Factors for Marketers to Consider in Attribution Rules

At the end of each campaign effort, a good database marketer is supposed to study “what worked, and what didn’t,” using attribution rules. Call it “Back-end Analysis” or “Campaign Analytics.” Old-timers may use terms like “Match-back.” Regardless, it is one of the most important steps in 1:1 marketing that is synonymous with what we used to call “Closed-loop Marketing.”

At the end of each campaign effort, a good database marketer is supposed to study “what worked, and what didn’t,” using attribution rules. Call it “Back-end Analysis” or “Campaign Analytics.” Old-timers may use terms like “Match-back.” Regardless, it is one of the most important steps in 1:1 marketing that is synonymous with what we used to call “Closed-loop Marketing.” (refer to my first article on Target Marketing from 11 years ago, “Close the Loop Properly”).

In fact, this back-end analysis is so vital that if one skips this part of analytics, I can argue that the offending marketer ceases to be a 1:1 or database marketer. What good are all those databases and data collection mechanisms, if we don’t even examine campaign results? If we are not to learn from the past, how would we be able to improve results, even in the immediate future? Just wild guesses and gut feelings? I’ve said it many times, but let me say it again: Gut-feelings are overrated. Way more overrated than any cheesy buzzword that summarizes complex ideas into one or two catchy words.

Anyhow, when there were just a few dominant channels, it wasn’t so difficult to do it. For non-direct channel efforts, we may need some attribution modeling to assign credit for each channel. We may call that a “top-down” approach for attribution. For direct channels, where we would know precisely who received the offers, we would do a match-back (i.e., responders matched to the campaign list by personally identifiable information, such as name, address, email, etc.), and give credit to the effort that immediately preceded the response. We may call that a “bottom-up” method.

So far, not so bad. We may have some holes here and there, as collecting PII from all responders may not be feasible (especially in retail stores). But when there was just direct mailing as “the” direct channel, finding out what elements worked wasn’t very difficult. Lack of it was more of a commitment issue.

Sure, it may cost a little extra, and we had to allocate those “unknown” responders through some allocation rules, but backend analysis used to be a relatively straightforward process. Find matches between the mailing (or contact) list and the responders, append campaign information — through what we used to call “Source Code” — to each responder, and run reports by list source, segment, selection mechanism, creative, offer, drop date and other campaign attributes. If you were prudent to have no-mail control cells in the mix, then you could even measure live metrics against them. Then figure out what worked and what didn’t. Some mailers were very organized, and codified all important elements in those source codes “before” they dropped any campaigns.

Now we are living in a multi-channel environment, so things are much more complicated. Alas, allocating each of those coveted responses to “a” channel isn’t just technical work; it became a very sensitive political issue among channel managers. In the world where marketing organizations are divided by key marketing channels (as in, Email Division vs. Direct Mail Division), attribution became a matter of survival. Getting “more” credit for sales isn’t just a matter of scientific research, but a zero-sum game to many. But should it be?

Attribution Rules Should Give Credit Where Credit’s Due

I’ve seen some predominantly digital organizations giving credit to their own direct marketing division “after” all digital channels took all available credit first. That means the DM division must cover its expenses only with “incremental” sales (i.e., direct-mailing-only responses, which would be as rare as the Dodo bird in the age of email marketing). Granted that DM is a relatively more expensive channel than email, I wish lots of luck to those poor direct marketers to get a decent budget for next year. Or maybe they should look for new jobs when they lose that attribution battle?

Then again, I’ve seen totally opposite situations, too. In primarily direct marketing companies, catalog divisions would take all the credit for any buyer who ever received “a” catalog six months prior to the purchase, and only residual credit would go to digital channels.

Now, can we at least agree that either of these cases is far from ideal? When the game is rigged from the get-go, what is the point of all the backend analyses? Just a façade of being a “data-based” organization? That sounds more like a so-called “free” election in North Korea, where there are two ballot boxes visibly displayed in the middle of the room; one for the Communist Party of the Dear Leader, and another box for all others. Good luck making it back home if you put any ballot in the “wrong” box.

Attribution among different channels, in all fairness, is a game. And there is no “one” good way to do it, either. That means an organization can set up rules any way it wants them to be. And as a rule I, as a consultant, tend not to meddle with internal politics. Who am I to dictate what is the best attribution rule for each company anyway?

Here’s How I Set Up Attribution Rules

Now that I am a chief product guy for an automated CDP (Customer Data Platform) company, I got to think about the best practices for attribution in a different way. Basically, we had to decide what options we needed to provide to the users to make up attribution rules as they see fit. Of course, some will totally abuse such flexibility and rig the game. But we can at least “guide” the users to think about the attribution rules in more holistic ways.

Such consideration can only happen when all of the elements that marketers must consider are lined up in front of them. It becomes difficult to push through just one criterion — generally, for the benefit of “his” or “her” channel — when all factors are nicely displayed in a boardroom.

So allow me to share key factors that make up attribution rules. You may have some “A-ha” moments, but you may also have “What the … ” moments, too. But in the interest of guiding marketers to unbiased directions, here is the list:

Media Channel

This is an obvious one for “channel” attribution. Let’s list all channels employed by the organization, first.

