A couple weeks ago, we did a case study on how bidding affected volume. The case study showed that after some point, increasing the bid had very little effect in increasing volume. In today’s case study, we tackle the other question: does changing bids affect traffic quality? Higher quality can be defined in terms of higher CTR’s, higher CVR’s, and higher EPC’s, but at the end of the day, what we really care about is: did it increase the bottom line? We will attempt to answer that today, and I think you’ll find the results incredibly interesting.

It’s important to note that since these case studies are based upon our own campaign data (and we’re not using all campaigns), there will always be some sampling error, not to mention data variance from performance volatility. What I want to demonstrate are only general trends across multiple campaigns. Individual campaigns behave vastly different from one another, so it’s important to treat each campaign as such and not just apply the trends we find here to all your campaigns across the board.

How the Data was Collected

The data was collected using a tool we built internally over the last 5 months (starting on December 11, 2011). We keep track of individual campaign performance every single day and log the CPM bid the campaigns runs at that day. For our stabilized campaigns (upon which this case study is based), we never change the bid during the day. We are disciplined enough to only change the bid once in the morning if we are changing bids at all, and therefore, we are able to assign that day’s performance data to that specific CPM bid. We’ve been tracking this data long before I thought of this case study simply so we can target optimal CPM bid points for our campaigns.

I selected data from 25 of our campaigns to use as the data for this case study. These campaigns are diverse across multiple countries, niches, and angles, but they are all under 100 login count. I’ve also filtered the campaigns for insignificant data such as not enough impressions, clicks, or conversions. The minimum amount of impressions for a narrowly targeted campaign had to be 5,000 per day in order to make this case study. We were able to extract a little over 200 total bid changes across these campaigns over the 5 months, so that’s how many data points we have. About 120 were bid decreases, and 80 were bid increases.

Using this data, we looked at all the times that we changed bids during the last 5 months for these 25 campaigns. We looked at the effect when we decreased the bid versus when we increased the bid, the effect of the magnitude of bid change, and their impact on campaign CTR, CPC, CVR, EPC, and PPM, or profit per thousand impressions, which is translatable to ROI.

Data Analysis

Since the data can be confusing to interpret, I’m going to explain everything with the help of graphs. We’re going to evaluate the effect of change on each of the metrics mentioned above based on the magnitude of CPM bid change. To keep the graphs easily readable, I’ve put outlier data points out of view. In addition, it’s important to note that these data points are not weighted, meaning some campaigns that got fewer impressions received the same treatment as campaigns that got more impressions (i.e. they each got one data point).

Unlike the bid vs. volume case study, I’ve made a decision to not release the raw campaign data for this case study due to the fact that it may reveal too much information regarding where these campaigns are bidding and their respective returns. If you have any data-specific questions, I would be more than happy to clarify.

How to read the graph. The x-axis indicates the magnitude of bid change. This is ubiquitous on all subsequent graphs as well. ($0.05) would mean that we lowered the bid by 5 cents, and $0.10 would mean we raised the bid by 10 cents. As you can see there are a lot of data points that line up exactly at the 5 and 10 cent marks. This accurately illustrates that we often change bids by 5 or 10 cent increments or decrements. The y-axis indicates the CTR change in points. For example, if a data point is at the 0.010% level, then that means that specific bid adjustment effectuated a 0.010% increase in CTR. So, after that bid change the campaign’s CTR may have increased from 0.100% to 0.110%.

What this means. As you’ll find in all subsequent graphs, the data points are all over the place, indicating relatively high variance, which fortifies the point I made earlier in that one individual campaign can react to bid changes radically differently from another. If we take a look at the bottom right quadrant, however, we can see clearly that there are very few data points there, which indicates that generally speaking, increasing your bid increases campaign CTR. The trend line shows us a positive trend that seems to indicate increasing your bid by $0.17 will increase your CTR by 0.010%. This may not seem like much, but I know from our data that for certain campaigns it increased CTR substantially. Remember again that the data has high variance and are just general trends, and outliers are outside the range of the graph you see here.

