Healthcare Open Enrollment Means Marketers Ask: Whose Patient Are You, Really?

It’s Open Enrollment season in health care. Marketers at physician groups, hospitals, and health plans compete for attention and selection. These high-profile marketing campaigns tend to overshadow a new wrinkle that adds complexity behind the scenes — patient attribution.

It’s Open Enrollment season in health care. Marketers at physician groups, hospitals, and health plans compete for attention and selection because the choices made now will drive patient traffic and revenue potential over the coming year. These high-profile marketing campaigns tend to overshadow a new wrinkle that adds complexity behind the scenes — patient attribution.

Patient attribution is rising in importance as part of the overall shift toward Accountable Care Organizations (ACOs), value-based care, population health, and social determinents of health. All of these trends require a provider or provider’s organization to be more engaged, proactive, and mindful of resource utilization. In an HMO model, the responsiblity rests with the assigned or chosen primary care physician (PCP), known as a “patient choice” attribution method. The PCP does an initial evaluation and determines when more specialized care is appropriate. But this obvious direct accountability is not present in other types of coverage, where the healthplan design does not require the patient to choose a PCP. In these situations, patients can bounce from physician-to-physician and hospital-to-hospital, with only the insurance company having an understanding of the patient’s health situation — based on the claims they pay.

This is where patient attribution comes into play. The idea is that clear lines of responsibility lead to more efficient use of resources and earlier resolution of health problems. But for a health plan to create accountability, it has to determine which provider or provider organization should benefit from the financial upside of success, or bear the downside risk for poorly managing a patient’s care. Providers and provider organizations with attributed patients can realize significant performance-based bonuses, if the oversight efforts result in more efficient use of resources, better clinical documentation, or improvements in their overall panel’s health. The downside is that a physician can be assigned — and remains responsible for — attributed patients who never come into the practice, which occurs most often if the provider does not have a strong outreach program.

Attributing a patient is not a simple matter. Each has strengths and different implications for the type of marketing service you might offer a provider or organization. If your hospital or physician group holds multiple value-based contracts, focus your marketing efforts on the agreements that offer the most financial “upside,” based on the number of lives under that health plan contract or based on whether the clinical performance targets outlined in the agreement are achieveable.

While there are several attribution models, the most common are Prospective Attribution, Retrospective Attribution, and Geography-Based Attribution. As a marketer, the outreach strategies and tactics you recommend may differ, depending on the the attribution method.

The retrospective approach relies on the patient’s visit history (sometimes called visit-based attribution). The patient is attributed to the physician or provider organization the patient went to most frequently. In some cases, that attribution may be to a specialist, rather than a PCP. This rear-view mirror approach has the advantage of retrospective accuracy, but also the weakness of assuming the patients’ behavior in the coming year can be based on their behavior in the prior year. As a marketer, your goal is to solidfy a relationship such that the patient would be strongly predisposed to returning.

In a prospective attribution model, the health plan will assign the patient to a provider or provider organization, with the expectation the provider will reach out to the patient to create initial and ongoing engagement. While the provider has clarity about who they are responsible for, patients may not respond to that outreach. As a marketer, your opportunity is to make getting the first appointment easy and the experience pleasant, so they do not feel a need to go elsewhere.

A geography-based attribution is an approach that assigns patients to nearby providers, often at the ZIP code level. The proximity of the provider’s office to the patient’s residence can be attractive to many consumers, increasing the odds the patient will remain with that practice. The weakness in this model is that it does not take into account the actual health needs of the patient who may need to see a specialist more frequently than a PCP. Geography-based marketing is a natural fit for most marketers, both in the real world or digital world.

As you execute upon this year’s open enrollment outreach, open a discussion with your contracting team about value-based agreements and the attribution models used. It may help frame some of the activities you pursue after the new year.

Don’t Settle for Last-Touch Attribution in Marketing

Last month, I talked about factors marketers should consider for attribution rules. Here, I would like to get a little deeper and discuss last-touch attribtuion, as just talking about contributing factors won’t get you anywhere. As in all data-related subjects, the devil hides in the details.

Last month, I talked about factors marketers should consider for attribution rules. Here, I would like to get a little deeper and discuss last-touch attribution, as just talking about contributing factors won’t get you anywhere. As in all data-related subjects, the devil hides in the details. How to collect the data, what to consider, how to manipulate clean and dirty data, and in what order one must execute different steps.

I wonder sometimes why last-touch attribution is such a popular industry, with all of the flaws embedded in that methodology. Without even getting into geeky programming details, let’s think about the limitation of last-touch attribution, in a logical sense.

First off, by giving all of the credit for a conversion to “one” medium of the last touch, you would be ignoring all of the previous efforts done by your own company. If you are the lucky channel manager of the last touch, you wouldn’t mind that at all. But is that fair? C-level executives should not accept such flaws in the name of efficiency or programming convenience.

