Using Data Mining in Digital Marketing: Implementation, methods, and performance metrics
The impact that digital marketing activities have on the community has become one of the essential factors in creative industries.
The value of data mining has increased, as has the rate at which it is being used in creating marketing decisions. Including that increment, the information held in agencies’ and companies’ databases becomes crucial for the business to make enhanced strategic decisions.
Tldr, Data Mining takes your unstructured data (all the gathered information in those .xlsx and .csv files) and formats it into a structured form or output that you want. In this context, it is crucial to provide this data for marketing experts to be more exact and realistic about making decisions about the future steps.
Therefore, we should seek to create an upgraded platform empowered by the latest technologies’ ethical use.
It can empower us to develop more advanced and socially responsible strategies. From the clients’ side, businesses must become aware that there is more to marketing besides creative and traditional approaches such as their websites, quality content, products, employees, and customer relationships to succeed in digital marketing activities.
Data Mining in Marketing
Yes, we should continue with the current approach. The only thing suggested is the upgraded version that responds better to both clients’ and customers’ expectations by using other tools and technologies.
Implementation of text mining in digital marketing can be seen in various areas, including personalized campaigns, product design, cross, and additional sales, and marketing campaigns themselves [1].
Depending on the business, optimizing the advertising strategy may mean different things. Regardless, any analysis’s end goal is to provide actionable answers to relevant questions that can drive business performance [2].
- Search Engine Optimization (SEO) — optimization of search results from major search engines
Text mining will help you identify your keywords. For example, starting with the main keyword and using semantic relationships, you can get a complete list of related words or phrases.
These new keywords can then be integrated into blog entries, meta tags, and permalinks.
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Using the RAKE algorithm on the previous two sentences, we’ll see how to extract keywords from our text data works. RAKE is an acronym that stands for Rapid Automatic Keyword Extraction.
!pip install nltk
import nltknltk.download('punkt')
!pip install rake-nltk
import pandas as pdfrom rake_nltk import Metric, Rake
text_array = ["Text mining will help you identify your keywords.",
"For example, starting with the main keyword and using semantic relationships, you can get a complete list of related words or phrases.",
"These new keywords can then be integrated into blog entries, meta tags, and permalinks."
]
rake = Rake()
rake.extract_keywords_from_sentences(text_array)
rake.get_ranked_phrases()
rake.get_ranked_phrases_with_scores()
rake.get_word_frequency_distribution()
rake.get_word_degrees()
rake = Rake(ranking_metric=Metric.WORD_FREQUENCY)rake.extract_keywords_from_sentences(text_array)rake.get_ranked_phrases_with_scores()
rake = Rake(ranking_metric=Metric.WORD_DEGREE)rake.extract_keywords_from_sentences(text_array)rake.get_ranked_phrases_with_scores()
That’s how the RAKE algorithm works on simple text. Imagine what it can do with your latest blog!
- Social Media Marketing (SMM), the strategy of interacting with users on social networks through social ads
Facebook users provide a massive amount of information to Facebook, creating a detailed profile of users’ personal information, habits, and interests. All you need to do is click on the extract button in Ads Manager and start exploring all the possibilities that mining data from text offers.
Advertisers who use Facebook as a platform for their ads can then tailor their advertising strategy accordingly, perhaps focusing on specific groups of users with similar characteristics for a particular product. To visualize one of many opportunities — in the following example, you can use standard metrics such as Click Through Rate (CTR), Cost Per Click (CPC), Impressions, and conversion to see the correlation between click data variables.
Conclusion and Future work
There is no need to speak about its further impact on sales, customer satisfaction, point of sale data analysis, supply and store placement optimizations, and profitability. Market segmentation, competitiveness analysis, customer valuation, and cross-selling analysis are conducted with data mining [4].
If businesses use data mining methods more, they’ll start developing personalized product and promotion activities for specific customer segments.
We may see it in certain areas such as pricing, customer preferences, and product positioning.
That’s why data mining may allow companies to understand hidden patterns in current data, hence planning and implementing new marketing campaigns smarter.
Are the creative marketing agencies and companies ready to start applying available tools like data mining in daily work?
Resources:
[1] Tekin, M. et al. Data Mining in Digital Marketing, Proceedings of the International Symposium for Production Research, 2019.
[2] J. R. Saura, Using Data Sciences in Digital Marketing: Framework, methods, and performance metrics, Journal of Innovation & Knowledge, Volume 6, Issue 2, 2021.
[3] C. Edwards, Exploratory Data Analysis of Facebook Ad Data with Python, C EDWARDS, 2018.
[4] P. Rajkumar, 14 useful applications of data mining, Big Data Made Simple, 2017.
