• Sales addressing a need for Revenue
• Product addressing a need for products/services the business sells
• Marketing addressing a need to evangelize said products/services

However, there often comes a time where a defined a Business Development team is formed, focused on establishing strategic deals with third parties to enhance the business.

This particular article gives insight into how traditional Business Development is not be synonymous with Sales, although many organizations today blur these lines.

— Understanding Sales in the Context of Business Development —

Many popular companies’ businesss identify as intermediaries between Clients and Sellers. Regardless of the terms used to describe how the intermediary self-identifies(eg. “platform”, “retailer”, “marketplace”), these companies often have:

A) a set of employees who engage with Clients and

B) employees who engage with Sellers.

I classify Sales roles as the former. I’ll provide some more insight below.

Non-Obvious Sales Roles💰

If your business gets paid for a product/service, that paying entity is a “Client”/ “Customer”/ “Buyer”. If your responsibility is to engage with clients for the purposes of getting these clients to pay your organization, you are part of the Sales organization. This may sound relatively straight-forward, but it’s worth stating in a defined manner, as a number of Sales related roles can be defined in much less transparent ways. Below are a few examples that may be a bit less obvious, but still fall serve a Sales function (I welcome commentary):

— -Customer Success — -

“Customer Success” represents an employee who maintains and seeks to grow existing relationships with clients. Maintaining revenue is such a key responsibility for Customer Success, that a key performance indicator often associated with Customer Success teams at SaaS companies is “churn”, how much money or how many existing clients has the company lost.

Not only does Customer Success play a critical role in maintaining clients by ensuring the client’s experience with the business remains positive, but having a Customer Success function focused on existing clients allows traditional Sales teams to focus on selling to net new clients. Some organizations have Account Managers who serve similar functions.

— Sales Development Representatives (SDR’s) —

There are a number of different roles that are worthy of their own more detailed article than this. But at a high level, some organizations have employees called SDR’s who do active outreach to potential clients to generate leads for Account Executives. While there can be various roles responsible for generating lead (eg. Channel Sales, SDR’s, etc.), these roles are not considered traditional Business Development.

— Platform Sales —

It seems hard to believe that someone in a role who’s title has “Sales” in the name would be confused about “Platform Sales” being a sales function. But what if this same role was entitled “Partnerships”? This actually happens more often than one might think. You can establish partnerships where increased adoption of the partner’s product/service by joint clients leads to incremental revenue for your organization. But if you are managing this partnership, and it’s central importance to your organization is the direct revenue you can achieve from this partnership, you’re now encroaching on a Partnership Sales role, which is different than traditional Business Development. I’ll take a moment to share why this happens, in my view.

Encroachment | Partnership Sales & Business Development

A company can create or enhance a product/service for clients through. partnership. Partners can be classified in a few different categories depending on the use-case that the partnership between the two organizations serve. A few category types include:

A) Product Sellers

B) Verifiers

C) Resellers

…and a few others depending on the business. For simplicities sake, I’ll keep the scope of this article to category A and expand on other category types in future articles 😁. That is, Product Sellers, partners who sell a product that your company uses to generate incremental sales.

When Business Development teams strike up partnerships in this category, the partnership is aimed at sales enablement. So a couple fair questions for a Business Development team to ask before signing a new partnership with a Product Seller are:

• “Will partnering with this Product Seller allow the Sales team at my company to generate revenue Sales could not generate currently?”
• “Will partnering with this Product Seller allow the Sales team at my company to generate revenue more efficiently than they can currently?”

With these questions in mind, Business Development teams strategize on which partnerships would present the optimal opportunities to grow and/or accelerate the revenue generated by Sales. This is where some of the nuance comes into play. If as a Business Development Team, you discover opportunities to generate increased revenue through a direct deal with a client, are you now synonymous with a Sales function? Yes. If you are striking direct deals to generate revenue, you are serving in a Sales function in that deal.