Why You Shouldn’t Settle for Last-Touch Attribution

Let’s use my own experience as a buyer to illustrate a typical customer journey in a multichannel marketing environment. Like any man who shaves daily, I’ve always felt that most quality brand blades were way overpriced. And I found it quite inconvenient that I had to visit a physical store to buy them, when I knew that I would need new blades at a regular interval. All of that changed when a few blade delivery services popped up in that lucrative men’s grooming market a few years ago.

I was one of the early adopters who signed on with one of the programs. But after cutting my face a few times with defective blades, I just canceled the delivery service, and went back to my favorite brand of my adult life, knowing that it would cost more. I considered that to be an affordable luxury.

Then one day, I saw an ad on Facebook, that my favorite brand now offers home delivery service, at a significantly lower price point in comparison to store purchases. Call that my first touch before conversion (to the newly offered service). But I didn’t sign up for it at that time, even though I clicked-through to the landing page of its website. I was probably on my mobile phone, and I also wanted to examine options regarding types of blades and delivery intervals further when I had more time.

That means, I visited the site multiple times before I committed to the subscription model. I remember using Google to get to the site at least once; and later, I hit on a bookmark with its URL a few more times. Let’s say that Touch No. 2 would be labeled as “Organic Search,” and touches No. 3 and No. 4 would be considered “Direct-to-Site.”

If you employ last-touch attribution, then Facebook and organic search would get zero credit for the transaction here. That type of false intel may lead to a budget cut for the wrong channel, for sure. But as a consumer, I “know” that it was the Facebook where I first learned about the new service from the brand.

Imagine if you, as a marketer, had a toggle switch between Last Touch and First Touch rules. When thousands, if not millions, of touch data points are aggregated in an attribution report, even a simple concept, such as “the most important acquisition channel,” will have a different look depending on the attribution rules. In one, Social Media may look like the most effective channel. In another, Organic Search may take the cake. The important lesson is that one should never settle for last-touch attribution, just because that is how it’s been done within the organization (or by the analytics vendors).

There Are a Few More Attribution Methods

The Last and First Touch rules are the easy ones — if you have access to all touch data on an individual level (because you’d have to line touchpoints up for each buyer, in sequence). As I briefly introduced last month, there are a few more methods to consider. Let’s dig a little deeper this time:

  • Last Touch: Although there could have been many touchpoints before conversion, this method would just give all of the credit to the last one. As flawed as it may be, there are some merits. For one, last touch would be the one that is directly attributable (i.e., connected) to the transaction (and the session that led to it) without any assumptions or rules. I suspect that the simplicity of it all is the main reason for its popularity.
  • First Touch: This would be useful for the study of acquisition sources. Timeline is an important consideration here, as effectiveness of channel or offer may decay at different rates, depending on product and channels in question. A consumer may have researched for a washing machine four months ago. And saw a newspaper insert about it three weeks ago. And then got an email about it a week ago. How far back can we go with this? A catalog that was mailed six months ago? Maybe, as we are dealing with a big-ticket item here. And are we sure that we have any digital touch data that go back that far? Let’s not forget that the word “Big Data” was coined to describe click-level data to begin with.
  • Double Credit: If a person was exposed to and engaged in multiple channels before the purchase, why not credit all involved channels? Overkill? Maybe. But we use this type of reporting method when dealing with store-level reports. There is no law that one customer can visit only one store. If one visits multiple stores, why not count that person multiple times for store-level reports? So, with the same reasoning, if a transaction is attributable to multiple channels, then count the transaction multiple times for the channel report. Each channel manager would be able to examine the effectiveness of her channel in an isolation mode (well, sort of).
  • Equal Credit: This would be the opposite of Double Credit. If there are multiple channels that are attributable to a transaction, create a discount factor for each channel. If one is exposed to four channels (identified via various tags and tracking mechanisms), each would get ¼ of the transaction credit. When such discounted numbers are aggregated (instead of transactions, as a whole number), there will be no double-counting in the end (i.e., the total would add up to a known number of transactions).
  • Proportional Credit: Some channel managers may think that even Equal Split is not a fair methodology. What if there were eight emails, two organic searches, three paid searches and a link on a Facebook page that was clicked once? Shouldn’t we give more weight to the email channel for multiple exposures? One simple way to compromise (I chose this word carefully) in a situation like this would be to create a factor based on the number of total touches for each channel, divided by the total number of touches before conversion.
  • Weighted Value: An organization may have time-tested — or politically prevailing — attribution percentages for each employed channel. I would not even argue why one would boldly put down 50 percent for direct marketing, or 35 percent for organic search. Like I said last month, it is best for analysts to stay away from politics. Or should we?
  • Modeled Weighted Value: Modeling is, of course, a mathematical way to derive factors and scores, considering multiple variables at once. It would assign a weighted factor to each channel based on empirical data, so one might argue that it is the most unbiased and neutral method. The only downside of the modeling is that it would require statistically trained analysts, and that spells extra cost for many organizations. In any case, if an organization is committed to it, there are multiple modeling methods (such as the Shapley Value Method, based on cooperative game theory — to name one) to assign proper weight to each channel.

