Archive for the Facebook marketing tag

Joni

How to do political marketing on social media? A systematic process leveraging Facebook Ads

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How to do political marketing on social media? A systematic process leveraging Facebook Ads

This post very briefly explains a process of using and scaling Facebook advertising for political marketing. It might not be clear for all readers, but professional online marketers should be able to follow.

The recipe for political marketing by using Facebook Ads:

  1. Create starting parameters (Age, Gender, Location, Message)
  2. Create total combinations based on the starting parameters
  3. Use prior information to narrow down search space: e.g., identify the 100 most important target groups (e.g., battle-ground states)
  4. Create Facebook Ads campaigns based on narrowed down search space
  5. Run the campaigns (the shortest time is one day, but I would recommend at least 2-3 days to accommodate Facebook’s algorithm)
  6. Analyze the results; combine data to higher level clusters (i.e., aggregate performance stats with matching groups from different campaigns)
  7. Scale up; allocate budget based on performance, iteratively optimize for non-engaged but important groups, and remove the already-converted voters.

The intuition:

You are using Facebook Ads to test how many different target groups respond to your message. You will cluster this data to identify the most engaged target groups. You will then try to maximize voter turnout within those groups (i.e., maximize conversion). In addition, you will create new messages for those groups which are not currently responding well but which you need to capture in order to win the election. You will keep testing these groups by creating new messages, one by one finding the most responsive groups for a given message.

Once a target group shows a high level of engagement, you will scale up your advertising efforts (think 10x or 100x increase). You will keep the test cycle short (a week is more than enough), and the scaling period long. Based on campaign events, you may want to revisit already secured groups to ensure their engagement remains high. Because you are not able to measure the ultimate conversion (=voting directly), you will use proxy metrics that reflect the engagement of different target groups (particularly, clicks, CTR, post-click behavior such as time-on-site, newsletter subscriptions). This enables you to predict likelihood to vote based on social media engagement. Once a person has “converted”, he or she is removed from targeting – this is done to avoid wasting your budget by preaching to the choir.

Here are some additional metrics you can consider, some of them are harder to infer than the basic ones: frequency of activity, sentiment level, interest in a single issue that cause votes, and historical voting records (district level). According to different metrics used, we can set a target level (e.g., time-on-site > 3 mins) or binary event (subscription to campaign newsletter) which represents conversion.

Overall, we try to mimic the best practices of online marketing optimization here by 1) testing with explore-exploit mentality (scaling appropriately), and 2) excluding those who converted from future targeting (in effect, they are moved into a different budget which is direct targeting by email – a form which is more personal and cheaper than ads). In addition, we delimit the search space by using our prior information on the electorate, again to avoid wasteful impressions and maximize ROI-efficiency.

Then, we fill the selected groups with data and observe the performance metrics. Finally, we cluster the results to get a higher-level understanding of each group, as well as find points of agreement between the groups that can be used to refine the communication strategy of the larger political campaign. Therefore, the data we obtain is not solely limited to Facebook Ads but can be used to further enhance messaging in other channels as well.

There. The methodology represents a systematic and effective way to leverage Facebook Ads for political social media marketing.

Also read:

Agile methods for predicting contest outcomes by social media analysis

Analyzing sentiment of topical dimensions in social media

Affinity analysis in political social media marketing – the missing link

Joni

Affinity analysis in political social media marketing – the missing link

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Introduction. Hm… I’ve figured out how to execute successful political marketing campaign on social media [1], but one link is missing still. Namely, applying affinity analysis (cf. market basket analysis).

Discounting conversions. Now, you are supposed to measure “conversions” by some proxy – e.g., time spent on site, number of pages visited, email subscription. Determining which measurable action is the best proxy for likelihood of voting is a crucial sub-problem, which you can approach with several tactics. For example, you can use the closest action to final conversion (vote), i.e. micro-conversion. This requires you have an understanding of the sequence of actions leading to final conversion. You could also use a relative cut-off point; e.g. the nth percentile with the highest degree of engagement is considered as converted.

