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Shreeraj Pillai's picture
September 08, 2014
Shreeraj Pillai

Facebook today offers a variety of bidding options ranging from CPC and CPM to OCPM and CPA. Each bidding option is associated with a particular objective and it’s important for every advertiser to understand each type to drive the best ROI from the platform.

In this post we are going to discuss each of these various bid types in detail. This post also aims to help you make better decisions for your next Facebook campaign!

Why bidding is important?
While running a successful Facebook ad campaign, every advertiser needs to focus on a few key ingredients like a powerful ad title, attention-grabbing images, crisp ad copies, precise targeting and lastly and more importantly the right bid. With the wrong bidding strategy, even with a great ad you might end up paying more than what you should actually pay Facebook or you might end up reaching out to a very small audience. With the right bidding strategy and a great ad creative you are poised to drive maximum returns on your investment from the platform.

What are the different bidding types on Facebook?
Facebook when it started off used to support only 2 types of bidding options i.e. Cost per Click (CPC) and Cost per Mille (CPM). But over the last couple of years, Facebook has simplified its bidding process in order to attract the less evolved digital advertisers and help them drive the best ROI from the platform.

During the ad creation process, once you decide on the creative and the targeting parameters you will be taken to pricing section where you have a choice about how you want to pay. Facebook currently offers 4 types of bidding options:

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Cost per Mille (CPM)

What: Cost per Mille (CPM) means cost per thousand impressions (Mille is 1000 in Latin). By choosing this option you indicate that you are ready to pay Facebook on the delivery of 1000 ad impressions. It is the simplest form of bid type available on Facebook. The system optimizes to deliver impressions within the target group chosen and not really focusing on accumulating actions.

Why: It’s meant for advertisers who are looking to create brand awareness. If you are not really looking at specific actions but intend only to show your ad to the users by delivering impressions then CPM could be apt for you.

Cost per Click (CPC)

What: Cost per click (CPC) means you will be paying Facebook on each and every click that happens on the ad. It’s the most commonly used bid option on Facebook. So if you are promoting an ad using CPC bid type, then you will only be charged on the clicks your ad gets irrespective of the fact that it may have taken millions of impressions for Facebook to get those clicks. The ad will be delivered to those set of people within the target audience who are most likely to click on the ad.
In the background, Facebook optimises on the ads which has the highest click through rate (CTR) and prioritises them. That’s why ads with higher CTRs record the lower CPCs whereas the ones with low CTRs record higher CPCs. From a revenue point of view, it makes sense for Facebook to show an ad which gets clicked 1000 times at $1.00 CPC than to show an ad which gets clicked only 100 times at $5.00 CPC.

Why: Cost per click (CPC) bidding is suited for you in few scenarios like below:

  • If you want to keep it simple and want to pay Facebook only when anyone clicks on your ad.
  • If you have limited advertising budget and don’t really want to experiment much
  • If you are targeting a specific group of Facebook users and you want them to perform a particular action, like clicking through to your website or buying your product through a sales landing page or maybe promoting a Facebook page for likes or post engagement

Optimized Cost per Mille (OCPM)

What: Optimized Cost per Mille (OCPM) is Facebook’s proprietary bidding type where Facebook optimizes to show your ad to a specific set of users who are most likely to execute the desired action and you pay them on 1000 impression. In OCPM, you have two options:

  • You choose the default setting and let Facebook take control of bidding and optimization towards the desired action
  • You manually set the maximum value you want to pay against a particular goal. Note these values are not bids but like soft goals to the system basis which the system adjusts its bidding strategy.

Currently, advertisers can optimize their campaigns based on the following goals:

  • Actions: Optimize on certain actions that happen on Facebook, e.g., Page Likes, App Installs, Post likes, comments etc. You can specify actions using conversion specs.
  • Reach: Optimize to maximize reach within the target audience
  • Clicks: Optimize to get clicks.
  • Social Impressions: Optimize for impressions with social context, i.e. with the names of one or more of the user’s friends attached to the ad who have already liked the page or installed the app.

While running an OCPM campaign, you can expect to get average cost per 1000 impressions much higher than your average traditional CPM bidding. In OCPM, Facebook carefully chooses the audience who are more likely to execute the desired action and hence you will end up getting more conversions than usual.

As OCPM tries to deliver your impressions to the people most likely to take the desired action within your target audience, there is one issue you should be aware of. OCPM needs a large target audience as a basis for the optimization, so you can’t target too narrow. Facebook defines this as greater than 1 million users. So if you are targeting a small audience chances are you may not find the desired audience within the target group who are likely to the action

Why: If you are looking at optimizing your campaigns to a desired action like page likes, post engagement, event RSVPs etc. with a large audience (preferably more than 1 million) then OCPM will be the right bid type for you. OCPM predicts who is more likely to convert and it needs time and training to understand your prospective customers. So in case your campaign is targeted to a small audience and you don’t have enough time to learn and execute, OCPM is not recommended.