I must point out that no one method would paint the whole picture. Choosing a “right” attribution method in an organization with vastly different interests among teams is more about “finding the least wrong answer” for all involved parties. And that may be more like Tony Soprano mediating turf disputes among his Capos than sheer mathematics spitting out answers. That means the logically sound answer may not void all of the arguments. When it comes to protecting one’s job, there won’t be enough “logical” answers as to why one must give credit for the sale to someone else.

While all of this has much to do with executive decisions, people who sit between an ample amount of data and decision-makers must consider all possible options. So, having multiple methods of attribution will help the situation. For one, it is definitely better than just following the Last Touch.

Start With Proper Data Collection

In any case, none of these attribution methods will mean anything, if we don’t have any decent data to play with. Touch data starts with those little pixels on web pages in the digital world. Pages must be carefully tagged, and if you want to find out “what worked,” then, well, you must put in tracking requests properly for all channels.

A simple example. In a UTM tag, we see Medium coded with values such as Paid Social. A good start. Then we go to Source, we would see entries like Facebook, Instagram, Twitter, Pinterest, etc. So far, so good. But the goal is to figure out how much one must spend on “paid” social media. Without differentiating (1) Company’s own social media page, (2) Paid ads on social media sites, and (3) Referrals by users on social media (on their Facebook Wall, for example), we won’t be able to figure out the value of “Paid Social.” That means, all of the differentiation must be done at the time of data collection.

And while at it, please keep the data consistent, too. I’ve seen at least 10 different ways to say Facebook, start with “fb.”

Further, let’s not stop at traditional digital tags, either. There are too many attribution projects that completely block out offline efforts, like direct mail. If we need to understand where the marketing dollars must go, why settle with one type of tracking mechanism? Any old marketer would know that there is a master mail file behind every direct mailing campaign. With all those pieces of PII in it, we can convert them into yet another type of touch data — easily.

Yes, collecting such touch data for general media won’t be easy; but that doesn’t mean that we keep the wall up between online and offline worlds indefinitely. Let’s start with all of the known contact lists, online or offline.

Attribution Should Be Done in Multiple Steps

Attribution is difficult enough when we try to assign credit to “1” transaction, when there could be multiple touchpoints before the conversion. Now let’s go one step further, and try to call a buyer a “Social Media” responder, when we “know” that she must have been exposed to the brand at least 20 times through multiple media channels including Facebook, Instagram, paid search through Google, organic search through some default search engine on a phone, a series of banner ads on various websites, campaign emails and even a postcard. Now imagine she purchased multiple times from the brand — each time as a result of a different series of inbound and outbound exposures. What is she really? Just a buyer from Facebook?

We often get requests to produce customer value — present and future — by each channel. To do that, we should be able to assign a person to a channel. But must we? Why not apply the attribution options for transaction to buyers, as I listed in this article?

That means we must think about attribution in steps. In terms of programming, it may not exactly be like that, but for us to determine the optimal way to assign channels to an individual, we need to think about it in steps.

Conclusion

Now, if you are just settling for last-touch attribution, you may save some headaches that come with all of these attribution methods. But I hope that I intrigued you enough that you won’t settle so easily.

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.

How to Connect Digital Media Spend to Revenue Results

Digital media spend is likely one of the largest pieces of the budget. But is it being well spent? How can you tell? The media team and their agencies use a lot of new buzzwords to describe where it is being spent, but at the end of the day CMOs want to know exactly how much revenue that budget drove.

Besides content and labor, digital media spend is likely one of the largest pieces of the budget. But is it being well spent? How can you tell? The media team and their agencies use a lot of new buzzwords to describe where it is being spent, but at the end of the day CMOs want to know exactly how much revenue that budget drove.

Understanding the Media Spend-Revenue Connection

Before we jump to answer how to ensure you are tracking the spend, let’s review what we spend the budget on at a high level. This chart provides a simplified view of the most common channels in North America:

Major North American Media Channels
Credit: Pedowitz Group by Kevin Joyce

So let’s agree the media spend can be divided between paid search, promoted posts, ads, retargeting on social channels, and display and banner ads hosted on other advertising platforms. We don’t have to blind ourselves here with which particular advertising technology (AdTech) is being used to target ads, we are just looking to understand how we track media spend to revenue.