Anyhow, this is very important because once you have secured a vote, you don’t want to waste your marketing budget by showing ads to people who already have decided to vote for your candidate. Otherwise, you risk “preaching to the choir”. Instead, you want to convert as many uncertain voters to voters as possible, by using different persuasion tactics.

Affinity analysis. The affinity analysis can be used to accomplish this. In ecommerce, you would use it as a basis for recommendation engine for cross-selling or up-selling (“customers who bought this item also bought…” à la Amazon). First you detemine which sets of products are most popular, and then show those combinations to buyers interested in any item belonging to that set.

In political marketing, affinity analysis means that because a voter is interested in topic A, he’s also interested in topic B. Therefore, we will show him information on topic B, given our extant knowledge his interests, in order to increase likelihood of conversion. This is a form of associative

Operationalization. But operationalizing this is where I’m still in doubt. One solution could be building an association matrix based on website behavior, and then form corresponding retargeting audiences (e.g., website custom audiences on Facebook). The following picture illustrates the idea.

Figure 1 Example of affinity analysis (1=Visited page, 0=Did not visit page)

For example, we can see that themes C&D and A&F commonly occur together, i.e. people visit those sub-pages in the campaign site. You can validate this by calculating correlations between all pairs. When you set your data in binary format (0/1), you can use Pearson correlation for the calculations.

Facebook targeting. Knowing this information, we can build target audiences on Facebook, e.g. “Visited /Theme_A; NOT /Theme_F; NOT /confirmation”, where confirmation indicates conversion. Then, we would show ads on Theme F to that particular audience. In practice, we could facilitate the process by first identifying the most popular themes, and then finding the associated themes. Once the user has been exposed to a given theme, and did not convert, he needs to be exposed to another theme (with the highest association score). The process is continued until themes run out, or the user converts, which ever comes first. Applying the earlier logic of determining proxy for conversion, visiting all theme sub-pages can also be used as a measure for conversion.

Finally, it is possible to use more advanced methods of associative learning. That is, we could determine that {Theme A, Theme F} => {Theme C}, so that themes A and B predict interest in theme C. However, it is more appropriate to predict conversion rather than interest in other themes, because ultimately we’re interested in persuading more voters.

Footnotes

[1] Posts in Finnish:

https://www.facebook.com/joni.salminen.33/posts/10212240031455606

https://www.facebook.com/joni.salminen.33/posts/10212237230465583

Joni

Total remarketing – the concept

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Here’s a definition:

Total remarketing is remarketing in all possible channels with all possible list combinations.

Channels:

  • Programmatic display networks (e.g., Adroll)
  • Google (GDN, RLSA)
  • Facebook (Website Custom Audience)
  • Facebook (Video viewers / Engaged with ads)
  • etc.

How to apply:

  1. Test 2-3 different value propositions per group
  2. Prefer up-selling and cross-selling over discounts (the goal is to increase AOV, not reduce it; e.g. you can include an $20 gift voucher when basket size exceeds $100)
  3. Configure well; exclude those who bought; use information you have to improve remarketing focus (e.g. time of site, products or categories visited — the same remarketing for all groups is like the same marketing for all groups)
  4. Consider automation options (dynamic retargeting; behavior based campaign suggestions for the target)
Joni

In 2016, Facebook bypassed Google in ads. Here’s why.

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In 2016, Facebook bypassed Google in ads. Here’s why.

Introduction

The gone 2016 was the first year I thought Facebook ends up beating Google in the ad race, despite the fact Google still dominates in revenue ($67Bn vs. $17Bn in 2015). I’ll explain why.

First, consider that Google’s growth is restricted by three things:

  1. natural demand
  2. keyword volumes, and
  3. approach of perfect market.

