What is LinkedIn? Ask around, and most people will typically tell you one of two things. They’ll probably say: “it’s a social media platform for businesspeople” or “it’s a platform for recruiters and job seekers.”
But there is a third option, an equally valid answer. Although few people spontaneously think about LinkedIn in this way, it is also a massive—in fact, the largest—global database of business professionals. Furthermore, it’s one of the most buzzing platforms. Unlike most other databases, LinkedIn stays refreshingly relevant thanks to active users who keep their profiles updated.
LinkedIn also differs from other business databases due to the richness of the actual data. LinkedIn not only stores each person’s name, company, and job title, but also incorporates details such as their full resume, skillsets, industries, company sizes, geographic location, interests, and education. It also provides a platform for individuals to write and share an unlimited number of posts reflective of their thoughts, findings, announcements, and so forth.
Using LinkedIn for Targeting? Here's What You Should Know.
This treasure trove of information can be used for targeted advertising, and it allows marketers to precisely find the optimal audience for their campaigns. However, there is one important caveat. The database of professionals that we see online when using LinkedIn is not directly used for advertising targeting. Instead, LinkedIn transfers the data from the profile database to a separate data warehouse, which is the basis for the advertising targeting. In that transfer of LinkedIn profile data, a sort of translation is conducted. Not merely a translation of languages (in some cases), but a translation and grouping of data into new categories. LinkedIn also pulls third-party data into the data warehouse to offer additional targeting capabilities.
Figure 1: Translation of data from profiles to data warehouse
To explain this through an example, let us look at the targeting by job “seniority” (i.e. managers and above). LinkedIn offers audience targeting by “seniority” in 10 different categories (explained in more detail further down). But no member will enter how senior they are into one of the fields on their LinkedIn profile. That option is not available to the member – you cannot tell LinkedIn about your own seniority. Instead, LinkedIn automatically works out an estimated seniority based on a person’s job title and their number of years in the workforce. This data exists only in the data warehouse for advertising and isn’t visible in the actual LinkedIn profile database.
The Data is Not Perfect!
The process of transferring and molding data in the data warehouse is imperfect and includes its own set of challenges. For example, all non-English job titles must be translated into their English equivalents, something that’s done with varied success. Decisions must be made about how to group, classify, and re-classify information. Regardless of how much or how little data the users’ profiles have, they are all imported to the same data warehouse. Because of these challenges, it is important for marketers to consider the data’s resulting quality in the warehouse before creating audience targeting for LinkedIn advertising campaigns.
We will further break down the data quality in the following sections, but first let’s clarify something. Even if the data is not perfect, it does not mean that it is bad for targeting. On the contrary: LinkedIn is a fantastic database to target B2B decision makers! Plus, it is without any real global competition; it is by far the largest and most high-quality database available for B2B advertising. Still, as a marketer, you’d best be aware of its imperfections and find ways to work around them.
That’s exactly what we’ll be exploring here.
The Seven Main Targeting Categories
At the highest level, LinkedIn offers targeting based on the information in the data warehouse, in the following seven categories.
Figure 2: Main targeting categories
It is fair to say that most B2B campaigns target three categories: Company, Job Experience, and Geography. That’s wise, for the most valuable targeting resides within these categories. It is, for example, possible to pinpoint specific job titles within a specific industry in a specific country. In many cases, this is exactly what marketers want. However, you can get creative with the targeting in unexpected ways and generate unexpectedly (wonderfully!) higher performance.
When defining our targeting, there are many pitfalls we must be careful to avoid. Because the database quality is not perfect, there is the risk of being too generic or too specific. Either mistake can lead to overlooking potential future customers. As a marketer, you need specific knowledge to determine optimal audiences. It is important to identify and segment audiences exactly, split your budget between different audiences, and remember to allocate a portion of the campaign budget to a somewhat vague audience that lives along the fringes. Note that this functionality isn’t available directly on LinkedIn, but the Markick Platform does enable this kind of complex targeting.