Cost per Action (CPA)

What: With Cost per Action (CPA) you pay for the actual actions your ad results in. You set a bid and indicate the maximum cost you are ready to incur for the particular action. You pay Facebook only when the desired action takes place and you won’t be charged for the impressions and clicks Facebook consumed to get that action.

Currently, Facebook supports Cost per Action (CPA) bidding on the below list of actions:

  • Page Likes (available only through a Preferred Marketing Developer)
  • Offer Claims (available only through a Preferred Marketing Developer)
  • Offsite Link Clicks (available only through a Preferred Marketing Developer)
  • Mobile App Installs ( available through Power editor)

Why: If you are looking to optimize your campaign on the CPA supported actions and you are looking to control costs then Cost per Action bidding type will help you drive the best ROI and minimize the spill over.

Key insights from our experience
We have worked with over 500 brands across 5000 campaigns over the last 5 years across Southeast Asia and India. With that kind of experience behind our back, our operations team are experts when it comes to bid management and driving the best ROI from the platform. Listing down a few insights from our campaign management experience which should help you in your decision making process while choosing the right bid type:

1. COMMON OBJECTIVES & BID TYPES

 

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2. REGIONAL INSIGHTS
India

  • Before the introduction of OCPM buy type, 95% of the campaigns ran on CPC bidding
  • Post the introduction of OCPM, advertisers have started flocking towards it for campaigns where they need to drive more actions.
  • CPC buys – 60%
  • OCPM buys – 30%
  • CPM buys – 8%
  • CPM buys – 2%

3. Brands under different verticals prefer different bidding options. To explain:

  • Brands under Banking & Financial Services, Travel, Automobiles and Consumer durables prefer a mix of OCPM and CPC buys
  • Ecommerce brands are more action-led and hence they do few CPA buys in addition to the regular OCPM and CPC buys
  • Entertainment and Retail brands have stuck to CPC buys and are yet to experiment with other bid types


Southeast Asia

  • Almost all campaigns here run on a mix of OCPM and CPC buys focused on getting more reach and driving actions.
  • Generally, 70% of the campaigns run on OCPM and the remaining 30% run on CPC.
  • Unlike India where there are certain brands who prefer running their campaigns only on CPC, in Southeast Asia brands run campaigns on a mix of CPC and OCPM buys

3. CHALLENGES
While every bid type helps an advertiser achieve a certain objective, each bid type also has its set of challenges. We are listing down a few challenges that every Facebook advertiser should be careful about while running their campaigns:

Cost Per Click (CPC):
At times, when the campaign objective is to drive actions and the campaign is booked on CPC, we have observed that the eCPA is higher in comparison to what you would have incurred in an OCPM campaign. Hence would recommend CPC model only if you are going to evaluate the success of the campaign on the basis of clicks you have received. For actions, ideally go for OCPM model.

Optimized CPM (OCPM):
Since OCPM targets people who are likely to engage with your ad, it reaches out to only a section of the entire target audience. Hence at times, if expected delivery volumes are high, OCPM falls short of delivering those high numbers. Apart from that, also since we cannot set a bid on OCPM, the eCPM recorded is higher than expected. OCPM is recommended only if the target audience is more than 500,000 and is not ideal for smaller audiences.

Cost per Mille (CPM):
CPM is particularly effective only for reach campaigns and is not recommended for driving actions or engagements. CPM campaigns generally record the low CTRs since the overall objective here is to burn impressions and not accumulate actions.

Cost per Action (CPA):
Since Facebook is optimizing towards actions it’s shown to only those users who are more likely to take that action hence the volumes at times are bit of a concern. If you are looking to drive volumes then you may face difficulties to achieve those numbers. It’s best recommended for campaigns with smaller budgets.

Komli’s Social Ads Platform makes it easier
Komli’s social ads platform supports all Facebook bidding options including Cost per Action (CPA) bidding on actions like page likes, offer claims, mobile app installs and offsite link clicks. Depending on what your objective is you could choose any bidding option.

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Our platform also allows you to seamlessly A/B test between different bid types. For e.g.- if you want to test if OCPM is working better than CPC then you could simply ‘Add’ an extra bidding option of OCPM to the existing CPC one and in a few seconds two campaigns which are exactly identical except for the budding type will be created.