How to read the graph. The y-axis indicates the % change in CPC from bid adjustments. For example, if a data point is at the 20% level, then that means that specific bid adjustment effectuated a 20% increase in CPC. So, that bid adjustment may have increased the CPC for that campaign from $1.00 to $1.20.

What this means. There is a clear trend between bids and CPC. Not surprisingly, increasing your bid increases your CPC. The CTR increase you gain from increasing your bid does not outpace the price you have to pay for it. Based on our data, in general, a $0.10 increase in bid effectuated a 20% increase in CPC. From the perspective of getting clicks only, you should only increase your bid if you want more click volume, but know that the marginal clicks come at a higher price.

How to read the graph. The y-axis indicates the CVR change in points. For example, if a data point is at the 10% level, then that means that specific bid adjustment effectuated a 10% increase in CVR. So, that bid adjustment may have increased the CVR for that campaign from 10% to 20%. Keep in mind that CVR could have moved for a variety of reasons. We can’t perform a perfectly controlled experiment, so we can only rely on the data we have.

What this means. This data set is very interesting. Of course, the data is again very scattered. Certain campaigns reacted positively to bid increases and some negatively. As a general trend, however, bid changes had virtually no effect on CVR. Based on the trend line, you can see a tiny positive trend as you increase bids, but in my opinion, it’s negligible. There’s been discussion in the past that higher bids gets you traffic that converts better. As a general trend, based on this data, not really.

How to read the graph. The y-axis indicates the % change in EPC from bid adjustments. For example, if a data point is at the 40% level, then that means that specific bid adjustment effectuated a 40% increase in EPC. So, that bid adjustment may have increased the EPC for that campaign from $1.00 to $1.40.

What this means. The trend indicated here is expected based on what we just found out regarding CVR. There is a slightly positive trend as you increase your bid. Based on this data, an increase of $0.20 in bid effectuated a 20% increase in EPC on average. The general positive effect certainly isn’t mind-blowing. You may have noticed that the trend line sits above the x-axis at all times, meaning that in general, we increased our EPC whenever we adjusted bids. This may have been caused by variance, but it might not have been an accident. Whenever we changed bids, it was probably because the current bid did not perform well for the campaign. We would change it to a bid that we thought would perform better. Therefore, there may have been some behavioral bias in producing the data, but the general trend should not be affected by this.

In addition, you can tell that across these campaigns, the rise in CPC outpaced the increase in EPC as bids were raised. This goes to show that, if you had no niche-specific or angle-specific knowledge, you should probably only increase bids if you are running enough positive margin that increasing volume may decrease that margin but will get you higher profitability.

How to read the graph. PPM is “profit per mile”, which is the profit received from each 1,000 impressions. The y-axis indicates the change in PPM from bid adjustments. For example, if a data point is at the $1.00 level, then that means that specific bid adjustment effectuated a $1.00 increase in PPM. So, that bid adjustment may have increased the PPM for that campaign from $1.00 to $2.00, doubling the profitability for every 1,000 impressions. This is a metric that is synonymous with ROI and measures profitability while controlling for volume.

What this means. Across these campaigns, changing bids really didn’t have too much of an impact on PPM. There’s even a slight negative trend as we increased bids. As a general trend, the higher bid equals higher quality is somewhat true: higher CTR’s and slightly higher EPC’s. Is it, however, worth the price? Based on this PPM graph, half the time it is, half the time it isn’t. Lower PPM, does not mean, however, lower overall profit! PPM takes out the effect of volume. It seems the general trend based on two case studies, then, is if a campaign is profitable and bidding low ($0.50′s or below based on volume vs bid case study) and has a relatively high ROI, it may be worth testing higher bids to see if you gain additional volume and see if that volume is worth the incremental cost. For the same campaign, if there isn’t much more volume above the bid it’s currently at, then there is less of a reason to bid higher since it doesn’t seem like the increase in quality is worth the cost.

