Saturday, January 31, 2015

Keeping up with digital - My Feedly reading list for analytics and digital

Just thought of sharing my Feedly list here to let everyone know how I try to keep up with all the articles that come out. Here goes:

Web Analytics:

  • Occam's Razor -
  • Justin Cutroni -
  • Google Analytics Blog -
  • Cardinal Path -
  • Adobe Digital Marketing Blog -
  • LunaMetrics -
  • Online-Behavior -
  • KissMetrics -
  • Web Analytics Demystified -
  • UX Movement -
  • UX Booth -
  • FastCo Design -
  • Search Engine Land -
  • Moz -
  • Search Engine Journal -

Sunday, November 9, 2014

Does Social Media Drive Conversions?

A little while ago, an acquaintance expressed concern that social did not really work for the brand in terms of conversions. At first sight, it might appear to be the case. However, what’s required is a deeper look to understand how channels work together to deliver assists or drive conversions.

How Is Conversion Reported and Why Is It Important To Understand Attribution?
The standard Conversions report uses the last-click model to account for conversions. Making marketing mix decisions based on last click models could become problematic if the budgets are skewed towards particular channels.
I couldn’t find a simpler and better analogy than this lightning of a goal scored by a certain Real Madrid player in the Copa Del Rey 2014 final against Barcelona.
Watch this clip for the first 30 sec (or more…) and answer the question:
Who scored the winning goal for Real Madrid in this match?

Easy one. Gareth Bale!
Now what if the question was tweaked to:
Which Real Madrid players helped score the winning goal in the dying minutes of the final?
Yep, you’ll get a very different answer….

Messi’s cross gets intercepted by Carvajal. He heads it off to Isco, who does a short pass to Coentrao. Coentrao sees Bale and send off a through ball. Bale gets the ball and makes the run of his life to score and probably justify the heavy investment in him by Real Madrid.
Think about your marketing efforts for a bit. You could almost replace the player names with channel names (and investment with your marketing budget) and have a similar scenario where a standard last-click conversion report always paints channel X as the hero. The truth is that convincing people takes time (and sessions). This is the same reason why return visitors almost always have a much higher conversion rate. When you look at the complete effort required in making that conversion happen, you would agree that the assisting channels are not too shabby after all (in the case of soccer, making a defender/midfielder’s job not so thankless…).

Understanding channel contribution is all the more important for “soft” channels such as social due to the possible disconnect in timing between the brand’s content and the customer’s needs. A first time visitor to the site who lands via social media might not be willing to make a purchase immediately, but could agree to sign up for a newsletter and probably convert at a later stage in the future.

Exploring Channel Funneling
A good starting point in understanding social (or any other channel) attribution is the Assisted Conversions Report in Google Analytics under Conversions > Multi-Channel Funnels. For each of the channels listed, the key columns to look at are Assisted Conversions (all assists except last click), Last Click Conversions and the ratio of these two metrics.
Assisted / Last Interaction Conversions Ratio helps explain the importance of the social channel.
·         A ratio of higher than 1 implies that social as a channel helps more with assists than actual conversions based on last click.
·         A ratio of 1 means that social equally serves in assists and last click conversion.
·         A ratio of below 1 is when social directly drives your sales than assists.
Knowing this piece of information is perhaps the starting point of deciding how much credit to give to your channels. Attribution modeling is a topic on its own and has been beautifully explained by Avinash Kaushik in this post. The key is in deciding the level of importance to give to the interactions that take place via different channels on the path to conversion.

If you’re not working in analytics, walk up to your friendly analyst and start a conversation around this topic. Hopefully, you will find some nuggets of gold related to social channel that will help in appreciating the true value of social at your brand. The truth is that social can serve differently for brands but the only way is to start digging out such data to help make more informed decisions. 
What are your thoughts on this topic? Does social media truly work for your brand?

Monday, October 27, 2014

Two Improvements Analysts Would Love To See In Facebook Advertising

Even though I love Facebook Ad Reporting tool for exporting just about any campaign related data, there’s scope for improvement. Any brand using Facebook Advertising understands the importance of segmentation – not all Facebook users are equal and not even all Facebook fans equal either. The key here is segmentation – the more granular you can get, the more differences you’ll see in behavior for key metrics such as Cost Per Reach, Cost Per Click, Cost Per Engagement etc.