More demand than supply

First, at any given time there is a limited number of people interested in a product/service. The interest can be of purchase intent or just general interest, but either way it translates into searches. Each search is an impression that Google can sell to advertisers through its AdWords bidding. The major problem is this: even when I’d like to spend more money on AdWords, I cannot. There is simply not enough search volume to satisfy my budget (in many cases there is, but in highly targeted and profitable campaigns many times there isn’t). So, the excess budget I will spend elsewhere where the profitable ad inventory is not limited (that is, Facebook at the moment).

Limited growth

According to estimates, search volume is growing by 10-15% annually [1]. Yet, Google’s revenue is expected to grow even by 26% [2]. Over the year, Google’s growth rate in terms of search volume has substantially decreased, although this is perceived as a natural phenomenon (after trillion searches it’s hard to keep growing double digits). In any case, the aforementioned dynamics reflect to search volumes – when the volumes don’t grow much and new advertisers keep entering the ad auction, there is more competition over the same searches. In other words, supply stays stable but demand increases, resulting in more intense bid wars.

Approaching perfect market

For a long time now, I’ve added +15% increase in internal budgeting for AdWords, and last year that was hard to maintain. Google is still a profitable channel, but the advertisers’ surplus is decreasing year by year, incentivizing them to look for alternative channels. While Google is restrained by its natural search volumes, Facebook’s ad inventory (=impressions) are practically limitless. The closer AdWords gets to a perfect market (=no economic rents), the less attractive it is for savvy marketers. Facebook is less exploited, and allows rents.

What will Google do?

Finally, I don’t like the Alphabet business. Already in the beginning it signals to investors that Google is in “whatever comes to mind” business instead of strategic focus on search. Most likely Alphabet ends up draining resources from the mother company, producing loss and taking human capital off from succeeding in online ads business (which is where their money comes from). In contrast, Facebook is very focused on social; it buys off competitors and improves fast. That said, I do have to recognize that Google’s advertising system is still much better than that of Facebook, and in fact still the best in the world. But momentum seems to be shifting to Facebook’s side.

Conclusion

The maximum number of impressions (=ad inventory) of Facebook is much higher than that of Google, because Google is limited by natural demand and Facebook is not. In the marketplace, there is always more supply than demand which is why advertisers want to spend more than what Google enables. These factors combined with Facebook’s continously increasing ability to match interested people with the right type of ads, makes Facebook’s revenue potential much bigger than Google’s.

From advertiser’s perspective, Facebook and Google both are and are not competitors. They are competitors for ad revenue, but they are not competitors in the online channel mix. Because Google is for demand capture and Facebook for demand creation, most marketers want to include both in their channel mix. This means Google’s share of online ad revenue might decrease, but a rational online advertisers will not drop its use so it will remain as a (less important) channel into foreseeable future.

References

[1] http://www.internetlivestats.com/google-search-statistics/

[2] http://venturebeat.com/2016/09/27/4-graphs-show-the-state-of-facebook-and-googles-revenue-dominance/

Joni

Facebook Ads: remember data breakdowns

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Here’s a small case study.

We observed irrational behavior from Facebook ads. We have two ad versions running; but the one with lower CTR gets a better relevance score and lower CPC.

This seems like an irrational outcome, because in my understanding, CTR as a measure of relevance should be largest impact factor to CPC and Relevance Score.

Figure 1  Aggregate data

So, we dug a little bit futher and did a breakdown of the data. It turns out, the ad version with lower aggregate CTR performs better on mobile. Apparently this adds emphasis to the algorithm’s calculation.

Figure 2  Breakdown data

Lesson learned: Always dig in deeper to understand aggregate numbers. (If you’re interested in learning more about aggregate data problems, do a lookup on “Simpson’s paradox”.)

Joni

Why social advertising beats display advertising

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Introduction

I’ve long been skeptical of display advertising. At least my students know this, since ever year I start the digital marketing course by giving a lecture on why display sucks (and why inbound / search-engine marketing performs much better).

But this post is not about the many pitfalls of display. Rather, it’s outlining three arguments as to why I nowadays prefer social advertising, epitomized by Facebook Ads, over display advertising. Without further ado, here are the reasons why social rocks at the moment.