Take-Aways

As a summary, our data indicates that increasing bids generally increases CTR, but not by a huge amount as an aggregate. We did experience high variability in all data collected, which means certain campaigns did increase substantially. CPC increases in tandem as CTR and bids increase. There isn’t as much of an effect on as bids increased. There is a slight positive trend with EPC’s, but the magnitude seems to be negligible. The increases in EPC was outpaced by the increases in CPC, and even in combination with the increases in CTR, the overall PPM was generally unaffected to slightly negative as bids increased.

The biggest take-away has to be that each campaign is so different that you really have to test each one. While general trends shown here may not point toward raising your bids across the board, you can see that the results were highly varied and campaign performance increased substantially half of the time. Over time, you may start to see the trends for specific niches and types of campaigns, at which point you will just have fewer data points you need to test. The most important thing is trying different things and actually collecting the data. It may seem, based on the PPM graph, that we’ve been fiddling around with bids the past 5 months and none of it mattered for these campaigns. I can assure you, however, that all of these campaigns are more optimized, and that’s because we were diligent in collecting all the data necessary to make better decisions. When something didn’t work, we were able to see that and change it. Nothing genius here. Collect data and optimize to maximize overall profitability, which is the only metric that matters!

Hope you guys found the case study interesting and useful!

As most of you know, we have been hard at work on a POF management/optimization/automation platform for the past several months. It’s taking us longer than expected due to the quality of the tool that I want to come out with. It will include 90% of the requested features that I’ve ever gotten, and I promise it will be an extremely useful platform and game-changer for POF. I’m not just referring to beating out your competition. I’m talking about monetizing your current traffic more efficiently so that the traffic source as a whole will return more to affiliates.

You will have the ability to market on POF from one, self-hosted location. No more exporting reports from multiple sources. You will be able to upload campaigns efficiently, track and generate reporting on all fronts, split test offers and landing pages with ease, automatically schedule day-parting, and much more. And we’ve found an official name for all this goodness: POF Pro.

I just wanted to reassure you guys that we are getting closer and closer to a beta on this since I know it’s taking longer than expected. To give you a sneak peak, I’m posting 2 initial concept designs that we’ve been through so far. The end product will probably look way different than these (i.e much better), but it’s a sneak peak that will hopefully satisfy you guys for the interim!

PS, thanks for the great response to the volume case study last week. We’ve been working on a bid vs. quality case study that I will hopefully be able to have up next week. Stay tuned!

POF’s self-serve platform implements a CPM-based bidding system that does not take into account incremental bids, meaning what you bid is what you pay. If you have even run a few campaigns on POF, you must have wondered what a higher bid actually buys you.

We know that a higher bid is supposed to give you more volume and higher quality traffic (i.e. lower session depth traffic), but what is the magnitude of the effects? In this case study, we tackle the volume question. We’ll save quality for a later date.

The results were very interesting: there is a price point where major volume drop-off occurs and a high price point where further increases could be achieved again.

Here is the data, followed by our interpretation:

How the Data was Collected

The data was collected using the POF Traffic Volume Monitor over an 8 hour period from 12pm CST to 8pm CST on a Saturday. We used 4 different campaigns. These campaigns weren’t broad campaigns but they aren’t targeting micro-niches either. They all had frequency caps of 3 per visit and distributed traffic ASAP.

Bids were adjusted between $0.21 and $1.01 by increments of $0.10 every 15 minutes. When the campaigns reached $1.01, we would lower the bid down to $0.21 for the next 15 minute interval. Since we were testing bid prices over 8 hours, performing the test in this manner controlled somewhat for the variation of traffic over time of day.

Since we didn’t perform the test for days and days, there was some volatility over a smaller sample size as manifested through the non-smooth line, but all we’re looking for is a general trend.

How to Read the Data

Looking at the table, the “CPM Bid” of course indicates the CPM price points we tested.

“Avg Ims Per Hour” is the average number of impressions gained per hour at those bid levels.

“Marginal Imps Per Hour” is where the data starts getting interesting. It refers to the incremental amount of traffic gained from increasing the bid by $0.10. For instance, when bid was increased from $0.21 to $0.31, our data showed that the campaigns gained 1,936 impressions on average.