Keeping this in mind, here are the two improvements that analysts could benefit from:

#1 – Ability to segment Connections Targeting under Data Breakdown
Connections Targeting is crucial as it helps determine whom to target (or not) i.e. fans, friends of fans, users not connected to the page. Each of these segments exhibit a very different pattern in terms of engagement rate, the type of engagement and the cost per reach/engagement. In my experience, it’s almost an inverse relationship between the cost per reach and engagement for these three groups. Reaching fans is the costliest (as it’s very targeted) but it also produces the highest engagement rate out of the above three groups. Moreover, users not connected to your page are more likely to like a post while fans and friends of fans are more likely to comment or share on content.

If you were planning post promotion for your next post, you would ideally want to know the pattern and how to allocate the total budget between the various connection targeting options possible. However, this option can only be utilized if you have already created separate ad sets for each of such connection targeting options. By doing so, you can rank the cost per reach, engagement, website click, video play etc. So, if you ran a post promotion for 25-44 and chose ‘All’ under Connection Targeting, you cannot retroactively find out how different groups behaved with your content.

#2 – Ability to know Negative Feedback for Advertised Content
If you’re onboard with point #1, then you’d also want to know the actions taken by users after seeing your ad (any type of ad content). Within the existing reporting tool, there are plenty of metrics that you can download depending on the type of your content. It could range from page likes to post engagement to conversions via Facebook. It can then be broken down by the existing options in point #1 to find out the best performing segments.

What’s missing right now is the Negative Feedback (hide post, page unlike) that you can get for the entire post from Facebook Insights. For any paid content I run on Facebook, I’d surely want to know the kind of paid content that triggers a negative reaction from the user. Example, if friends of fans feel overwhelmed from promoted posts in their newsfeed and end up hiding the post, I would want to keep that in mind for the upcoming post promotions. There are way too many possibilities to list here but the idea is clear. Avoid the low performing ones (or negative performing) and go on with the positive performing ones.

What do you think can be improved within the existing reporting tool to help analysts make better decisions?

Wednesday, October 8, 2014

3 AdWords Video Optimization Tips To Improve View Rates and CPV

When it comes to any channel, segmentation is the key to understanding the differences in performance with regard to the peaks and valleys in trends.

As you'd expect from AdWords for Video, there are several ways in which data can be segmented to bring out the best in your campaign. If you don't have any data available from past campaigns, it'd be best to gather statistically significant data and then turn on the optimizations.

1. Segment Performance By Hour Of Day and/or Day Of Week

While it's currently not possible to create a pivot out of both these variables, the next best option is use them independently to find out patterns in viewership. When are users more likely to watch your content )(especially when it comes to pre-rolls via In-Stream Ads?

In my experience, both of these especially 'Hour Of Day' for shorter duration campaigns can help understand how your ad fits in between the busy lives of your audience. Is your ad viewed more during late evening / early morning hours (or not) when your audience is unwinding?

View Rates can be laid out by the hour (0 for midnight, 1 for 1:00 am...till 23, 11:00 pm - midnight) and be judged against the average view rates for the campaign. This would result in any of the three possible scenarios:
  1. Hours during which View Rate = Average View Rate
  2. Hours during which View Rate > Average View Rate
  3. Hours during which View Rate < Average View Rate 
As you can imagine, the third possible scenario will likely have higher CPVs and therein lies the opportunity to pause campaigns during low performing hours. This helps you improve your Avg. View Rate along with decreasing Avg. CPV (win-win!)

Note: In trying to show ads only during certain hours, care needs to be taken that the audience is not over-exposed to impressions. While this might not be an issue when targeting a wider demographic, capping impressions (on a per day/month basis) could helpful.

2. Making The Best Of Video Remarketing

While this is definitely a topic on its own, Remarketing in AdWords For Video is very interesting to me based on the options offered. 