1) Geographic Targeting
Geographic targeting may seem simple, but it demands more consideration than you may initially think. Different geographies have different prices in the LinkedIn advertising auction (advertising pricing is determined by an auction where the highest bidder’s advertisement is shown to a given audience). The data of users from different countries differs in quality in the data warehouse, and variations in language require special attention. Cultural differences further differentiate user behavior on LinkedIn. It is therefore important to cluster geographies in your campaigns in structured ways.
A common mistake is to throw all geographies of interest into a single campaign. Marketers subsequently lose control of budget allocation per geography, pricing per geography, delivery per geography, and so forth. The Markick Platform allows for budget allocation and pricing optimization per geography, a function that isn’t native to LinkedIn.
2) Language Targeting
Many members choose to access LinkedIn in their own language. A Spanish speaker, for instance, can choose the option in their personal settings to view the entire platform in Spanish, just as you can choose the language setting on your smartphone. It’s worth noting that the user does not need to be in Spain when choosing the Spanish language setting; he or she could be in located in any country in the world.
LinkedIn currently offers targeting based on user profile language settings in 19 different languages. It is critical to understand the “English” language targeting behaves differently from all the other language targeting options. LinkedIn slipped up when devising the data warehouse for advertising, and it is difficult for LinkedIn to change this now for legacy reasons. When you select “English” for targeting, LinkedIn actually interprets this as “any language, including English.” Therefore, selecting language targeting in English will not exclusively target users who’ve opted for the English language setting. This is a shame, since it makes it impossible to target, say, people who live in Thailand and who prefer English. That would have been a nifty feature.
Most people choose their own local language, but it varies by geography. In Figure 3 below, we compare Sweden and France to show what percentage of people in each country have chosen their local language setting. Evidently, a higher proportion of users in France (92%) have their local language setting than in Sweden (70%). This is likely because Swedes are more comfortable using English as their business language.
We can also see how many people in these two countries have chosen Spanish as their platform language—around 0.5% in each country (a minority, but still sizeable)—despite not living in Spain. Note how we cannot know how many people have chosen English as their language setting due to LinkedIn’s aforementioned oversight. That said, it is a reasonable assumption that most people who have not chosen their local language will have chosen English.
Figure 3: Illustrative example of language settings in France and Sweden
3) Company Targeting
Looking closer at the targeting options within the Company category, we see there are 8 sub-categories which can all be used to create audiences for targeting. While examining this data, please remember that LinkedIn has a total of 820 million members globally (820,000,000) as of 2022.
Figure 4: Targeting criteria within the Company category
When analyzing the above data, there are a few things that stand out regarding the numbers under “Total audience size (global)” and the percentages under “Percentage of total members.” The numbers you see here provides an answer to the following question: if we select all options available for this targeting category, how many members do we reach, both in absolute numbers and as a percentage of LinkedIn’s total 820M members? If we select all industries that can be targeted on LinkedIn, for instance, the audience is only 590M members (not 820M). This is because some users do not have industry data attached to their profiles, and therefore LinkedIn cannot know which industry they work in.
In the Figure 4, some of the percentages under “Percentage of total members” may seem oddly low. As an example, only 34.1% if members can be targeted by company size. Why not 100%? What is going on there? We will explain this odd phenomenon in the next section, where we deep dive into Company Size targeting.
Company Size
LinkedIn offers nine different categories to choose from when considering a company’s size by its number of employees. The options are Myself Only (1); 2-10; 11-50; 51-200; 201-500; 501-1,000; 1,001-5,000; 5,001-10,000; and 10,000+ employees. In the column “Total audience size (global),” if we select all of these options, we still only count 280 million users (34.1%), despite the total number of users being 820 million. This means that LinkedIn is lacking company size data for a whopping 540,000,000 members, or 76% of its members. Why is that?
