One of the problems that a firm may face is marketing. A firm may experience challenges getting new customers for its business, expanding brands, or keeping up with global industry changes. As such, having working solutions is imperative in keeping the company running. Data analytic tools, including prescriptive, predictive, descriptive, cognitive, and visualization, are methods that firms can use to scale their marketing and get good results. In the wake of digital marketing, data analytic tools have helped transform the business landscape and unveil the significance of data-driven decision-making.
Data Analytic Tools
Descriptive
Descriptive Analytics is one of the simplest forms of analysis that answers the question, “what happened?” It involves interpreting historical data to determine the changes occurring in the organization. Using a range of historical data, the company can identify performance from previous years (Emeritus, 2022). A company struggling with poor marketing can benefit from descriptive analytic tools as it helps in benchmarking progress and creating a foolproof marketing strategy using historical and present data.
Predictive
Unlike descriptive marketing, which uses historical data to analyze current trends, predictive analytics uses data to predict marketing trends. Companies can create effective marketing strategies by leveraging data with predictive Artificial Intelligence (Lobinski & Tarka, 2014). The analytic tool allows firms to be forward-looking and make decisions based on collected data, not assumptions. Firms can use marketing tools to get new consumers, retain existing ones, and increase profits.
Cognitive
Cognitive analytics applies human intelligence to specific tasks within the firm to solve business problems. By initiating human brains to do tasks, the firm can draw inferences from existing patterns that allow it to make informed decisions. Cognitive analytics collects and makes real-time data sources such as videos and audio used to make business decisions. To maintain their competitive advantages, data analysts must examine the trends and potential impacts and adjust their business models effectively (Ghosh, 2020). Such tools can effectively make informed marketing decisions for companies to scale their operations.
Prescriptive
This analytic tool uses data and machine learning to help firms determine their next course of action. Marketers can use the tool to determine the best marketing strategy for their customers. By using prescriptive tools such as lead scoring, firms can increase sales and turn potential customers into actual customers (Cote, 2021). It can inform marketers of product developments and improvements, which enables them to survey customers and determine their likes and dislikes.
Appropriate Data Analytic Tool.
The predictive analytic tool is the most appropriate tool for a company with poor marketing strategies. The device uses data models, statistics, and artificial intelligence to predict business events. In marketing, organizations can use these tools to make informed decisions regarding media planning and buying. Using this tool will enable marketers to determine the working campaigns and choose the appropriate marketing strategy that can boost sales. Unlike other data analytic tools, predictive tools allow firms to run scenarios for business planning, which helps determine business prospects (Camilleri, 2020). This is significant in helping the firm make informed marketing decisions that ultimately increase sales.
The predictive analytic tool is significant in strengthening marketing strategies as it reveals the effects of increasing or decreasing expenditures to maximize the marketing mix. This helps guide spending allocations in particular media (Camilleri, 2020). Since a company with poor marketing strategies already lacks profit maximization, using analytic tools that highlight expenditure patterns and help the company cut costs is essential in reducing losses. The tool could also reveal the impact of advertising on consumer behaviors as they show the relationship of customers with the market conditions and competitors.
Consumer response data on the marketing activities are fed to the analytic tool to investigate the organization’s spending. Such a method can generate realistic information and marketing recommendations to help the firm scale its profits (Camilleri, 2020). For example, the analytic tool can create what-if scenarios and measure outcomes, which help in making informed marketing decisions. With the increasing competition in many industry sectors, using the predictive analytic tool will help understand the marketing strategies that generate the best results for the company. An effective strategy allows the firm to have a competitive advantage in the market leading to increased profits.
The fast-changing technology means that marketers must introduce strategies that allow them to stay in the competition. Predictive analytics reduces the uncertainty that firms may experience in the business world. It enables an organization to identify a targeted group likely to respond to a product positively (Lobinski & Tarka, 2014). Descriptive techniques such as clusters would allow marketers to remember customers close to each other. The method helps them identify the consumers that will stay, identify the period that a consumer will likely make a purchase, and determine methods to maximize revenue (Lobinski & Tarka, 2014). The analytic tool is crucial in ensuring marketers get returns from marketing by creating strong marketing strategies that guarantee success.
