NCDM Presentation: Unlocking Customer Value with Cutting Edge Customer Lifecycle Marketing Approach

I came in late to this presentation, due to a flight cancellation yesterday.  I’m picking up my notes where I jumped in.  The notes may seem a little fragmented, but that’s because I’m only noting down the key points, and I’m trying to do it quickly, so grammar may suffer.  If I add my own comments, I’ll make them in a blue font, so you can distinguish between what the speakers say and my own comments.  I’m not completely sure of the context behind the first couple notes…


Make registration easy.  Don’t load prospects up on survey data, but instead implement a light registration.  Capture limited info early, but be persistent.  More customers that we load into the front of the funnel, the more that comes out the backend.  (I could add some humor that my teenager would appreciate, but I’ll restrain myself.)

Remember, that behavioral data is more valuable than survey data which is more valuable than appended data.  Behavioral data is also live data, you don’t have to wait for it, buy it, or append it.  Appended data can take 30+ days to get into your database.  (This isn’t really true anymore.  There are plenty of vendors that provide realtime customer level data in realtime, using APIs.)

Problems with email: not only that people change email address, but that they have multiple addresses, each with a multiple purpose.  Retailer’s frequently are given a “junk” email account – one that we know will collect lots of spam.  If a customer changes their email address, is it a better address?  (I wonder if people still do change their core email address as frequently as in the past?  I have one or two that are used only for family, friends, and other critical communications, but I have plenty that I use for other, public purposes.  I will replace a public email address if it gets to much spam, but spam filters are pretty good these days, so that hasn’t been an issue in a while.)

Measure open/click rate by age of email address.  It is very likely that engagement drops significantly after 5 or so email.  One goal is to postpone and diminish this fatigue rate.  Make email personal and engaging.


The first 90 days after a 1st purchase are critical.  It can be a good idea to deliver a welcome package via email, or online; it never pays to mail it.  Mailing it can “destroy value”, mostly because it looks like a “mass piece”; they never pay for themselves, even over a customer’s lifetime.  Recognizing the customer with a canned “thank you” can actually hurt your relationship.

There was lots of discussion about making sure any loyalty program is not perceived as marketed to the masses.  Find out what categories they purchased in or viewed and send them offer in that category.

Have planned and tested loyalty program.   Don’t implement it within 30 days; wait till they’re more comfortable with their purchase, at approximately 45 days in their experience.  Note that the 45 days might not be the sweet spot for every company.  You need to experiment and measure loyality program signup rates.

Cementing can be accomplished by quickly getting second purchase.  “Use it or loose it” offers can be “huge”.

Reinforce purchase decision by reinforcing whatever emotion triggered purchase in emails and mailings.  (I think this is key.  If a customer just made a big purchase, make them feel good about it.  There was probably some emotion behind the decision, so leverage that emotion.) He presented the example of BMW.

Customize experience online.  This can provide 60-70% lift.  (Really!?) This can be as simple as the segmentation of products based on purchase or visit history.  It could also be personal area of website with purchase history, product information, ways to use your product, etc.)

One way to personalized online content is with the use of ad serving software, which is (supposedly) pretty easy to plug into most websites.

Make order process as simple as possible.  The goal is to make it simple for a person to re-order.  For example, pre-populate any fields that can be pre-populated.

They provided a grocer example: most grocers (even the big national chains) do an awful job of leveraging the massive amount of data they have.  Tesco (UK) is the exception.  Their offers lead with things they know you buy from them, but also include products you don’t buy but which are good cross-sell opportunities.  This way they try to shift some of your wallet share to them.

Try merging primary consumer research data with behavioral data.  Customize offer based on “potential” value instead of “historical” value.  Quote: it is very hard to determine a customer’s potential value.  You need to know wallet share.

Watch out for silent attrition.  Listen to triggers, changes in usage patterns.  He provided an example of a bank.  Instead of watching for closing of accounts (when it is frequently too late), watch account balance, number of transactions, etc.  Declines will help determine who is going close account.

Some products are different.  With insurace, the last thing you want to do is remind them they are a client.  It tends to remind them to shop around for insurance.

Think about what other kinds of rewards can be presented. (He mentioned foursquare, as an example.)  Is there some form  of recognition that would reward the customer.

Loyalty programs – things to track:

Watch out for loyalty programs destroying value.  It may just give better deals to people who would buy anyway.  Loyalty programs rarely provide direct benefit for this very reason.  Value is in capturing data that helps you market, especially if that data is sold.

It is very tricky to measure incremental value.  (No kidding!)

There was some discussion on “share of wallet”: only way to get at this is metric is through a survey.  Need to ask them where else they shop, and how much.  (I think even the survey approach is limited in its value.  How many people are going to tell one vendor how much they spend with other vendors?  How reliable is that data when provided?)

Customer Level Data/Analysis

It is important to measure EBITDA by customer segment: new, spent less, spent more, lapsed.  If you know where your EBITDA is coming from, then you determine how to allocate your marketing budget.  (There were some great charts and graphs.  I’ll have to see if I can find them online.  If so, I’ll post.)

Also measure $GM by various RFM segments or other metrics (# channels used by customer, for example).

Look at various marketing drivers (cross/up sell efforts, frontline sales, loyalty, marketing, etc.) and the value they provide.  (But that raises the issue of order allocation – crediting)!  Best way to get at this is by survey data: by asking what triggered purchase.

Always “test & learn”.  Test everything you do, otherwise you don’t know.  That’s the one thing to takeaway, if we take away nothing else.  This is the biggest lever you can pull.  Learning is the key element.  Need to be able to quickly implement results of test.

This usually involves tracking results down to the customer level.

Always include “hold-out” samples that helps you evaluate mailing efforts.  (This might be difficult for vendors with high ticket/low conversion products.)

Cutting edge approaches to maximizing customer value take years to implement.  For that reason, long-term executive commitment is critical.

Final comments…this presentation was crowded, or maybe the room was just too small.  I got a seat because I brought one in from the lobby and placed it in the back.  One person, who came in later, sat on the floor.

Overall, a good presentation.  Takeaways for me:

  1. Test & learn, which I already knew.
  2. Make registration process easy, and incrementally capture data.
  3. Really think about the actions within 90 days of a first order.  This is a high value group to target.
  4. Need to focus on making emails valuable to the readers, so they won’t fatigue and so the prospect will give us their most read email address.

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