Five elements justify this puzzling result:
(1) Not all company pages include their company size (today when setting up a new company page, this information is mandatory, but that wasn’t always the case).
(2) Some company pages are duplicate; when people select which company they work for on their profile, sometimes they accidently select the wrong company page which doesn’t have this information.
(3) Some members enter a company name that does not exist in the LinkedIn database (e.g., it’s misspelled or simply hasn’t been included as a company).
(4) Some users don’t connect their profile to any company or employment, either because they want to be discrete about who they are or what they do, or because they are not that active on LinkedIn.
(5) The data quality on the LinkedIn database varies by country.
Figure 5 exemplifies how the data quality of users and companies differs between countries, like between the U.S. and Brazil. You can see that in the U.S., you can target 45% of members by company size, vs only 29% in Brasil.
Note also that if we target by each company size range individually and add them together, we get a higher number than if we select all company sizes at once. This is because some people have more than one active place of employment on their profile, e.g., they work for two differently-sized companies. Around 6% of the global population has more than two active jobs listed (see Figure 5).
Figure 5: Investigating Company Size targeting in detail
Understanding these differences is critical when it comes to high-performance targeting. It is common to target, for example, people working only at “large” companies (this is something we frequently do and it’s recommended). However, if you want to target people who work for large organizations, there is an alternative way. Instead of choosing to include people working for companies of 501+ employees, it is sometimes better to exclude people working for companies with less than 500 employees. With this second option, you are basically saying: “Please exclude people who are known to work for small companies, but include everyone else.” This ultimately includes a lot of people whose company size is unknown.
Initially this may seem like a bad idea. Why include a lot of “poor quality data” in this targeting? There are four reasons for why this strategy may be beneficial in some cases:
(1) You get to include people (potentially really high-value, like discrete executive directors and board members) who work for a large but incorrectly listed company.
(2) You can improve the data quality by adding additional targeting such as “job title” (e.g., a “compliance director” is unlikely to work for a small company that doesn’t have such titles).
(3) You gain a competitive advantage over companies that don’t leverage this negative targeting option (and therefore may be the only advertiser reaching certain important decisionmakers among your competitors!).
(4) Your cost per click will be lower, as the LinkedIn pricing is based partly on inclusive targeting (meaning you get more traffic with the same budget).
As an example of this, please look at the LinkedIn profile below. This person is the Chairman of the Board, of a company with more than $2 billion in revenue, over 1,600 employees (see Figure XX), and listed on the stock market. This is the very highest-level decision maker for this company, and as such, highly desirable for advertisers to target. But this Chairman has for some reason not connected his LinkedIn profile to the correct company page on LinkedIn (at the time of writing). Perhaps this is a mistake on his behalf, or perhaps he has chosen to do so in order to avoid being found in searches on LinkedIn. Either way, he would not be included in advertising targeting for large companies with 1,000+ employees. But by using targeting to exclude small companies, he would be included.
Figure 6: Example of a high-profile decision maker, who would be missed with inclusive targeting, but who would be in the audience if exclusive targeting was used instead.
Figure 7: The company page his profile should have been connected to
If you do decide to try exclusive targeting by company size, begin by setting up an A/B test. Is it better to include large companies or exclude small companies? Either option could work better for you; it all comes down to what audience you have, which industries you are in, and which markets you target. The Markick Platform supports this type of advanced A/B testing.
Company Category
LinkedIn offers 30 different categories created by LinkedIn editors. These lists can change over time. For example, there’s a “Fortune 100 Fast-Growing Companies” list. This can be great for advertisers wanting to sell into fast-growing companies, but these lists are also a little counterproductive. The competition to sell into the fast-growing companies makes LinkedIn auction pricing higher, and these companies may be hard targets. It is a targeting option that is good to try for a few campaigns, but we don’t consider it a solid base for any marketing strategy.
Company Connections
LinkedIn allows the targeting of “first degree connections of a company’s employees.” This may initially seem like an odd targeting option, but there are several examples of when this can be very useful. The cheekiest one is what we internally call “steal your competitors’ clients” (or, less provocatively, “gain market share”). How would this work?
