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In this post, I’ll list few examples from various industries to help you differentiate between business intelligence and data science problems.

 Business Intelligence.(WHAT & WHY) Data Science & advanced analytics. Bike Rentals How many bikes did we rent in Q3 2014? How does that compare to Q3 2013? What is the trend of total bike rentals at week level? Can you break it down by geography? Can you predict bike rentals on an hourly basis? Credit Risk How many customers have a credit risk of ‘C’? Can you rank customers by their payments due amount that have a credit risk ‘C’? Can you predict the credit risk of the customer during contract negotiations stage? Customer relationship management How many account cancellations occurred this year (broken down by month and customer segmentation)? How does percentage of account cancellations this year compare to that previous year? Can you predict customer churn? Flight Delays What is the trend of % of flight delayed this year? Can you break down flight delays this year by their reasons? Can you predict whether a scheduled flight will be delayed by more than 15 minutes? Customer feedback What is the customer satisfaction % trend this year? What is the customer satisfaction % broken down by customer segments and product segments? Can you classify a customer feedback comment into “positive”, “negative” or “neutral”?

I hope this helps!

# How to get descriptive statistics in Excel?

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#### Problem:

you are analyzing a dataset and before modeling/analyzing you need to generate descriptive statistics on a field. you have the data loaded in Excel and wondered if there’s a way to do that in Excel.

#### Solution:

There’s an out of the box solution that will support your needs to generate descriptive statistics on a field. Here are the steps:

Note: for the purpose of this blog post, I am using Excel 2013 but data analysis toolpak is available in Excel 2007+.

1. Active “Data Analysis” toolpak.

2. make sure to check the “analysis toolpak” checkbox.

3. Now you should see a “data analysis” option under the “Data” pane:

4. Now click on “Data Analysis” and select one of the following options:

Anova, Correlation, Covariance, Descriptive Statistics, Exponential Smoothing, F-Test Two-Sample for Variances, Fourier Analysis, Histogram, Moving Average, Random Number Generation, Rank and Percentile, Regression, Sampling, t-Test, z-Test.

in this case, let’s go with descriptive statistics but you can see that you can perform other tasks as well.

5. Once you click on the descriptive statistics, a dialog box will show up and you will have to enter some data like your input range to generate descriptive statistics. Once you have filled the data needed, click on OK and it should generate descriptive statistics for you in EXCEL!

I hope that helps!

#### Conclusion:

In this post, we saw how to generate descriptive statistics in Microsoft Excel.

Author: Paras Doshi

# Cost Driver’s Dashboard for a Supply Chain Executive:

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Summary:

Profitability equals revenue minus costs – To that end, A supply chain executive is mostly focused on optimizing cost elements to drive profitability. Here’s a mock up of a dashboard created for an executive to help him keep an eye on the overall health while making sure he gets alerted for key cost categories.

The Dashboard was created using profitability data-set & also had drill down capabilities to analyze numbers for cost buckets like Raw materials, manufacturing & logistics.

Mockup:

# PASS September 2014 Outstanding Volunteer

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Dan English and I got the “PASS Outstanding Award” for our work with Business Analytics Virtual Chapter. Thanks & Congrats Dan, It’s great to have you on the virtual chapter’s leadership team 🙂

# Business Metric #5 of N: “Conversion rate” (online marketing)

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Summary:

In this post, we will see an important metric in online marketing called “conversion rate”.

Description:

so, what is conversion rate?

Conversion rate = (Number of Goals Achieved)/(Total Visitors)

why is this important to track?

In my previous blog on leads (marketing), I mentioned it’s important to track number of people interested in your products/services but along with that it’s important to provide context while reporting on Leads – this is where conversion rate comes into picture and provides the necessary context. Conversion rate can tell us the Quality of the leads & visitors that you get from your online marketing efforts.

