Text and web analytics provides one of the most efficient methods of addressing the issue of customers churn in business. However, the texting analysis also requires the construction of a predictive model that would inform the trends of customers churn or retention. In the entire process of trying to reduce customers churn, the use of unstructured data as well as listening to the customers’ insight is fundamental. Before carrying out both the prediction model and texting analysis, the company must prepare all their customers’ information such as mapping their journey and using a wider sample to get better results ( Aleksandar, et al, 2016 ). If a company manages to curb the rate of customer churn, some of the benefits it could reap include higher profit margins from the sales which assure the survival of the business.
The importance of unstructured data in the churn analysis
Unstructured data unlike the structured one is more useful in handling customers churn since it contains information of the consumers’ needs, wishes, complaints, and wants. If the data is utilized to the fullest, it could reduce the problem of customers churn in the company. Moreover, if the analysis works, the company reaps extra profits and revenues. The use of unstructured data in the text and web churn analysis is crucial in reducing the cost of acquiring customers. The analysis is practical in the sense that it recognizes the ease of retaining existing customers than finding new ones ( Lu, et al 2014 ). The texts and web posts that will be offered either free of charge or at low costs to the customers will foster closer relationships between the company and its especially loyal customers. The analysis will also be helpful for the company to know the right strategies of increasing the number of right customers.
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Using unstructured data in the churn analysis also facilitates sustainable business growth as well as creating more cross sales than before. In essence, the analysis will increase the number of customers retained which will eventually lead to higher profit margins. Even though the process takes time to yield desirable results, the company will persist on the churn analysis using unstructured data as a form of competitive advantage (Lin, 2014). Additionally, both upsell and cross-sale opportunities will increase gradually since the existing customers will be more willing and easier to market to than acquiring new ones while also letting others leave without intervention.
List other structured and unstructured data other than the memo and Web blogs that you need to use in your churn analysis
When attacking customers churn with text and web analytics, both structured and unstructured data forms will be needed. Other than memo and web blogs, the analysis could rely on structured data especially the customer relationship management (CRM) and structured query language (SQL). Considering the fact that attacking customers churn with web and text analysis may not be easy, the use of structured data with regards to groups of words, numbers and date strings will be crucial. On the other hand, text files, email, social media feeds, digital surveillance, business applications, and communications from unstructured data will also be necessary for the churn analysis ( Bendre & Thool, 2016 ). In the list of unstructured data, multimedia and text content including videos, audio files, photos, presentations, and webpages will also come in handy while working out on customers churn with text and web analytics.
Propose a series of steps for deriving a predictive model using text and Web analytics
Predictive modeling and analytics process involves a few steps to analyze future trends on the matter of managing customers churn. The steps of formulating a predictive model require the knowledge of the rules of the company first to make the improvement on quality of data easier. Therefore, the first step will involve understanding the business by evaluating all the customers that left. The next step will be understanding the data that is shared in the text files even those exported anonymously ( Lu, et al, 204 ). Data pre-processing would be the next step whereby rough data from contract, demographic and customer behavior attributes would be revised to form finer and reliable data. When everything is ready, the following step involves the transformation of data where the results are normalized. The last step of the predictive model consists of feature selection in the forms of relevant attributes with the help of algorithms.
Identify technologies that you can use to construct the predictive model and highlight their pros and cons
The construction of a predictive model for customers churn involves several technologies that have both some advantages and disadvantages. Predictive algorithms are one of the technologies used in the construction of the predictive model. The advantages of the predictive algorithms include the ease of sorting natural language, providing solutions to problems and is easy to understand. Unfortunately, algorithms could give bias data and therefore unreliable predictions. The machine learning technology does the data labeling role of categorizing information appropriately. Machine learning enables automation of tasks and provides quality information from large and complex data ( Aleksandar, et al 2016 ). If the technology isn’t checked properly, it could result in numerous errors such as improper categorization or generalizability. Data mining technology helps in the extraction of the required information from large volumes of databases. The technology is great at flaw detection and pattern generation. The cons of data mining include privacy and security issues of the data collected.
Why “voice of the customer” carries much more insight into churn analysis and prevention
The voice of the customers is the most relevant aspect in attacking customers churn for a number of reasons. The most fundamental reason for the consideration of the customers’ voice and opinions is the fact that the cost of retaining old customers is usually lower than that of seeking new ones who no one can guarantee their loyalty. Competitive business enterprises put a lot of emphasis on their customers’ lifecycle to ensure that they remain loyal to the brand. Hence, by listening to the voice of the customers, a company can keep its customers pleased ( Bendre & Thool, 2016 ). When a company uses text analytics, it can identify the factors that cause churn and address them immediately to stop the possible drop-outs. The insights of the customers usually drive changes in any organization. If the organization consider the voices of its customers, the results would be improved customer experiences, better decision making, high revenues, and excellent operations which are good for business sustenance.
Suggest at least one churn prevention method that you can use to reduce your churn rate based on your model.
Text analytics is one of the best ways of attacking customers churns yet it also commits some mistakes that may not reduce the rate of churn even when using the predictive model. Therefore, one suggestion of churn prevention method would be to include a wider variety of customers in the survey and data analysis. For instance, instead of only sampling the company’s existing customers and those who left, the model could also involve samples of the competitors’ customers as well (Lin, 2014). The other prevention method would be for the company to first map all their customers’ journey before proceeding to predict the churn rates based on the model then implement the necessary marketing actions that would cause customer retention on all.
The issue of customers churn has been a worrying case in most businesses especially in telecommunication and using text and web analytics alone may not provide the desirable solutions. Apart from relying on the unstructured data alone, it is prudent to also use a few tools from the structured data to help in resolving the customer churn crisis. When coming up with a predictive model for the customers churn, the company should also ensure to watch out for the pros and cons of the technologies used and take up prevention measures for best results. Above all, listening to the customers’ voice is usually the most vital aspect in attacking the customers churn with text and web analytics since it is less costly to work on retaining existing customers than attracting new ones.
References
Aleksandar, P. J., Biljana, S., Slobodah, Kalajdzi, & Kire, Trivodaliev. (2016). Analysis of Churn Prediction: A case study on Telecommunication Services in Macedonia. 4 th Telecommunications Forum TELFOR 2016. Serbia, Belgrade, November 22-23. www.academia.edu>Analysis_of_Churn ...
Bendre, M. R., & Thool, V. R. (2016). Analytics, challenges and applications in big data environment: a survey. Journal of Management Analytics , 3 (3), 206-239.
Lin, N. (2014). Applied business analytics: Integrating business process, big data, and advanced analytics . FT Press.
Lu, N., Lin, H., Lu, J., & Zhang, G. (2014). A customer churn prediction model in telecom industry using boosting. IEEE Transactions on Industrial Informatics , 10 (2), 1659-1665.
Sharda, R., Delen, D., Turban, E., Aronson, J., & Liang, T. P. (2014). Businesss Intelligence and Analytics: Systems for Decision Support-(Required) . London: Prentice Hall.