Firms can use descriptive analytic tools to boost marketing by employing data visualization tools such as pie charts and statistical tools that provide information on the firm’s functions. They provide inventory and sales reports that help marketers spot anomalies and outliers that require further assessment (Chatterjee, 2022). When used together with predictive and prescriptive tools, descriptive analytics can help identify business strengths and weaknesses. Marketers can use strengths and weaknesses to identify marketing areas that need rebranding or strengthening, hence increasing profits.
Descriptive analytics also help organizations identify customer preferences and how they change based on different factors. Such behavioral patterns help companies use data-driven methods to strategize marketing campaigns (Chatterjee, 2022). An example of a company that uses descriptive analytic tools is Netflix. The company uses large amounts of data in platforms to identify trending movies, which allows it to make accurate recommendations to users. This also helps the company to make internal decision-making for future production.
Marketing research is an area that significantly benefits from descriptive analytic tools. It allows firms to gain valuable insights from focus group data such as surveys. These data are essential in identifying diagnostic tools that help to investigate correlations and make plans for the company. Product improvements and effective marketing campaign strategies are significant in improving company profits. Predictive analytics provides a course of action for future events based on the predicted outcome. For a company struggling with poor marketing strategies, this analytic tool will help them analyze prospects and consumer behavior patterns, influencing marketing campaign strategies that guarantee maximum returns.
Potential Challenges
Despite the solutions that predictive analytics brings to businesses, the tool also has some challenges. Like any other new technology, predictive analytics requires expertise for effective implementation, which may be costly or challenging. This can be inherently limiting as firms must hire dedicated science analysts, which is expensive. It can also lead to difficulty integrating analytics into information technology systems (Attaran & Attaran, 2018). When there is no skilled personnel for implementation, firms may fail to use the predictive results in a manner that generates profits. Predictive analytics may fail to bear suitable fruits for companies that do not employ qualified personnel.
Another challenge in implementing the predictive analytic tool is that it can be uncertain and complex. Its completeness is limited to the data accuracy collected (Lepenioti et al., 2020). Therefore, any deficiency in the data may result in inaccurate results. Companies that want to integrate the analytical tool must ensure that the data used are accurate and depict the company’s actual state for reliable results. For example, in marketing, since a customer retention model is built using customer service histories, the model must use data that shows accurate sales and returns for accurate prediction. The complex nature of predictive analytic tools might be challenging for firms unfamiliar with the techniques. Therefore, companies must hire professionals familiar with the analytical tool for effective results. For effective marketing strategies, companies must ensure that they use reliable data to avoid uncertainties in predictions.
Visualization Tools Beneficial to the Firm
Data visualization tools are software designed to help firms visualize information being processed. The capabilities of these tools vary, but all of them, at basic, allow data managers to visualize and manipulate data. By using visualization tools, companies are able to understand and assess trends, outliers, and patterns in data that can be used to make informed decisions. They include Microsoft excel, Google charts, Zoho analytics, and tableau. Tableau is the visualization tool most beneficial to a firm struggling with marketing.
The hallmarks of great marketers have the flexibility to promptly adapt to change and leverage existing tools to achieve objectives using single programs. Tableau allows marketers to manipulate data easily by watching them drag and drop. Its comparative advantage over other visualization tools is that it can use huge data sets to infiltrate data in user dashboards where they can analyze patterns (Batt et al., 2020). It has a user interface that is easy to use and hence can be easily implemented in firms that are not familiar with visualization tools. Compared to other visualization tools, the non-complex nature of tableau makes it easier for firms to install and get positive results that aid in marketing campaigns.
