Digital Solutions and Machine Learning Can Improve Niche Market Reach




Segmentation, digital marketing, machine learning, targeting, Google Ads


Digital solutions in marketing can help reach niche markets. Marketers have the greatest opportunity ever to address segments whose needs have not yet been met. Online segmentation techniques allow to better know their characteristics. The aim of this article is to investigate the segmentation and targeting possibilities of the Google Ads system, which helps to explore consumer patterns more deeply. Digital marketing solutions help marketers reach niche markets to maximise the effectiveness of their activities. The goal of this social constructivist research was to find an answer to the question of whether the segmentation and targeting options of the Google Ads advertising system can sufficiently ensure this. To this end, we examined the presence of the “target market category” label in 37 individuals using a face-to-face survey method. The occurrence of the labels and the actual interests often overlapped.

Author Biographies

Zoltán Somosi, University of Miskolc

Ph.D student

Noémi Hajdú, University of Miskolc

Associate Professor


Abdallah, E. E., Eleisah, W., & Otoom, A. F. (2022). Intrusion Detection Systems using Supervised Machine Learning Techniques: A survey. Procedia Computer Science, 201, 205–212.

Alzahrani, L., A. (2021, July 14). Customer Segmentation: Unsupervised Machine Learning Algorithms in Python. Towards Data Science. Retrieved September 2022, from

Atkinson, G., Driesener, C., & Corkindale, D. (2014). Search engine advertisement design effects on click-through rates. Journal of Interactive Advertising, 14(1), 24–30.

Barysevich, A. (2021, July 19). Long-Tail Keyword Strategy: Why & How to Target Intent for SEO. Search Engine Journal. Retrieved October 2022, from

Belavagi, M. C., & Muniyal, B. (2016). Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection. Procedia Computer Science, 89, 117–123.

Bleoju, G., Capatina, A., Rancati, E., & Lesca, N. (2016). Exploring organizational propensity toward inbound–outbound marketing techniques adoption: The case of pure players and click and mortar companies. Journal of Business Research, 69(11), 5524–5528.

Das, A. (2020, April 17). Segmentation using Unsupervised Learning Technique - Clustering. Medium. Retrieved October 2022, from (2019). How Google uses Machine Learning to revolutionise the Internet World? Data Flair. Retrieved October 2022, from

Decker, A. (2021). The Ultimate Guide to Customer Acquisition for 2022. Hubspot. Retrieved September 2022, from

Desai, N. (2019, August 14). How Is CAC Changing Over Time? ProfitWell. Retrieved September 2022, from

Digital Marketing Institute (2018, June 05). 7 Ways Machine Learning Can Enhance Marketing. Digital Marketing Institute. Retrieved September 2022, from

ECLAC (2016): Social Panorama of Latin America, 2016. (LC/PUB.2017/12-P), Santiago: Economic Commission for Latin America and the Caribbean. Retrieved September 2022, from Published: 2017.

ECLAC (2022): Digital technologies for a new future. (LC/TS.2021/43), Santiago: Economic Commission for Latin America and the Caribbean. Retrieved September 2022, from Published: 2021.

Ehrlich, J. (2019, April 19). What is Customer Acquisition? Demand Jump. Retrieved October 2022, from

El Bouchefry, K. & de Souza, R. S. (2020). Learning in Big Data: Introduction to Machine Learning. In P. Škoda. & F. Adam (eds.), Knowledge Discovery in Big Data from Astronomy and Earth Observation, (pp. 225-249).

Fidan, H., & Yuksel, M. E. (2022). A comparative study for determining Covid-19 risk levels by unsupervised machine learning methods. Expert Systems with Applications, 190, 116243.

GKID. (2022, March 24). 70 millió online vásárlás pörgeti az e-kereskedelmet. GKI Digital & Árukereső. Retrieved October 2022, from

Google AI. (2022). Google AI. Retrieved October 2022 from

Google Developers. (2022). Supervised Learning Machine Learning Google Developers. Retrieved October 2022, from (2022). About audience targeting. Google Ads Help. Retrieved October 2022, from

Haley, C. B. (1984). Valuation and Risk-Adjusted Discount Rates. Journal of Business Finance & Accounting, 11(3), 347–353.

