- In the end TVs will just be a higher fidelity outlet for all your existing content. All content will live online and you’ll choose when, where and one which device to watch it. Pick up where you left off on your phone on the way to work where you left off last night on TV.
- The antiquated way Nielson tracks viewership will finally be called out. Real time viewership stats of shows will be available for all to see. Sort all live content by most viewers.
- Clickable credits. If you’re going to show me who made the show then make their name a link to other stuff they’ve made for me to discover.
- Product placement will be a lot bigger source of advertising in shows. A character walks into the scene wearing a leather jacket and a subtle alert invites you to pause and buy that jacket from an online retailer.
- Algorithmically generated content suggestions based on past shows viewed, what friends are viewing, what’s trending, etc., spread out across all content: YouTube, tv shows past and present, movies, paid content, etc.
- What starts playing after the show is over? On TV it just keeps going while online the show stops. I think after a show ends it will keep running and you’ll be able to tell it what to show next: something from your queue, what’s trending, ect.
- TV shows will host forums, ratings, commentary, and discussion around each episode after it airs. You can subscribe to commentary from writers about the shows you watch.
- Real time RottenTomatoes-like meter of each show’s rating while it’s airing
- You can sync your calendar with your content so that it can filter for content that fits your time frame. If you’ve got 10 minutes until the bus arrives your phone will automatically suggest all the relevant content that is under 10 minutes
- TV shows created from data, not from pilots. Instead of studios shelling out tons of money to create a bunch of pilots where only a fraction will make it, shows will be created based on the big data of viewer trends
- With so much content available individuals will be able to make money selling subscriptions to their curated content. What is a network but a curator of content?
Not so with direct traffic because unlike other sources, it doesn’t work alone. Direct traffic is not really direct for two reasons. 1. The way all analytics tools work is that if they can’t identify the source, they will call the visit direct. 2. Even if it all really was direct – people typing the url in the browser bar, direct traffic is hard to analyze on it’s own because something else always has to cause it to happen. You don’t go typing in a URL in your browser without learning about that URL from somewhere else. You can never really know what initiated someone to come to the site directly. So direct isn’t really a source, it’s an action. A better label for direct would be unknown.
Direct being unknown is not necessarily a bad thing. Direct traffic should be used in conjunction with analyzing all other channels. All the work those other channels do will contribute to direct. This forces you to think of your site’s acquisition strategy in terms of an ecosystem rather than channels working in silos, independant of each other.
According to the product adoption curve, the majority of people will wait for the feedback from the innovators and early adopters before deciding it’s worth their time. Money can carry it faster to the early and late majority, but it takes a lot of it. If the product is relevant for long enough, and you’re patient long enough, the product can carry itself into the consciousness of the majority.
It’s tempting to blurt out everything you have to say all at once. For the customer, participating in buzz is fun. Being the one to recommend something to others builds trust – both with their friends and the creator. What your audience wants from you is not just your product, but the ability to be the one to share it with others.
A less than 2% conversion rate seems bad when you think about it; 98% of people don’t buy. But if you were to whittle down the people who were actually there to buy your conversion rate would be a lot higher.
Take a look at all the different reasons someone would come to your site. There are visitors there to check their order status, contact customer service, browse, compare prices, find a store locator or are not interested in buying at all and leave after one page view.
If you can measure all the actions on the site that infer a certain kind of customer cohort than you can build something like this:
In this graph I’ve spit up all traffic into five segments:
- Service – visitors checking order status or contacting customer service.
- No Shot – visitors that show no interest in buying, visit <=1 Page. Who knows why they showed up we have no information on them to infer anything from.
- Browsers – visitors reading blog posts, relase calendars, looking at
- Buyers/Instore – choosing the pick up in store option, visiting the store locator, getting driving directions
- Buyers/Online – behavior that matches that of the Visits With Conversion segment
With this new view in mind you can measure accurately how well your site is converting those shoppers who actually have an intent to purchase. And you can accurately measure the task completion rate of those other visitors there to go to customer service or shop offline. You can also segment these buckets by marketing channel to see how many qualified leads each channel is providing.
