For my Mother’s birthday, I created her very own website on www.etsy.com.  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.

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.

Cluster segmentation is a descriptive, multivariate technique that creates distinct, homogeneous groups within your customer base.  The goal of cluster segmentation is to classify consumers or businesses based on behaviors, demographics or firmographics, and/or attitudes.  In this way, you can develop more targeted programs and tailor messages based on the needs and characteristics of specific groups.  One client reorganized their marketing department as a result of a segmentation project I worked on, assigning one marketer to each segment so that consistent messaging and product offers could be employed against each customer group.  Further,the segments that are developed can be combined with models or other segmentation schemes to identify the best customers to target for particular campaign or offer. 

Determining what methodology to use for clustering depends on many factors including your clustering software, the type of data you have, and the number of consumers or businesses available for segmentation.  You should also consider the optimal number of segments to meet the business objective and which behaviors or other factors are most important in defining customers.    

Regardless the methodology chosen, you will need to do data prep.  You typically start with data summarized to the household level for B2C analysis and establishment or enterprise level for B2B analysis.  You might also need to do missing value substitution, transform categorical variables to binary or scaled variables, weight variables to drive preferred ones into the solution,  and standardize continuous variables.

Data reduction might also be necessary if you have many variables.  Tools for data reduction include correlation analysis, principal components and factor analysis.  

Once that is complete, you can create your segmentation schemes.  I run many more segmentation solutions than I show to a client because I want segments that are actionable within the client’s marketing plans and that are intuitive as well as not overly complicated.  In addition, I test the validity of my cluster solutions through goodness of fit statistical measurements and by replicating my results on a hold-out sample.   The end result is that a company can align its marketing efforts against segments, taking a customer-centered approach rather than treating every customer the same.  Cluster segmentation can be a tool for giving the right message at the right time to the right person.

My clients often know who their best customers are.  Typically the best are the top 20% of customers that generate 80% of the profits.  These are the customers you most want to retain.  The question becomes who are the customers that you should try to migrate into your best customer segment?  Figuring out who are the next best requires research into their behaviors, demographics or firmographics, and attitudes. 

 Segmentation is one way to separate your customer base into differentiated groups against which relevant marketing communicationsand strategies can be developed and executed.  There are many different types of segmentation and techniques including cluster analysis, RFM and CHAID.

Regardless of what method you choose, bear in mind that a good segmentation scheme is often a result of art and science.  Segments should make sense intuitively and, if they are data driven, should be sound statistically.  In my next post I will describe clustering and how that is used for segmentation.

I recently received a mailing from the deCordova Sculpture Park and Museum, formerly known as the DeCordova Museum and Sculpture Park. 

I was puzzled by the name change.  In this case, why would you essentially just reorder the words in the name?  Yes, the sculpture park is beautiful and unique in the metro Boston area.  It is well worth a visit I might add.  However, the museum is excellent as well.  There was a fascinating exhibition called Drawn to Detail which I saw last fall. 

Perhaps, the ICA in Boston is cornering the contemporary art museum “market”, with the recent Shephard Fairey show and before that an Anish Kapoor exhibition.   It also has received a lot of attention for its new building and new location on the waterfront in Boston.  The deCordova’s name change might be a bid to distinguish itself from the ICA.  If that is the case, I am not convinced that re-branding is the answer.  But if you are going to re-brand, at least be consistent. 

The website uses the new name at the top on the right…

The top of the deCordova home page

 and then uses the old name under the History and Mission title.

Bottom of deCordova home page

Thursday’s Wall Street Journal had a quote from Carol Bartz, the CEO of Yahoo, “Are we leading up to “I’m both too old and too stupid to know what the Internet is’?”  Her remark was in response to a question about her experience but it made me think about a potential generational gap in Internet usage. 

At a book club meeting almost a year ago, several members asked for a description of Twitter, as if it was a foreign country or new-fangled religion they had heard about.  One member provided an excellent summary based on his usage of the site but several were left trying to get their heads around why anyone in their right mind would use Twitter.   Once again Twitter came up during a recent book club meeting.  No one new had tried out the service in the 10 months since our last discussion.  It made me wonder if the technological gap is not just rich versus poor but also young versus old.  As the remark by Carol Bartz indicates, there is a general perception that the Internet is a young person’s game.  High profile anecdotes have reinforced that assumption.  According to a July 2008 Frank Rich Op Ed piece in the New York Times, John McCain doesn’t know how to use a computer.