The first thing to do is to come up with a list. This list can bucket users who have either visited your website (placing remarketing tags on your site), or visited your channel, seen an ad/vdieo, engaged with your channel etc. The full screenshot is below. The key idea is that remarketing cookies need to be placed on user devices to be tagged as individuals who have shown interest in your brand via various means. 

Once you know whom you want to remarket to, the list needs to be populated with users (minimum 100) for it to be used as a target. 

Think about the original ad for Mio Squirt ("Eye of the squirter") and how annotations were used to create back stories via separate videos. 

Remarketing the backstory to audiences who have viewed the main ad would most definitely result in our win-win (Higher View Rate / Lower CPV)!

Another example is how Crazy Egg realizes that I have visited their website in the past and shown some interest in tool. As a result, I have been targeted on YouTube (on a few occasions) to watch their pre-roll ad containing an introductory video to Crazy Egg and the product benefits explained. Suddenly, the content becomes much more relevant and makes me likely to pay attention to the video content. 

3. Segment Performance By Format and Network

Starting out your campaign, you might not have sufficient data on it but once it starts rolling in, it's a good time to checkup on Ad Formats + Network to see the difference.

Ad Formats can be In-Stream / In Display while Networks can include YouTube Videos, Search and GDN.

Using the screenshot option to breakdown performance metrics by these factors helps us understand our win-win targets. Is the View Rate much higher for In-Stream vs In Display (Note: In-Display counts a view after it has been clicked on while In-Stream counts it after 30 secs) or is there any significant difference in the CPVs?

How this helps is because Max CPV bids can be customized (increased / decreased) for the two formats based on information that we have now analyzed. Again, the two key variables that we are looking to improve are the View Rates and CPVs.

What are your ideas for optimizing AdWords For Video? Do let the readers know via comments.

Saturday, August 23, 2014

Difference Between Boost Post and Promoted Posts In Facebook

Since both options are available in Facebook, thought I'd do a quick post on them and what are the pros/cons of each. 

Option 1: Boost Post
Boost post allows admins to quickly promote a post from the post itself or from the Insights > Posts tab.

Option 2: Promoted Post
Requires ad managers to manually enter demographic targeting for posts. This obviously takes longer in setting up.

Here's a quick summary of what are the main differences and why Promoted Posts option is much better than Boost Posts.

Criterion Boost Post Promoted Post
How to find it? Insights > Post tab Ad Manager / Power Editor
Duration Maximum 7 days None
Ad Placement Control  None Desktop Newsfeed, Mobile Newsfeed and Right Column
Interest Targeting None Keywords can be chosen
Behavior Targeting  None Digital Activities, Mobile users and Travel
Connection Targeting Fans and friends or demographic targeting used in posts All users, People connected to page (or not), Friends of fans
Bidding Options None For engagement, Clicks or Impressions
Ad duplication for multiple posts None Ad details can be copied and used for multiple posts

Here's a longer version of the main stuff from above:

Ad placement control: Having tested Right Column ads in Facebook, I think that paying for these would not be wise. It's almost as if users develop blindness to right column ads (on Facebook or other sites as well). If in doubt, you can always sort your post promotion data by placement to see check how your past promoted posts have performed (CPM, CPE etc).

By choosing Boost Post option, you lose control over where your post gets promoted. Under Promoted Post option, you can always decide if you want your promoted posts to appear on (desktop or mobile) Newsfeed and / or Right columns.

Interest / Behavior Targeting: Interest Targeting allows ad managers to choose keywords that may be directly related to their brand. Behavior Targeting helps choose particular mobile OS, device manufacturers among other options (think promoting a post to competitors).

Connection Targeting: This option lets Promoted Posts have a big upper hand on Boost Posts. The former allows users to create separate Ad Sets for friends, friends of fans, People not connected to the page, All and then monitor performance of each Ad Set (Also, because Ad Manager cannot separate ad data to show performance levels for such connections...Creating different Ad Sets is the only way out!). 

Boost Post has only 2 options: Promoting to all friends and fans OR using demographic targeting (applied in a post) to show ads to that audience.

Hope this post was helpful in understanding the difference between Boost Post and Promotion. Looking at the two options, Post Promotion is definitely the way to go for Ad Managers and allows for narrower targeting and detailed performance review.