Relevancy, the Currency of Conversion

More. The marketer’s mandate will always be “more” — more traffic, more sales, more margins. Add to it that in order to get more, we’ll need to test more ideas, try new strategies, new media and mediums — not all of which will work.

Oliver Twist moreMore.

The marketer’s mandate will always be “more” — more traffic, more sales, more margins. Add to it that in order to get more, we’ll need to test more ideas, try new strategies, new media and mediums — not all of which will work.

More ultimately means sales conversion, and there’s a data-driven approach to getting more that leverages a new currency. Not Bitcoin, but relevancy, because relevancy is the “Currency of Conversion.” That conversion currency is based on the intelligent use of data.

Truly accomplishing data-driven success requires focus and simplifying — one of the few constants in business marketing.

Through advising dozens of organizations on the intelligent use of data to inform and improve performance, it is often helpful to come back to some of the fundamentals in thinking about the application of our data to business problems. And while often we focus on the “what” that has to do with data, let’s consider perhaps the most important question — “Why?”

Why Should I Inform Marketing With Data?

While it’s likely considered risky nowadays to lack a data strategy or better yet, a data-driven strategy, we do need to ask why. I’ve been surprised at the lack of fluency even experienced IT people and all kinds of marketers have when asked why they need to invest in data strategies. That’s despite the “reality” that everyone knows they “should.” Let’s deal with that.

  • Reporting. Many organizations still desire better reporting, Key Performance Indicators (KPIs) being the most important. It’s a baseline use of data, and it’s important. So data serves a purpose and provides consistent, specific solutions to the questions “how are we doing?” It’s hard to operate without it, but it should become “table-stakes” in short order.
  • Analysis and Insights. Data, if organized and governed reasonably well, can yield insights. This requires you have an analyst with a big brain to pore through it. The analyst needs to know enough about your business to understand what is relevant and what is not. The analyst must also consider materiality and the difference between correlation and causality.

This last point being an all-too-common mistake. For example, “our customers are rich” so we need to target rich people. Being affluent may be correlated with buying your product, but it may not be causal! We’ve found this example many times when actually statistically testing to see what attributes have the most causal/predictive relationship. For a full study on causality vs. causation, see this piece from Stats.org.

  • Customer Intelligence. Customer Intelligence is the next-level beyond analytics. In CI, we now use purpose-specific algorithms to derive new data and to identify valuable patterns that arise in large amounts of data. It’s fair to call it “the union between marketing and data mining.” Customer Intelligence provides us the answers to questions we don’t ask because we don’t know how to answer them.

The Most Important Reason to Inform Marketing With Data

The low cost of communicating digitally has, in some cases, left relevancy underrated. This is no coincidence. When you spend real money to send a quality, brand-appropriate direct mail piece or even more money on television — you care a lot about relevancy. This message has to be right, it has to be on-brand, it has to resonate. Today, that mass-market TV ad isn’t a winner if it doesn’t “break the Internet.”

But when it’s an email that costs a fraction of a cent to deploy and just a few fixed dollars to create amortization over millions of recipients, we as marketers can get impressively lazy. Relevancy is trumped by low cost and high ROI. Who cares if the message is perceived as irrelevant? The email drop “worked.”

Let’s consider this further.

Let’s say the “less than relevant” drop had an out-sized 35 percent click rate. We know the sender names were likely those they anticipated email from, and the subject line was likely relevant. We can’t know the breakout of which send carried more weight without testing them. But if you subscribe to the school of thought that relevancy isn’t important, then testing probably is irrelevant to you, too. Before you decide “well, of course we think relevancy is important” — think about whether you’re really using it as a principle in your outbound marketing.

Focus Group of One

If you’re sending your marketing campaigns without benefit of A/B or multi-variant testing—most companies admit to fewer than five tests per month—you are effectively acting as a focus group of one. You are assuming all of your constituents feel the same way about your campaign as you do. Big mistake.

If you’re sending your marketing campaigns without benefit of A/B or multi-variant testing—most companies admit to fewer than five tests per month—you are effectively acting as a focus group of one. You are assuming all of your constituents feel the same way about your campaign as you do. Big mistake.

Most of us have a least a bit of familiarity with A/B testing and have integrated it into some of our deployments. Testing subject line A against subject line B is likely the most common test, but with A/B testing you can go so much further—both simple and complex—for instance:

  • Best time of day for sending each of your email types (e.g., newsletter, offers)
  • Best day for sending each type of email
  • Frequency of sending each type of email
  • Length of subject line
  • Personalization within the subject line
  • Personalization within the message
  • Squeeze page vs. landing page
  • Conversion lift when video, demo or meeting booking are included
  • Diagnosing content errors
  • Challenging long-held behavior assumptions
  • Calls to action
  • Color
  • Format and design
  • Writing style (casual, conversational, sensational, business)
  • From name and email address (business vs. personal)

A/B and multi-variant testing enable you to learn what makes your prospects, leads, subscribers and customers tick. When you adopt a consistent testing process, your accumulative results will provide you with the knowledge to implement dramatic changes producing a measurable impact across campaigns, landing pages, websites and all other inbound and outbound initiatives.

We have a client whose singular call to action in every email is to discount their product, and each offer is more valuable than the last. When I asked how well this worked, they admitted, the bigger the discount, the more they sold. When pressed, however, they could not tell me the ROI of this approach. Sure, they sold more widgets, but at the discount level they offered, they also made far less profit.

I suggested an A/B-laden drip campaign offering no discounts, and instead providing links to testimonials, case studies, demos of their product, book-a-meeting links, and other inbound content. In this way, we were changing their position from asking for the business to earning the business. While I admit this usually lengthens the sales cycle, it also means money is not being left on the table unnecessarily.

For this client, the change in approach was simply too dramatic and they found they couldn’t stick with it long enough to gather the data needed to make long-term business decisions. The limited of data they were able to collect in the first few emails did show, however, an inbound approach deserved strong consideration by their organization.

Not all A/B testing need be this dramatic—we could have started them off with a less-committed approach. My takeaway was: You don’t have to learn it all now; A/B testing can be integrated in a small way. Whether you go all out or an occasional test, A/B data is useless if you do not set measurable goals. Measurable goals mean you will establish:

  • Required return on investment
  • Vehicle (email, direct mail, other)
  • What to test
  • Audience
  • Time frame
  • Testing protocol
  • How to integrate what you’ve learned into future campaigns

If your email application does not support A/B testing, you can use a more automated approach. Simply create two versions of your marketing campaign and divide your list randomly in half—unless, of course, what you’re testing is something within your list, such as gender or locale.

I often am in search of information well beyond opens, clicks and visits, so I turn to Email on Acid for email heat maps and Crazy Egg for landing page and website heat maps. While these are effective on live pages and campaigns, it’s not required you deploy A/B testing to a live audience. Testing can be just as effective with a small focus group, just be sure it’s not a focus group of one.