Halligan, B. (2021, October 08). Inbound Marketing vs. Outbound Marketing. HubSpot. Retrieved October 2022, from

He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv (Cornell University). Retrieved October 2022, from

Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., & Kingsbury, B. (2012). Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups. IEEE Signal Processing Magazine, 29(6), 82–97.

Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651–666.

Jones, R. (2011). Keyword Intelligence: Keyword Research for Search, Social, and Beyond. SYBEX Inc., USA.

Keenan, M. (2022, June 07). A Complete Guide to Customer Acquisition for Startups. Help Scout. Retrieved September 2022, from

Kotler, P., & Keller, K. L. (2016). Marketing Management (14th edition). Shanghai: Shanghai People's Publishing House.

Kotler, P., Kartajaya, H., & Setiawan, I. (2021). Marketing 5.0: Technology for humanity. Hoboken, NJ, USA: John Wiley & Sons.

Kühl, N., Goutier, M., Baier, L., Wolff, C., & Martin, D. (2022). Human vs. supervised machine learning: Who learns patterns faster? Cognitive Systems Research, 76, 78-92.

Lawrence, R. (2021, September 27). How to segment your website audience with unsupervised machine learning. Rise at Seven. Retrieved October 2022, from

Leonel, J. (2018, June 02). Supervised Learning. Medium. Retrieved September 2022, from (2023). What is Machine Learning? MathWorks. Retrieved October 2022, from

Microsoft Research. (2022). Machine Learning Area. Microsoft Research. Retrieved October 2022, from

Molnár, Cs. (2021, May 17). Mindent visznek a webáruházak. (The websites take it all). Retrieved June 2022, from

Peng, X., Li, H., Yuan, F., Razul, S. G., Chen, Z., & Lin, Z. (2022). An extreme learning machine for unsupervised online anomaly detection in multivariate time series. Neurocomputing, 501, 596–608.

Price, G. D., Heinz, M. V., Zhao, D., Nemesure, M., Ruan, F., & Jacobson, N.C. (2022). An unsupervised machine learning approach using passive movement data to understand depression and schizophrenia. Journal of Affective Disorders, 316, 132-139.

SOCO Sales Training (2019). Traditional Prospecting VS Modern Prospecting Strategies: How To Get Results. SOCO Sales Training. Retrieved September 2022, from

SOCO Sales Training (2021). Outbound Sales Explained: Techniques, Strategies & Best Practices. SOCO Sales Training. Retrieved October 2022, from

Sutton, M-R. (2021). Customer Acquisition: How to Calculate It and Create a Strategy. Shopify. Retrieved September 2022, from Updated: 23 March 2023.

Tu, N., Dong, X., Rau, P. & Zhang, T. (2010). Using cluster analysis in Persona development. In Proceedings of the 8th conference on Supply Chain Management and Information Systems. 8th International Conference on Supply Chain Management and Information, Hong Kong, China, IEEE Conference Publication, 1–5.

Turkmen, B. (2022). Customer Segmentation with machine learning for online retail industry. The European Journal of Social and Behavioural Sciences, 31(2), 111-136.

van Leeuwen, R., & Koole, G. (2022). Data-driven market segmentation in hospitality using unsupervised machine learning. Machine Learning with Applications, 10, 100414. (2022). The Ultimate List of Google Ads Targeting (Complete List of Audiences, Topics, and More!). Vid Hoarder Blog. Retrieved October 2022, from

Wang, C. (2022). Efficient customer segmentation in digital marketing using deep learning with swarm intelligence approach. Information Processing & Management, 59(6), 103085.

Wang, J., & Biljecki, F. (2022). Unsupervised machine learning in urban studies: A systematic review of applications. Cities, 129, 103925–103925. (2022). 4 Types of Market Segmentation with Real-World Examples. Yieldify. Retrieved October 2022, from Published: 06. December 2021.




How to Cite

Somosi, Z., & Hajdú, N. (2023). Digital Solutions and Machine Learning Can Improve Niche Market Reach. Theory, Methodology, Practice - Review of Business and Management, 19(01), 31–39.