Predictive Analytics Critical Thinking Example

Predictive Analytics covers procedures about the aspects of questions of what is expected to happen, and when it is expected to happen. Predictive analytics involves using of algorithms, data, and rules involved in the business to facilitate a forward oriented initiative process. Predictive analytics is not a definable science rather discipline majorly concerned with business issues that involve using mathematical procedures in its implementation. Customer Service refers to the practice of ensuring satisfaction of the customer with a service or product. In most circumstances, customer service happens while executing a transaction with the customer, like conducting a sale or receipt of returned items. Customer service may involve a phone call, self-service, an in-person interaction, or other methods of ensuring customer satisfaction. The after- sales service offered by contact centers is covered under customer service. The after-sales services could either be non-technical or technical concerns that are experienced by the customer with regards to goods or services that were rendered by the company. In most cases, there exist ease of contact, through email chats, phone contact, and web comments (Game Changer, 2015). Field Service is terminology that reflects the after-sales service offered by an organization through directing an expert or a team of experts to the customer location to resolve a concern-technical or non-technical- raised by the customer while using a product or service rendered by the company. Servicing in the field usually takes place in three steps- the customer sends a formal communication to the company concerning the issue, the customer services division deliberates on whether to involve a field service team and after a decision has been made the field team is dispatched to the customer's location.Challenges addressed in the ArticleEnsuring customer satisfaction and loyalty to counter competition The article examines customer satisfaction as a post-consumption judgment by the customer concerning service quality and product quality against pre-consumption expectations. The article indicates that the critical nature of the relationship between loyalty and satisfaction is considerably influenced by the setting differences in competition. Accounting for the differences that exists allows organizations to elevate their abilities significantly to transform customer satisfaction effectively to customer loyalty by that maintaining a competitive edge. Customer satisfaction and loyalty is indicated as a major challenge in marketing due to the profitability impact of maintenance of a loyal customer base (Wu & Coggeshall, 2012). Customer loyalty and satisfaction is pointed out in the literature as leading to organizational profitability since customer loyalty influences product performance in the marketplace positively. Additionally, customer loyalty is an indication of customer retention. Loyalty is a metric for an organization`s profitability since loyalty is a measure of a customer`s intention to repurchase a service or product. Competition has a direct impact on customer satisfaction. One the visible effect is that increased competition that results in substitutability of substitute brands lowers a customer’s will to retain or stick to one brand. Competition effects loyalty in the level of satisfaction derived by the customer from a product. In a conclusive, note the article emphasizes the need to ensure customer satisfaction that leads to loyalty as a key challenge that must be addressed to maintain a competitive position against other brands available in the market (Game Changer, 2015).Cost reduction and building a profit-oriented portfolio Cost reduction is essential to an organization to ensure profitability. Successful implementation of cost cutting strategies proves to be a challenge to many organizations. The potential negative impacts on reputation of an organization and meeting of key consumer needs because of cost cut are a challenge to driving customer service. Cutting costs at the expense of the customer satisfaction could potentially lead to losing of customers by an organization. Containing costs is essential to ensuring a stable profitability but it is key to ensuring a customer satisfaction. The current economic situation present both an opportunity and risk to business organizations. Consumers tend to display characteristics that extend through economic cycles. Customers loyal to brands tend not to forgive or forget violations or opportunistic actions against their trust, but excellent service will always be appreciated. The risk of damaging reputation with core loyal customers as an organization implements cost reduction procedures are high because of increased customer awareness. As much as the cost reduction procedures present challenges, it creates an opportunity to smart organizations to take advantage of organizations that fail in their execution of cost reduction strategies (Wu & Coggeshall, 2012).Recommended solutions to the challenges Customer service department heads are charged with duties of monitoring and managing a variable metrics known as KPIs – key performance indicators. The performance indicators are used to track the procedural progress towards the meeting of the challenges and attaining organizational objectives. The key performance indicators used to steer performance towards objectives are satisfaction and loyalty, resolution, operations and productivity, cost and revenue. The Satisfaction and Loyalty category comprises indicators that are precisely aimed at understanding whether a customer feels satisfied by the after-sales service offered by the contact center of the company and if the customer will likely continue to transact with the company in future. Examples of the parameters of satisfaction and loyalty include brand satisfaction, net promoter score, recommendation likelihood, satisfaction with an agent, and repurchase likelihood. Some categorizations levels involved in the resolution include first