The optimism of #Purpose & science of #design in forging #digital customer experiences! Opinions my own!

In an effort to build long-term, profitable relationships, many companies systematically engage in multichannel customer interactions —personalized messages sent to existing customers through various channels as part of a broader relationship marketing strategy.

There are in fact, three key drivers of relational communication effectiveness:

• volume of communication
• mix of communication channels, and
• alignment of those channels with customers’ preferences.

The customer response curve follows a continuum in which reciprocity explains response to lower levels of communication – there is an inflection point where customers become “saturated” and subsequent contacts actually erode effectiveness. Studies have shown that these inflection points differ by channel type, and by industry.

Sadly, most of us have failed as direct marketers to tailor our communication strategies accordingly. There are many reasons to this; sometimes we just lack the technology infrastructure to capture individual customer preferences over time. As a result, we fall back to the following:

• Shaving the target selection list (hard limits placed per campaign – based on “experience”)
• Placing hard limits and constraints on the number of contacts
• Select cost-effective, digital channels (e-mails and text messages in most of the cases)
• Increasing the gap, lowering the frequency between contacts

These are practical and a simple way to get campaigns out the door. I’m not sure we’re squeezing the most out of every contact made though. In other words, a blanket (vanilla) contact strategy is applied to the entire target selection. With this approach, we fail to appreciate the inherent uniqueness of every customer + channel combination: e.g. response likelihood of a specific customer given a particular frequency, timing of contacts, gaps and quarantines in communication and choice of channel.

To illustrate, let’s take a case study of an onboarding program for a Bank’s credit card where the as-is communication strategy is aggressive and 2 emails are sent followed by a text message in the first 7 days. Average response rate realized is X%.

Micro-segmentation

If we build a statistical model we could glean the following from historical data before grouping customers (with healthy response rates) in similar focus buckets: 

• Day and time the message was sent
• Day and time the message was opened/clicked (for digital channels such as email and the Internet)
• Day and time the response was recorded (e.g. a call to action to click on a banner on the Bank’s website); Facebook stats show higher levels of engagement in the mornings, between hours of 8am to 10 am (presumably when we’re on our way to work?)
• Number of contacts sent till a response was captured
• Channel and offer types used

Simple logic would dictate that customers requiring just 1 email with a higher likelihood of response on a certain day e.g. Wednesday would be identified – so instead of the standard 2 emails X 1 SMS on Tuesday, Thursday and Saturday for the entire target customer base, why not have another communication cycle that fits in a Wednesday? If we continue along this path we arrive at a tailored contact strategy for every customer.

Why tailor communication and contact strategies?

Simple – one size doesn’t fit all. Before, we faced challenges, such as insufficient historic experimental, contact and response data. That’s not an excuse today, it’s basic hygiene. There’s plenty of upside doing this well:

1.         Significantly reduced campaign costs – “less is more”

3.         Higher response rates to campaign

4.         Reduced subscription opt-outs and DND registrations

For item 1, less really is more. Consider our frontlines – the PFCs, the RMs and the branch folks. These guys they’re under pressure to deliver on channel efficiencies such as the number of daily contacts, leads acted on etc; reducing the lead volume but dialing up quality translates into better conversion rates. Every time.