1. Quality of contacts

It’s commonly known Facebook advertising is cheap in comparison to many advertising channels, when measured by CPM or cost per individual reached. Display can be even cheaper, so isn’t that better? No, absolutely not. Reach or impressions are completely fallacious metrics — their business value approaches zero. Orders of magnitude more important is the quality of contacts.

The quality of Facebook traffic, when looking at post-click behavior, tends to be better than the quality of display traffic. Even when media companies speak of “premium inventory”, the results are weak. People just don’t like banner ads. The people who click them, if they are people and not bots to begin with, often exit the site instantly without clicking further.

2. Social interaction

People actually interact with social ads. They pose questions, like them and even share them to their friends. Share advertisements? OMG, but they really do. That represents an overkill opportunity for a brand to interact with its customer base, and systematically gather feedback and customer insight. This is simply not possible with any other form of advertising, display including.

Display ads, albeit using rich media executions, are completely static and dead when it comes to social interaction. Whereas social advertising creates an opportunity to gather social proof and actual word-of-mouth, even viral diffusion, in the one and same advertising platform, display advertising is completely lacking the social dimension.

3. Better ad formats

Social advertising, specifically Facebook gives a great flexibility in combining text, images and video. Typically, a banner ad can only fit a brief slogan (“Just do it.”), whereas a social advertisement can include many sentences of text, a compelling picture and even link description that together give the advertisers the ability to communicate the whole story of the company or its offering in one advertisement.

But isn’t that boring? No, you can craft it in a compelling way – the huge advantage is that people don’t even need to click to learn the most essential. If the goal of advertising is to inform about offerings, social advertising is among the most efficient ways to actually do it.

Conclusion

That’s it. I don’t see a way for display advertising to overcome these advantages of social advertising. Notice that I didn’t mention the superior targeting criteria — this is because display is quickly catching up to Facebook in that respect. It just won’t be enough.

Joni

5 questions to ask your Facebook marketing agency

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Facebook marketing is not magic, although it might seem like it if you have no clue how to do it. Therefore, before anything else, the first piece of advice is: get to know the basics. Jonloomer.com is a good resource for that, as well as Facebook’s free training modules.

Now, to the actual point. A company may run Facebook marketing in-house or via an agency. For small companies, it often makes sense to do it yourself, but larger budgets require a deeper know-how and more time to get the best results. For these reasons, outsourcing is often chosen by many medium and large companies. When outsourcing, an agency can take care of organic Facebook marketing, paid advertising, or both.

But how to test the quality of your agency?

Well, remember the first advice – learn the basics of Facebook marketing. If you don’t know something, you cannot manage it. Second, you can ask these questions, before engaging an agency or during your relationship with them.

  1. What goals would you set for our Facebook marketing?
  2. How would you measure the achievement of those goals?
  3. Describe your strategy in achieving the goals.
  4. Describe your optimization process for Facebook marketing.
  5. Based on our Facebook posts, tell me something that I don’t know about my business

The first question reveals how well the agency grasps your business, and how they would fit your business goals to the Facebook environment. The goals don’t have to be exactly what you had thought of — it’s more important that they show innovativeness and general understanding of your business.

The second question reveals the metrics they would choose to measure performance – the more they are aligned with your general business goals, the better. In addition, if they are able to argue efficiently for both ROI- and non-ROI-oriented metrics, it’s a good sign as it shows an understanding of the general complexity of multichannel consumer behavior.

The third question tells how they would go about creating a Facebook marketing strategy — here you can pay attention to their proposed split between organic and paid, frequency of posting/optimization, target group definition, ad creation process, etc. You can ask specifying questions, e.g. about the suggested size of budget. That shows how they approach campaign planning on the fly – the better they know the environment, the better answers they can give.