In the Old Days, It Was Simpler

10 years ago, most of our digital media spend was display ads and paid search.

Digital marketing 10 years ago
Digital marketing 10 years ago. | Credit: Pedowitz Group by Kevin Joyce

Back then, the expectation was that a prospect gave you their identity after just one click.

But once you paid for the click, and the visitor didn’t fill in your form, you had no further ability to interact with that person. So a lot of your spend was for naught.

But tracking the spend was easy. We used UTM parameters that were picked off by the forms on the website and this enabled connecting a lead source directly to the media spend. If our digital team was smart, we had all the UTM parameters and knew what campaign and ad generated the leads. All that was left to do was carry this info over to our contacts and accounts, and onto the opportunities and we had a media spend to revenue connection. Fast forward 10 years.

Channel Complexity From Digital Media on Social Platforms

The addition of extra channels, including the social channels makes tracking media spend back to revenue more difficult because there may be multiple interactions between a prospect and you, many of them paid for by you before they become known to you.

Digital marketing today.
Digital marketing today. | Credit: Pedowitz Group by Kevin Joyce

So now we can expect that a prospect won’t surrender their identity until they click eight or more times on your ads and content. But there is good news here. When they do click on an ad, or a blog post we are paying to promote, they get a cookie placed on their device. This allows us to target only people with that cookie with very specific ads (retargeting). So, it keeps our costs down, and those retargeting ads lead to forms that capture all the UTM parameters we talked about earlier. Also, if we are using a marketing automation platform, and that was the cookie we placed on their device, then all of their anonymous behavior is recorded and associated with the lead when they finally fill in a form.

Two important things happened in that last scenario:

  1. We got the ability to continue to market to individuals who clicked, but didn’t fill in a form by virtue of the social platforms providing the retargeting feature.
  2. We got to associate all the multiple interactions of an anonymous prospect, to our content, drive by media spend, because we were using a marketing automation platform.

Connecting the dots – media spend to revenue

So now the complete picture for showing attribution for media spend looks like this:

  • The top of the funnel focus is on getting people cookied (no form required here)
    • Once cookied we can track their behavior with our digital properties and content
  • The next step is to retarget those cookied people to get them to a form to identify themselves
    • Once we know who they are we connect their past anonymous behavior to the new lead
  • The next step is to start email nurturing to them with very targeted offers
  • Finally, they warm up, become SQLs, we connect them to opportunities.
  • All the UTM information, and the anonymous interactions captured in the marketing automation platform can associate your media spend with the closed won opportunities.

I simplified a few things, but the key point is this. It is possible to connect media spend to closed won opportunities. The proper usage of UTM parameters and your marketing automation platform can make this possible. Make sure your team can tell you how much revenue comes from the media spend and calculate the lag time.

Why Attribution Matters in Content Marketing

Why does attribution matter in content marketing? Money, that’s why. More pointedly, attribution matters because the denizens of the C-suite don’t care about clicks, likes, follows or friends. They care about business outcomes and you need to be able to show that your content marketing is contributing to your firm’s profitability. If you’re just another cost center, you’re going to get cut.

Why does attribution matter in content marketing? Money, that’s why.

More pointedly, attribution matters because the denizens of the C-suite don’t care about clicks, likes, follows or friends. They care about business outcomes, and you need to be able to show that your content marketing is contributing to your firm’s profitability. If you’re just another cost center, you’re going to get cut.

But what exactly is attribution in this context? It’s the ability to know how prospects found you and once they did, what influenced their decision to become a client.

Determining Lead Source

Sounds easy enough, but determining a lead’s source can be tricky. Determining what influenced the lead’s decision can be even tougher. There are steps you can take to help increase the degree of certainty with which you identify lead sources and their paths to purchase.

Let’s start with a look at your website. If you think you’re being helpful by cheerfully having your email address accessible on every page of the site — or even just on the contact page — you should re-evaluate what your website is supposed to do. It has to help your prospective clients, of course, but if it’s not helping your marketing, it shouldn’t be part of the program.

Instead, each page of your site — or perhaps just the contact page — should have a simple mail form through which visitors can contact you. This allows you to track what page prospects were on when they were motivated to reach out to you.

Depending on the sophistication of your site’s coding, it may enable you to see what other pages the prospect spent time on, as well. If not, you may was to discuss the possibility with your web developers, as this is valuable information for your sales team. And it’s valuable to your marketing team, too. It can guide what content to present to the prospect as you move that prospect toward the hand-off to the sales team.

Mail forms also cut down on the spam you receive through your website, which is a nice side benefit. They can also be coded to help automate the marketing process, by routing messages to the appropriate team member depending on the prospect’s needs and interests. Again, check with your web dev team if this isn’t happening already.

