and FTP (File Transfer Protocol) server [37].

3. Results

This section presents the results of the analysis and implementation of the selected automation tool. First, the organization in which the analysis was performed is introduced. Then, the current campaigns and their status before implementation are analyzed, and finally, the results of the campaigns after the implementation of the Dotidot tool are evaluated.

3.1. Results of Current Campaigns (Google Ads) of Selected Company

The main purpose of this section is to analyze the results for the company’s product campaign and category campaign in 2024 through the results of the GADS (Google Ads) advertising network.

3.1.1. Product Campaign Data

The product campaign focuses on products in the form of hotels. During the active season, there are 234 active products that the customer can purchase.

Table 4. Product campaign data for 2024.

Time Range	1 January 2024–31 December 2024
Type of budget	Daily
Score of optimalization	67.20%
Type of campaign	Search
Click-Through Rate	30.03%
Spend	EUR 12,498
Impressions	642,883
Bidding strategy	Maximize conversion value
Clicks	189,316
Conversion rate	0.22%
Conversion value	EUR 1,188,541
Conversion value/price	95.10
Conversions	414
Average price per click	EUR 0.07
Price/conversions	EUR 30.14

shows the results of the product campaign in 2024.

The lowest CTR (Click-Through Rate) in 2024 was in April with a value of 27.78%. The highest ad CTR in 2024 was recorded in November with a value of 34.21%. The fewest impressions in 2024 were in October with a value of 9941. The most ad impressions in 2024 were recorded in June with a value of 115,908. The fewest clicks in 2024 were in October with a value of 3136. The most ad clicks in 2024 were recorded in June with a value of 33,504. The lowest Conv. rate (Conversion rate) in 2024 was recorded in May with a value of 0.06%. The highest Conv. rate in 2024 was recorded in January with a value of 0.42%. The lowest Conv. value (Conversion value) in 2024 was in October, specifically EUR 6 691. The highest Conv. value in 2024 was in January, specifically EUR 221,612. The fewest conversions in 2024 were recorded in October, specifically two. The most conversions in 2024 were recorded in January, specifically 77. On average, a travel agency paid the most for one conversion in 2024 in October with a value of EUR 344.32. On average, a travel agency paid the least for one conversion in 2024 in January, with a value of EUR 8.55.

3.1.2. Results of Category Campaign Data

The category campaign targets specific product categories. In the active season, the company advertises 35 product categories.

Table 5. Category campaign data.

Time Range	1 January 2024–31 December 2024
Type of budget	Daily
Score of optimalization	74.10%
Type of campaign	Search
Click-Through Rate	21.85%
Spend	EUR 4182
Impressions	268,509
Bidding strategy	Maximize conversions
Clicks	55,275
Conversion rate	0.14%
Conversion value	EUR 235,525
Conversion value/price	56.32
Conversions	83
Average price per click	EUR 0.08
Price/conversions	EUR 50.38

shows the results of the category campaign in 2024.

The highest CTR rate was in October 2024 with a value of 28.41%. The lowest CTR in 2024 was in July with a value of 16.89%. The lowest value of impressions in 2024 for a categorical campaign was in October, namely, 10,280. The highest number of impressions for the calendar year 2024 for a categorical campaign was in June with a value of 43,507. The most clicks for a categorical campaign in 2024 were in June with a value of 7507. The fewest clicks in 2024 for a categorical campaign were in October with a value of 2921. The lowest value of Conv. rate for 2024 for a categorical campaign was in October, namely, 0.03%. The highest value of Conv. rate in 2024 for a categorical campaign was in January, specifically 0.24%. The highest Conv. value for 2024 for a categorical campaign was in January with a value of EUR 48,202. The lowest Conv. value in 2024 was recorded in October with a value of EUR 10. The most conversions in 2024 in a categorical campaign were in January, exactly 14. The fewest conversions in 2024 in a categorical campaign were in October, specifically one. In a categorical campaign, the company paid the most for one conversion in 2024 on average in April, with a value of EUR 263.56. The travel agency paid the least on average in 2024 in January, with a value of EUR 10.85.

