In this episode of Intended Consequences, we discover how to implement website surveys without affecting conversions and we evaluate some great tools to measure and analyze the gathered data.

Implementing website surveys is always a great idea. Unfortunately, if wrongly implemented, they may lower conversions. Our visitors may decide to respond to the survey and forget what they added to the shopping cart. Today, we’ll analyze the importance of well crafted website exit survey questions that will shield results. We will also share with you some AI-powered tools that can help you find out how to diagnose your webpages and get visitors past the obstacles that most of us unintentionally create.

Intended Consequences: Interview with Curtis Morris of Qualaroo

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Resources and Links Discussed

How to Implement Website Surveys without Affecting Conversions Key Takeaways

  1. Thank you page survey: Find out why this should be a part of every website that processes sales, subscriptions or registrations of any kind.
  2. What almost kept you from buying today?: In this episode, learn what’s more effective than Net Promoter scores or pre-sale feedback queries.
  3. “Liking” In Action: Curtis shows us when is the best time to ask someone to take desired action.
  4. Data Tools: Find out which tools to use that allow you to be more creative, all while gathering data to be effective.

An Interview with Curtis Morris of Qualaroo

Our guest, Curtis Morris formerly with Qualaroo

Curtis Morris formerly with Qualaroo

Qualaroo let’s you discover issues — good and bad — that are affecting your prospects and customers. It provides a business with the ability to ask website visitors questions, collect answers, and process high quantities of input. The tools uses sentiment analysis and AI-driven text recognition to summarize inputs from hundreds or thousands of participants.

There is no better focus group than your prospects and customers. Qualaroo keeps you in touch with them.

How Automatic Solved Their Sales Problem with a Website Exit Survey

The people at a company called Automatic had an idea. What if we created a device that would connect your smartphone to your car’s computer. The idea was great, but then they ran into a problem.

How do you get people to buy the more profitable version of your product? How do you get people to click on the things you want them to click on? How do you get them to

When you take any car built since 1996 to a mechanic, one of the first things they will do is plug your car into a computer. The mechanics computer will essentially ask your car what’s been going on.

This makes me think of Star Wars, when Han Solo tells C-3PO that he needs him to talk to the Millennium Falcon.

It turns out that there’s a lot that your car can tell the mechanic, most of it uninteresting to the mechanic.

When one of the many sensors around your car detects a problem — your oil is low, or your engine temperature is getting high — your car shows you a “check engine” light, as if you couldn’t handle the details.

But your car knows more. Much more.

You car knows how fast you’re accelerating. It nows how fast you’re slowing down. It knows if your airbags have been deployed. It knows the levels of all of the fluids, the pressure in the tires, even the quality of the emissions coming out the tailpipe.

For your mechanic, all of this information becomes available through a special port in your car, called the OBD-II port. They get an engine code from your car’s computer and can lookup the problem, probably online.

The people at a company called Automatic had a idea. What if we created a device that would plug into the port on your car, and connect your smartphone to your car’s computer. Then your pocket C-3PO could talk to your four wheeled Millennium Falcon, translating engine codes and much more.

It turns out that Automatic was on to something. Their device connected your car’s computer to your phone, and then their app tracked your trips, monitored your acceleration and deceleration — to help you save gas — and even connected to a variety of apps so you could expense travel miles and turn on your Nest thermostat when you pulled into the driveway.

How Implementing Website Exit Surveys Increased Conversions

In 2016 the company released a more advanced version of the product. Automatic Pro had its own always-on 3G connection. This meant that it didn’t need your smartphone to communicate with the internet. This opened up new opportunities.

Automatic Pro could alert someone if your airbags deployed, even if your phone was broken in an accident. If your car was stolen, you would know exactly where it is. The site touted “event-based apps” and “streaming apps” and “parking tracking.”

The old device was recast as Automatic Lite and sold online for $80 beside the Automatic Pro at $130.

And most people bought the Lite version.