Phone numbers can similarly be tracked. Various services allow you to replace your “real” phone number with one that will automatically ring through to the appropriate department and can be tracked as having come from your website. (Or anywhere else the number is published.)

Some services also offer the ability to record calls so you can get a sense of whether your telephone reps are a strong or weak link in your marketing process. Even just tracking call length can provide valuable insights.

Other Content Attribution Tools

There are other attribution tools, as well. The key to use them effectively and to managing the attribution chain well include:

•    Plugging the leaks — know where every lead is coming from
•    Connecting the dots online and off — not everything happens on your website or in your inbox
•    Integrate sales and marketing and your CRM tools in the process
•    Create a consistent data framework

The last bullet may be the most important. Tracking attribution over time helps smooth over the inevitable inaccuracies by allowing you to view trends rather than just individual data points. You’re never going to get to 100 percent accuracy of all lead generation online, offline, and via all branding activity,  so trends may be as useful as the data itself.

Now, there are always going to be imperfections in any attribution attempts you make. You simply have to embrace the imperfection, be aware that you don’t know it all and likely never will, and use the data you’ve gathered to guide your decision making. 80 percent certainty is a lot better than 0 percent. The bottom line is that if you can’t attribute any of your firm’s revenue or profit to your content marketing, you shouldn’t be doing it.

6 Tips for Interpreting Content Marketing KPIs

In my last post, I addressed the metrics that should be a part of your content marketing KPIs. Today, I’d like to dive into what to look for in the marketing metrics you are measuring.

content marketing KPIsIn my last post, I addressed the metrics that should be a part of your content marketing KPIs. Today, I’d like to dive into what to look for in the marketing metrics you are measuring.

1. Think Motion Pictures, Not Snapshots

Perhaps the most important concept you can take away from this column is the truth that although data points can be valuable individually, you’ll gain the most insight from your KPIs by tracking them over time.

This will generally give you a more accurate picture of the health of your online marketing efforts since the trends can help you filter out more of the noise. The shorter the period you’re examining the more likely something anomalous will impact the accuracy of your data.

Over time, you’ll see trends develop and you’ll gain an understanding about the metrics where small changes are important and the metrics where even wild swings aren’t cause for alarm or celebration.

2. Think About Context

Here again, I am suggesting that you not look at individual data points discretely, but rather as part of a larger whole. For example, seeing an important page on your site with a higher-than-average exit rate might look alarming, but it might simply be the nature of that page’s content combined with the fact that it is a more popular page.

That’s not to say that you shouldn’t try to decrease the exit rate on your key pages, but you probably shouldn’t be comparing a page like that to a low-traffic page that appeals only to a small segment of your overall audience.

3. Understand the Metrics

Most metrics are going to be fairly straightforward and easily understood even by those not already familiar with reading analytics, but as a marketer, you’d be wise to assume nothing, and work an explanation of key metrics into your presentation so more senior people don’t have to ask.

You should also make sure you and your team really do know what, say, bounce rates are and how they work. This keeps you and anyone who reviews your metrics from reacting to results incorrectly.

For example, we frequently see contact pages with bounce rates that are higher than the average for the site overall. Invariably, a client will ask about this – isn’t it a “bad” signal? In fact, it’s probably not. As you can imagine, a fair amount of your contact page traffic may be from folks who search for how to contact you by phone or email. So what do they do?

  1. They go to their favorite search engine and enter, “phone number for Andigo” or “Andigo email address.”
  2. They click on the link to your contact page
  3. Once they get to your page, they pick up the phone or send you an email.

That’s it. And that’s good. Actually, that’s great since it means they’ve “converted” and moved their relationship with you beyond simply consuming your content.

4. Looks For Gaps in Attribution

While we’re on the subject of contact points, you should look for gaps in attribution that open email addresses and phone numbers can cause. If your contact page simply has “info@mycompany.com” as a way to contact you, you’ll never be able to tell whether the email that lands in your “info” inbox is from the website or elsewhere. (To say nothing of spam you’ll be receiving.) Instead, use a mail form that can be coded to let you easily identify the inquiry as having come from your website and even feed it directly into your CRM, alerting the appropriate team members based on information in the form. (Zip code or area of interest, for example.)

Smart Attribution Modeling

Depending on the size and scope of your advertising and marketing spend, you may have spent time and effort thinking about attribution modeling. Different organizations have very different approaches to attribution.

analyticsDepending on the size and scope of your advertising and marketing spend, you may have spent time and effort thinking about attribution modeling. Different organizations have very different approaches to attribution.

To this end, developing a valuable attribution model that serves your goals and your business can take many forms. Herein, I’ve put together some criteria that’ve been used effectively by a number of organizations we’ve worked with to inform decision-making and use of attribution methods and models.