Tableau visualization tool can be used as an effective methodology to test market structures. Testing the market using tableau helps marketers gain insights into marketing competition structures (Batt et al., 2020). The methodology enables users to analyze trends in the marketing environment, which help them create strategies in their marketing campaigns that influence sales positively. Through it, managers can use simple charts and trends without learning to code. Its user-friendly interface allows technical and non-technical data experts to use visualization to analyze market trends, which is useful for making informed marketing decisions.
Trend lines are significant in data visualization. Tableau gives users multiple trend lines that they can use to make marketing decisions after analysis. These trend lines show different patterns and trends in data that can help understand the market (Akhtar & Tabassum, 2020). For marketers, this is vital in investigating customer preferences, tastes, and sale patterns which is helpful in influencing marketing campaigns. The trend lines from tableau allow marketers to create products that match demand, increasing sales. It also helps marketers with visual figures used to benchmark figures and compares them with competitors.
Tableau visualization can help forecast market trends when used alongside predictive analytic tools. It shows recommendations that are then used to create machine-learning algorithms (Akhtar & Tabassum, 2020). Through it, marketers can suggest views from data that they viewed and the ones with similar preferences. Using tableau also enables users to create dashboards and get insights into understanding the competitive market. Marketers can make decisions from the data displayed in the dashboard that reveals consumer patterns and behaviors.
Effective marketing strategies are vital for a firm to reach its maximum potential. However, with effective data analytic tools, firms can boost their profits and sales. Predictive data analytics and tableau visualization tools are effective marketing strategies, especially for firms with problems breaking even. The two tools allow the company to make informed decisions based on future forecasting. Marketers should consider using analytic tools that guarantee techniques to help their firms have a competitive advantage.
References
Attaran, M., & Attaran, S. (2019). Opportunities and challenges of implementing predictive analytics for competitive advantage. Applying Business Intelligence Initiatives in Healthcare and Organizational Settings, 64-90. https://www.researchgate.net/publication/325934828_Opportunities_and_Challenges_of_Implementing_Predictive_Analytics_for_Competitive_Advantage
Batt, S., Grealis, T., Harmon, O., & Tomolonis, P. (2020). Learning Tableau: A data visualization tool. The Journal of Economic Education, 51(3-4), 317-328. https://www.tandfonline.com/doi/pdf/10.1080/00220485.2020.1804503?needAccess=true&role=button
Camilleri, M. A. (2020). The use of data-driven technologies for customer-centric marketing. International Journal of Big Data Management, 1(1), 50-63. https://www.inderscienceonline.com/doi/pdf/10.1504/IJBDM.2020.106876
Charterjee, D. (2022). What is Descriptive Analytics and Why a Data-Driven World Needs it. Emeritus. Retrieved 16 February 2022, from. https://emeritus.org/blog/business-analytics-what-is-descriptive-analytics/
Cote, C. (2021). What Is Prescriptive Analytics? 6 Examples. Havard Business School. Retrieved 16 February, 2021, from. https://online.hbs.edu/blog/post/prescriptive-analytics
Emeritus (2022). What is Descriptive Analytics and Why a Data-Driven World Needs it. Retrieved 16 February 2022, from. https://emeritus.org/blog/business-analytics-what-is-descriptive-analytics/
Ghosh, P. (2020). Fundamentals of Analytics. Dataversity. Retrieved 16 February 2022, from. https://www.dataversity.net/fundamentals-of-cognitive-analytics/#
Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020). Prescriptive analytics: Literature review and research challenges. International Journal of Information Management, 50, 57-70. https://www.sciencedirect.com/science/article/pii/S0268401218309873
Łobiński, M., & Tarka, P. (2014). Decision Making in Reference to Model of Marketing Predictive Analytics–Theory and Practice. Management and Business Administration. Central Europe, 22(1), 60-69. https://www.researchgate.net/publication/271312087_Decision_Making_in_Reference_to_Model_of_Marketing_Predictive_Analytics_-_Theory_and_Practice
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