  • Email
  • Direct Mail (or different types of DM, such as catalog, First Class mail, postcards, etc.)
  • Social Media (and specific subsets, such as Facebook, Instagram, etc.)
  • Display Ads
  • Referrals/Affiliates
  • Organic Search/Paid Search
  • Direct to Website (and/or search engines that led the buyers there)
  • General Media (or further broken down into TV, Radio, Print, Inserts, etc.)
  • Other Offline Promotions
  • Etc.

In case there are overlaps, which channel would take the credit first? Or, should “all” of the responsive channels “share” the credit somehow?

Credit Share

If the credit — in the form of conversions and dollars — is to be shared, how would we go about it?

  • Double Credit: All responsible channels (within the set duration by each channel) would get full credit
  • Equal Split: All contributing channels would get 1/N of the credit
  • Weighted Split: Credit divided by weight factors set by users (e.g., 50% DM, 30% EM, 20% General Media, etc.)

There is no absolutely fair way to do this, but someone in the leadership position should make some hard decisions. Personally, I like the first option, as each channel gets to be evaluated in pseudo-isolation mode. If there was no other channel in the mix, how would a direct marketing campaign, for example, have worked? Examine each channel and campaign this way, from the channel-centric point of view, to justify their existence in the full media mix.

Allocation Method

How will the credit be given out with all of those touch data from various tags? There are a few popular ways:

  • Last Touch: This is somewhat reasonable, but what about earlier touches that may have created the demand in the first place?
  • First Touch: We may go all of the way back to the first touch of the responder, but could that be irrelevant by the time of the purchase? Who cares about a Christmas catalog sent out in November for purchases made in May of the next year?
  • Direct Attribution: Or should we only count direct paths leading to conversions (i.e., traceable opens, clicks and conversions, on an individual level)? But that can be very limiting, as there will be many untraceable transactions, even in the digital world.
  • Stoppage: In the journey through open, click and conversion, do we only count conversions, or should the channel that led to opens and clicks get partial credit?

All of these are tricky decisions, but marketers should not just follow “what has been done so far” methods. As more channels are added to the mix, these methods should be reevaluated once in a while.

Time Duration (by Channel)

Some channels have longer sustaining power than others. A catalog kept in a household may lead to a purchase a few months later. Conversely, who would dig out a promotional email from three weeks ago? This credit duration also depends on the type of products in question. Products with long purchase cycles — such as automobiles, furniture, major appliances, etc. — would have more lasting effects in comparison to commodity or consumable items.

  • Email: 3-day, 7-day, 15-day, 30-day, etc.
  • Direct Mail — Catalog: 30-day, 60-day, 90-day, etc.
  • Direct Mail — Non-catalog: 7-day, 14-day, 30-day, 60-day, etc.
  • Social: 3-day, 7-day, 15-day, etc.
  • Direct Visit: No time limit necessary for direct landing on websites or retail stores.
  • General Media: Time limit would be set based on subchannels, depending on campaign duration.

Closing Thoughts

The bottom line is to be aware of response curves by each channel, and be reasonable. That extra 30-day credit period on the tail end may only give a channel manager a couple extra conversions after all of the political struggles.

There is really no “1” good way to combine all of these factors. These are just attribution factors to consider, and the guideline must be set by each organization, depending on its business model, product composition and, most importantly, channel usages (i.e., how much money bled into each channel?).

Nevertheless, in the interest of creating a “fair” ground for attributions, someone in a leadership position must set the priority on an organizational level. Otherwise, the outcome will always favor what are considered to be traditionally popular channels. If the status quo is the goal, then I would say skip all of the headaches and go home early. You may be rigging the system — knowingly or unknowingly — anyway, and there is no need to use a word like “attribution” in a situation like that.

Creepy Marketing—When Database Marketing Goes Awry

With Halloween over and the holidays on their way, I thought that Creepy Marketing made a timely subject for today’s blog. Now I’m not referring to marketing for ghouls, witches or mummies. I’m talking about adding a creepy factor to your marketing program—a major pitfall of 1:1 marketing.

With Halloween over and the holidays here, I thought that Creepy Marketing made a timely subject for today’s blog. Now I’m not referring to marketing for ghouls, witches or mummies. I’m talking about adding a creepy factor to your marketing program—a major pitfall of 1:1 marketing.

Creeping people out is, after all, contrary to what we’re trying to achieve as marketers, which is namely to use promotion to advance the brand’s sales and branding objectives. That is, of course, unless it’s your goal to damage your brand and drive away customers. Assuming that’s not the case, let’s assume that creepy is bad. Very bad. In the age of social media, one creeped out customer can very easily spread the word to hundreds of thousands of customers and prospects. In other words, better safe than sorry.

