A colleague and I had a very interesting discussion today over lunch.  I was arguing for the importance of industry in guiding the type of business questions you ask and hence the type of analyses you perform.  He believes that industry or vertical does not matter. 

My professional experience tells me otherwise.  Currently two of my clients have very different challenges.  One is a retailer trying to drive a repeat visit among its customer base.   Given the volume of customers they have and the average basket size, increasing the number of repeat visits can greatly impact revenue.  The other client is a software maker that sells to large manufacturers.  Identifying the right customer who would be interested in their product is key.  They have a much higher price point and much longer buying cycle than the retailer.  For them, understanding lead generation and lead conversion is vital in order to make their sales process more efficient.

However, there was on thing we could agree upon.  It all comes down to giving the right person the right message at the right time.

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.

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.

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?

I ran a 5K race recently and so I have been thinking about my pre-race routine.  I may not have a lucky rabbit’s foot but the morning of a race I have a ritual of sorts.  I eat my usual breakfast and of course drink coffee.  There are some things I just can’t do without!  I wear the same clothes and sneakers for the race as I wore training.  I will not do anything new or different.   

This ritual keeps me from being distracted so that I can concentrate on the race.  In this case, my ritual helps me.  But in the office, rituals can be limiting.  Always doing something the same way can get old and stale.  A colleague asked me about identifying best customers.  My first thought was an RFM or RAD segmentation because I was in the midst of a RAD segmentation.  It would have been easy to stop there.  However, I couldn’t stop until I also suggested clusters and CHAID.   If she had let me, I would have added modeling and NPV.   The trick is knowing when to stick with rituals and when to avoid them.

I was catching up on Tangyslice’s blog and enjoying his 5 meaningless marketing metrics post, when I thought of another meaningless metric.  Last week I was reading a presentation which described the response rate of one group as slightly greater than the control group.  What does slightly greater mean in this context?  Well, it turns out the difference was statistically significant once I did the math.  What I find meaningless is when analysts do not look for statistical significance when comparing two groups.  This is known as A/B Testing.

Conceptually A/B testing is very simple.  You are comparing Group A to Group B.  A might be a control group and B the test group.  Alternatively, A and B might be two different offers, landing pages, e-mails, direct mail lists, or landing pages.  As the name suggests, this is a test which is why A/B testing is also known as split testing.  Ultimately, you want to know if A and B differ in a way that is statistically significant.

Here’s an example to make it concrete. Let’s say that you marketed to 50,000 customers encouraging them to purchase product A and 5,000 of them responded. That is a 10% response rate. In addition, there were 5,000 customers that you could have marketed to but that you did not.  Instead, you assigned them to the control group.  They look and act just like the 50,000 customers that you mailed. The reason for the control group is that some customers might buy product A regardless of whether you market to them or not.  In this example, 450 of them or 9% purchased the product. Is the difference between 10% and 9% statistically significant?  Was the campaign successful?

In this case, we perform the two-proportion z-test for equal variances using the following formula:

and

where…

p1=10% (response rate for Group A)

p2=9% (response rate for Group B)

x1=5,000 (number of responders in Group A)

X2=450 (number of responders in Group B)

n1=50,000 (quantity mailed in Group A)

n2=5,000 (quantity mailed in Group B)

If the value of z is greater than 1.96 then the difference is significant at 95% confidence.  In this case, the z value is 2.26 so the difference is statistically significant.

In order for the test to be valid a few assumptions must be met:
1. Your control group needs to contain customers or prospects that look and behave like the treatment group
2. You need to have sufficient numbers of direct mail recipients and responders such that n₁ p₁ > 5 AND n₁(1 − p₁) > 5 and n₂ p₂ > 5 and n₂(1 − p₂) > 5 and n2>29 and the groups contain independent observations

The math might look scary but really the hard part is making sure that the test is done properly.  It is vital that the control contains a random selection of customers who are similar to the treatment group.  If not, you could end up with very strange results

Last week I attended a Marketing Analytics conference  in Boston sponsored by iKnowtion.  At a time when companies are cutting expenses, including staff and marketing budgets, iKnowtion is investing in their future.  They are also engaging in a dialogue with the larger Marketing Analytics community through the conference and their blog.  (In the interest of full disclosure, I know several people at iKnowtion but have never worked there.)

The conference began with a talk by Tom Davenport, the Author of Competing on Analytics with Jeanne G. Harris.  He set the stage by providing examples of how companies recognize the importance of analytics but reminded us that marketing is still a combination of art and science.  As the emphasis shifts more towards the science of marketing, we need to recognize that the “art” is still relevant.  He further challenged us to move beyond reporting to provide more value and insight.

Next was a panel on driving business value featuring speakers from GM, CVS Pharmacy, and ConstantContact.  Each speaker provided a brief case study of how analytics has helped their business.  In one case, analytics changed the focus of the business.  In another, it led to the rebalancing of product marketing.  Finally, the rigors of “test, measure, and learn” enabled one company to optimize media effectiveness across channels.

After lunch there was a lively digital panel discussion around social media, the future of web-enabled communities and the challenges of measuring the impact of companies’ efforts in this space.  Given the evolving nature of social media, it is no surprise that there were divergent opinions.  I, for one, appreciated the candor and the healthy discussion that ensued.  To quote Jane Austen, “My idea of good company…is the company of clever, well-informed people, who have a great deal of conversation.”

The conference wrapped up with a return to the theme of competing on analytics.  This free flowing discussion touched upon a range of topics, including how to become a company that uses analytics for competitive advantage.  Interestingly, one of the panelists thought that finding good talent was the biggest challenge we face.  As a Marketing Analytics professional who hires and develops staff, I am in complete agreement.  There is stiff competition for the best analytic staff and I have found it difficult to find technical competence coupled with business acumen.  In fact, the discussion about finding, training and retaining analytic staff continued at the bar, after the conference formally ended.

iKnowtion has plans to hold the conference again next year and I encourage you to attend.

Do you spend a lot of time on YouTube? If so, you may have already seen this but I was surprised to see the MC Hammer video on YouTube.  Am I the only one surprised to hear the words “behavioral targeting” being spoken by MC Hammer?  Who knew that MC Hammer and I would have something in common.  We both believe that analytics enables you to allocate your marketing dollars effectively.

If you haven’t seen it, watch MC Hammer on Analytics from YouTube.