Multivariate testing allows marketers to test unlimited combinations of elements on a web page in a live environment and measure the significance of those changes on the site’s conversion rate by allowing the visitors to vote with their clicks.
The typical mindset for testing content on a website is to find which variation of site elements performs the best, and then implement those changes permanently: make a test, discover the winner, turn the test into what 100% of visitors see, and then move onto the next test.
There is a big flaw with this mindset: it assumes visitors don’t change preferences over time. The winning variation was good this week but that is no reason to think that it will be the winning combination the following week. One of the least understood aspects of e-Commerce is how much web visitor behavior changes from one time interval to another.
Giving the test more time to run doesn’t really solve this problem because all visitors who visit once the test has been declared over could be different than the ones that visited during the test. A winning variation is only winning during the time it was running.
The solution is to have the variables run constantly. A winner is never declared and implemented permanently because there isn’t only one clear cut winner. As the seasons and visitor preferences change, the system changes with it. For example: the combination of variables that are successful in the summer, when the products are full price and customers weigh pros and cons, read reviews and analyze costs to benefits, will be very different than the variables that succeed during holiday, when visitors are under a time crunch, want nothing more than a low price and to get in, buy and get out.
For this reason saying something like, “we need to do some testing to make sure our site is in tip top shape for holiday,” can cause some serious problems. How visitors act now is not how they will act then.
Online marketing has up to this point claimed that big data which allows more precise targeting is the solution to all of marketing’s woes. I think that data can go a long way but there is a point
when more data isn’t the answer. Focusing on the data too much can have some negative impacts.
First, left to their own devices, the data-driven direct response people will compromise and dumb-down everything to the point of complete blandness with the excuse of “it’s what the visitors want!” People don’t always know what they want. This is something that Steve Jobs knew well according to Guy Kawasaki:
“Apple market research” is an oxymoron. The Apple focus group was the right hemisphere of Steve’s brain talking to the left one. If you ask customers what they want, they will tell you, “Better, faster, and cheaper—that is, better sameness, not revolutionary change. They can only describe their desires in terms of what they are already using—around the time of the introduction of Macintosh, all people said they wanted was better, faster, and cheaper MS-DOS machines. The richest vein for tech startups is creating the product that you want to use—that’s what Steve and Woz did.”
Second, as soon as managers pick a numerical metric as a way to measure whether they’re achieving their desired outcome, everybody starts maximizing that metric rather than doing the rest of their job.
Third, data isn’t enough to motivate others. From Seth Godin: “In my experience, data crowds out faith. And without faith, it’s hard to believe in the data enough to make a leap. Big mergers, big VC investments, big political movements, large congregations… they don’t usually turn out for a spreadsheet. The problem is this: no spreadsheet, no bibliography and no list of resources is sufficient proof to someone who chooses not to believe. The skeptic will always find a reason, even if it’s one the rest of us don’t think is a good one. Relying too much on proof distracts you from the real mission–which is emotional connection.”
Generally, SEO is simple, but not easy. The majority of it is common sense stuff. You can learn the lion’s share of SEO tactics directly from Google here.
But, SEO is not necessarily common sense to the average non-tech savvy business owner and unfortunately SEO is often sold as something only experts with very technical expertise can do – sometimes even thought of as “secrets to rank higher in Google”.
I read 35 books
World War Z
The Power of the Dog
Best Nonfiction: Unbroken: A World War II Story of Survival, Resilience, and Redemption
Watched 88 movies
Silver Linings Playbook
Gone Baby Gone
It’s A Disaster
Safety Not Guaranteed
Side By Side
Kings Of Summer
Movies that messed with my head for days: Capturing The Friedmans, Compliance
Worst Movies: Upstream Color, A Serious Man
Watched 216 episodes of TV
Breaking Bad season 6
Newsroom seasons 1 & 2
Arrested Development was mostly a disappointment
Couldn’t get into House Of Cards or Game Of Thrones like everyone else
Swingin’ Utters – Poorly Formed
Vampire Weekend – Modern Vampires of the City
Off With Their Heads – Home
Plow United – Marching Band
Telekinesis – Dormarion
Biggest Purchase: 2005 Toyota Sienna
BIggest Holy Crap! Moment: Discovering the elusive buzzing sound in the house that lasted for weeks was a woodpecker pecking on a pipe on my roof
Most Satisfying Moment: Seeing my first short story published on Amazon
Best Purchase: Acer H5370BD Home Theater Projector
Most Embarrassing: Falling over at a stop light on bike while towing both my kids
Scariest Moment : Seeing my kid choke on asthma induced frothy, white sputum
Runner Up: Driving a rental car in downtown Sevilla, Spain
Saddest Moment: Watching Boulder flood
Biggest investment in time, smallest return: the delusion of making passive income from advertising on a niche content site – movingtobouldercounty.com
Firsts: played poker, sold a car on Craigslist, used an ear candle, ran out of gas on motorcycle at a stoplight, our backyard chickens laid their first eggs
Biggest Bonehead Move: Packing too much wood in van from Home Depot causing windshield to break when stopping at a light
Best Vacation: Trip to Spain & Germany
Most Frustration Inducing Project: Building a pergola in my backyard
Biggest Bummer: Hundreds of shirts for Minimalist Tees misprinted and ruined by incompetent screen printers
Biggest Point Of Pride: My appearance on this video at the Andrew WK Concert in Denver at the 1:29 mark
Best Moment: Becoming a foster parent to an awesome little boy
I measure the value of any website by the quality of information that I get from it (Snapchat is by definition made up of trivial content, stuff not worth keeping around, therefor I have no interest in the medium). Facebook has also dwindled in value for me. I think there are three main trends for the decrease of quality content on Facebook:
1. The assumption that what gets clicked is high quality. Facebook’s algorithm EdgeRank filters what will be at the top of my newsfeed based on multiple factors. One of them is what my friends are clicking, Liking or commenting on the most. Just because my friends read something doesn’t mean it’s good. It could be that the piece of content has been refined and tested to the point of becoming irresistible click bait. It’s just like how I continue to eat Oreos until the package is empty while wishing for something more nutritious to save me from my own compulsions.