First Things First: Determine Your End

The most important questions senior marketers need to ask going into an attribution initiative, at any level of investment, include:

  • “What is the purpose for attributing (estimating) media value?” You may be surprised how often that answer is ill-defined. Make sure you can answer, in simple business outcome terms, what the purpose of your attribution is. All else fails if this step is missed.
  • “How logical, defensible and credible is a potential attribution methodology?” While attribution, by its nature, is rarely deterministic, it is requisite that a methodology is credible and has robust basis, or a raison d’etre, if you will, if it is to add value. The understanding individuals often develop is an appreciation that the assumptions underpinning any attribution strategy are tenants of the strategy itself.

The right answers for any brand depend on keeping the end in mind and knowing the expected outcome. So the logical starting point is defining your purpose for attributing media value, as described in that context. For example, “to get the best ROI from our advertising investments.”

3 Strategic Attribution Model Levers

In the spirit of keeping it simple, we think in terms of three strategic attribution levers that an organization can benefit from. These strategic levers are used to inform both the attribution model selection and the weighting of channels. They are as follows:

  • Engagement: Measures a customer’s depth of interaction and potentially, the relationship with the brand.
  • Recency: The amount of time lapsed since the last touch. For example, all other things being equal, a touch yesterday is more valuable than a touch 45 days prior.
  • Intent: Identifies a need the user has or information the user is seeking. Intent is specifically valuable in search, and sometimes in social media. Lead generation programs demonstrate intent, as well. The point of considering “intent” is that it prequalifies traffic in a meaningful way. If the consumer exhibits intent-driven behavior — that should be weighted heavier in your attribution thought process.

While the decision to “attribute” always means judgment is incorporated, the credibility of the attribution is higher when media touches are evaluated within the three strategic levers and should always be based on the nature of the interaction — or lack thereof. If a user did not engage with an ad, then the amount of interaction is lower or even zero.

The following chart breaks out major channels and how you might evaluate each of the strategic levers discussed above.

Ferranti display ad chart

Ferranti display ad chart part two

The ‘Bonus’ Lever: Measurability

Measurability is the “fourth” strategic lever, and can be considered optional for very large brands utilizing traditional non-digital channels extensively. A channel that has evidence associated with its performance is one that can be weighted accordingly. When a channel is measurable, the weighting in the attribution model can be scaled to leverage the predictability of that channel; thereby, improving the efficacy of the attribution. It is a reality that some channels however, will have hard measures, while others require more assumptions and inferences.

Brands should give thoughtful consideration to not inadvertently “reward” a channel, simply because it is hard to measure — and, by the same token, not unnecessarily punish them, either.

Over- or under-weighting channels that have weak evidence of conversion value can actually reduce the performance of the overall media mix.

Viewability and Display-Weighting

While reach, frequency and targeting are hallmarks of display advertising, it has the widely known challenge of “viewability.” Viewability is when an ad is served (and paid for) but a consumer does not see it.

When the objective is to improve the ROI of the media mix, ads that are never seen (un-viewable) should be accounted for in the attributed value of the channel.

One way marketers simplify account viewability concerns is by deducting the percentage of ads that can never be seen on a percentage basis when weighting online display in the model. Bear in mind, “viewable” generally means that only part of the ad was viewable for 1 second. Specific viewability metrics should be discussed and negotiated with media outlets or networks you work with.

How Much Is Viewable or Unviewable?

A recent study done by Google identifies that many display ads are never viewed; therefore, the weighting of display ads should consider this reality (opens as a PDF).

Here are some of the issues with viewability that should influence the weighting of display.

  • 1 percent of all impressions measured are not seen, but the average publisher viewability is 50.2 percent.
  • The most viewable ad sizes are vertical units. Above the fold is not always viewable … Worth considering when weighting display.
  • Page position isn’t always the best indicator of viewability.
  • Viewability varies across industries. While it ranges across content verticals and industries, content that holds a user’s attention has the highest viewability.
  • The most important thing is to give viewability consideration and weight based on your own experience.

Frequently Used Attribution Models

Let’s summarize the most popular attribution models in order of frequency of use, and as based on field experience. There are many more models you may consider, and this list is not intended to be exhaustive.

  • Last Click: 100 percent of the sale is credited to the last click, given its immediacy in driving the sale.
  • Linear Attribution: Equal weighting is given to all touchpoints, regardless of when they occurred. Its strength and weakness is in its simplicity. Not every touch is equal and for good reasons that we’ll describe in some detail below.
  • Time-Decay Models: The media touchpoint closest to conversion gets most of the credit, and the touchpoint prior to that will get less credit. This is the best of the simple approaches. It does not, however, account for brand discovery.
  • Position Model: Position model utilizes intuition and assumption to spread the weights of touches over time, heavying up the first and last touches, and considering the middle touches to spread the difference evenly across them. To be clear, this model presupposes “zero” brand awareness — and, therefore, that every customer “discovered” the brand from a (display/banner) ad impression, for example. Blanketing an audience in advertisements can provide great reach and frequency. It also sets a lot of cookies, which can be used to set the first “position.”