“% Traffic Increase” is the magnitude of marginal traffic received. For instance, when bid was increased from $0.21 to $0.31, traffic increased 127.5%, or more than doubled.

“Eff. CPM of Marginal Imps” refers to the effective CPM of the additional impressions received by raising the bid. For instance, when bid was increased from $0.21 to $0.31, the campaigns gained 1,936 impressions per hour on average. Those 1,936 additional impressions effectively cost us $0.39 per 1,000 impressions, since we could have gotten the first 1,519 impressions by only spending $0.21 per 1,000 impressions.

“Marginal Cost as % of Prev” refers to the increase in cost for the additional traffic gained by bidding $0.10 higher. For instance, when bid was increased from $0.21 to $0.31, the 1,936 additional impressions we received cost us 85% more than the first 1,519 impressions.

What does the data tell us?

We can see that there is a huge boost in traffic going from $0.21 to $0.31. We still see pretty good increases in volume up to $0.51. We still get small gains from $0.61 to $0.71, but we really don’t see any incremental traffic as we bid $0.71 and higher. That is, until we get to over $1. At $1.01 we see another increase in volume. We did not test any bids higher, but my hypothesis is that there is a clear divide between the average performing and broad-targeted campaigns, which bid in the $0.50’s and lower, and the niche-targeted, higher CTR campaigns, which bid over $0.70. Once you’re bidding over $1, you start to leave the majority of the pack behind, and the campaigns start to see more volume, beating out a majority of the campaigns. From this data, it looks like you shouldn’t be bidding higher than the $0.50’s unless you can bid over $1 as you start to see very little gain in traffic (only 4.9% more traffic from $0.51 to $0.61).

Let’s also take a look at how much the additional traffic cost you when you increase bids. As you can see, the cost goes up exponentially. While additional impressions received by increasing bid from $0.21 to $0.31 cost only $0.39 effective CPM, by $0.61, the extra traffic you gain from increasing from $0.51 cost us $2.64 effective CPM. By $0.81, it got as high as $30.46 (yes, that’s effectively $30.46 for 1,000 impressions), which clearly indicates to us that increasing from $0.71 to $0.81 just isn’t worth it for our campaigns.

Based on this data, the general takeaway seems to be to stay in the $0.21 to $0.50’s range unless your campaign truly justifies a high bid greater than $0.91. Remember, we have not taken into account the quality of traffic and its effect on campaign performance, so when you make decisions for specific campaigns, you need to analyze the overall and marginal profitability of those campaigns. What we’ve uncovered here, however, can be used as a general rule.

We hope you found this experiment interesting and helpful. If you’d like to download a copy of the data from this case study, please do so below. It will give you a good idea as to how to go about doing similar analyses for your own campaigns, if you so choose.

Hey guys, this is Aziz, and this is my debut post on the blog. I hope you guys enjoy it!

 

If you’ve ever had the chance to check out the ads running on POF, I’m sure you’ve noticed that 99% of the ads you see have the typical image-on-the-left, copy-on-the-right placement. Why is this the case? This widespread phenomenon in creative layout is molded by 2 commonly accepted principles in the marketing industry. The first is called the Gutenberg Rule, which states that when people scan a page of content, they scan left-to-right and top-to-bottom, making the top left area the focal starting point. The primary optical area is where the content needs to convince the viewer that what they are about to read or click on is going to be worth their while.

Along with the Gutenberg Rule, experienced marketers will tell you that a successful creative weighs largely on the effectiveness of your image to attract attention (50-80%), followed by your headline (10-30%), and lastly your body copy (5-10%). Simply put, according to studies, an ad will get more clicks when it includes a powerful image, placed on the left of your banner since it is the most effective when used in the primary optical area in order to get the viewer to invest more time in the creative.