[If you are asking yourself, how is the author defining a “Direct Deal”, you’re asking yourself a great question. In the interest of keeping this article brief, here’s a link to a Sales Hacker article that talks more about it.]

If this only applies to a Business Development team on far occasions but isn’t your core responsibility, I’d argue you’re still serving in a traditional Business Development role since, by our definition, Business Development teams “should focus on developing the business by satisfying a strategic need”.

The Wrap Up

Frankly, revenue growth is often a business need, so Business Development teams need to keep in mind how they can contribute to revenue lift of the organization. However, If a business needs to generate more revenue, every team should play their part to help the company generate revenue when the opportunity arises. Therefore its important to recognize the difference between a team’s current KPIs (eg. Business Development team to establish five strategic partnerships to press releases to evangelize our products) versus the team’s core responsibility (eg. Marketing . The core responsibility of a traditional Business Development team is to develop the business by satisfying a strategic need.

Synchronizing Cookie ID’s

The goal of a cookie synch between two adTech platforms is to create a Mapping Table that lets the holder of the mapping table translate another platform’s cookie ID to the holder’s own ID. Here’s a way of visualizing a mapping table between two platforms, PlatformX & PlatformY:

In order to do so, the parties can exchange pixel tags that are placed on each other’s sites that visitors come to. These pixel tags are called upon a user’s visit to the site, and the platform that owns the site drops their cookie ID for the user, which is subsequently recorded by the holder of the Mapping Table. Take a look at an example pixel tag that may be sent to an adtech platform to implement on their site.

Example Pixel Tag:

http://idsync.platformx.com/4495994.gif?partner_uid=<partner’s cookie ID placed here>

To illustrate the process, below is an example of the pixel tag activity between two adtech platforms, PlatformX & PlatformY, which are looking to synchronize their cookie ID’s such that PlatformX holds the Mapping Table.

PlatformX Publisher Traffic Pixel:

1. PlatformX sees a ‘cookied’ user on its publisher Network (ie. a user visited one of PlatformX’s publisher websites or one of it’s referral partners)
2. PlatformX sends this user to the PlatformY’s designated webpage URL (http://dmp.partnername.com)
3. PlatformY labels this user with their cookie ID and sends this ID to PlatformX (“PlatformY calls this user: PlatformY_CookieID123”)
4. PlatformX records this label and ties it to the internal PlatformX ID for this same user, thereby building a Mapping Table of PlatformY’s cookie ID to the PlatformX cookie ID for a given user (ie. PlatformY_CookieID123 = PlatformX_CookieID456)

PlatformY Traffic Pixel:

1. PlatformY sees a user on its website (could be specific webpages, advertisement/inventory, emails, etc.)
2. PlatformY labels this user with their cookie ID and sends this ID to ConnectivityPlatformX (“PlatformY calls this user: PlatformY_CookieID123”)
3. PlatformX records this label and ties it to the internal PlatformX ID for this same user, thereby building a Mapping Table of the PlatformY’s cookie ID to the PlatformX cookie ID for a given user (ie. PlatformY_CookieID123 = PlatformX_CookieID456)

The major difference between the two pixel tags is that one is representative of PlatformX’s traffic while the other represents PlatformY traffic. By establishing a two-way sync, Connectivity platforms like PlatformX can recognize and thereby transact with its partners based on the cookie IDs the partner understands.

Being able to scale your cookie overlap (“cookie pool”) with a given partner allows the two parties to exchange more data with one another because they can match more users with one another. For those looking to read more into the subject, check out Chris Kane’s post ID Syncing in Action or any of the links below for more insight.

Sources:

Cookies 101 | LiveRamp

http://clearcode.cc/2015/12/cookie-syncing/

http://www.krux.com/blog/ceos-corner/cookie-synching/

The digital advertising ecosystem is stacked full of platforms offering their own specialized services to enable digital advertising. When we think of the overarching process, Advertisers have a message that they want to send to a particular audience at a given time. Since Publishers are the one’s drawing in different audiences, Advertisers look for ways to get their message into the ad spaces that Publisher’s have designated for advertisements (“Ad Inventory”).