Pointers for Getting Started

The closer you can get to individualized attribution vs. broadcast attribution, the stronger the returns. For example, attribution by segment can provide insights you miss when measuring the aggregate.

Channel measurability should be weighted accordingly. Non-measurable channels should be measured by depth of observable engagement.

The Time-Decay model is widely considered a good place for brands to use when getting started in media attribution. Brands can simply insert logical and evidence-based assumptions and customize the half-life of decay based on the Three Strategic Levers described above.

Follow-up discussion and analysis can refine your thinking and allow you to provide a rationale that helps achieve the most credible, logical and valuable attribution capability.

 

‘Who Moved My’ Multichannel Measurement on a Budget?

There are several great enterprise data platforms that can put you on your way to algorithmic attribution, but many marketers don’t have the budgets to support that investment. So how do you determine which channels are performing best for you without relying on simple but unsatisfactory attribution methods like first exposure, last click or arbitrary weighting?

Who moved my multichannel attribution?[Editor’s note: Chuck McLeester is speaking during a Target Marketing webinar on Oct. 20 titled “Who Moved the Sales? Why marketing attribution is so crucial to track, yet so hard to do.” As a preview, he’s re-running his April blog post, “3 Steps to Multichannel Measurement on a Budget.”]

There are several great enterprise data platforms that can put you on your way to algorithmic attribution, but many marketers don’t have the budgets to support that investment. So how do you determine which channels are performing best for you without relying on simple but unsatisfactory attribution methods like first exposure, last click or arbitrary weighting?

Here’s a relatively easy way to measure the incremental value of each marketing channel to determine which channels are performing best so you can optimize your marketing mix.

First, pick a set of geographically similar markets — one for each channel that you’re using plus one to act as a control cell. Make them as closely matched as you can in terms of size and demography — so don’t mix big markets like Chicago with smaller markets like Waco. You also want to stay away from markets that have competing media — for example, Princeton, N.J., is exposed to both New York and Philadelphia media. Data from the Statistical Abstract of the United States and Census.gov can help you select markets that work for you.

Second, create a test matrix where one of your markets serves as a control, and the balance of your markets eliminate one of the channels you’re evaluating. For example, in the matrix below all channels are used in the control cell, and one channel is eliminated from each of the test cells.

Chuck McLeester chartConduct your test long enough to get a statistically reliable number of responses. With 250 to 300 in each column and each row, you can be 90 percent confident that your results won’t vary by more than 10 percent in a rollout scenario.

Third, examine your cost per response by market in a matrix like the one below. (These numbers are for illustration only and are not meant to reflect actual costs or responses from these channels.)

Cells that have a higher cost per response from the control indicate that the channel you eliminated from that geo area is valuable to you because it was lowering the average cost per response in that cell. In the example below, the geo areas where email, search and social were eliminated had a higher cost per response overall, indicating that these channels were important parts of your media mix. Cells with a lower cost per response from the control indicate that the channels eliminated from those geo areas were increasing your overall cost per response. In the example below, direct mail, display and mobile all had higher costs per response than the control cell which included all the channels.

Chuck McLeester measurement chartYou can do the same analysis on revenue and profit if you are engaged in catalog or e-commerce. The difference in profit between the control profit and the profit in each equally matched geo cell provides the incremental value, whether positive or negative, of the channel that was omitted in that cell.

Chuck McLeester money chartThis experiment has its limitations. Your markets will not be perfectly matched and external factors can affect your results. However, it will provide valuable insight about the interplay among the different elements of your media mix.

Finally, remember that eliminating different channels from your media mix will also have an effect on your response or sales volume. To understand how to best manage volume within your allowable cost per response or cost per order, check out this former Here’s What Counts post.

3 Steps to Multichannel Measurement on a Budget

There are several great enterprise data platforms that can put you on your way to algorithmic attribution, but many marketers don’t have the budgets to support that investment. So how do you determine which channels are performing best for you without relying on simple but unsatisfactory attribution methods like first exposure, last click or arbitrary weighting?

There are several great enterprise data platforms that can put you on your way to algorithmic attribution, but many marketers don’t have the budgets to support that investment. So how do you determine which channels are performing best for you without relying on simple but unsatisfactory attribution methods like first exposure, last click or arbitrary weighting?

Here’s a relatively easy way to measure the incremental value of each marketing channel to determine which channels are performing best so you can optimize your marketing mix.