In an endless sea of left-justified image placements in creatives, I started wondering what would happen if a few images defied tradition. So I decided to take that idea and make it into a POF case study with our little friend, the 310×110. First, I grabbed a batch of images that performed well in a previous campaign that targeted women age 18-21. Preserving the same targeting, I set up 3 templates: one with an image on the left, one with an image on the right, and one with 3 images across the banner. Finally, I ran the ads until each creative had at least 10,000 impressions and kept the top 2 performers from each template.

Case Study Creatives

The Results

It seems that contrary to widespread usage, left-justified image placement didn’t always produce the best CTR. We were actually pretty surprised at the clear divide in this case study. Images placed on the left performed the worst, while the images on the right got a slightly better CTR, leaving the 3-image placement as the victor.

Keep in mind that the above creatives were targeted toward one specific demographic and had collected less than 15,000 impressions each. Across data greater sample size and wider range of demographics, the results could certainly vary. What we can learn from this little case study is to never stop challenging the norm and thinking outside the box. While I still believe that the aforementioned principles hold, much of marketing is about differentiation and standing out from the crowd to catch the attention of the traffic that suffers from technology ADHD.

What are commonly accepted principles are rooted in logic and are time tested, but the magnitude of potency depends on many factors. In the world of POF, the principle of left-justified images suffers from overuse, which is especially noticeable in POF small ads that are shown 3 at a time side-by-side.

While the advertising industry seems to accept that left-justified image placement is always the most effective, I took the “test for yourself” mentality, and I urge you to step back and do the same wherever you’re advertising.

We used 310x110s here, but what would be interesting would be to see how mimicking 110x80s but testing image placement (left vs. right vs. middle) would fare in a similar case study.

If you’ve tested image placement or went against the grain another way, please share your results with us!

As a follow up to the post a couple of weeks ago on creating your own image variation packs for split testing, which some of you guys had a little trouble making the variations, we decided to put together some ready-to-go variation that you can apply to batches of your images using Photoshop actions. All you do is open up the actions using your copy of Photoshop and apply it to a batches of images on your hard drive.

We have a lot of these and have chosen some basic ones to get you started. These are by no means to guarantee you instant profits but to help get your thinking hats on with ideas to split test variations of images and ads that are already working. From our experience, you really never know what works, and the correct simple border could make a world’s difference. So, you can never test enough. At least for us, we are never going to wake up one day and think that we’re done testing.

If you haven’t already, you really need to watch the Photoshop Batch Processing tutorial. Not only will you totally understand how to use these actions, you will be able to edit the variations from this free pack and customize them. For example, you can edit the widths or the colors of the border variations included here.

Since this variation pack was put together by our very own Aziz Kamara, I’ll let him give you guys a quick guide to the usage of the download. Also lookout for some posts in the coming weeks from Aziz as he makes his posting debut on the blog.

Aziz’s Guide on How to Apply the Variation Pack

Hey guys, this is Aziz. In the zip file, you’ll fine Actions, Samples, and Shapes. The Samples give you a sample of all the variations that we’ve included in the pack and what they look like. The Actions folder contains all the kinds of variations you can apply. The Shapes folder you can largely ignore, but it’s just different cut outs that the Actions use on the images. This variation pack can only be applied to 310×110 or 110×80 images. Certain actions can only be applied to one of the two, and they are clearly labeled by their file names. You’ll see when you take a look in the Actions folder.

Here’s how you easily apply one of the variations in Photoshop:

1. Simply double click on any of the available variations in the Actions folder.
2. Have the batch of images you want to apply the variations to in a single folder.
3. After opening the Action file in Photoshop, Go to File > Automate > Batch.
4. For “Set”, pick the Action that you just opened.
5. Then, for “Action”, pick the sub-type of the Action that you want run.
6. For “Source”, choose the folder that you have your images prepared.
7. For “Destination”, choose the folder that you want the new image variations to be saved in.
8. Finally, hit “Ok”, and your variations should be created automatically.

Talk to you guys soon!

One of the most frequently asked questions: Should I build a landing page or direct link?