Well, it is reasonable to assume that the salespeople working for your competitors are connected to their clients via LinkedIn. Let’s say your competitor is named Company A, and the people typically buying your products are “Finance Directors” in the shipping industry. If you then run a campaign targeting “1st degree connections of Company A, who are Finance Directors in the Shipping industry,” you will have pretty much pinpointed the likely customers of your competitors.
There is one specific trick that can be used to make such a campaign extremely effective. We won’t disclose it here—it’s too powerful to just give away—but if you decide to partner with Markick in the future, setting up such campaigns could certainly be part of our service to you.
Company Followers
This targeting allows you to target the “followers” of a company page, but with one important limitation. You can only select your own company, i.e., the company you are creating advertising for. If it was possible to target the followers of any company, this would also be a great way to target your competitors’ clients, but LinkedIn deems this too sensitive or upsetting for companies, and does not allow it. Even with the limitation of only being able to target your own followers, this avenue can be useful.
One option is to target your followers with the marketing objective of upselling / cross-selling. We all know the easiest person to sell to is an existing client, with a typical deal closing rate of 60-70% (as opposed to 5-20% for prospective clients!). Well, if I post organically on LinkedIn, my followers will see it anyway, you may be thinking, and organic posts are free. The problem is that organic posts quickly drop down in users’ newsfeeds and basically vanish in the hubbub. If you have 10,000 followers and post organically, you are lucky if 1,000 (10%) of your followers see your post; even then, many of these 10% will be your own employees. By paying for advertising, you ensure your sponsored post is shown in Placement 2 and Placement 5 in the users’ newsfeed. That way, you can increase the reach to nearly 100% of your followers.
The other option is to negatively target your followers. Basically, you’d be targeting everyone but your own followers. This ensures that your campaign only reaches people who are not already following you.
Company Growth Rate
This targeting may be slightly misleading since it is easy to assume that a company’s growth rate is based on financial growth. Instead, however, the growth rate is based on a company’s number of new employees. While these two numbers often are aligned, that’s not always the case. This targeting should be used with cross reference to a control set, and with A/B testing in place.
Company Industry
LinkedIn has their own industry classification which does not follow the ISO standard. LinkedIn offers targeting in 149 separate industries, assembled into 24 groups. Industry targeting is valuable to use, but it is worth noting two things:
(1) One single company can belong to more than one industry in this classification.
(2) Only 72% of members have a profile connected to an industry (as shown in Figure 4 earlier).
It is also interesting to observe the discrepancy in the data quality between Company Industry and Company Size. 72% of LinkedIn’s users can be targeted by the former, but only 34% by the latter. This is because of quality differences in the data warehouse for advertising, but also because many companies and members belong to more than one industry.
Company Names
For anyone interested in Account Based Marketing (ABM), this is a fantastic targeting option. By selecting several specific, named companies, you can streamline your marketing to the prospects who matter most to you. This targeting can be combined with further targeting metrics to whittle down the audience.
One common mistake with ABM on LinkedIn is making the audience too narrow. By only targeting named prospects, you miss out on all the opportunities you simply had not thought of. ABM targeting should never run on its own, and should always be complemented with broader marketing to generate the best overall results.
There is a second intriguing option available for targeting specific companies: upload a list of company names. We’ll delve into greater detail in another article about Retargeting and Data Uploads (soon to be published). That said, the first type of targeting by company names described here is more accurate, since the “upload list” option will not have a 100% match.
Company Revenue
The final company targeting option is by Revenue. LinkedIn does not have revenue data in its own database, but instead buys and adds this data from a third party. This makes this data fairly incomplete, with only a 17% overall match with user profiles. This means that using this data for targeting will exclude many potential prospects. We recommend using company size by employees instead of company size by revenue for finding large companies, as this dataset is of higher quality and entails data that comes directly from LinkedIn. That said, it can be interesting to A/B test the two and compare; in some cases, for some clients, one alternative can perform better than the other.