First, pick a set of geographically similar markets — one for each channel that you’re using plus one to act as a control cell. Make them as closely matched as you can in terms of size and demography — so don’t mix big markets like Chicago with smaller markets like Waco. You also want to stay away from markets that have competing media — for example, Princeton, N.J., is exposed to both New York and Philadelphia media. Data from the Statistical Abstract of the United States and Census.gov can help you select markets that work for you.

Second, create a test matrix where one of your markets serves as a control, and the balance of your markets eliminate one of the channels you’re evaluating. For example, in the matrix below all channels are used in the control cell, and one channel is eliminated from each of the test cells.

Chuck McLeester chartConduct your test long enough to get a statistically reliable number of responses. With 250 to 300 in each column and each row, you can be 90 percent confident that your results won’t vary by more than 10 percent in a rollout scenario.

Third, examine your cost per response by market in a matrix like the one below. (These numbers are for illustration only and are not meant to reflect actual costs or responses from these channels.)

Cells that have a higher cost per response from the control indicate that the channel you eliminated from that geo area is valuable to you because it was lowering the average cost per response in that cell. In the example below, the geo areas where email, search and social were eliminated had a higher cost per response overall, indicating that these channels were important parts of your media mix. Cells with a lower cost per response from the control indicate that the channels eliminated from those geo areas were increasing your overall cost per response. In the example below, direct mail, display and mobile all had higher costs per response than the control cell which included all the channels.

Chuck McLeester measurement chartYou can do the same analysis on revenue and profit if you are engaged in catalog or e-commerce. The difference in profit between the control profit and the profit in each equally matched geo cell provides the incremental value, whether positive or negative, of the channel that was omitted in that cell.

Chuck McLeester money chartThis experiment has its limitations. Your markets will not be perfectly matched and external factors can affect your results. However, it will provide valuable insight about the interplay among the different elements of your media mix.

Finally, remember that eliminating different channels from your media mix will also have an effect on your response or sales volume. To understand how to best manage volume within your allowable cost per response or cost per order, check out this former Here’s What Counts post.

Measuring Customer Engagement: It’s Not Easy and It Takes Time

Here’s what’s easy: Measuring the effect of individual engagements like Web page views, email opens, paid and organic search clicks, call center interactions, Facebook likes, Twitter follows, tweets, retweets, referrals, etc. Here’s what’s hard: Understanding the combined effect of your promotions across all those channels. Many marketers turn to online attribution methods to assign credit for all or part of an individual order across multiple online channels. es as the independent variables.

Here’s what’s easy: Measuring the effect of individual engagements like Web page views, email opens, paid and organic search clicks, call center interactions, Facebook likes, Twitter follows, tweets, retweets, referrals, etc.

Here’s what’s hard: Understanding the combined effect of your promotions across all those channels.

Many marketers turn to online attribution methods to assign credit for all or part of an individual order across multiple online channels. Digital marketing guru Avinash Kaushik points out the strengths of weaknesses of various methods in his blog, Occam’s Razor in “Multichannel Attribution: Definitions, Models and a Reality Check” and concludes that none are perfect and many are far from it.

But online attribution models look to give credit to an individual tactic rather than measuring the combined effects of your entire promotion mix. Here’s a different approach to getting a holistic view of your entire promotion mix. It’s similar to the methodology I discussed in the post “Use Market Research to Tie Brand Awareness and Purchase Intent to Sales,” and like that methodology, it’s not something you’re going to be able to do overnight. It’s an iterative process that will take some time.

Start by assigning a point value to every consumer touch and every consumer action to create an engagement score for each customer. This process will be different for every marketer and will vary according to your customer base and your promotion mix. For illustration’s sake, consider the arbitrary assignments in the table in the media player, at right.

Next, perform this preliminary analysis:

  1. Rank your customers on sales volume for different time periods
    —previous month, quarter, year, etc.
  2. Rank your customers on their engagement score for the same periods
  3. Examine the correlation between sales and engagement
    —How much is each point of engagement worth in sales $$$?

After you’ve done this preliminary scoring, do your best to isolate customers who were not exposed to specific elements of the promotion mix into control groups, i.e., they didn’t engage on Facebook or they didn’t receive email. Compare their revenue against the rest of the file to see how well you’ve weighted that particular element. With several iterations of this process over time, you will be able to place a dollar value on each point of engagement and plan your promotion mix accordingly.

How you assign your point values may seem arbitrary at first, but you will need to work through this iteratively, looking at control cells wherever you can isolate them. For a more scientific approach, run a regression analysis on the customer file with revenue as the dependent variable and the number and types of touches as the independent variables. The more complete your customer contact data is, the lower your p value and the more descriptive the regression will be in identifying the contribution of each element.

As with any methodology, this one is only as good as the data you’re able to put into it, but don’t be discouraged if your data is not perfect or complete. Even in an imperfect world, this exercise will get you closer to a holistic view of customer engagement.