Unless you are the Advertiser or the Publisher on either end of this workflow, you play a role in the placement of the Advertiser’s intended message on the Publisher’s Inventory.

But, making this workflow come to fruition can entail numerous steps, particularly in programmatic advertising where the goal is to automate this entire workflow to happen in 100’s of milliseconds. Since AdTech platforms tend to specialize in one or a handful of these intermediary steps between the Advertiser and the Publisher, AdTech platforms tend to rely on one another to complete the chain by integrating their systems. What was a seemingly simple workflow in the previous diagram can become a bit more involved.

Identifying Users Online

Setting up an integration between adtech platforms enables the two systems to exchange online datasets with one another. An online dataset identifies the users that the advertiser wishes to target by anonymous online identifiers. Some common online identifiers include:

Browser Cookie ID’s — Represent a user using an internet browser. As many users access websites on multiple devices (desktop, tablets, phones, etc.), a cookie ID can represent a user in several facets. As cookie ID’s expire or are deleted (such as when an internet user decides to clear their cookies), a new cookie is created for subsequent online sessions. There are different methodologies in deciding how long the average cookie may be actively used by the user prior to deletion, but many major industry players believe a cookie is viable for ~60–120 days. The adtech platforms thereby set a filter for these cookies for a particular duration, called a Recency Filter.

(example Cookie ID: 2a5e10g0850c34fc9ff21d1dc0f3dacf)

Mobile Device ID’s — Represent a user within mobile applications. The two most common Mobile Device ID’s you’ll see in the market today are iOS’s Identifier for Advertisers (IDFA’s) and Android Advertising Identifiers (AAID’s). Since it’s less common for mobile users to reset their Mobile Device ID’s, these identifiers are perceived as more persistent than cookie ID’s. Major industry players do recognize that when a user switches to a new phone, their ID can change, and therefore set the recency filter on Mobile Device ID’s for a year or less.

(example IDFA: AECE53E6–03DE-465A-B3D2-E87293876239)

(example AAID: ee2q35f1–88d8–128e-5f89-d67348784394)

There are other identifiers that are sometimes used, but the aforementioned are the common currencies leveraged in data exchanges on individuals within the adtech ecosystem. For cookie ID’s in particular, it’s important to note that a cookie ID is domain-specific, meaning that each autonomous website or platform has it’s own cookie labels for each of the users it sees. That means that Joe Smith who visits www.fictiouswebsite.com has a different cookie ID than Joe Smith who is also simultaneously browsing www.examplesite.com.

This may beg the question: “If there are multiple digital currencies in the ecosystem, how do these platforms recognize one another’s ID’s to exchange data on users?”

As Mobile Device ID’s remain the same between platforms, the obstacle of understanding the ID’s of another platform often comes about in the cookie space. These connectivity platforms have synchronized cookie ID’s with several adtech platforms, enabling the connectivity platforms to exchange data with ID’s that both parties can interpret. For more on the intracies of the cookie synching process between AdTech platforms, take a look at my article: “Who Stole the Cookie from the Cookie Jar?”

In conclusion, having an integration in place allows for the distribution and ingestion of audience data segments, whether first party or third party data. A connected digital advertising ecosystems enables marketers to move their data through different platforms that specialize in different market nichés, thereby empowering markets to make their dream campaigns come to fruition.

Sources:

SSP to DSP Cookie Syncing Explained - Ad Ops Insider

Plug & Play was originally published in Ad-Ham on Medium, where people are continuing the conversation by highlighting and responding to this story.

The AdTech ecosystem is fraught with different platforms that specialize in particular use-cases to assist Marketers with getting the right message in front of the right people. Despite all of the hard work and sophistication that goes into presenting a message to the right people, successfully locating or matching to that individual online remains a challenge many marketers face.