3.2. Implementation of PPC Automation Tool Dotidot

Sysoiev [52] described in their study that e-commerce projects advertise their products based on data feeds, which help automate products or services. Data feeds are integrated into the CRM (Customer Relationship Management) systems of the given e-commerce projects. This data source is imported through a tool that works with product parameters and advertises ads based on them. A similar solution is analyzed in this section.

To create an automated PPC campaign, data sources called feeds are needed. Based on these feeds, the automated tool can (after entering conditions) create a group of PPC ads, which are then imported into the advertising system. It was proposed to create such a feed for the selected company. The created feed was a product and a categorical feed, which was then implemented into Dotidot. The feeds will be used to create both a product and a categorical campaign.

After agreement with the internal programming team, it was agreed that the design and implementation of feeds for PPC campaigns in Dotidot should adhere to the following procedure:

• The mandatory feed attributes that must be included are defined.
• The feeds will be created in CSV (Comma-Separated Value) format in the form of hypertext links (due to possible changes and updates).
• The feeds are then imported into the selected Dotidot tool.

3.3. Results After Implementation

The product campaign had the following results after automation in the Dotidot tool for the time period 14 March 2025–31 March 2025, which can be seen in

Table 6. Product campaign results in Dotidot.

Time Range	Name of Metric	Product Campaign (Results in Dotidot)
14 March 2025–31 March 2025	Click-Through Rate	21.80%
	Impressions	5130
	Clicks	1118
	Conversion rate	0.09%
	Conversion value	EUR 8499
	Conversions	1

.

The product campaign earned EUR 8499 (one purchase). CTR was at 21.80%. The bidding strategy was set to “Manual cost per click”. CPC was at EUR 0.50 throughout and the daily budget was set to a maximum of EUR 65.

The category campaign had the following results after automation for the period 14 March 2025–31 March 2025, which can be seen in

Table 7. Category campaign results in Dotidot.

Time Range	Name of Metric	Category Campaign (Results in Dotidot)
14 March 2025–31 March 2025	Click-Through Rate	20.54%
	Impressions	6602
	Clicks	1356
	Conversion rate	0.37%
	Conversion value	EUR 16,914
	Conversions	5

.

The category campaign earned EUR 16,914 from five conversions over the period. CTR was at 20.54%. The bidding strategy was set to “Maximize conversions”. The cost per conversion was not set, but there was a daily budget of EUR 10 per day.

In addition to the quantitative aspect, it is also necessary to compare the qualitative aspect of manual and automated PPC campaigns for a more comprehensive view. A comparison can be seen in

Table 8. Comparing manual PPC campaign and automated PPC campaign.

Factor	Manual PPC Campaign	Automated PPC Campaign
Bidding control	Full control over bidding at the keyword or ad group level.	The algorithm continuously adjusts bids based on dozens of signals (device, time, audience, context) and targets CPA/ROAS/conversions.
Performance at different data rates	It works better in a low-data environment (new campaigns, small budgets, low conversion volume), where the algorithm has nothing to learn from.	In a data-rich environment, the algorithm can usually reduce CPA/increase ROAS because it uses more signals and continuously tests.
Time-consuming	Useful for smaller accounts with a few dozen ad groups and quick daily/weekly adjustments.	Useful for larger accounts with large amounts of data. Saves time for strategic actions.
Focus on KPIs	Manual KPI preference based on user needs.	Auto-bidding can directly optimize for conversions, conversion value, ROAS, and work with predictive models (e.g., purchase probability).
Targeting	Full control over the selection of demographics, audiences, and placements.	Can dynamically create and optimize audiences based on behavior and purchase likelihood.
Flexibility in business rules	The user can make decisions outside of the data into account according to the company’s situation.	They mainly use data when making decisions, but it is possible to set restrictions in the form of budgets or the number of ads displayed.

Source: processed according to [53,54]. Note: ROAS—return on ad spend; CPA—cost per action.