2. What gets shared is for the value of the author, not the reader. Most of Facebook is made up of image crafting – where the author’s sole motivation is to affect the way people think of them. This content is insufferable because it holds no value for me, the reader. Friends who publicize their consumption of content that they think reflects well on them isn’t content I want to consume.
3. Content from pages you “Like” won’t show up in your feed if the page admins don’t pay to boost it. So even if I want to use Facebook as a way to deliberately stay updated by specific brands or business I like, it won’t let me. EdgeRank has tweaked posts from pages to have decreased organic reach in an obvious move to make more money.
I’ve been a hardcore RSS fan for years because it keeps me from being at the mercy of a blended, algorithmic deluge of incoming news. I like to pick what I’ll see and when, and not rely on luck and the self interested filtering of a social network.
I recently read Malcolm Gladwell’s David and Goliath: Underdogs, Misfits, and the Art of Battling Giants
where he discusses the concept of the inverted U curve which explains how having more of something can be counterproductive. I think attribution analysis in online marketing applies to the same curve.
In my graph you can see that more information from attribution modeling does in fact produce better investment returns. But each additional piece of information yields less marginal utility – known as the law of diminishing returns – and, at some point, additional information begins to have the opposite effect.
Count of visits before purchase is increasing. Amount of sites visited before purchase is increasing. Time spent researching before purchase is increasing. Amount of devices used to access the internet is increasing. Amount of sales offline influenced by online research is increasing. Amount of time spent online generally is increasing. All these factors combined turn attribution analysis into a brutal rabbit hole.
Eventually, assigning specific value to any individual interaction is an exercise in futility. Gone too far and it will begin to take away value. I think the online marketing manager of the future will employ a kind of marketing mix strategy rather than a channel ROAS strategy.
There are four main categories to think about when you categorize your keywords into campaigns: retailer, manufacturer, product and category. And then there are the multiple inter combinations of those four.
Prioritizing which category you should focus on depends on which category your business falls into. If you’re a brand manufacturer than the Manufacturer bucket will give you the best return and you wouldn’t worry about the retailer category. If you’re a retailer without your own brand than you need to worry about all four categories. Generally ROAS and volume looks something like this (varies depending on industry):
In Google Analytics under Audience > Behavior > Frequency & Recency you can find the count of visits report. Add the Built-In segment Visits with Transactions. This way you can see how many visits it takes for people to purchase. Compare year over year count of visits for purchasers. There is a good chance the amount of visits it takes for people to convert has gone up year over year.
A few thoughts on this:
- As the internet becomes more ingrained in our daily routines we all browse more. A visit to an ecommerce site is no longer a signal of high purchase intent like it used to be.
- Retargeting is more important. Visitors are taking more time comparing prices and sites. While they’re weighing their options it’s worth it to remind them of your offering with display ads.
- Do you offer discounts so often that visitors waiting for your products to go on sale?
Modifying a bid for someone who not only has been to your site before but you also have an idea of their activity on your site is powerful.
So a site that sells guitars can create a remarketing list for a certain brand of guitar by making a list based on any URL that contains that brand keyword. There are lots of cool things you can do with this.
To take this a step further you can create audience lists based on cart, product detail page, and category page. Then make a custom combination so that your bid increases a little if they have been to your site before, or increase it more if the person has not only been to your site before but has been to a product detail page or added something to cart before.
To go a step further still, you can use import remarketing lists made of audience segments from Google Analytics. Here are a few ideas:
- Make a list consisting of visits with 10+ page views or a count of visits greater than 2 so you can bid highest for visitors who have shown a high level of purchase intent
- Visitors who came through your adwords brand keyword campaign initially can be served an ad that will take them to a page other than the homepage
- Exclude visitors who bounced. If they didn’t show interest the first time don’t serve them an ad again
- Use local ad text referring the city or state they live in using the city and metro data in Google Analytics
- Use Days Since Last Visit to bid up on those visitors who have lapsed
- Show different adtext to new visitors vs returning visitors using the visitor type segment
- Exclude return visitors from seeing your ads if they search on your brand terms. If they already know about you don’t bother paying for a click to have them when they return
Really, there are an infinite amount of ways to make your paid search campaigns efficient by combining audience lists to search ads.