For marketers, this represents a challenge and an opportunity.  To me, it is further proof that we need to develop integrated campaigns with both online and offline channels for outbound communication and inbound response.  You cannot assume that everyone will be on the Internet 24/7.  Broadcast media, print ads, direct mail, etc. can play an important role in reaching an older audience that may not be on the Internet as frequently and they reinforce your message to those who are active on the Internet.  A recent report found that displaying a URL within a  Yellow Pages print ad drove an increase in online leads.  Of course, you should measure the interaction of online and offline behavior to see what drives the most responses and to optimize future campaigns.

Modeling is a powerful tool that is worth considering when determining how best to spend your marketing dollar.  At its simplest, modeling looks for patterns in data to predict future behavior.  That data could be past behavior.  If someone bought diapers last week, it is very likely they will buy them again this week.  It could also include demographics such as age and gender or, in a B2B context firmographics, the number of employees and annual sales volume.  Attitudinal information, such as willingness to purchase a product, could also be used in a model.  The power of modeling comes from the fact that it weighs all of the factors and results in a unique algorithm that predicts future behavior.  Instead of the usual “spray and pray” approach, modeling enables you to focus your dollars where they will have the most effect.

Two articles in the Wall Street Journal last week offered real life examples of how models can solve business problems.  I have seen clients use attrition models and proportional hazard models to determine which customers are likely to leave.  Google is building an attrition model to identify which of its employees are most likely to leave the company for another opportunity.  Presumably Google will target those employees most likely to leave and be able to retain valuable talent that might otherwise walk out the door.

Chrysler’s digital agency has designed a media modeling system according to the Wall Street Journal.  It sounds like a marketing mix model and is being used to allocate Chrysler’s marketing dollars.  At a basic level, this model tells Chrysler how much money needs to be spent on marketing to drive a certain number of vehicle sales based on the web traffic generated.  By monitoring online activity and tying it to their marketing campaigns, Chrysler has determined how many web visits translate into sales.  The media modeling system, including enhancements based on the ongoing performance of television advertisements, has helped Chrysler determine how to structure their marketing campaign and tweak marketing in real time to drive results.

These two examples may not fit your exact situation but they highlight the power and value of modeling.

Let’s face it. It takes time and patience to develop a good e-mail subscriber list. First, you have to make it easy for individuals to add and update their e-mail addresses. Second, every time you e-mail them, you run the risk that they might unsubscribe. Third, maintaining the e-mail list requires that you clean the file (e.g., remove hard bounces), e-mail frequently to keep subscribers engaged and send targeted, timely and relevant e-mails.

 It is not surprising then that I am routinely asked about purchasing e-mail addresses. My standard answer is to be prepared to pay a lot and to get few responses relative to your investment. A recent Limeduck post illustrates what can happen when you purchase e-mail addresses.

Accountable marketing is a lofty goal.  It is the idea that marketing can and should be measured.  It sounds simple but is difficult to implement and execute.  It starts with planning and identifying metrics for success up front and ends with calculating ROI and other relevant metrics as well as incorporating lessons learned into future marketing efforts.

I have written about metrics before.  In fact, my New Year’s Resolutions post included a suggestion to test, measure and learn.  Even in social media there are now agreed upon metrics.  The Interactive Agency Bureau (IAB) has released social media ad metric definitions. 

Given the current tough economic climate, there is no reason not to measure and evaluate your marketing efforts.   How else can you know what worked, what did not work and whether your efforts have met your threshold or definition for success?

James Surowiecki writes in the April 20, 2009 edition of The New Yorker that during hard economic times, companies cut costs including labor, advertising spend, and research & development, as well as forego acquisitions even though prices are lower.  However, companies that remained market leaders during the 1990-1991 recession increased spending according to a McKinsey study.  Research by Bain found that recessions can be opportunities for companies to leapfrog over their competitors.

Thus, CEOs and senior management have a difficult choice.  They can choose to slash costs in order to win a war of attrition – becoming lean to survive the recession assuming that their competitors drop out of the market or business altogether.    Alternatively, they can invest in advertising, research & development and acquisitions in order to grow market share and transform themselves into market leaders.  However, there is no guarantee.  Do you risk sinking the boat in order not to miss the boat?