contract, the second contract resolutions depending on the urgency level required (Game Changer, 2015). In the event a resolution is needed, the resolution performance indicator is employed. When customers contacts the organization`s customer service department to seek assistance in resolving an issue, be it technical or non-technical, that they might be facing from the use of a product or service they procured from the organization. The customer service department involves the resolution performance indicator to address the customer`s concerns as quickly as possible to ensure their satisfaction hence instilling loyalty. Some categorizations levels involved in the resolution include first contract, the second contract resolutions depending on the urgency level required (Wu & Coggeshall, 2012). Operations and productivity performance indicators are applied primarily in monitoring the organization's production system to ensure quality that meets the customer’s needs. Some of the elements monitored and managed under the category to ensure quality include utilization by the customer, incoming contact volume, occupancy adherence to schedule and handle time. Cost and Revenue category focus primarily on cost reduction practices that improve a company`s profitability. The cost reduction is achieved through selling by the service channel established by the company`s customer service. Elements that are considered in cost reduction and revenue increase include revenue per contact, the actual cost vs. budget estimates, and sales conversion rates.How predictive customer service facilitates operations With the introduction of key performance indicators, customer service has been successful in attaining the objectives of improving customer satisfaction and ensuring quality hence ensuring a competitive position, reducing of cost per contact and transformation from cost centering to profit centering. All this is facilitated through motorization and management of satisfaction and loyalty category, resolution category, operations and productivity category and revenue and cost category. Predictive analytics facilitates significantly impact on customer service for introduction of new products. When product lifecycles contract and innovations levels continue to diminish, prediction what the contact centers expect from new product lines continues to get difficult and complex. Leading organizations employ predictive analytics in predicting incoming contact volumes, to appropriately employ staff to their contact centers in anticipating the new products (Game Changer, 2015). Technological advancements have significantly influenced predictive analytics- with the introduction of social media and video technology. The aspect has facilitated the ability to incorporate different data types -text, audio, video, numbers, and text- the huge data capacities make it easy and accurate making predictions.Takeaways The cost incurred in the implementation of the key performance indicators is massive. The successful implementation of the technique to achieve the desired results is time-consuming. As a result, small organizations with limited financial capabilities cannot afford to implement predictive analytic techniques to drive customer service and compete effectively.Limitations of the techniqueHistorical data must be available for predictive analytics to be successful Predictive models depend on historical data and require quite large data set for it to be efficient in determining significant aspects from fewer significant aspects. So it is important to collect data dating back in time or the ability to recreate data representing different time periods. Predictive analytics requires consideration of the minimum rate of the characteristic that is tried to be predicted. An example is trying to identify spam emails from targeted emails of customer communications, it is essential to create a predictive model based on past email qualities to successfully identify spam traps. Though if spam emails consist of a figure less than 0.1 percent of the email database available it is challenging for the created model to identify spam emails efficiently from significant emails. Predictive modeling is characterized by high resource and time consumption during the initial creation stage; the need for constant maintenance as the models depreciate over time because of changes in the variables involved. So before initiation of a predictive model project, careful balancing of the benefits that be experienced from the model against the resource and time invested in the model (Wu & Coggeshall, 2012). Occasional low accuracy levels. Predictive model have excellent prediction accuracy, but it occasionally proves imperfect. So in cases where accuracy is not guaranteed, predictive model cannot be relied on rather the involvement of other techniques should be applied. When the accuracy levels required are relatively low, simple techniques like business analytics, logic, correlation and profiling models can be applied successfully. The simple techniques are much cheaper and quicker. Complexity in its nature is a challenge. It’s significant to have stakeholder accept the use of the predictive model before investing heavily in a complex process that is less understood by shareholders. Predictive modeling is complex and not widely understood concept this aspect creates difficulty to analyst who articulates findings obtained from models. Neutral Network is an example of techniques with extreme accuracy level, but complex to explain. This aspect often lead to stakeholders questioning the effectiveness of the model and sometimes overlooking the model during decision-making.

References

Game Changer. (n.d.). Retrieved February 5, 2015, from http://www.analytics-magazine.org/july-august-2010/128-predictive-analytics-game-changer
Predictive Analytics: 4 Key Constraints. (2012, January 10). Retrieved February 5, 2015, from http://www.aryng.com/blog/predictive-analytics-4-key-constraints/
Wu, J., & Coggeshall, S. (2012). Foundations of predictive analytics. Boca Raton, FL: CRC Press.