Analytics for JC Dollar

¶ … Business Analytics Project

JC Dollar Analytics Strategy

The $10M investment in creating a customer loyalty program has set the foundation for capturing, aggregating, analyzing and making recommendations from customer's preferences and expectations that are not being delivered in experiences today. Continuing to pursue a price reduction strategy in an attempt to increase sales has proven ineffective, which is further validation to JC Dollars store management that their stores operate in an inelastic market. Additional data on price elasticity captured as part of the customer loyalty program will over time will continue to underscore just how inelastic the chain's pricing is based on the commodity-like nature of clothing and accessory retailing. Of the many benefits of analytics, one of the most valuable is being able to gain insights into pricing and purchasing behavior for specific product and service categories, providing decision makers on which value-add strategies are most effective (Gnatovich, 2007).

The business problems that JC Dollar is fixing revolve around incorrect assumptions about customer loyalty. Combining customer loyalty data and predictive analytics will provide a unique glimpse into their customer base not seen before. One of the most significant contributions of predictive analytics is the ability to take massive data sets and succinctly gain insights through the use of advanced predictive analytics and Big Data-oriented analytical tools (Sharma, Dadhich, 2014). In addition, JC Dollar's modifications to their pricing strategy and merchandising mix may be contributing more indirect customer loyalty than the company realizes. With predictive analytics, the individual contributions of each component of a strategy can be isolated provided a suitable methodology has been defined (Davenport, Harris, Emberson, 2007). Competing on price alone, making the incorrect assumption JC Dollar customers are only driven on price, will be proven wrong when this level of granularity is applied to the current and future conditions of their business. What's missing from the scope of business problems the company is the measurement of the customer experience being delivered in the stores. One of the most underrated aspects of predictive analytics is the ability to track attitudinal data in addition to the many more easily quantified metrics of customer behavior (Sharma, Dadhich, 2014). It's reasonable to assume that the JC Dollar sales problem may not have anything to do with pricing but instead with the in-store experience and shoppers in their target audiences may just be bored with the experiences of being in their stores. The lack of enthusiasm for their products and the stores is what's potentially driving the sales drop, not their pricing. Using analytics to measure how pricing reductions may have actually hurt their brand is also possible and often done in industries that have commodity-like pricing structures (Schauer, 2004). Another business problem that the company must contend with is the grim reality that their product lifecycles may not be fast enough to keep up with the preferences, perceptions and expectations of their customers. In retailing the speed of new product development and introduction often dictates which store chain will be profitable or not. Predictive analytics also needs to be applied to the challenging issue of planning product lifecycles that are very short, which are common in retailing. Using predictive analytics for product development and the timing of new product introductions can significantly improve the execution of this critical strategy (Schauer, 2004). With nearly all retailers getting at least 70 of their revenues from their new product introductions, this issue of the product strategy, its timing, seasonality and measuring customers "expectations vs. experiences all needs to be taken into account in the context of this study. Finally customer loyalty is not a fixed or predictable investment, it is rather a relationship built with customers over time that must be invested in with new, exciting, interesting products and serves. Analytics will help to show that customer loyalty must be constantly earned over time.

Planned Process

The two hypotheses that senior management have initially chosen for this study include the following:

Whether or not the loyalty data at JC Dollar can be effectively leveraged to increase customer loyalty, and lead to improved business results.

A broader, enterprise-wide analytics strategy for JC Dollar and how it would enable the company to more effectively compete through analytics in the future.

Based on these two hypotheses, the methodology for the study needs to concentrate on accurately capturing and analyzing the results of an ongoing effort to capture customer data. This needs to begin with the selection of the best approach to interviewing customers and gaining their insights. Best practices in retailing research continue to rely on a multichannel approach to data collection, especially as it relates to loyalty programs. In designing a predictive analytics model to better understand any business, respondents (or customers being interviewed) need to have the flexibility of participating using channels, methods and tools thy are most familiar with (Yeoman, 2009). As the demographics of JC Dollar customers are young, affluent and very fluent with social media and networks, the survey will be heavily promoted on Facebook, Twitter, Snapchat and other social media sites. Based on this insight it is recommended that JC

Data Collection Phases

Dollar take the following phased approach to the data collection effort:

Phase 1: Using an online survey application such as SurveyMonkey or comparable application, JC Dollar needs to create an online survey that can be easily repurposed across all online channels from e-mails, to landing pages configured on their website, to embedding the questionnaire in their blog posts and all other forms of online media.

Phase 2: Announce the customer survey through the customer loyalty program first, offering a $10 gift certificate to everyone who participates. This will significantly improve response rates with the resulting data set providing inviable insights into what can be done to improve JC Dollar's sales performance.

Phase 3: Launch to all customers and offer a $10 gift certificate if they become part of the loyalty program and complete the survey. Using e-mail combined with landing pages on the website, this approach will prove highly effective in gaining greater response levels as well. Index Phase 4: Create an online customer advisory council of the most vocal customers on social media and across all other channels of communication with customers.

Analysis Phase

As all online survey tools today have a baseline level of functionality that includes statistical analysis features, the surveys will be immediately analyzed as they come in. Once thirty days has passed from the surveys being delivered, a formal report will be produced and presented to management. From that point on, monthly reports will be produced that provide insights into how the loyalty program is working.

Potential Outcomes

Creating an entirely new knowledge base to plan and execute retail strategies is essential for JC Dollar to move beyond their downward spiral in sales and revitalize their business. Using predictive analytics the company can more effectively understand how each component of their strategy is assisting in attaining that goal (Sharma, Dadhich, 2014). In addition, HC Dollar will understand how their customers define value, which is unknown today as the company is competing primarily on price.