The gift that keeps on giving

For my Mother’s birthday, I created her very own website on  My Mom was speechless when she saw a site dedicated to selling her handcrafted jewelry online. 

This was a labor of love.  I spent many afternoons taking pictures of her necklaces, earrings, bracelets and lanyards and then researching the materials she used.  Using the etsy template, I created her “shop” by loading the pictures of her inventory, creating descriptions for each piece, setting up tags, outlining her shop’s terms and conditions (including shipping costs) and setting up a Google Analytics account so that I could track the performance of the website.   

It is so rewarding to receive feedback from customers that they love my Mother’s jewelry and think it is well made.  I also enjoy analyzing the web site’s performance and playing with Google Analytics.   In case you haven’t had a chance to use Google Analytics, here’s a screen shot from one of the standard Google Analytics’ reports.

The top graph shows the number of visits by day for the most recent month.  You can look at the metrics by day, week or month and set the time period to be analyzed.

Next on the report is site usage metrics including visits, pageviews, pages/visit, bounce rate, average time on the site, and percent new visits.  Most of these metrics are straight forward but you do need to be mindful of anomalies.  There are some weeks when I will see a huge spike in visits; however, those correspond to times when I was loading jewelry to the site and thus frequently visiting the site to see how it looked.

Her bounce rate is 39%.  Google Analytics defines it as follows, “bounce rate is the percentage of single-page visits or visits in which the person left your site from the entrance (landing) page.”  Not everyone who comes to her page will be interested in her jewelry.  Three visitors who typed in the keywords “buddha inspired chinese” were directed to her website.  I doubt they found what they were looking for!  Bounce rate is a powerful metric and I will be discussing it in another blog post.

Next is the visitor overview.  This is the number of new and existing visitors that came to the site.  It looks very similar to the Dashboard chart but the difference is that it measures visitors and not visits.  The Map Overlay World shows me at a quick glance where visitors to the site are coming from in the world. 

The pie chart below shows the traffic sources — direct traffic, search engines and referring sites.  Finally, the report shows an over view of the pages that had the most pageviews.  The first is the home page of her site and the subsequent ones are the pages for particular jewelry.

 I have no idea what I will do for my Mother’s next birthday but I will probably still be playing with Google Analytics until then.

A three letter word you should know

Continuing on the theme of segmentation, RFM Analysis is another tool for understanding and identifying different types of customers.  RFM stands for recency, frequency and monetary value.  This tool will help you:

  1. understand customer value quickly when limited data are available (e.g., just purchase data)
  2. develop a basic value segmentation that can be used to determine if your customer strategy is optimal
  3. find untapped markets if there are segments which are not targeted
  4. gain insight into gaps that might exist between accepted wisdom about the customer base and actual purchase behavior

The name suggests that recency is the most important factor for determining a customer’s value followed by frequency and monetary value.  However, you can set different priorities.  For one of my clients, monetary value was more important than recency and frequency.  Thus, their analysis was driven by monetary value first, recency and finally frequency.  It all depends on your product and the typical buying cycle.

The actual analysis involves calculating the R, F, and M dimensions, specifically:

  1. creating a reasonable number of categories based on the date of most recent purchase (e.g., date was within the last month, within most recent 2 to 6 months, within prior 7 to 12 months, etc.)
  2. breaking the number of purchases into a reasonable number of categories similar to recency
  3. summing all revenue and creating a reasonable number of categories similar to recency

The number of categories you create depends on how you intend to implement the RFM analysis and should be guided by the means and standard deviations of the variables.

The fun part comes when you bring all of this together.  You first need to decide which dimension is most important and which is the least important.  Next, you need to determine the number of segments you want.  Will it be high, medium and low or 1 through 10?  If there are too few segments, then the segmentation will not be very targeted.  If there are too many segments, it may become a burden to implement and may ultimately be considered too complicated to use.  Business judgement and knowledge of the customers’ behavior should drive the creation of the segments. 

Once the segments have been decided, business rules or code can be written so that the segments are applied to your customer base on a regular basis.  This has the advantage of identifying new best customers or up and comers that can then be targeted with a special welcome communication.    Further, the segmentation can be used with other tools to drive marketing messages and campaigns.  However, you may need to revisit your RFM segments from time to time as your business changes significantly.   For example, if you raise or lower prices significantly after the segments are put into production, you will want to reassess the original recency categories.