From my experience, I’ve rarely had properly tested landing pages perform worse than direct linking. This goes back to who’s good at what. Typically, an advertiser’s expertise is in their product and niche industry, which is the precise reason why they are paying you to generate leads for them. They are not specialized in marketing, and for that reason, the offer’s original landing page probably isn’t optimized to get the conversion. Keep in mind that the advertiser also has to keep their landing page generic enough so that it caters to the spectrum of traffic that is sent to them.

As a marketer, the biggest value add you can provide is creating a sales funnel that converts. The part that you can most effectuate this is through landing pages, particularly because you can create landing pages that cater to specific niches and play specific angles to sell to targeted traffic. So, to become a marketing force to be reckoned with, you absolutely cannot avoid the landing page. You can only hide for so long until you realize you need to buckle down and get this right.

For dating campaigns, if you are using generic offers (e.g. True, Mate1, etc.) and trying to sell them to niche demographics, then a landing page is an absolute requirement to do some intermediate selling. Going from an ad targeting Jewish men that jumps directly to True’s landing page isn’t going to do well. Instead, if you present it as a site where they need have a need for Jewish members and show them a bunch of profile pictures of other Jewish members of the opposite sex, then when they click sign up, they are only focused on the form that pops up and the sales pitch is still in their mind.

Even with niche dating sites, from what I’ve seen most of those (e.g. Jewcier) are not high performing landing pages. I am always able to eventually increase conversion with a landing page. As you gain experience through repetition and seeing what works and what doesn’t, creating working landing pages gets easier and faster. Being able to produce high performing, consistent landing pages is a difficult, but highly valuable, skill in my opinion and will set you apart greatly. It takes a lot of time and trial and error, so don’t give up if your first 10 landing pages didn’t work as well as you’d hoped.  You’re building your competitive advantage.

The Hook, Then the Sell

I’ve gotten pretty tired of seeing ads that are really really generic. Ads that seem like they use some mashed up copy taken out of a book but do not attack the user’s attention are a sure sign of a newbie marketer. When you think of marketing something generic to a group of people, you’ve got to have a gimmick or “hook”. Something that’s different and arouses a sense of need, of urgency, happiness, etc. When making the ad, read it and ask yourself, “Does this interest me if I were seeing this?”

For POF, you need to create the initial ad to hook your traffic, but the landing page is where you SELL. You need it to bridge the gap between your ad and your hook to the page where you get them fill out their personal information. I mean think about what that really takes: to have a people fill out multiple fields of information about themselves on a consistent basis. The person who clicked has given you a tiny tiny piece of mind and if anything seems at all inconvenient after they click, they’re gone. One click is not much investment for them to let go.

However, if you get them looking at a landing page, and taking the time to READ through your landing page. And then they click AGAIN, well, they’re a lot more invested. Then it comes down to whether they’re interested in taking a chance with their information. To get a sense of what it all feels like from their shoes and get some ideas, survey your competition. When you’re browsing on the internet, actually notice the ads, click on them, and follow through to see what you can learn from it. If advertising through social sites, sit in a spy account and really analyze what marketing techniques work or don’t work from the ads you see. Click on them and see where they lead you. Then, revamp your campaign with a hook, or test a few different hooks. Lastly, absolutely obliterate the landing page. Split test a landing page with direct linking as the control variable.

Some of this stuff should be common sense, but we all need a flick on the head and kick in the butt to remind ourselves once in a while.

In addition to the little POF trick I’m going to demonstrate in this video, I also show you how to apply multiple image variations to any ad, be it POF or Facebook. Through Photoshop Record Actions and Batch Processing, you can create your own library of border, or any other type of, variations that you want to apply to your images for testing by recording and saving those actions. If you’re too lazy to make your own variations, then check out CrazyCTR or FB Ads Tool Box.

For people who are relatively new to POF, I’ve started to recommend to do 110×80 ads first, making some profitable campaigns with those first, then diving into 310×110 ads. I do believe that 310×110 ads have more potential if done correctly, but that also means in the hands of the inexperienced, it could do more harm than good. 310×110 ads tend to burnout a lot faster if they are very distinct. The design of the banner almost overwhelm the impact of the image, which is what you usually consider to be the burnout factor. I’ve seen a lot of 310×110 ads that are just over the top, over-designed, and too flashy. Marketers are more constrained by 110×80 ads, and the style of those blend in well with the rest of POF. I would even recommend that one use a couple different designs in a campaign as well as test 110x80s with the banners.