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Apart from providing you with flexibility on bidding options, our platform can optimize campaigns by focusing on one particular action you desire. Goal driven optimization helps you drive higher volumes at lower costs and helps you achieve specific business or marketing goals. For e.g.: if your overall objective is to drive link clicks on your post, you could define the same in your rule (with an expected CPA attached to it) and the system will then optimize with that soft goal in the background to keep your costs within the limits.

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Conclusion
Ideally, if you are not an experienced Facebook advertiser it’s better to use the Optimized CPM option where the bidding and optimization is automated. You will pay Facebook on a CPM basis but in the background Facebook will study your data and deliver impressions only to those users who are likely to take that desired action.

After gaining some experience on running Facebook ads, you can start playing with manual bidding, specifying how much you are willing to pay for each click (CPC) or for every 1,000 impressions (CPM) or each desired action (CPA).

Komli’s Social ads platform gives you total control over your campaigns and flexibility with support to all bidding options. In addition to that, the goal-driven optimization works towards driving the best ROI from the platform. Flexibility also exists in terms of service you could choose to run your own campaigns on our platform or else use are in-house Facebook advertising experts to run the campaign for you.


If you would like to know more about our social ads platform, please visit our website
http://www.komli.com/in/content/komlis-social-ads-platform
or
Contact our sales team at http://www.komli.com/in/contact for a live demo.

Amar Agrawal's picture
August 22, 2014
Amar Agrawal

Since its launch in 2012, Facebook Exchange has seen phenomenal success in delivering ROI to clients and the birth of several ad technology companies that help leverage the power of real time bidding to drive the very best out of FBX. Komli has been working with clients to scale remarketing on FBX through innovations in dynamic creative technology and advanced optimization algorithms since early 2013.
While FBX is a powerful channel on its own, there is more to remarketing on Facebook. For one, FBX does not allow for cross-device targeting i.e. being able to target customers who have dropped off the website on one device and showing relevant ads when they are logged into Facebook from their other devices. This is largely a limitation of cookie based user identification used by FBX ad partners to identify and bid on users in real-time.
FBX also does not allow marketers to leverage their customer relationship database to target users who haven’t been to the website recently but have interacted with the brand in the past. Lastly, marketers lack the ability to target their mobile app users to drive conversions on the app.
This is where Facebook’s Custom Audience (CA) and Website Custom Audience (WCA) ad products come into play. Through these ad products Facebook allows marketers to leverage their own customer data in the form of email/phone lists, device IDs or website cookies to run cross-device campaigns by identifying those users on Facebook. This is how it all happens -

Custom Audience

  1. Marketers upload a list of device IDs, email addresses or phone numbers into Facebook
  2. Facebook maps the uploaded identifiers to Facebook user IDs based on its own login data
  3. The matched user IDs are available as a named audience list for targeting on Facebook across all user devices

Website Custom Audience

  1. Marketers implement a data collection tag on their website which is fired every time a user views a page
  2. Facebook maps the incoming pixel hit to register the page event against its own user ID
  3. Marketers can write rules on page events to segment users into different audience lists for cross device targeting

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Marketers should start evaluating these options as it not only provides additional reach, but also enables them to leverage multiple data formats.

Remarketing strategies should have an ideal mix of FBX, CA and WCA so that marketers can extract the full potential of Facebook marketing. The key lies in consolidating audiences and spends on a single platform that can optimize between these channels to deliver maximum performance. Komli is actively working on making Facebook advertising easy for marketers, watch this space for more!

Gaurav Jain's picture
July 23, 2014
Gaurav Jain

A digital identity is data that uniquely describes a person. This unique identity may not necessarily be traceable to an actual person, yet it helps in providing a very rich experience. Publishers can use this information to show personalized content on their websites and DSPs may use this to show highly personalized creative.

A digital identity is the lifeblood of the advertising ecosystem and it is this identity that helps differentiate an online advertising medium from its mass channel counterparts. Identity is used in all stages of advertising – namely collection, identification, personalization and attribution.

For years, cookies were the main mechanism of user identification on the digital medium. Though it was not the most efficient mechanism, it was highly functional. Cookies are unique to a domain and are also unique to the browser being used. The Advertising ecosystem built its own layers on top of cookies so as to leverage the data in a much more efficient way.

When Apple launched its app store, advertisers started using UDID to track installs. UDID had a problem - It is a unique identifier based on hardware and had no user opt out. In 2011, with the launch of iOS 5, access to UDID was denied by Apple to reduce the privacy concerns. In absence of any identifier, mobile technology companies started building their own identifiers such as OPENUDID and ODIN. In 2012, Apple launched a resettable ID named IDFA which allowed advertisers to use a privacy compliant ID.