In this piece, we’ll talk a bit about what is often considered a key performance indicator when a marketer is transmitting data into online advertising platforms — Match Rates. We’ll highlight some of the more common concepts discussed when evaluating Match Rates, such as:

• Defining a Match Rate
• How Different Entities Play a Role in the Match Rate
• Deterministic vs Probabilistic Methodologies (the Accuracy vs Reach Debate)

My goal is provide foundational knowledge on how match rates impact a market’s campaign and its subsequent influence on the thousands of platforms in the ecosystem (over 3,600 today per Chief MarTec’s 2016 diagram).

Defining a Match Rate

A Match Rate is the percentage of individual users successfully translated from one entity to the data’s final entity. In our post, The “Who,” we described 1st Party and 3rd Party data that may be leveraged for ad targeting. Much of the data that we outlined came from the offline perspective, whereby an Advertiser has CRM data (1st party) or uses data from a Data Vendor (3rd party). In either case, if the data being used for targeting has Personally Identifiable Information (PII) tied to it, this can be deemed “offline” data, or data that has yet to be onboarded into the “online” world. There are some digital platforms that specialize in this offline to online translation, essentially acting as a rosetta stone between an Advertiser/Data Vendor and the Destination platform (where the Advertiser/Data Vendor is trying to push their data to for a successful “Match”).

Crossing the Chasm into Digital

When these entities send their 1st or 3rd party data to a Data Onboarding platform, their initial data set could identify individuals by parameters such as: email address, name, postal information, phone number, etc. The idea is that if a human being could look at that information and know exactly who that information represents, it could be considered PII. [For those looking to learn a bit on Data Onboarding, check out my other post “The Who”]

However, those looking to reach these individuals online need to locate/Match these parameters to an online identifier in order to send them targeted messages online (ie. JSmith@adham.com → Online ID123). This is because when our friend John Smith is browsing the internet, the webpages John visits recognize him by his online ID on that webpage.

At this stage, an Advertiser has sent a Data Onboarder offline data on an individual (“Record”), which the Onboarder has ingested and reconciled with their own internal ID. The ratio of recognized records divided by the total input records is called the “Recognition Rate.” Meaning if the Advertiser sent a data file with 10mm records and the Data Onboard could locate 6mm internal ID’s for those individuals, the Data Onboarder achieved a Recognition Rate of 60%.

But, the fun doesn’t stop here. While Data Onboarders can take on other tasks beyond this offline to online translation, the Advertiser may want the Data Onboarder to distribute the onboarded records to another AdTech platform. When the Data Onboarder distributes the data to the Destination platform, this is called the “Distribution Rate”. So if the Advertiser initially sent 10mm records to the Data Onboarder which located 6mm internal ID’s for that segment, and the Data Onboarder in turn translated these 6mm ID’s into the 4mm known ID’s it has for the Destination platform, then:

Recognition Rate = 6mm/10mm = 60%

Distribution Rate = 4mm/6mm = 66%, BUT…

…Match Rate = 4mm/10mm = 40%

Since the marketer is simply focused on how many individuals from their CRM list they can target, Match rates tend to become the primary KPI in these use-cases. However, when AdTech platforms are evaluating (to put it nicely) their connections with other platforms or trying to troubleshoot data leakage during a particular workflow between platforms, Recognition and Distribution rates can come into focus.

Further, in providing a pretty standard definition of match rates, we’ve assumed that the marketer’s goal is to target the right individuals during the campaign. However, there are some marketers who may be less focused on the exact individual, and may think it’s equally important to get their message in front of a person within a household. If you’re a marketer advertising a new family mini-van, it may matter less to you whether John Smith receives the ad specifically or if any member of the Smith household receives that message. You believe that just getting the marketing message in front of a member of the household will increase the likelihood of a purchase by the family. What we’ve just touched upon, is the debate between accuracy and reach.

Deterministic vs Probabilistic

The handful of data onboarding specialists in the Adtech space have a very real debate that they’re faced with within this niche — whether to “Match” deterministically or probabilistically. Now, within the niche, a “Match” can have numerous definitions/formulas, but the general concept is: Can the Onboarder receive PII on an individual and find a corresponding online identifier to associate with that individual?