4) Job Experience Targeting
The options of targeting by Geography, Language, and Company are all essential for defining a good audience on LinkedIn. Equally important are the options for targeting by Job Experience. Figure 6 below outlines these in brief.
Figure 8: Options for targeting by Job Experience
Just as with Company targeting, the data quality in the warehouse varies again by our targeting criteria. For example, only 47% of LinkedIn members have Job Function derived from their profile data, and only 52% of members have Job Seniority. Despite these limitations, each targeting criterion is very powerful in its own way.
Job Title
Targeting by Job Title is one of the most accurate strategies, as most people keep their profile data updated and job titles can be very granular. Still, it’s worth remembering the data quality for this type of targeting is not perfect either. Individual job titles are classified with the following challenges:
(1) A whatever-goes title. LinkedIn starts off with ANY text that the member has entered as their job title. This is a free-text field in the LinkedIn profile database, so a user can give themselves any job title they like. Meaning, there could be tens of millions of options of text strings entered in the job title field in a profile database. Remember:
People can make spelling mistakes.
Some people like to make up fancy or creative job titles.
Many people write job titles in their own language.
(2) Translation errors. LinkedIn attempts to translate all foreign job titles into the English equivalent (and when this isn’t possible, the job title is lost).
(3) Haphazard groupings. Translated titles are grouped into a set of fixed job titles as predefined by LinkedIn, which are stored in the data warehouse and used for targeting. As an example, “Chief Executive Officer” and “CEO” would both be grouped as “Chief Executive Officer,” a job title you can select for targeting. There are 5,400,000 people with this job title globally (see Figure 6). The job title “CEO,” written as a three-letter abbreviation, is not targetable on its own; the only choice is to select and target “Chief Executive Officer,” which will be an aggregate of all CEO-equivalent job titles across all languages.
There are 10,000+ such predefined job titles in the advertising data warehouse, so you can still get specific. But different job titles are priced differently in the advertising auction. A common mistake is to group low-cost and high-cost job titles together. That may mean you pay a premium for the low-cost job titles, lowering campaign performance.
The good news is that the Markick Platform has a way to segment job titles and bid differently on different job titles within the same campaign. By structuring job titles more proficiently, we decrease the overall cost and increase the campaign’s performance. The Markick Platform also enables advertisers to bypass LinkedIn’s limit of 100 job titles to be targeted in any campaign, a limit that means some job titles cannot be used and advertisers must consider broader types of targeting.
Job Function
Job Functions are collections of Job Titles grouped together. Targeting by Job Function is very popular, as in many cases advertisers want to reach decision makers who have specific roles. However, job functions are defined by a LinkedIn algorithm which groups the various job titles into only 26 different classifications of job functions. This means that from millions of free-text job titles, LinkedIn first creates 10,000+ job title options, which are then aggregated into only 26 job functions. The result is that job functions are very broad—spanning, on average, 15 million users each—so this targeting option should always be complemented with additional targeting.
Job Seniority
Targeting by Job Seniority is also a prevailing practice in B2B marketing. LinkedIn offers seniority targeting with 10 different categories which include “Owner,” “CXO,” and “Senior.” While this one is a powerful targeting tool, it does come with its own share of shortcomings—both on the platform’s end and the advertiser’s.
(1) Not everyone have seniority derived from their profile. Only 52% of LinkedIn members have some sort of seniority attached to their profile. This means that by including seniority in your targeting, you miss out on many prospects. This can be avoided if negative seniority targeting is sometimes used in alternation.
(2) LinkedIn’s classifications of seniority are often misunderstood (especially the meaning of “senior). “Senior” means “Senior Individual Contributor” (i.e., non-manager), and “Entry” means “Entry-level Individual Contributor” (also a non-managerial profile). The difference between these two is determined by looking at a combination of profile data. Many advertisers wrongly assume “Senior” refers to people at the managerial level, and they end up paying for traffic they did not intend to target.