But before we go any further, however, let’s attempt to define creepy. This is important because many marketers I speak with cite there often is a razor thin line between casual and its inappropriate Cousin Creepy, between making a sale and detonating a potential long-term relationship. Fair enough. Creepiness is also a bit slippery because, like fashion tastes, standards for creepiness definitely do tend to change with time. To quote Sean parker, former CEO of Facebook, “Today’s creepy is tomorrow’s necessity.”

When it comes to detecting creepiness, I’m a firm believer of what I’ll call the ad oculos school of thought. For those of you who do not understand Latin, ad oculos means “to the eyes,” and roughly translates into “obvious to anyone that sees it.” In other words, if it looks creepy and feels creepy, then it probably is creepy and you shouldn’t do it.

You shouldn’t, for example, write out your customer’s names on a postcard or landing page—or anywhere that might be, or seem, visible to the general public. Nor for that matter should you display your customer’s age, marital status, or medical condition on any piece of marketing collateral. This doesn’t mean that you shouldn’t send offers for dating services to a customer you know is single, or information on chiropractors to someone who has acknowledged a back problem. What this means is you need to be careful with the language you use in these offers, taking care not to publicize information your customers want to remain in the private sphere.

It’s also important to keep in mind that 1:1 marketing works because it focuses like a laser on your customer’s interests and presents them with compelling and compatible product information and offers. Personalized communication is not an exercise in regurgitating your customer’s personal data in an effort to prove to them how much you know.

Remember, successful database marketers use profile data to run highly compelling and relevant campaigns to their customers. What makes the campaign successful is the fact that the offer and marketing message contain relevant information that the recipient will have a strong affinity for—not simply because it is personalized. Personalization for the sake a personalization is nothing but a gimmick—it might work once but that’s it. Successful and sustainable personalized marketing programs ultimately find a formula for identifying customer interests based on key data points and indicators, and use this formula to create and disseminate offers that will strike a chord with prospects and customers.

Have you ever been creeped out? If so, I’d love to find out how and get your feedback.

The Database Marketer Superhero: Expanded Role, Big Impact

Riddle me this, Batman: What sort of marketing strategies today require deeper, strategic database insight? Not so puzzling, is it? Pretty much everything a marketing team does today is driven by data — e.g., digital outreach, content, media, attribution, return on investment analysis, lead nurturing, PR and social community participation. In fact, the list would be shorter if we tallied up those marketing functions that don’t benefit from data-driven decisions.

Riddle me this, Batman: What sort of marketing strategies today require deeper, strategic database insight?

Not so puzzling, is it? Pretty much everything a marketing team does today is driven by data — e.g., digital outreach, content, media, attribution, return on investment analysis, lead nurturing, PR and social community participation. In fact, the list would be shorter if we tallied up those marketing functions that don’t benefit from data-driven decisions.

Database marketers were traditionally the geeks of the marketing department. They kept to themselves, ran queries to answer questions posed by other strategists, and worked hard to keep data clean and updated. Today’s database marketers are part of an emerging and essential marketing operations team that’s driving a lot of brands’ strategies. One marketer said to me recently, “Whomever knows the customers best gets to make the call.” Who knows your customers better than the people working with the data every day? All of a sudden, database marketers are superheroes — or at least have the opportunity to wear capes if they choose to accept the challenge.

There are two factors driving this trend, one being consumer habit. Given the ability and choice to interact with brands in many ways and across many channels, consumers are taking full advantage. It’s a me-centered consumption world where customer preference and whim create habits. At the same time, marketing automation technology is advancing and data integration is possible. Marketers can track and, more importantly, react to customer behavior in order to meet needs across channels.

Consider these five initiatives that have become imperatives for many chief marketing officers today:

1. Obtain a 360-degree view of the customer. One B-to-C marketer told me that there are more than 25 ways customers can interact with her brand, from a kiosk to a store counter to email to mobile commerce to branded website to call center to social communities. Most consumers participate in three or more of those channels. Communications can only be optimized if those habits and experiences are captured — and actionable — in your database.

2. Respond to customer behavior in the channel where the interaction occurred. This also has to be aligned with self-selected preferences.

3. Select the optimal channel for your next offer. A hotel owner uses past booking behavior to send last-minute alerts via SMS to those who have opted in and accessed the brand’s mobile commerce site. All others get the information via email. Response has boosted overall 8 percent.

4. Outline personas representing key customer segments. Do this in order to profile audience types and improve communication messaging and cadence.

5. Test and optimize your mix of channels for lead nurturing campaigns. For a live seminar event, one B-to-B marketer emailed reminders and offers based on interaction with previous email campaigns. Those who didn’t respond got simple reminders on date, location and keynote speakers. Those who did respond got more robust offers. Revenue from the offers increased 50 percent over the previous year and spam complaints dropped 25 percent. This is surely because those who demonstrated a willingness to engage prior to the event were nurtured with offers that made sense to their actions, and the others were left alone.

I’m sure there are infinite variations of these opportunities. Perhaps you’re testing some of them now. It will also be great to see how database marketers react to this new level of attention and interest from the C-suite. Will you embrace it and join the strategists, or will you run back to the corner and take orders?

How are you and your team embracing the need for a data-driven marketing approach? Please tell us by posting a comment below.