As you might imagine, this is a difficult process regardless of whether you choose to match deterministically (meaning your have a high degree of confidence that this particular online ID can be readily associated with this individual) or probabilistically (where you either have less confidence in the match or that you simply believe one of several different online ID’s you identify can be attributed to that individual).

Probabilistic Matching

Particularly when you speak about probabilistic matching capabilities, you may start a discussion around the importance of “Reach” (ie the ability to capture more of the individuals in the initial input file even if that means including ID’s associated with individuals you didn’t mean to include). You may also hear about things like matching at a Household-level instead of finding an online ID that can accurately match to an individual (think about a Desktop at home that multiple family-members use).

Deterministic Matching

When you hear discussions predicated on “Accuracy” as opposed to “Reach,” you’re likely taking the more deterministic perspective. Some Onboarders can argue that the central point of targeted advertising is about tailoring your message to specific audiences. If that audience is full of online ID’s that don’t map to the exact individuals you’re intending to communicate with, than what’s the point of targeted advertising in the first place?

5 Key Questions about Match Rates (courtesy of LiveRamp)

In sum, a marketer’s underlying dataset can be robust, with some identifying information on many people. However, the ability to reach those same users online is often predicated on the matching capabilities of the digital platforms the marketer is working with. Therefore, Match Rates become a central KPI when running a digital marketing campaign.

Not as Simple as “Swiping Right” was originally published in Ad-Ham on Medium, where people are continuing the conversation by highlighting and responding to this story.

Not all advertisements should be looked at with disdain. After all, many of the websites and mobile apps we frequent today don’t charge us to view their content since they are supported by advertising dollars. Further, marketers are getting better and better about sending the right advertising message, to the right person, at the right time. In fact, good advertisements can be entertaining in and of themselves (I’m sure a few Superbowl ads come to mind).

Key to having an ad resonate with a marketer’s audience is having a better understanding of who, where, and when particular audience members are available to receive your message. In digital advertising, understanding the “Who” has multiple facets.

Observing Tradition

Long before the advent of digital advertising, there have been several companies that use data to advertise to you already. Ever receive an envelope in the mail addressed specifically to you promoting a product? Well the company who sent you the envelope may have received your name and mailing address from information you provided directly to that company. But in other cases, that credit card company may have purchased a data set from a third party with the names and mailing addresses of individuals they thought would be likely to buy their product.

As you might imagine, many of the same methodologies apply in digital advertising as well. In the digital realm, you may have seen ads about a pair of boots that seemed to come just when you were looking into buying a pair. You might also notice that the brands you see advertisements for may differ from your parents, children, friends.

But how does the marketer know where to find me online to show me relevant ads? How does a marketer know what types of ads I want to see or don’t want to see? That’s where Data Onboarding comes into play.

What is Data Onboarding?

Data Onboarding is the concept of moving “offline” data into the digital (“online”) world. “Offline” data usually consists of information on a person’s identity in the physical realm, or put differently, outside of internet properties. This offline data consists of Personally Identifiable Information (PII). There are several different data elements that could be leveraged to identify a person “offline.” One way of thinking about this PII is to consider data elements that make you distinguishable as an individual in the “real” world.

For example, someone could identify me in the real world by my name, home mailing address, phone number, or email address.

At times, these different data fragments can be pieced together to more narrowly identify an individual.

Defining 1st Party vs 3rd Party Data

In the first case where the company advertising the credit card (“Advertiser”) collected the information from you directly, this would be considered 1st Party Data. In other words, data that is generated by/owned by the advertiser is 1st party data. In the Marketing industry, you may hear of the data that an advertiser has on its customers as a “CRM” (Customer Relationship Management) database. This is one type of 1st party data, along with “POS” (Point of Sale/Transaction) data. This 1st party data is often held very close to the advertiser and often kept separate from any other 1st party data when flowing through different ad tech platforms. If you’re a retailer, you don’t want the information you have on your clients to be shared with your closest competitor.