(3) Overlooking the real decision makers. Many B2B advertisers think decision makers are those who sign contracts. And, of course, they are correct; those are the ultimate decision makers, and advertisers need to influence them. But it is a mistake to exclude non-managers from B2B advertising campaigns. Many experts belong in the non-manager categories, and while those experts perhaps aren’t signing off on any deals, they majorly influence the people who do, and are often the inhouse guides. Advertisers must therefore have a strategy to target both high-level decision makers and non-managerial experts. The Markick Platform can structure campaigns that cater to both groups, ensuring a budget is allocated to each group separately.
Skills
Due to LinkedIn being a social media platform, all its members have a set of specific skills allocated to them. These skills accumulate in their profile as each user’s connections “vouch for” or “vote” for the user’s strength in a certain skill. The Skills data is a little flimsy initially for a newly created profile, but over time, these votes accumulate in a statistically relevant way. The data of these skills are translated into the data warehouse and can be used for targeting.
There are more than 100,000+ skills that can be used for targeting, which is great for detailed targeting. There are however some caveats to be aware of. One such caveat is that a person may have any number of “votes” for a particular skill. Perhaps they had just one vote for a particular skill, or perhaps they had 100+ votes. This means that if we target a skill, the audience will include both people with few votes and with many votes for this skill. The advertiser is probably more interested in reaching people with higher skill-counts, but LinkedIn can serve ads broadly, and include any members with this skill mentioned on their profile. It’s therefore best to use this method along with other targeting options, and conduct an A/B test.
Another caveat is that people will keep their skills in their profile even if they change jobs or careers. So, while a person may still rank highly in a specific skill, it is not guaranteed that they still work in this field or exhibit the same proficiency.
This skill targeting option is still a great way to create desired fuzziness in certain targeting scenarios. While “fuzziness” might sound like a bad thing, sometimes, it is an effective way to capture the fringes of a more precise targeting – and can as such be a valuable tool for the marketer.
Years of Experience
This targeting facet is based on how many years a user has been in the workforce (i.e., when they began their first job in their job history). However, LinkedIn has made a mistake (in our view) when selecting the brackets available for targeting. Their highest bracket is “12+ years,” with each year between 1-12 years serving as an option. But there is a difference between someone who has 13 years of experience and someone who has 45! Unfortunately, LinkedIn doesn’t enable advertisers to specifically target people with, say, 30+ years of experience. The overall data quality is also a fairly low 31%. We therefore recommend not using Years of Experience for targeting, except for possibly very specific scenarios (e.g., recruitment).
5) Groups, Interests, and Traits
Figure 7 demonstrates several additional options for targeting users, which we’ll examine in detail below.
Figure 9: Options for targeting by Groups, Interests, and Traits
Groups
The best of these options, in our opinion, is the choice to target by group membership. LinkedIn has a staggering 2.4 million different groups, created and moderated by members themselves. Some of these are massive, with numbers in the millions. But even groups with tens of thousands of members (of which there are many) are valuable targets. Two great advantages of group targeting are:
(1) Many group members are often very active LinkedIn members, and
(2) They’re also typically interested in the group’s topic.
The challenge lies in finding the right groups, given their tremendous scope and the limitless variety of their names. Finding the best ones demands clever research. It is also important to not rely on group targeting alone, as many potential prospects will be missed. Group targeting should be part of a bigger overall LinkedIn strategy and deployed carefully over time in the marketing mix.
Interests
LinkedIn offers 29 predefined professional interests for targeting, somewhat along the lines of what Twitter provides in the consumer space. The value of this targeting is debatable. LinkedIn tries to assess interests based on member engagement, but the data is far from perfect. There are also only 29 interests defined (at the time of this article’s writing), which means that there is not something here for every advertiser. It’s still worth having a look, since a suitable interest may exist. If so, these campaigns should be deployed with A/B testing and as part of a broader mix.