Let’s take an example of an e-commerce site:

An e-commerce site decides to increase their monthly online marketing budget and they see a spike in the number of visitors – so that’s great, right? They should continue to increase their marketing budget, right? Well – that might not be true. While the number of visitors may have increased how do we know that increased number of visitors results in increased revenue? It all depends on the quality of the visitors that’s being generated – so how do you quantify the quality of the visitors? That’s right – conversion rate with the goal: number of visitors clicking “buy” button. So you want to make sure that with increased online marketing budget 1) Conversion rate is good or better 2) Number of visitors/leads have grown.

This was a basic scenario helping you appreciate the power of tracking the conversion rate for your online marketing efforts.

Now, If your marketing funnel is more complex then you might also create multiple conversion rate metrics to track conversions at each stage of a marketing funnel. This is VERY powerful. Here’s an Example:

Conversion Rate #1: (Number of Leads)/(Number of Total Visitors)

Conversion Rate #2: (Number of Actual Customers)/(Number of Leads)

Note: Conversion Rate #1 is great to evaluate effectiveness of marketing campaigns and conversion rate #2 is great to evaluate sales effectiveness.

How can you capture this data?

Conclusion:

In this post, we saw that tracking conversion rate is very important metric to track your online marketing efforts.

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Summary:

In this post, we will discuss about a common metric used by Sales & Marketing teams called “leads”.

Description:

In simple terms,

Leads = number of individuals (or companies) that have expressed an interest in your goods or services.

why do we want to measure this?

For a business to grow, it’s important that the sales & marketing department work to make sure that there is a growing interest in company’s goods or services. It’s important to track this metric to make sure that it’s a positive upward trend!

Word of caution: It’s important to also note that this metric on its own can be misleading. It might be a good idea to also track “conversion ratios” (converting leads or potential customers into actual customers) to make sure that high-quality leads are being generated.

where can you get this data?

Depending on the channel that you use to capture potential customer’s information & the technology maturity of the company, it varies. I’ve seen CRM systems used to report “leads” data and I’ve also seen manual excel files that are used to generate leads report.

Are there any sub-categories?

Yes, it’s usually subdivided into 1) Marketing Qualified Leads and 2) Sales Marketing Leads.

Conclusion:

In this post, we saw a high level overview of a business metric used in marketing and sales called “leads”.  As mentioned earlier, don’t report on just “leads” – it can be misleading for marketing & sales executives since upward trend in number of leads doesn’t necessarily result in increased sales unless the quality of new leads is same or better. Marketing and sales executives would really appreciate any context  (example: conversions) that you can provide to their “leads” report. I hope that helps!

# Time Intelligence in MDX: last N days

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it’s a common requirement to create a report that shows last N days of a business metric – so I thought I’ll post a template here for SQL server analysis server’s MDX query:

[code language=”SQL”]

WITH
MEMBER [Measures].[Sales_last_15_days] AS
Sum
(
{
[Calendar].[Date YYYYMMDD].CurrentMember.Lag(14)
:
[Calendar].[Date YYYYMMDD].CurrentMember
}
,[Measures].[Sales]
)

MEMBER [Measures].[CurrDate] as
"[Calendar].[Date YYYYMMDD].[" + Cstr(Year(Now())*10000+month(now())*100+day(now())) +"]"

SELECT
{
[Measures].[Sales_last_15_days]
} ON COLUMNS
FROM
[CubeName]
WHERE
STRTOMEMBER([Measures].[CurrDate])

[/code]

Here are things that you’ll need to adjust to make it work for your scenario:

1. Date Dimension Attribute & it’s format. The example shows yyyymmdd but you could have different format of the date.

2. Measure name. Instead of [Measures].[Sales] you’ll have to replace it with your business metric. Also, make sure you are using the right aggregate function, in the example above I have used SUM but you’ll have to change this based on your requirement.

3. Create a parameter and use it in index for the Lag function.

4. change [cubename] to your cube name.

I hope this gives you a good starting point to create last N days for your business metric.