How Natural Language Processing (NLP) Systems Change the Business Intelligence Arena and Enhance Measurement Systems
A firm’s success lies in its ability to sell products and services in a competitive market place. When the firm achieves this objective, it gets the reward of higher margins, increased revenue and increased shareholder value. A lot of human to computer information is in the form of text language. This requires a large staff number to do research and sort the relevant information, then compile their findings into a knowledge database. In this section, this essay examines how natural language processing enhances the functionality of business intelligence and aids the development of performance dashboards.
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Natural Language Processing is a computerized approach to text analysis. It uses a set of theories and a set of technologies to do text analysis, and presents only relevant information to the user. This happens as the system identifies concepts and relationships using a given domain ontology (Hladky, n.d.). Semantic web is an example of a NLP system technology that promises to facilitate this kind of work. With this technology, data processing happens directly or indirectly by machines. The result is a reduction in people-hours and an acceleration of decision-making (Gruber, 1993).
Business Intelligence encompasses software tools for querying, reporting and analysing. It may be summarised as the processes and tools that turn data into information (Parsons, 2007). The information obtained leads to the creation of knowledge and plans that drive effective business activities (Eckerson, 2011). Business intelligence (BI) is not the same as operating systems. Although both systems generate reports, BI provides the necessary solutions to the inefficiencies of information gathering that otherwise require a lot of people-hours every year (Eckerson, 2011). Business intelligence creates accurate and consistent data that promotes sales and credibility of the firm.
NLP techniques allow managers to describe their situational awareness (SA) using a natural language and input the SA description to the system in a simple way (Niu, Lu, & Zhang, 2009). The use of NLP systems reduces the need for management to use structured language to obtain or input raw information into the system. The NLP system enhances data capture since managers relate to the system as they would with another human in a natural conversation.
The combination of NLP and BI leads to a real time situational awareness for managers, which greatly aids their decision making process. NLP systems create a natural input output function to Business Intelligence. The enhanced business intelligence system becomes more capable of data analysis and output visualization. In addition, BI systems that integrate into all departments of the firm provide a simple and reliable groundwork for the establishment of a performance dashboard.
Appropriate Metrics for the following positions CEO, CFO, VP Manufacturing, VP Sales
A number of metrics exist to measure different business effectiveness in terms of meeting the market demand at the minimum cost (Nugent, 2002). As a result, there is a need to have a clear understanding of the relevant available metrics to choose the best (Petersen et al., 2009). This section suggests appropriate metrics for different management positions in a firm.
Brand value metrics would be appropriate for CEO and CFO positions. Brand equity positively relates to the firm value and the main task of the CEO is increasing the firm value (Madden, Frank, & Susan, 2006). CEOs answer to shareholders who need information relating to the performance of the firm. Under brand equity, Knowledge metrics measure the brand’s awareness in the various stages of recognition. Successful brands have a high score on awareness and association. Another brand equity metrics is performance metrics that measures the competitiveness of the brand in the market in relation to competing brands. The CFOs position would measure the effectiveness of its strategy using the financial metrics that measure the brands monetary value (Branding Strategy Insider, 2009).
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Customer value metrics measure the customer lifetime value to the firm for the basis of selecting the most profitable customers during marketing campaigns. Word of mouth and referral value metrics are appropriate for VP sales because they measure the effectiveness of the sales department of the firm. Satisfied customers are likely to engage in word of mouth referrals of the company than unsatisfied customers (Bourne, 2008). Customer retention and acquisition metrics would suffice for VP sales positions and CFO positions because it measures the number of customers the business acquires, and how many remain loyal to the business. Cross-buying and up-buying metrics classify the type of customers that the firm acquires into categories based on their spending pattern and therefore fall under the VP sales position (Petersen et al., 2009).
Multichannel shopping metrics measure how the different shopping channels belonging to the firm influences the customer. In this metric, the customer behaviour and profitability form prominence. Product return metrics fall under the VP manufacturing docket. The metrics measures the number of faulty products that reach the market. It indicates the efficiency of the manufacturing department in avoiding errors and delivering quality products. The VP manufacturing position’s effectiveness can also use customer value and word of mouth and referral metrics to measure the superiority of the firm’s product quality in the market (Petersen et al., 2009).
Bourne, M. (2008). Performance measurement: learning from the past and projecting the future. Measuring Business Excellence, 12(2), 67-72.
Branding Strategy Insider. (2009). The metrics of brand equity. Web.
Eckerson, W. W. (2011). Performance dashboards: measuring, monitoring, and managing your business (2nd ed.). Hoboken, NJ: John Wiley & Sons.
Gruber, T. (1993). A translation approach to portable ontologies. Knowledge Aquisition, 5(2), 199-220.
Hladky, D. (n.d.). Sustainable advantage for the investor relations team through semantic content. Web.
Madden, T. J., Frank, F., & Susan, F. (2006). Brands Matter: an empirical demonstration of teh creation of shareholder value through branding. Journal of the Academy of Marketing Science, 34(2), 224-235.
Niu, L., Lu, J., & Zhang, G. (2009). Cognition-driven decision support for business intelligence: Models, techniques, systems and applications. Chennai: Springer.
Nugent, J. H. (2002). Plan to win: Analytical and operational tools – Gaining competitive advantage. New York, NY: McGraw-Hill.
Parsons, J. (2007). Measuring to learn whilst learning to measure. Measuring Business Excellence, 11(1), 12-19.
Petersen, J. A., McAlister, L., Reibstein, D. J., Winer, R. S., V, K., & Atkinson, G. (2009). Choosing the right metrics to maximize profitability and shareholder value. Journal of Retailing, 95, 95-111.