The first thing we can do is eliminate those who do not fall into the age category or are males.

In the next section, let's evaluate the names further to get the best candidates for A Matter of School's email campaign.

We now have a list that includes only females in the 18-25 age category. Although all of the women on the list have purchased from A Matter of School, the truth is that some have a better chance of contributing to the bottom line than others. Let's use the "3,3,3" method to find these people.

In the "3,3,3" method, we rate each candidate in terms of their Recency, Frequency and Monetary characteristics, attempting to narrow to those having:

1. bought in the last 29 days (Recency)
2. bought three or more times total (Frequency)
3. built up to a total purchase of $50 or more (Monetary)

Here's the parameters on spreadsheet.

As mentioned, we want to find only the candidates who rate a "3" in all categories. Here's what my analysis came up with.

We are left with seven women who fall into the "3,3,3" category:

Ami Stake
Anita Bath
Ester Bunni
Jan Edick
Mary Schultz
Nicole Dest
Tara Dactyl

We now have seven women in the 18-25 age group who have:

1. bought from A Matter of School in the last 29 days
2. purchased at least three times total in the last 90 days
3. spent a total of $50 or more.

​We could send a broadcast email to the whole group and highlight products that we sell. Or, we could rely on past experience and try to find at least two groups with similar characteristics within the seven and match them with products.

To find a pattern in the data, I put together a spreadsheet that details the characteristics of each of the seven candidates.

All seven candidates have purchased from A Matter of School using a mobile device, either through the website on the phone or through a downloaded app. This will be important as we develop content in the next section.

We will be sending out two emails - one to females 18-22 and another one to females 23-25. The first thing to do is create a list for each of the two emails in Constant Contact. 

Here's the other list:

For the younger group, 18-22, we'll use the email below. Remember, in both groups, email recipients have shown that they buy things with their phones. So there should be "Buy Now" buttons by each product. This email design also sets up well for mobile viewing.

Here's the email that we'll use for the 23-25 group.

For this assignment, we do not send any actual emails to the lists above. We send both emails to an audience of one - instructor Matt Hanson.

We are presented with two different set of expectations of statistical guidelines regarding delivery rate, open rate and click-through rate (CTR).

The Oracle numbers are the midpoints of the range of the data given in each category. It should be noted that Oracle's test emails deliver at a 99.3% rate vs. about 99% of the population as a whole.

Since A Matter of School is sending emails to people who have previously purchased from them, it is reasonable to conclude that the open rate and CTR would be closer to the Stukent averages than the Bronto/Oracle numbers. Also, the delivery rate for A Matter of School should be closer to the test email at 99.3%.

Expected rates for A Matter of School email marketing campaign:

As the numbers roll in on the campaign, there will always be areas to improve.

Delivery rate - No matter where the numbers come in at - 97% or 99.9% - we should continually strive to maintain and improve our lists. We suggest that A Matter of School devote resources to this constant cleaning process by:

1. authenticating email addresses
2. improving the opt-in process
3. checking IP's and links
4. reviewing Delivery rate data in gender, geographical, time of day, and other categories to see if there are more bounces in specific categories.

Open rate - As with Delivery Rates, the data will tell us the story. We will look at the data and attempt to maintain and improve in four categories:

Segmentation - There is clear evidence that companies that segment ruthlessly experience better open rates. We should stress to the client to spend the money to comb the data for better ways to target and segment.

Timing - The data will tell us when it's best to send our emails in different segments and how often. 

Subject lines - Although we did not do A/B testing of subject lines for this exercise, we could certainly do that going forward to improve open rates.

Content - One of the best ways to improve open rates is from repeat openers. If recipients know that you have in the best sent them content that is relevant to their lives, they will be more likely to open future emails.

Click-through rate - Content is king when it comes to CTRs. We must continually do A/B testing of different content to random subsets to ensure that we are achieving the maximum possible CTRs. The data will tell us a lot in this area.