After you’ve seen some success with 110×80 ads, you may want to ease into the use of 310×110 ads by adding a border, changing some colors, or adding some flair. To do that, you should first replicate your successful 110×80 ad into 310×110, starting adding small tweaks to it, then start testing. This way, you’re able to start seeing what kind of elements of 310×110 ads can make your ads better. In today’s tutorial, I show you how to replicate an exact version of any 110×80 ad you would want to create into 310×110. Further, I show you how to apply some border variations to these ads via batch processing.

It was SUCH a hassle to switch the site’s theme, but after a few days of hard work, it’s finally live. This new theme runs faster, looks better, and is easier to read. I’m personally very satisfied. Change is good. What do you guys think? Are you a fan?

I realized a week ago that some of you were having some trouble posting comments on the old site. I’m really sorry that some of your comments didn’t go through on multiple occasions! We’ve since switched over to Disqus for comments so they will definitely go through from now on.

Unfortunately, due to the theme switch, all the like, tweet, and plus counts were reset . Please contribute in spreading the word by going to your favorite posts again and clicking on those social buttons. That and re-posting your comments would go a long way.

Recent POF Changes

POF has been extremely volatile recently. The team over there started tinkering around with when users see ads beginning around Valentines last month, and the changes have yet to fully manifest and stabilize into some sort of clear pattern. If you are new to POF, this might not be the time to start testing your first campaign.

Essentially, they have made a change to when new users begin seeing ads after registration. POF’s thought is that users who see ads too early before they’ve fully invested time into POF end up spending too little time on POF before leaving the site. They believe that if new users are focused only on the benefits of POF in the beginning, they will become higher quality users who stay and interact with the site longer, hence providing POF with more impressions to deliver.

New users, however, produce the highest ROI for POF marketers. A main reason of that is brand new users are not used to POF’s interface, and they will click on anything that interests them. An ad has an easier chance of drawing them in when they’re new, hence resulting in higher CTR’s in the login count < 50 demographic. Ads will now be shown much later, when users have gotten relatively used to the POF interface. Whether that has an impact on when they first see ads is still up in the air. They could do just as well.

We haven’t been able to prove with certainty what the criteria is for ads to start appearing to new users. The criteria is not based on a specific login count nor number of days logged in. For example, several accounts we created between 3/7/2012 and 3/14/2012 all started seeing ads on 3/18/2012, with varying login counts and days logged in. We’ve also had accounts that were created on a weekend start seeing ads right away. We’re not sure of the exact criteria, but from our tests, it seems that it could take up to 11 days or so for ads to start showing. Our tests also show that ads could start showing up for profiles created a day ago or even same day. The only conclusion we can draw right now is that it varies, but typically, it will take 4 days. If you have any insights, please share!

Fundamentally, this is an attempt by POF to manufacture more impressions while possibly sacrificing small segment of impressions that produce the highest ROI. The long term question, then, is: will this potential increase in impression quantity trump the potential sacrifice of this quality? Time will tell as marketers adjust their bids to accommodate the changes and to maintain profitability. One thing is for sure though: the interests of POF and the marketers are aligned. So, as long as POF remains a sizable traffic source, I have zero worries that there will be money to be made even though the changes are a pain.

What to do in the meantime? The recent changes mostly impacted campaigns targeting login counts < 50, since most users probably are seeing ads in the > 50 range. If your <50 LC campaigns that were consistently profitable before are not anymore or are seeing low volume, duplicate that campaign and test the 50-100 login count range. Compare the results. If it's still consistently losing money, then it may be time to start testing campaigns under the new scheme. Since the change does affect user behavior, certain campaigns just might not be viable any longer.

