Smartly Avoiding Dumb Messaging

In a perfect world, your marketing database would be compiling near real time data on each and every customer and their every move on your web properties.  And in a perfect world triangulating that data would empower you to provide for perfectly SMART messaging.

But there isn't a perfect world outside of that aspirational model.  And so we knew it would happen sooner or later:  A client recently asked whether there is a simple test to know if they're achieving "smart messaging."  Readers may recall that we like the SMART acronymn to guide highly tailored and targeted messaging for customer relationship management:

  • Succinct
  • Meaningful
  • Actionable
  • Relevant
  • Timely

And indeed, for the simplest test, we think there is the antithesis mnemonic: DUMB...

  • Disingenuous
  • Useless
  • Meaningless
  • Boring

All one needs to do is put their messaging to the test.  If an intended campaign message falls into any one of these traps of DUMB, avoid the message at all possible costs.  "Duh," you say, "Why would any marketer worth their title ever submit a message guilty of any of those characteristics?"  Well, actually, it happens all the time.

It may be a fault in the business rules.  It could be a missed but imperative piece of data capture.  Or it could be a misinterpreted analytic.  It could even be worse (and more likely): the necessary analytics to determine the applicable business rules and resulting message template don't even exist.  And all of these conditions are often present, without the marketing team even realizing the situation.

The point is, its not enough to craft a conversation engine if you don't test the messages.  Actionable messages for one recipient are unusable for another.  Relevant messages for another turn out to be boring for a different recipient.  Seriously, we know this seems like block and tackle stuff, and to an extent it certainly is, but what is often missed is the necessary testing, whether by A/B method or even more simply manually auditing a proposed message<->target sequence.

Finally, a small point of nuance.  Some have asked, "What is the difference between meaningful and relevant, and aren't they one in the same?"

In fact, the distinction is important to achieving "SMART" messaging.  A meaningful message speaks to a recipient's current wants or needs.  A relevant message actually catalyzes a conversion of the want into a need. 

For example: you may want to acquire a new widget, but it is an aspiration until a point in time -- an event horizon that can convert that want into a need.  If that opportunity can be ascertained from recognizing a condition or set of conditions based on the recipient's (for example) recent behavior on your web site, then a succinct, meaningful, actionable, relevant and timely message will convert that want into a need.  The key is recognizing the proper triggers to conversion for a cross-sell, new-sell, or up-sell opportunity.  Triggers could include price sensitivity, enhanced experience of existing products or services, or specific behaviors suggesting a replacement opportunity.

Here's a Use Case.
Suppose your data informs you that your customer is the owner of a particular model of shoe you offer.  Suppose further that your analytics detect recent web site behavior that included a combination of visting product pages for the same shoe model they already own as well as pages for a more premium model.  Suppose further that behavioral analytics show the customer spent 2/3rds of their time looking at the existing model of shoe.  Finally, you notice that based on other data points -- perhaps last date of purchase or more ideally, some sort of wear data either acquired or intuited -- that their current shoes are due to be replaced.  Importantly, no purchase was made.  Their emerging need is to replace, but they may want to upgrade.  Based on their RFM score, purchase history, or other data, it is not hard to predict the trigger(s) to move their want to a need.  Now it becomes a matter of crafting a succinct, meaningful, actionable, relevant and timely message to do so.

But wait, one more element must come into play: your brand strategy.  If your brand is positioned to be upscale, then rather than a pricing trigger, the strategy could be a combination purchase opportunity or an exclusive offer.  So it is equally important to stay on message with your brand.  We see this botched all the time when it comes to thinking about Loyalty, but that's another story.

OK, a couple of reality checks.
First, we cannot underestimate the level of computational capability required and complexity incurred in implementing the neccesary automatic mechanisms to run these types of individually oriented analytics (as we wrote at the outset, in a perfect world with perfecrt resources this would be easy).  While some are doing this precise level of targeting, its not affordable for everyone (yet). 

Likely is the case that rather than a fine grained approach of 1:1 messaging, a reasonable (and more affordable) solution will amount to class-messaging wherein customers fit into a category of message recipient.  Second, not every message campaign need result in a conversion (i.e., an up-sell, new-sell, or cross-sell).  So long as the message(s) catalyze behavior, which may well include customer feed back depending on the type of message (e.g., offer, survey, feedback request, ratings/reviews, exclusive participation opportunity, etc.) you are using SMART messaging to continue moving the customer from merely satisfied, to loyal, and on to advocate.

The simple answer then is to test your messaging -- always, in fact, continuously is the ideal.  Compile and apply data to help shape messaging.  And in as much as you strive for SMART messages, make sure you avoid the DUMB ones as well.


Gregory Miller, CTO

Greg has been in the tech sector as a software architect and engineer, product manager, marketing and biz dev exec., and even IP and privacy lawyer for 3 decades. He is currently on the Board of a non-profit tech foundation reinventing America's election technology, is a venture adviser in the Silicon Valley, and serves as the CTO for C[IQ] Strategies, Inc.