In the second case where the Advertiser purchased the data from a third party (“Data Vendor”), those in the advertising industry would consider that data set 3rd Party Data. Advertisers and ad tech platforms alike can purchase 3rd party data to get a more in-depth view of consumers. Beyond enriching a pre-existing customer profile, third party data can also be used to find other, like-minded individuals, a use-case defined as Audience Expansion (or “Look Alike Modeling”).

The definitions themselves aren’t that interesting on a standalone basis, but the concept of properly combining 1st Party and 3rd Party data sets to effectively target your audience becomes all the more interesting. The biggest marketers in advertising industry understand that the more informed they are about their target audience the greater yield they can achieve in their advertising campaigns.

The Complexities of Offline Identification

There are a handful of businesses that have the ability to translate these pieces of PII into anonymous online identifiers. But doing so effectively has its challenges, including:

Data Fragmentation — The Advertiser may only have one of the pieces of PII, which can lead to uncertainty around the individual being targeted. For instance, if the Advertiser has a John Smith who’s zip code is 94104 (in San Francisco, CA), there could be multiple John Smith’s within that geography (no offense to the John Smith’s out there, just an example).

Data Validation — Advertisers want to make sure that the data they have in their system or purchased from a Data Vendor is actually valid. Who’s to say that the John Smith the Advertiser is targeting, who may have signed up for a loyalty card while interning in San Francisco for one Summer, still lives in San Francisco? When purchasing from 3rd party Data Vendors, the validation problem can be exacerbated if the vendor hasn’t performed proper Data Hygiene on the data it’s supplying. This goes beyond just a location mishap, they can even mistaken your gender or age (“oops”), perhaps impacting the Advertiser’s marketing campaign based on this demographic information.

Simply put, offline identification itself isn’t quite so… simple. But assuming the Advertiser does have access to valid, well-connected personally identifiable information, translating these data elements to identifiers that can be leveraged online is an arduous process.

Do Marketer’s Ever Combine 1st & 3rd party Data?

Yes. It’s common for the Advertiser to enlist a Data Management Platform (“DMP”) to combine 1st and 3rd party data. DMP’s can take many different forms, but a central commonality is that DMP’s can house multiple datasets, whether is is offline (think PII) or digital (think cookie ID’s) data. Working with a DMP allows the Advertiser to parse or segment their online dataset by certain parameters/attributes related to the individual identities in the data set.

This allows an Advertiser to take a list of individual identities (“records”) within their DMP and only focus on a specific subset. For example, pre-existing Customers of the Advertiser who identify as Female between the ages of 34–55 and live in the state of Maryland. Then, if the Advertiser wants to send a tailored advertisement to these individuals online, maybe urging their customers in the Mid-Atlantic to buy a new winter jacket just before wintertime, the Advertiser can select just this subset of its customer database and send online ads to just those individuals.

Or, an advertiser may decide that they want to send a targeted message to most of the customers in their CRM, but not a particular subset. For instance, the ad for a winter coat may be great for those customers living in the Maryland, but less so for those in Florida. The advertiser can then create a segment of customers they specifically don’t want to target, often referred to as a “Suppression” file.

In sum, Data Onboarding is the process of finding real individuals online, providing the basis for the Ad Tech industry. By leveraging both onboarded 1st and 3rd party data sets, advertisers can develop fairly sophisticated digital targeting campaigns.

Want to read more about Data Onboarding and Identity Recognition? Check out some of these sources below:

Sources:

http://www.adopsinsider.com/ad-exchanges/cookie-syncing/

http://liveramp.com/blog/data-onboarding-101-match-rates/

http://liveramp.com/cookies-101/

http://liveramp.com/blog/a-primer-on-data-onboarding/

The “Who” was originally published in Ad-Ham on Medium, where people are continuing the conversation by highlighting and responding to this story.