Question

Can someone please explain these steps from this data description

All of Bubba Gump’s data has recently been integrated in a data warehouse. That enterprise data warehouse was built specifically to support data mining initiatives like the one you have been assigned to conduct, by consolidating data from multiple operations and channels in one place and integrating the data across sources for a complete view of the customer experience. For the first time, Bubba Gump analysts can link sales transactions to specific customers at specific restaurants, for example. It also means that you can link customer transactions across channels; that is, for any given customer, you can link to both their restaurant purchases, their online purchases, and (in some cases) their purchases from third-party retail partners.

You have been selected to develop and execute the data mining analysis plan for Bubba Gump’s customer analysis project. Your project will be the first major data mining project conducted against the new Bubba Gump data warehouse. Because Bubba Gump’s data was not previously integrated in a single data warehouse, company leadership has never been able to analyze its customers across their complete experience. In other words, customer restaurant purchases, online purchases, and third-party retailer purchases could not be analyzed together previously; each channel had to be analyzed separately.

As a first step, a sample of 500 customers has been selected from the analytics data warehouse and given a survey in exchange for purchase credits at one of Bubba Gump’s sales channels. The survey sample was selected from the universe of customers who have made purchases from at least one Bubba Gump outlet (restaurant, web store, etc.). Responses to various customer satisfaction questions were recorded, and historical purchase information has been extracted from the data warehouse for each customer in the sample.

To answer these questions I am having trouble to know what these questions wants if it is ok can someone please explain this to me thank you.

Your task is to analyze the survey responses to understand whether there are natural “clusters” within Bubba Gump’s customer population. You are then to
create a visualization of this survey data that describes Bubba Gump’s customers across any dimensions that define those subgroups.
Your Assignment
In your response, address the following critical elements:
Analysis Tools
What data mining tools will you use to perform the analysis?

Why these particular ones?

Data Visualizations
What data visualizations will you use in your report, and why?

Research Question
What is the specific research question that needs to be addressed?

What research question will you work from in order to analyze the given data for meaningful
patterns?
Research Measurement
How will you determine if your research question was answered or if your hypothesis-generation was successful?

How will you measure progress?

Follow-Up Questions
What are cogent follow-up questions or explorations that should follow from your initial research?
Research and Support
Are there any published sources or other resources that address your line of inquiry? Where do they fall short? How will they help guide your analysis?

Answer 1

ANSWER:

In previous phase Bubba Gump Shrimp be doing the analysis unconnectedly for all its commerce lines i.e. offline restaurants, online services and third party retailers. So, the customers were not interlinked with every additional in all 3 categories. It may be probable that the “same customers” availed the check from all 3 channels but Bubba Gump would never know this insight about its customers because the channels were not organized according to their previous methodology.

So, they misused their style by keeping all the data in a data-warehouse.

Current Phase of Analysis:

At the present, next onto the current phase, we have statistics from all 3 major categories store in a well arrange scheme or we can state the data is now in regular form where the profit of such form is that:

1. We can simply map individuals ‘same customers’ who are availing our defense force from different channels. For example: Suppose there is a customer named “xyz” and he/she availed services from all 3 sectors i.e. one business from each ‘offline restaurants’, ‘online services’ and ‘third party retailers’, now depending on consumers details in each category we can map his/her all the transactions under his/her unique ‘CustomerID’ using the power of data warehousing, so instead of having 3 different transactions in which we didn’t knew that whether these 3 dealings belong to the same customer or not which was the case in previous style. So, once we do this type of map we will get to know about our customers and can prioritize our customer base consequently.

2. since the sales of Bubba Gump have decline in last 2 years, to examine the sales data and to figure out the potential explanation we can opt for a data mining system called Time Series Analysis, as the name suggest examine the data with deference to time. Because there was a decline in sales for last 2 years, we can look out at the factor due to which this declination is happen. A person can look at the present trend, or can look at cyclic disturbance in the data i.e. something like a produce which Bubba Gump is offering for last 5 years but had gone out of tendency for last 2 years so there is no logic in continuing with that result as it is out of trend instead of that they can look for a product related to their product by doing a promote research and the one which is in fashion.

3. Talking about the ‘data mining technique’ that can be used to separate out the customer base into “High Priority”, “Medium Priority” and “Low Priority” customers is usually known as   
RFM-Analysis, it stands for Recency Frequency Monetary-Analysis.

(i) RFM (Recency, Frequency and Monetary): model is widely applied in many practical areas, mostly in direct market. By adopt RFM model, decision makers can efficiently identify priceless consumers and then develop effectual marketing strategy.

(ii) Integration of RFM analysis and data mining technique provide helpful in order for present and new customers. cluster based on RFM attribute provides more behavioral knowledge of customers’ actual selling levels than other cluster analyses. Classification rules discovered from customer demographic variables and RFM variables provides useful information for managers to predict future customer behavior such as how recently the customer will probably purchase, how often the purchaser will purchase, and what will the value of his/her purchase.

4. Using a sample of their 500 customers that they collected from different sources, they can run above RFM analysis to form out their high priority and low precedence customers and finally take actions on them for that reason by giving some discount coupon and all to maintain a good bond with the customers.