Similarly, advertisers have been relying on Android-ID for tracking devices in the Android ecosystem since Android Froyo release. This is a non-resettable ID, which is specific to hardware. Just recently, Google too came up with a new Advertising ID which is resettable and privacy compliant.

Android ID and IDFA, at times, are passed raw and other times hashed by using different algorithms such as md5 and sha1.  

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Device ID mapping is required not only for targeting and showing relevant content but also to maintain the frequency capping. Since correct attribution is the backbone of digital advertising, fragmentation of device IDs is severely hampering the ability of attribution. Without a reliable identifier, advertising platforms have no way to understand the user’s interaction with the ads.

There are three places where DSPs interact with the user – Data collection, Targeting and Attribution. For a non-retargeting platform, user identity mapping is not important in the first two stages.

Data collection: Interaction happens between advertiser and DSP

Targeting: Interaction happens between SSP and DSP

Attribution: Interaction happens between advertiser and DSP  

The advertising industry is moving towards converging of passing raw device IDs in IDFA format for iOS and Google Advertising format for Android. In the meantime, advertisers may choose to use a single identity which binds these multiple identities. There are broadly two ways – probabilistic and deterministic.

Some advertisers have chosen to use probabilistic approach, commonly known as fingerprinting, in which on the basis of different properties such as browser version, device type, country, time zone, language settings, user agent, browser resolution, browser add-ons etc. This might help reach huge scale but it may not be very accurate. Different players claim to achieve accuracy levels from 60-80%.

Other advertisers are using a deterministic approach in which a common identity such as an email ID, phone number or social network IDs are used. This can be used to tie different identities to a single user.

The next level of user identification lies in binding these multiple identities across devices to a single common identity which can then be leveraged for cross device marketing strategies. Given the nascent stage of the industry and the high fragmentation, this is a very challenging problem to solve. At Komli, we are excited about these challenges and the opportunity it provides for marketers.

Sanjiv Jha's picture
June 19, 2014
Sanjiv Jha

Using a combination of predictive and causal modeling techniques to deliver higher ROI

First, let’s get on the same page for the definition of predictive modeling and causal modeling. Causal modeling is used to understand what events or actions influence others. It is an estimation approach based on the assumption that the future value of a variable is a mathematical function of the values of other variables.  On the other hand, predictive modeling is the process by which a model that best predicts the probability of an outcome is created. However, when it comes to predicting human behavior such as clicks and conversions, predictive model has its limitations. At Komli Media, we use the combination of both causal and predictive modeling practices to overcome the limitations and best optimize a campaign. We call it “Predictive Causal Modeling”.

We use predictive modeling to target advertisements to consumers and we observe that targeted consumers purchase at a higher rate subsequent to having been targeted. However, we cannot say with certainty whether the advertisements influenced the consumers to purchase or predictive models did a good job at identifying those consumers who would have purchased anyway. So it’s reasonable to assume that both are true and accordingly optimize campaigns.  We use site map analysis of advertiser's web sites to identify audience segments. We then determine their purchase intent intensity using various user signals, through randomized controlled experiments. Based on these observational data we typically create two kinds of campaign strategies. One strategy that targets high intent segments which largely consists of consumers who would have purchased anyway. While the second strategy targets relatively low intent consumers to whom we then show advertisements multiple times, converting them to high intent consumers over time.

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When it comes to user behavior, historical data can be unreliable to predict the future. Any model based on historical data implicitly assumes that there are certain steady state conditions or constants in the system. This cannot always be true when involving people. For example, purchase intents change drastically when a flash sale is announced. What makes predicting accuracy difficult is that with more data there is more probability of noise. To overcome this problem, we use an in-house developed casual modeling technique of min/max bid price analysis. This technique removes tipping points from the predicted value of conversion that pushed the bid price over the edge leading to wastage of impressions. After filtering outliers or tipping points we significantly reduce the wastage of impression. This helps us improve margins across campaigns and deliver significant value to advertisers as we can now target a wider audience for the same budgets.

By combining the best practices of predictive modeling driven by prediction of outcome and casual modeling driven by observational data, we have significantly improved our capability to accurately predict the purchase intent of a consumer. It has helped us improve our margin and also reach out to a wider consumer set for the same budget. It has also helped to target relatively lower intent consumer and convert them to purchasers. After applying the casual modeling technique to predictive modeling we have observed that the number of new consumer’s purchase has doubled, providing evidence of the success of Predictive Causal Modeling techniques. 