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The table compares manual PPC campaigns with automated PPC campaigns across several key factors. Manual PPC management provides full control over bids, targeting, and KPI priorities, making it more effective in low-data environments such as new or small-budget campaigns. However, it is time-consuming and depends heavily on the advertiser’s expertise. Automated PPC campaigns rely on algorithms that continuously adjust bids using large amounts of signals to improve CPA and ROAS. They perform best when abundant data is available, saving time and allowing for strategic focus. Automation can dynamically optimize audiences and conversions but tends to follow data-driven rules, limiting flexibility compared to manual decision-making, where broader business contexts can be applied [53,54].

4. Discussion

This study set out to select and empirically evaluate an automated tool for managing PPC campaigns in the Czech and Slovak markets. Using the SMART multi-criteria method, Dotidot was identified as the most suitable option among three shortlisted tools (Dotidot, Conviu, and BlueWinston), mainly due to its number of functionalities in the web application interface, number of functionalities in the feed setting, number of feed import options, or maximum number of products in the cheapest package. The summary result of the comparison analysis of the automated PPC tools can be seen in

Table 9. Overall success rate—comparing automated tools.

Tool Name	Average Count of Points for One Factor	Modus	Gathered Points	Maximum Available Score	Overall Success Rate
Dotidot	2.7	5	27	35	77%
Conviu	2.3	5	23	35	66%
BlueWinston	1.7	2	17	35	49%

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The summary evaluation between product and category campaigns in Dotidot versus product and category campaigns in Google Ads for the same time period (14 March 2025–31 March 2025) can be seen in

Table 10. Summary evaluation of Google Ads and Dotidot.

Name of Metric	Product Campaign (GADS)	Product Campaign (Dotidot)	Category Campaign (GADS)	Category Campaign (Dotidot)
Click-Through Rate	24%	21.80%	23.25%	20.54%
Impressions	37,244	5130	3106	6602
Clicks	8923	1118	722	1356
Conversion rate	0.21%	0.09%	0.00%	0.37%
Conversion value	EUR 59,023	EUR 8499	EUR 0	EUR 16,914
Conversions	19	1	0	5
Cost/conversions	EUR 18.22	EUR 430	EUR 0	EUR 36
Costs	EUR 348	EUR 430	EUR 0	EUR 180

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The implementation of the automated tool (Dotidot) led to mixed results. In the product campaigns, the original manually managed Google Ads outperformed Dotidot in terms of CTR (24%) and conversion rate (0.21%). However, in the category campaigns, Dotidot generated five conversions and a conversion value of EUR 16,914, where the original setup achieved no conversions. On the other hand, Dotidot campaigns are more expensive than manual Google Ads campaigns.

From the perspective of data feed implementation, several recommendations can be used. The STDC (See–Think–Do–Care) model can serve as a framework for data feed segmentation. The See phase is communication aimed at the widest possible audience who may be inspired by the product (headlines or photos in PPC ads). In the Think phase, the initial interest has already been created within the audience, and a closer acquaintance with the services is taking place. The audience is narrowing down and showing the first signs of purchase intent. They are considering the offer through the USP (Unique Selling Proposition), which is highlighted in the headlines or descriptions of ads. In the Do phase, the audience is already determined to take action. They are ready to purchase the product or service. At this stage, it is essential to include elements such as pricing, discounts, or special offers to encourage conversion [55]. In the Care phase, the focus shifts to customer retention. The product feed can be expanded with repeat purchase items, add-ons, and related products that promote ongoing engagement. Data from existing customers can also be leveraged to create personalized remarketing segments [56].

Chaffey [57] defines the RACE (Reach–Act–Convert–Engage) model as a pragmatic framework for measuring the performance of a data feed at individual stages. The Reach phase can analyze feed coverage across search and content networks and track the share of products that are displayed at least once a day (impression share). The Act phase evaluates engagement metrics (CTR, adds to cart) for individual feed categories. The Convert phase tracks the conversion rate based on feed attributes (for example, availability, price, brand). The Engage phase measures repeat purchases, reviews, and customer cycle length. In practice, the feed is not just a “product database”, but a tool for dynamic performance measurement according to the interaction phase, which enables iterative optimization within automation systems.