These questions can be answered by looking at the amount of money a company invests at the different levels of Return On Ad Spend (or CPA). If you compare time periods you can see how paid search strategies change.
In this example you can see that a focus on direct response was increased year over year as that sweet spot of ROAS from 1 to 4 was much more heavily invested in – budget was spread much less evenly across higher ROAS levels. There is always a balance between profit and volume the more evenly the cost is spread out the more evenly the company values both metrics
in 2012 there seems to be more experimentation going on as the amount invested that got 0 return was higher. Interestingly, in 2013 as the amount of money invested on the > 21 ROAS was much higher. Maybe there were a few pet keywords that even though got low return were important from a branding or competitive perspective. Or impression share on those high ROAS keywords was tapped out and additional budget was given to the wrong keywords.
Ideally the first bubble chart gives you insight into where you sit in relation to your competitor’s impression share, avg. position and top of page rate. How competitive are you with these other advertisers? If you’re similar size and position expect high costs and aggressive bidding.
The second bubble chart shows the relation of avg. position and top of page rate with the competitors you overlap with the most. The more bubbles of similar size, the more competitive the auction is.
The Top Of Page Rate column chart gives you an idea of how aggressive you are relative to the competition for that data you’re analyzing. Are the competitors that are ranking higher than you also winning out the most when you are in the same auction?
I copied over the colors from the bubble charts to the bar charts so that you can follow one competitor across all comparisons. This way it’s easy to see if a competitor with high overlap rate ranks higher than you when in the same auction.
Is ranking higher than one of your competitors important to you? With the Who Is Ranked Higher chart you can see how often that is happening and with the other column chart you can see how competitive they are overall.
Download the Paid Search Competitive Analysis Template (.xlsx)
I built a dashboard for measuring the success of these two silos on a monthly basis. Check it out:
Measuring engagement has multiple facets. How well does your content attract new visitors, get people to come back, bring in others via social sharing and just be all around worth reading? The top left column pulls in those metrics of measuring how engaging the content is by using comments, shares and likes as a proxy for quality. Likewise the first row of bar charts shows the count of visits, days since last visit and page depth – pulled from Google analytics, over the last 4 months. Seeing these trended out gives you an idea if your content is getting better or worse over time – same with the line social graphs in the bottom row.
Next, how well does the blog get people to buy from your site? The first step to driving a sale is to get people from the blog to the site, so the Visits to Website line graph and the Blog to Site CTR shows how many, and with what frequency people are clicking through. The middle chart on the middle row shows overall revenue and per visit value.
A dashboard is only as good as the actionable insights one can glean from it. This dashboard shows (the numbers are all made up mind you) that last month the content got better – people commented more, shared more and Liked more. The next step here would be to pull the All Pages report for the month and see what kind of content resonated so much, and then make more of it.
Despite less traffic overall the quality of traffic to the site increased as Per Visit Value increased – too bad not more people aren’t clicking through to the site, maybe more links to the site could help that. Were the links to the site product pages, category pages, the homepage?
More questions: Did the new visits this month convert? The count of visits from last month were higher, was there a theme of content that you stopped using this month? Anyway you can add onto the content that drove the spikes in visits from previous months? How does your cadence of posting affect the volume of traffic?
Download the Ecommerce Blog Dashboard in Excel.
Of course we want to drive awareness and/or increase engagement. What is marketing if not one or both of those things? The point of marketing proposals are to allow the thinkers on the team to collaborate and then give those ideas outlined to the doers on the team. Proposals, like most endeavors, are garbage in – garbage out. The more vague the objectives and strategy the more vague the results will be. So let’s how we can boil down “awareness” and “engagement” into unique actions that we can measure and get better results.