Fourth, it is important to know how they would run the accounts in practice. For example, how much time are they willing to invest? Facebook marketing is a time-consuming activity, which is actually a major reason the optimization workflow has to be efficient to achieve the best results. For an agency it’s easy to spend money precariously because Facebook takes all the money you can throw at it — but optimization is a different ballgame.

The fifth question tells how well they have analyzed your accounts and prior Facebook marketing activities. Not all agencies bother to analyze the status quo in your Facebook marketing this before meeting you — or even when they are doing marketing for you — but obviously doing so communicates a genuine interest in closing/keeping you as a client, as well as attention to detail. If they are able to tell you something about your customers, for instance, that you didn’t know, it’s a very good sign.

There. Asking these questions and going through the associated discussion is, in my opinion, an excellent way to vet a Facebook marketing agency.

In addition, one of the by far most neglected aspect of managing digital marketing agencies is auditing. You should frequently have a 3rd party, such as another agency, audit your campaigns. Never be “forever happy” with an agency but instead always push for more. You want to show commitment so they see value in investing in the relationship, but you also want to keep them a little bit on their toes so they actually bother doing their best for you, as oppose to only chasing new clients.

Joni

Here’s Facebook cheating you (and how to avoid it)

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Here’s how Facebook is cheating advertisers with reporting of video views:

How is that cheating? Well, the advertiser implicitly assumes that ‘video views’ means people who have actually watched the video which is not the case here. Say, you have a 10-second video; this metric does not show people who have watched that video till the end, but only those who have watched the first three seconds — possibly just scrolling their newsfeeds and letting the video autoplay accidentally while quickly browsing forward. Essentially, that kind of exposure is worth closer to zero than the 1 cent Facebook usually reports.

Indeed, other view-based metrics such as CPV should be calculated based on somebody watching the video till the end, but in FB it’s 3 SECONDS after which they calculate it as a view. In effect, this will multiply the real CPV by order of several magnitudes, in some cases I’ve seen it’s 10x more than the figure reported by Facebook.

But aren’t they telling this honestly? Sure, they show the correct definition, but a large part of advertisers do not bother looking at it, or are unsuspecting misguiding definitions. After all, you should be able to trust that a big and reputable player like Facebook would not screw over advertisers. However, those of us who have played the game for many years know it’s not the first time (remember their definition of “click” a couple of years ago?).

What’s more, there’s no metric for the real CPV in the reports, so advertisers need to calculate it manually (at which point, based on my experience, it’s revealed that Facebook video views are are typically 5x more expensive than on Youtube).

How to avoid this shenanigans? Simply look at the metric ‘video views to 100%’. This is the real video views metric you should use – calculate your spend with that number, and you will get your true CPV. In other words:

ad cost / views to 100%

Keep your eyes open, my fellow advertisers!

UPDATE: Another good tactic, pointed out by my colleague Tommi Salenius, is to bid for 10-second views in your video campaigns. This is a relatively novel feature in Facebook, and although it doesn’t fix the problem, it’s a decent workaround. He also recommended to optimize “average % viewed” metric – you can do that e.g. by comparing different demographic segments. Finally, Facebook video ads can be seen to have a “social advantage” which refers to people’s ability to comment and like videos – sometimes this does take place 🙂 The advertiser can also include more text than in Youtube video ads which has a positive effect on ad prominence. It is then up to the advertiser to consider whether these advantages are worth the cost premium Facebook tends to have in comparison to Youtube.

Joni

Negative tipping and Facebook: Warning signs

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This Inc article states a very big danger for Facebook: http://www.inc.com/jeff-bercovici/facebook-sharing-crisis.html

It is widely established in platform theory that reaching a negative tipping point can destroy a platform. Negative tipping is essentially the reverse of positive tipping — instead of gaining momentum, the platforms starts quickly losing it.

There are two dimensions I want to look at in this post.

First, what I call “the curse of likes“. Essentially, Facebook has made it too easy to like pages and befriend people; as a result, they are unable to manage people’s newsfeeds in the best way in terms of engagement. There is too much clutter, leaving important social information out, and the “friend” network is too wide for the intimacy required to share personal things. The former reduces engagement rate, the latter results in unwillingness to share personal information.