The AIDA (Attention–Interest–Desire–Action) model provides a structured theoretical foundation for improving product feed-based creatives by aligning individual feed elements with the sequential psychological stages of customer decision-making. Its application to product feeds is justified by the fact that modern advertising systems (Google, Meta, Seznam) dynamically generate creatives directly from feed attributes; therefore, the quality, structure, and persuasive strength of feed elements directly influence the generated ads and, consequently, campaign performance [58].

In the Attention stage, interventions target the user’s perceptual filters. Research on digital advertising effectiveness shows that users allocate only fractions of a second to assessing whether an ad is relevant (e.g., “last minute”, “limited edition”, “new arrival”). Enhancing feed attributes such as titles or image overlays with attention-triggering cues increases the probability of initial cognitive engagement and improves impression-to-interaction ratios [59]. Hmurovic [60] pointed out in a case study focused on an email campaign that by attracting attention through various phrases such as “limited-time”, consumers will open such emails 23% more than a control version without these phrases.

The Interest stage focuses on deepening the user’s cognitive processing. Additional feed-enriched attributes such as customer ratings, review counts, or specific product differentiators (materials, specifications, certifications) act as relevance signals. Studies on heuristic-driven evaluation processes show that such informational cues significantly improve ad dwell time and click-through rate because they reduce uncertainty and help the user quickly evaluate product suitability [61]. These claims are supported by a case study in the United Kingdom. Consumers were more than four times more likely to click on an ad that displayed a 5-star rating, high TrustScore, 3000+ reviews, and a customer testimonial, compared to a baseline ad without ratings and reviews [62].

In the Desire stage, the objective is to activate motivational mechanisms. Optimizing feed fields to highlight unique product benefits (e.g., durability, sustainability, local production) supports value-based positioning and aligns with research on persuasive message design, which demonstrates that benefit-oriented messaging triggers affective responses that correlate with higher purchase intent. By embedding these benefit-driven fields directly into the feed, automated ad systems are able to generate creatives that communicate value propositions without manual intervention [63]. Tran [64] shows that affective and benefit-oriented content increases purchase intention compared to advertising creatives without such content. The effect is statistically significant (p < 0.05).

The Action stage directly addresses behavioral conversion. Adding explicit calls to action (CTAs) such as “Buy now”, “Order today”, “Add to cart” into CTA-related fields corresponds with proven behavioral science models that emphasize that clear, directive prompts significantly increase the likelihood of completing the final step of the customer journey. When these CTA cues are encoded at the feed level, platforms can consistently apply them across thousands of dynamically generated creatives, leading to higher conversion probabilities [65]. Chae [66] studied over 2000 branded social media posts and found that posts that use CTAs obtain significantly more likes or shares than posts that do not.

Importantly, these interventions transform the product feed from a static data source into a communication asset. This approach is aligned with recent studies on data-driven personalization, which consistently demonstrate that the performance of automated advertising systems improves when input data are semantically enriched, contextually relevant, and structured to support persuasive message generation. Therefore, modifying feed attributes according to the AIDA model is not only theoretically justified, but also strategically necessary for maximizing creative quality and overall campaign effectiveness [67].

These recommendations may not apply only to tourism. A complementary view comes from an e-commerce case study of Arzanka Store, a fashion business selling men’s chinos via social media and marketplace platforms. Before adopting digital advertising, the company relied mainly on word-of-mouth and organic social media activity. After implementing a broader digital marketing strategy, combining social media campaigns, marketplace advertising (Shopee), SEO, and content marketing and explicitly measuring click-through rates, conversion rates, and ROI on marketplace ads, the business reported an estimated 60–70% increase in sales. Customers began to purchase more items per transaction and were more likely to buy immediately after seeing paid promotions or special offers. In STDC terms, this illustrates the Do phase very clearly. When the feed explicitly surfaces price, discounts, and limited-time offers in ad titles and descriptions, the probability of conversion rises. Under the RACE framework, Arzanka’s approach shows how feed-based performance can be tracked across Act (ad clicks and add-to-cart events) and Convert (orders and revenue), with conversion rate and ROI metrics directly reflecting the quality of feed attributes such as price competitiveness, promotional flags, and product availability. The case thus operationalizes the recommendation that the feed is not just a static catalog, but a dynamic measurement instrument linked to behavioral data in automation systems [68].