Analyze Multiple Campaigns in Campaign Viewer

We’ve just updated the POF Campaign Viewer with the ability to now analyze multiple campaigns at the same time in the same spreadsheet. This will work, however, only if you have unique keywords in the campaigns you want to analyze (i.e. your P202 or CPVLab report must not have duplicate keywords pointing to different ads). In addition, it can now handle dynamic tokens in your click-through URLs. Just select “Yes” for “Use Creative ID”. Please consult the instructions accompanying the tool to make sure you’re using it properly.

As users have reported bugs, we’ve been updating the other tools to fix them as well. If you’ve experienced a bug in the past with any of our free tools, it might be worth your while to re-download those tools to see if they work now. If any of the free tools still don’t work, we are always here to try to fix your specific bugs as long as you are able to provide us the debug screenshots.

Upcoming Private Beta for Mobile Uploader

We’ve been working hard and making great progress on a mobile uploader, starting with uploading to JumpTap. We are going to ask a few mobile guys to test a private beta in a few days. As soon as things seem to run along smoothly, we will open for a semi-public beta. If you run volume on mobile, or know someone who does, and are interested in helping us put forth a great product to serve the industry’s mobile needs, please contact me directly.

In a previous Excel tutorial on pivot tables, I showed you how you can easily analyze data when you have one large complicated table. What if, however, you had 2 tables?

As marketers, we work with multiple networks, tracking platforms, and traffic sources. They each generate their own reports. It’s always been a hassle to try to line up multiple reports. For those that are not familiar with Excel formulas, it can be pretty frustrating.

This tutorial will make that task a lot simpler. I show you, using POF and Prosper202 reports, how to quickly join the two reports so you can see the all the performance metrics by ad. This technique, of course, can be applied to any traffic source and, in reality, to any task that involved combing data tables.

If you haven’t checked out our previous Excel tutorials, I highly recommend you do so before beginning this one, at least the previous pivot table tutorial. As a marketer, I’ve found Excel to be an instrumental tool for data analysis. It’s a skill that will surely benefit you in the long run.

Without further ado, please enjoy the video!

If you’ve been in the marketing game for quite some time, you’ve probably built up a huge image library over time.  As this collection gets bigger and bigger, it becomes very difficult to find the images that you need when you need them.

Personally, I have a very specific system of organizing my own images.  Each unique image is assigned a serial number.  That serial number’s purpose is so I can use it in click-through URL’s and keywords in order to track its performance across multiple campaigns. This way, I can see what images have performed the best across my entire portfolio as well.

In addition to assigning serial numbers to file names, I also utilize the power of descriptive tagging to categorize the images.  For example, if I wanted to find Latin men, I am able to filter out only the images that I have previously tagged as Latin men.  This eliminates so much wasted time in trying to locate the correct folder, if that’s how you’re organizing images currently.

The tool I use to tag and organize my images is Windows Photo Gallery.  I’m sure there are a ton of similar free tools for both Windows and Mac, but I’ve found this to be adequate.  Here’s a video on how I use it:

When tagging images, I have 3 must-have tags.  These are tags that I put on every applicable image because these tags should be universal when it comes to images of people.  Obviously, this doesn’t apply to animals and everything else.

1. Age.  I have 3 categories that I use to identify age: young, middle age, mature.
2. Gender.  Pretty straightforward: either male or female.
3. Ethnicity.  It’s important to tag by ethnicity for future campaigns that target specific niches.  Sometimes I input multiple ethnicity tags if the person can pass for multiple ones.

Outside of the 3 must-have tags, it’s always better to include as much of the special attributes of the image as possible.  If the person has tattoos, is a Christian, is a hipster, is muscular, has a uniform, or anything else that you may want to search by, I will include those in the tag.  Just make sure to keep your descriptions consistent and not use “single dad” for one image and “single dads” for another.

You can spend a lot of time thinking of tags for each image.  Don’t burn your time here.  Do the best you can quickly and move on to the next image.  If you already have a huge catalog, it might be worth it to come up with a list of tags you want applied to the images and outsource the tagging on oDesk.

Do you have your own tips and tricks for organizing images and other digital media?