Objective #1 Drive Awareness
Awareness to who? Certainly not everyone. Moms with kids? Traveling businessmen? Baby Boomers? Teenager jocks? Go one step further and say “Drive Awareness to ________.” Then the doers can measure themselves against how well they are reaching that desired audience. Age and gender reporting are easily accessible in AdWords from the Google Display Network.
Also by using the myriad ways to overlapping contextual, interests one can measure the CPM and CTR of getting in front of the coveted demographic.Targeting new customers or return customers is also a consideration – how well can you reach past purchasers of product x who might have an affinity for the new product y? Remarketing lists can also be a useful tool to measure awareness. Getting the message in front of visitors who browsed certain categories or similar products can be counted as success. All of these tactics drive awareness and give you ways to measure how well that awareness was achieved.
Objective #2 Increase Engagement
Engagement is subjective and web analytics tools are inherently unable to measure the kind, positive or negative, of engagement and are left to only measure the degree of engagement. Measuring the degree of engagement is going to be unique to the experience of the site. Maybe it’s the amount of contest submissions, tweets, comments, video plays, likes, it all depends. There are a few standbys however: loyalty, recency, length of visit and depth of visit (all located under Audience > Behavior in Google Analytics. With all of these metrics see what the site average is and then use that as a baseline to achieve against.
So instead of being meaningless, an example objective on a marketing proposal could say:
“Drive awareness to our target 25 – 35 mothers with children who are trying to save time and increase the value of quality time spent with their families.”
“Increase engagement by reaching more than X contest submissions while driving X new visits through user generated social shares.”
Avinash has multiple great posts on this subject micro-conversions, net income & goal values. I’ve tried taking all of this in and meld together an approach to make all of these ideas work together. You should read Avinash’s posts first and then take a look at my conclusion. So I see three steps to putting together a strategy that values all the objectives of a website.
Step 1: Quantify All Actions Taken On The Site.
Look at all the micro-conversions that take place on the site and calculate their worth. This takes some creativity. You end up with something like this:
Step 2: Extrapolate Those Value Across All Channels
You’ve deduced how much a new email subscriber is worth, now multiply that value to the amount of email subscribers organic search has driven in the last week. Do this with each metric and each channel and you’ll end up with a report that encompasses the value that each channel has for each micro-conversion. This is a good looking weekly report to show how the site is doing overall. But where should you focus?
Step 3: Focus Strategy Going Forward Based On Categorization Of Micro-Conversions
At a very simplistic level most businesses work under a pretty basic premise: buy stuff at one price and then sell it for more than you bought it for. There are four main strategies to do this: price strategies – sell at a higher price, cost strategies – sell at the same price but lower your costs, market share strategies – take more customers from your competitors, & market size strategies – go into new places where you haven’t sold before.
Divide your micro-conversions and other metrics that are important to your business into one of the four buckets. Now if you want to focus your strategy on volume you know the micro-conversions and metrics that each marketing channel should be driving to.
Online marketers have always stuck their noses up at TV advertising because they couldn’t believe advertisers would spend so much money on a medium that was not trackable, was interruptive, was not precisely targeted, had no ability to engage the user further once the ad ended, was not shareable. Surely TV ads are inferior.
But when the amount of time people spend online is constant you need new math. The number of sites visited before a purchase as reported by google is growing exponentially – is this because people do more research or is it just because people spend more time online? When browsing is something that never ends, creating attribution models around touch points that weave in and out of constant browsing habits start to look futile. The sheer fact that someone showed up at your ecommerce site used to be a pretty strong signal of purchase intent and every time they didn’t convert was deemed a failure. Now, with mobile usage skyrocketing the value of a visit is dropping fast.
In the end the traditional principles of TV advertising – where you interrupt and grab attention by inserting advertising into an appealing environment and then make that advertising message entertaining, beautiful or interesting is maybe all that may really works after all. The majority of online advertising hasn’t been focused on that as much as it’s been focusing on precise targeting, number of “likes” and optimization.