Besides the fashion industry, there are also examples of using digital marketing in the sports industry. In the field of sports management, the same feed-based strategies structured around the mentioned models can be translated directly to fan acquisition, ticketing, and merchandising. Quantitative research on football clubs in Iraq shows that approximately 97.5% of respondents use social media, with Facebook preferred by 32.5% over traditional media, and most users are under 30 and spend 2–5 h per day on social platforms while actively liking, commenting, and paying attention to sports advertising. In STDC terms, this indicates that the See phase for sports organizations is almost entirely digital. Broad, inspirational content (highlights, behind-the-scenes, star players) must be systematically encoded into feed fields (thumbnails, headlines, short descriptions) and distributed across the dominant platforms and age groups. The Think phase then relies on richer feed attributes such as competition type, opponent, seat category, family offers, or membership benefits, which help fans evaluate the club’s proposition. From the “Reach” and “Act” metrics, impressions and interactions on feed-driven creatives are early indicators of how well clubs are positioning their content and products across intent stages [69].

PPC marketing is also represented in the sports management industry. Childs [70] claims that only 23 college sports programs out of 250 analyzed use online paid promotion within sports organizations. The research was divided into two groups, where it was found that Division 1 purchased an average of 20.4 keywords per month, while Division 2 purchased only 6.5 keywords per month.

A fundamental difference was also demonstrated in terms of spending on online advertising in sports organizations. Childs [70] found that some college sports programs spend as much as USD 18,000 per month, while others “only” spend USD 500 per month. The most frequently searched keywords according to college sports organizations are “tickets” (frequency of 96 searches per month) and “football” (frequency of 59 searches per month).

Automation in PPC marketing brings many benefits. The increase in work efficiency is achieved by reducing the manual work of PPC specialists in setting up and optimizing campaigns, which allows marketing agency employees to focus more on more strategic tasks. Compared to Google Ads, it can significantly speed up the time spent creating and editing campaigns using automation rules [71]. Another advantage is that PPC advertising automation provides quick responses to changes and easy feed updates. RPA is a technology that automatically synchronizes data and responds without the need for manual intervention [24]. Automation enables the management of multiple systems from a single platform. It enables the transition from isolated tools to integrated systems [72].

Social media has an enormous impact on the marketing and reputation of sports clubs and athletes [73]. Engaging in marketing in the context of sports organizations via PPC tools contributes to the digitalization of sports. Sports represent an undeniable value associated with people’s health and leisure time, which can support financial sustainability (e.g., by selling tickets/merchandise) [74]. Human capital management helps manage and develop human capital throughout modern industry, which presents new challenges. Industry 4.0 is a driving force for automation and digitalization (automation of PPC) [75,76]. Automated PPC tools and their selection (Conviu, Dotidot, BlueWinston) as a tool to simplify the process of creating and managing campaigns also need to be applied to employee recruitment processes. Companies switch to e-recruitment, utilizing information and communication technologies [77]. It is necessary to put a common emphasis on digitalization and the use of information and communication technologies and automation in key business processes. The aim of the research is to provide a comprehensive view of building an innovative intelligence system in a company from managerial, information, and organizational support [78]. Managerial decision-making regarding the implementation of innovative/digital systems/tools and their impact must also be linked to the marketing communication of organizations, which focuses on PPC tools.

Limitations and Future Work

There are several limitations within the methodology. Time constraints could have narrowed the range of selection options. Poor indexing and the “untraceability” of other tools on the market make it difficult to systematically explore available solutions, thus narrowing the selection set. A lack of knowledge about the foreign market also leads to misinterpreting the results when selecting from the available tools.