Second, if people are sharing less about themselves, the platform has it more difficult to show them relevant ads. The success of Facebook as a business relies on its revenue model which is advertising. Both of the aforementioned risks are negative for advertising outcomes. If relevance decreases, a) user experience (negative effects of ads) and b) ad performance decrease as well, resulting in advertisers reducing their ad spend or, in worst-case scenario, them moving on to other platforms.

To counter these effects, Facebook can resort to a few strategies:

  1. Discourage people from “over-liking” things – this is for their own benefit, not to clutter the newsfeed
  2. Easy options to unsubscribe from people and pages — e.g., asking “Do you want to see this?” in relation to posts
  3. Favoring social content over news and company posts in the newsfeed algorithms – seeing personal social content is likely to incite more social content
  4. Sentiment control of newsfeed algorithm – to many, Facebook seems like a “negative place” with arguing on politics and such. This is in stark contrast to more intimate platforms such as Instagram. Thus, Facebook could incorporate sentiment adjustment in its newsfeed algorithm to emphasize positive content.
  5. Continued efforts to improve ad relevance – especially by giving incentives for high-CTR advertisers to participate by lowering their click prices, thereby encouraging engagement and match-seeking behavior.

Overall, Facebook as a platform will not be eternal. But I think the company is well aware of this, since their strategy is to constantly buy out rivals. The platform idea persists although individual platforms may perish.

Joni

Facebook Ads: too high performance might turn on you (theoretically)

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Introduction

Now, earlier I wrote a post arguing that Facebook has an incentive to lower the CPC of well-targeting advertisers because better targeting improves user experience (in two-sided market terms, relevance through more precise targeting reduces the negative indirect network effects perceived by ad targets). You can read that post here.

However, consider the point from another perspective: the well-targeting advertiser is making rents (excessive profits) from their advertising which Facebook wants and as the platform owner is able to capture.

In this scenario, Facebook has an incentive to actually increase the CPC of a well-targeting advertiser until the advertiser’s marginal profit is aligned with marginal cost. In such a case, it would still make sense for the advertiser to continue investing (so the user experience remains satisfactory), but Facebook’s profit would be increased by the magnitude of the advertiser’s rent.

Problem of private information

This would require that Facebook be aware of the profit function of its advertisers which as for now might be private information to the advertisers. But had Facebook this information, it could consider it in the click-price calculation. Now, obviously that would violate the “objective” nature of Facebook’s VCG ad auction — it’s currently set to consider maximum CPC and ad performance (negative feedback, CTR, but not profit as far as I know). However, advertisers would not be able to monitor the use of their profit function because the precise ad auctions are carried out in a black box (i.e., asymmetric information). Thus, the scenario represents a type of moral hazard for Facebook – a potential risk the advertisers may not be aware of.

Origin of the idea

This idea I actually got from one of my students who said that “oh, I don’t think micro-targeting is useful“. Then I asked why and he said “because Facebook is probably charging too much from it”. I said to him that’s not the case, but also that it could be and the idea is interesting. Here I just elaborated it a bit further.

Also read this article about micro-targeting.

Micro-targeting is super interesting for B2B and personal branding (e.g., job seeking).

Another related point, that might interest you Jim (in case you’re reading this :), is the action of distributing profitable keywords by the platform owner between advertisers in search advertising. For example, Google could control impression share so that each advertiser would receive a satisfactory (given their profit function) portion of traffic WHILE optimizing its own return.

Conclusion

This idea is not well-developed though; it rests on the notion that there is heterogeneity in advertisers’ willingness to pay (arising e.g., from different in margins, average order values, operational efficiency or such) that would benefit the platform owner; I suspect it could be the case that the second-price auction anyway considers this as long as advertisers are bidding truthfully, in which case there’s no need for such “manipulation” by Google as the prices are always set to maximum anyway. So, just a random idea at this point.