Atul Shrivastava's picture
June 03, 2014
Atul Shrivastava

Leveraging data to drive higher ROI

Website browsing data helps you understand what your user was looking for along with the strength of his intent. In the previous post of this series (Data is the difference!), we saw different user data signals that can be extracted from your website or app. This post will focus on using those signals for crafting a personalized message and segmenting valuable customers for ROI optimization.

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Communication strategy using Contextual signal

The contextual browsing data signals like category, product visited, etc. can be used to devise a relevant communication message for the user. A customized and relevant message goes a long way in driving a positive response towards the ad. Few examples are:

  • Somebody who's searching for mandarin collar shirts or was found browsing multiple shirts of that type can be shown an ad showcasing other mandarin collar shirts in your collection matching the price range or color that the user was looking for.
  • Somebody who put a certain pair of leather shoes in the shopping cart (but abandoned the transaction) can be shown an ad with picture of those exact shoes saying "Your favorite shoes are waiting for you! Use this Coupon code XYZ123 to avail a special 10% discount and make them yours!"

There are two ways you can achieve this:

  • Manual method: In your remarketing platform, you can create segments for each category and target these segments to show the top 5-6 static creatives in their respective categories. Needless to say, this method is not scalable if the number of categories or subcategories in your product catalog is huge.
  • Automated method: This method is far more effective in delivering a personalized communication and is scalable to the longest of product catalogs. If your remarketing platform supports Dynamic Creatives, the task of Ad generation is taken over by its recommendation engine and is executed for each user in the runtime. Products similar or equal to the ones browsed or cart-ed by the user are filtered out from the product catalog (remember the inferred signals!) and are used to generate an ad specifically for that user.

ROI optimization using Intent signals

The intent signals help you tell an accidental visitor or casual onlooker from somebody who's seriously considering a purchase. These signals can be leveraged to spend selectively on the users (in terms of advertising cost or individual discount offers) and hence boost your ROI. Since most of the remarketing platforms use RTB to buy inventory from Ad exchanges, this translates into placing high bids on a strong intent user to win every opportunity of serving an impression to him and not bidding or bidding conservatively on someone who is not likely to convert. Few examples of selective bidding are:

  • Somebody who was seen on the website regularly (3-4 sessions) for the last 1 week, checking out multiple Items in the Bed category with significant time spent and page views per session can be considered to be deep in the research phase with high category coherence, contemplating a purchase soon and hence, should be bid at aggressively.
  • Somebody who put a certain product in shopping cart (but abandoned the purchase), or compared it with other items in the same category or added it to his wishlist is considered to be deep in the purchase funnel and should be bid at aggressively or even offered a personalized discount to give the final push.

Just like in case of contextual signals, there are two ways to achieve this:

  • Manual method: In your remarketing platform, you can create segments based on recent views, frequency, funnel stage etc. and set a different bidding strategy for each of them.
  • Automated method: If your remarketing platform supports Rule-based bidding, you can assign different bid-boost values to each intent signal like cart, recent views, category coherence etc, so that a user scoring high on multiple intent signals is computed a high bid value as well. Some sophisticated platforms leverage machine learning to compute dynamic bids for each user based on the intent signals. These predictive models decide the weight for each intent signal (in computing the bid value) by looking at the conversions and intent signals registered then.

Limitations of Browsing data based retargeting

This post cannot be complete without mentioning the caveats in browser data based retargeting. Since the entire user browsing pattern is tracked against a cookie here, this method suffers from limitations around cookie persistence and matching.

  • Few browsers like Safari don't allow third party cookie persistence, while some users surf in the incognito mode where cookies can't be set. Systems with high safety settings flush the cookies after every session, making the retargeting effective only within the session.
  • Cookies work in the silos of a browser i.e if a customer uses multiple browsers or devices to access your property, he will be treated as multiple customers and the unified view of his browsing pattern will be lost. This also means a cookie can be retargeted only on the same browser/device. However, the industry is trying to overcome this limitation with cross-device user identification which is essentially tying up multiple cookies/devices using keys like email id.
  • If multiple users are sharing the same machine, their browsing pattern gets tracked against the single cookie. The remarketing platform then thinks of them as a single user which compromises the efficacy of remarketing.

Conclusion

Contextual data signals can be leveraged to deliver a personalized and relevant communication for the user based on his past browsing behaviour. While intent signals can be used to optimize the ROI by spending selectively on your retargeted base. Creating segments is the easiest way to use these signals, but if your website has a huge product/category catalog or you want to use multiple intent efficiently without exploding the segment count, you should go for Remarketing platforms with dynamic creatives support and automated bidding controls. Cookie-based tracking used to capture these signals has some limitations which the industry is trying to overcome by using fingerprinting techniques or tying cookies with actual user account.