The new year has begun. Now is the time to measure the success of your holiday campaigns. How did your campaigns perform? This is an opportunity to look at their effectiveness in terms of building awareness, generating revenue, increasing retention and aiding customer acquisition? How do your metrics compare to industry benchmarks as well as internal benchmarks? How much revenue did they generate and were they profitable? In addition, what worked and what didn’t? Now is the time to evaluate any tests that were done - date/time, subject line, creative, etc. Finally, compare the results of this past holiday campaign to the one before and analyze the differences. The insights from the holidays can inform your strategy for 2012.

Even though it still feels like summer outside, now is the time to start planning for the holidays.

The first step is to evaluate all of the tests that have been done throughout the year in order to put your best foot forward.  In addition, it involves reviewing the results from the prior holiday season.  That means determining the most effective:

• communication method (e.g., email, direct mail, multi-channel) by customer segment
• timing (both day of the week and time of day)
• creative (hero images, placement of links, etc.)
• subject lines (when and where to mention free shipping offers, brand or product offers, etc.)
• offers (discount percentages, dollars off, buy one get one free)

Next step is to evaluate any implementation issues from the prior holiday season.  Before coming up with your holiday strategy it is important to determine any limitations or challenges with respect to execution.  Your strategy cannot be developed in a vacuum.  Thus, I recommend that you review what has worked and what did not work with the entire team.

Once all of this information has been gathered, you can develop a holiday strategy.  It should incorporate the lessons from past tests and holiday campaigns as well as encompass:

1.  Start Date. The average holiday campaign begins in October.  Some retailers hold pre-holiday clearance sales and send informational emails to start their holiday campaigns.

2.  Black Friday. For Marketers, the holiday campaigns have been starting earlier and earlier on the calendar.  The same is true for Black Friday.  It is now beginning on Thanksgiving Day for some retailers.  When will yours start?

3.  Cyber Monday. While many digital sales are made on the Monday after Thanksgiving, digital sales are occurring earlier as consumer shop from home.  Will you wait for Cyber Monday or start earlier?

4.  Sequence. If you are using email, you can easily send at least an email a day.  It is important to determine the contact frequency and cadence.  Will all or a segment of your customers receive an email a day, every other day, every third day, etc.?  Will emails be sent only on weekdays or only weekends or a mix?  Will there be a resting period or a maximum number of emails that can be received?

5.  Free Shipping.  Many consumers expect to get free shipping online, especially during the holidays, and will not pay for shipping.

6.  Social Sharing.  Consider how to tie in Facebook, Twitter and other social sites with your campaign.

7.  After Christmas. Lastly, there is also the opportunity for follow on sales after Christmas.  It is the time to promote use of gift cards and purchases of parts or refills.

To be most effective, loyalty programs need to continually evolve.  Loyalty programs need to be regularly re-evaluated as customers, products and the competitive environment change.  Stagnation can cause a once valued loyalty program to be seen as old and tired.

To determine the health of your loyalty program, monitor its performance and perception.  The key performance indicators (KPIs) will be tailored to your program, your goals and your business.  However, there are four general metrics which are important for most programs:

1.  The time it takes to earn a loyalty reward

2.  The percentage of loyalty customers who earn a reward

3.  The percentage of loyalty customers that redeem the reward certificate

4.  The percentage of loyalty customers that take advantage of the program

A loyalty reward must be attainable.  If it takes too long to earn a reward, the customers may get bored and give up.  The appropriate time to earn a reward varies by your business and customer behavior.  That said, do not consider this a static number.  It may be that an average of 6 months was appropriate two years ago but 3 months is more appropriate now.

Similarly, if you have a program that requires a particular spending or mileage threshold, the minimum at which a customer receives a reward must be chosen carefully.  If you are a retailer whose median customer spends $250 per year and customers only receive a reward after spending $1,000 annually, very few customers will likely attain the reward.  If you want to encourage increased spending, you can always create tiers.  The basic loyalty membership level could be annual spend of $250 to $499 per year, the silver level could be $500 to $749 per year, the gold level could be $750 to $999 per year and the platinum level could be $1,00 or more per year.  Tiers encourage customers to strive to reach the next level.  Plus, it does not have to be expensive to add additional services for the higher tiers.  For example, you could e-mail platinum level customers in advance of sales or invite them to special in-store promotions.  However, customers must see the value of achieving a higher tier.  It is very easy to see the benefits of tiers when you see fliers with higher tier levels board the plane first or see the shorter check-in line for premier members.

The value that customers see in your loyalty program is evidenced by how many of the customers redeem the certificate.  In addition, if customers do not redeem the certificate then you have lost the revenue that would have been generated by the incremental trip.  Certificate redemption should generate revenue and continued loyalty to your brand.  If not, the loyalty program needs to be re-evaluated.

Finally, customers should be taking advantage of the program.  If not, you should be asking yourself why not.  Do customers not see the program as valuable?  Does my competitor have a better program?

As I mentioned in my prior post, loyalty programs are a valuable tool.  They can help retain customers and companies can win greater share of wallet as a result.  If a customer can buy the same goods or services from multiple sellers, a loyalty program encourages customers to consolidate their purchases.  It might also create additional demand.  For example, a reward certificate can spur an incremental trip or customers may splurge in order to meet a spending threshold.

Another benefit of loyalty programs is the insight into customer behavior.  This has far reaching benefits.  Take the example of a retailer.  This customer insight can help both marketing and merchandising.  Using the data collected, a retailer can segment their customers based on past behavior so that they can tailor their messages and offers appropriately.  For example, marketers can use this information to personalize product promotions, cross-sell products and identify new customers that have the potential to become to best customers.

Further, this data will provide insight into what products bring new customers into the store, what products drive repeat purchases and what products are typically purchased together. Merchandisers can use this information to plan promotions and make buying decisions.

To be valuable, the data must drive actionable insights and be used to continually improve the loyalty program.  I will write about using data to evaluate the health of a loyalty program in my next post.

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?