Traits
This is a fascinating category; some of the traits available are based on user behavior, and can provide some savvy and rare forms of targeting. Take, for example, “frequent travelers,” which defines people who frequently log in to their accounts from different countries. Some of these individuals may be using a VPN to get an IP from a different country, of course, which would create a false positive. The majority, however, are likely traveling and can be high-value targets for some advertisers. Even though only 11 different traits are currently defined on LinkedIn (as of the writing of this article), it is worth having a look. Perhaps one of them is applicable to you.
6) Education Targeting
When it comes to B2B advertising, there is an ongoing debate about how important education really is as a targeting metric. Some of the world’s most successful business leaders never finished college (Bill Gates and Steve Jobs are perhaps the most often quoted examples). By targeting business decisionmakers by their educational degree, you risk missing out on a massive portion of your intended audience. Furthermore, many LinkedIn users who have degrees don’t include their educational background in their profile at all, opting to only list their job history (if that).
That said, sometimes targeting by degree is useful, like if you’re selling some form of higher education to business professionals. In that case, their educational background may be most helpful in helping you define who you want to target with your offer of subsequent education.
Education targeting options are listed below in Figure 8. Note that 95,000 schools worldwide have a “company profile” on LinkedIn.
Figure 10: Options for targeting by Education
7) Demographics Targeting
This is the seventh and last option we’re covering in this article. It’s a rather limited one on LinkedIn. Generally, Demographics targeting is more important for B2C marketing than B2B marketing, so it makes sense that LinkedIn does not focus on this data. As with several other targeting options, the data quality is not perfect, as both age and gender are derivative. Gender, for instance, is derived from a member’s first name. But many first names are gender-neutral, like Sam or Kim for example. Such names, and other name ambiguities, cannot be correctly classified by the system. For this reason, LinkedIn only knows the gender for 82% of its members.
The age data is tricky, too. Apart from there only being four age brackets available for targeting, it’s based on the educational years and first job year, resulting in further miscorrelations. Not everyone attends college in their 20s and not all members have their first job listed on LinkedIn. Due to these discrepancies, we don’t recommend using demographics for targeting other than for a few very specific marketing scenarios.
Figure 11: Options for targeting by Demographics
Retargeting and List Uploads
In the above sections, we covered all types of targeting on LinkedIn based on data in the LinkedIn data warehouse for advertising targeting. This warehouse derives its data from five sources:
(1) Original data from the LinkedIn profile database (i.e. company size),
(2) Derived data based on data in the profile database (i.e. job function),
(3) Data put together by the LinkedIn teams (i.e. Fortune 100),
(4) Real-time user data (i.e. frequent travelers), and
(5) Third-party data that LinkedIn buys to complement the internal data (i.e. revenue).
In addition to the data warehouse, LinkedIn also offers targeting based on Retargeting (targeting customers who have already shown an interest in your brand), List Uploads of companies and contacts, and Lookalike parameters (when targeting audiences similar to an audience you have defined). This is such a big topic that it deserves an article on its own—and it’ll get it. We’ll soon be publishing a separate article about our thoughts and recommendations around retargeting, so stay tuned!
Summary
LinkedIn is an enormous and high-quality database of millions of business professionals. The data warehouse for advertising is different from the LinkedIn profile data, with additional data and many steps of categorization and translation along the way. Advertisers can learn to aptly leverage the data warehouse for more precise targeting. Since the data quality is not perfect, it is essential to first understand its limitations and then find ways to work around them to attain optimal results. A comprehensive targeting strategy is crucial for sustainable success. Markick has a proprietary platform that can help you effectively execute your campaigns by targeting in ways that aren’t directly accessible on LinkedIn.
Contact Markick
If you would like a free advisory assessment of your company’s opportunities on LinkedIn, feel free to contact us here and we will get back to you within 24 hours.