Implementation also brings certain limitations. High dependence on the quality of data feeds and the need for regular updates pose a risk in long-term use. Another limitation is the concern about automation and whether automation can perform the actions that the marketer really wants. The loss of the human factor in decision-making or possible technical problems with incorrectly set feeds also remains debatable. Automation can save time, but on the other hand, it is necessary for the user to learn to work in a given tool, which takes a certain amount of time.

The internal validity of this case study is limited, as the observed outcomes cannot be clearly attributed either to the intrinsic capabilities of the automation tool or to the specific way it was implemented (including the team’s initial settings and optimization approach). Different configuration choices and campaign strategies could lead to different performance results, which also raises concerns regarding the reliability and reproducibility of the findings.

The different level of the digital maturity of companies in the Czech and Slovak environments is also another limitation. As Knihová and Hes [29] point out, many companies show insufficient integration and professionalization of digital activities, which may limit the effective application of the theoretical framework of this study.

From a longer-term perspective, it is appropriate to consider expanding automation rules to include elements of artificial intelligence that enable the predictive adaptation of ad content. The overall benefit of the proposal can be assessed as practically usable in tourism conditions, while its applicability is also transferred to other segments of digital marketing. The topic can be further explored in future research, particularly in relation to the implementation of AI and its potential use in business decision-making.

5. Conclusions

The main contribution of this paper is an empirical case study that evaluates the implementation and performance of an automated PPC management tool (Dotidot) in practice, by comparing its campaign results with standard manually managed Google Ads campaigns. As a supporting step in the research design, the study first identifies and evaluates automated tools for creating and managing PPC advertising campaigns in the Czech and Slovak markets. Dotidot, Conviu, and BlueWinston are compared using the SMART multi-criteria method based on functional and budgeting criteria to determine the most efficient solution for business applications.

The analysis showed that Dotidot achieved the highest overall score (4.19 points/77% success rate) and proved to be the most suitable solution for the automation of PPC advertising. The tool demonstrated wide compatibility with advertising systems, a high number of functionalities, and comprehensive feed management options.

The product campaign managed through Dotidot achieved a CTR of 21.8%, which is only slightly lower than in Google Ads (24%). However, it recorded fewer impressions (5130 vs. 37,244) and clicks (1118 vs. 8923). After automation, the campaign generated a conversion value of EUR 8499 with one conversion, resulting in a cost per conversion of EUR 430.

The category campaign in Dotidot reached a CTR of 20.54%, comparable to Google Ads (23.25%). Despite a smaller number of impressions (6602), it achieved a higher conversion rate of 0.37%, while Google Ads recorded no conversions. The campaign generated a conversion value of EUR 16,914 with five conversions and a cost per conversion of EUR 36.

In terms of cost efficiency, Dotidot showed lower total expenses in the category campaign (EUR 180) compared to the product campaign (EUR 430). Overall, the results indicate that Dotidot delivered mixed outcomes. It underperformed the original Google Ads setup in the product campaign, but in the category campaign it generated additional conversions at a moderate cost per conversion.

The practical implementation of Dotidot in a travel agency confirmed its applicability in real conditions. After automation, both the product and category campaigns reached solid performance results, proving that automation can effectively streamline campaign management and reduce manual workload.

Only 9.2% of college sports programs (23 out of 250) actively use paid online advertising, indicating very limited adoption of PPC strategies in the sports sector. Among those that do, spending varies drastically. Some programs invest up to USD 18,000 per month, while others spend as little as USD 500, reflecting a significant disparity in digital marketing resources.

In summary, the research confirmed that automation in PPC marketing increases efficiency, allows faster campaign setup, and enables data-driven optimization. The use of automated tools such as Dotidot represents a significant step toward more effective and scalable digital advertising management.

This issue will be investigated in further research as part of a dissertation and will be based on the use of AI in managerial decision-making.