This was a bit of a problem as the Lite version was a lower margin product. Why weren’t people buying the clearly superior Pro version of the product? Should Automatic just accept that car owners are cheap and adjust their expectations?

Fortunately, Conversion Sciences was working with them, and tackled this problem for them. Using our sophisticated scientific minds, we devised a strategy for finding out why buyers weren’t jumping on the Pro product. We asked them.

Whenever someone bought an Automatic Lite, we served up one question in a popup box: “Why didn’t you choose the Automatic Pro?”

Within two weeks, we had over 150 responses. And these responses were from people who had already been all the way through the purchase process. The popup had no negative effect on conversion, because it appeared AFTER THE SALE.

And it told us what was wrong.

After analyzing the responses, one comment really summed things up.

“I don’t think I need crash alert. I have apps that track where I park just fine, nor have I ever needed it. I don’t know what Live vehicle tracking means. I don’t know what event-based apps means. I don’t know what streaming apps mean, either.”

In short, the site wasn’t doing a good job of helping them choose the right solution for them. So they defaulted to the cheapest option. This is the classic problem of the Pricing Page. The job of the pricing page is not to show off all of the features. It’s to help the buyer choose the right plan, the right level or the right feature set.

By modifying the way the features were presented on Automatic’s pricing page, we were able to significantly increase the number of units sold overall, and increase sales of the profitable Automatic Pro as a percentage. This was proven with an AB split test.

Things were going well enough that Automatic was acquired by SiriusXM, the satellite radio people, for 100 million dollars.

This is the power of qualitative data. Qualitative data is that delicious, juicy input that comes directly from buyers, prospects and pretenders. It’s typically gathered in surveys, focus groups and polls. These can deliver quantitative data, but qualitative data is prized for its messiness. It helps us understand how people think about products, how they talk about their problems, and what really is important to them.

The downside of this kind of data is that it is harder to process. We had 150 responses to analyze for Automatic. Imagine if you got thousands a day. Every day.

These are the problems that Curtis Hill thinks about. He is CEO of Qualaroo, and believes, as I do, that quantitative data means nothing if it’s not supplemented with qualitative data. So, listen to the Podcast for all the juicy details on how to implement website surveys without affecting conversions.

Intended Consequences: Interview with Curtis Morris of Qualaroo

Subscribe to the Podcast

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Discover how AI marketing tools truly work and find the answer to the question: Can they really increase your website’s conversion rates?

Do you know how machine learning is impacting conversion rate optimization for marketers? We all know what the acronym “AI” stands for: “As If”. Data scientists are telling us that by using AI, they’ll will be able to create a predictive model of the visitors to your website that will tell you exactly who is ready to buy.

I say, “As if.”

We may marvel that such things can be done, but we also recognize that these things require a great deal of data and the skills of some serious brainiacs to get a machine to tell us something we don’t already know.

The truth is, you are probably already using “AI”, or more accurately, machine learning in your marketing. It’s hiding in the tools we use, like monsters under our bed. Machine learning and the more sciencey-sounding AI will change the way you take products to market, but your human mind will still be needed and loved.

Unless you resist – “as if.”


Augmenting Our Brains: AI-powered conversion optimization

Things like AI-driven predictive models are exciting, because our job as marketers is to predict the future. We’re like that exotic fortune teller gazing into an empty tea cup or a crystal ball.

We say things like, “If you give me a budget, I’ll generate six times that amount in revenue.” This is is like saying, “If you put a chicken foot under your pillow, you will find true love.” As if.

But this is what we do, and the data on which we base our predictions is often no more valid than the layout of tea leaves at the bottom of a cup. Our brains are wired to find patterns in anything, even when a pattern isn’t really there.

If I came into your office and said, “The last three leads we generated were all visiting the website using a Firefox browser,” your brain would jump to the conclusion, “If I can get more Firefox users to visit our website, I’ll generate tons of leads.”

Do AI marketing tools impact your website conversion rates?

Do AI marketing tools impact your website conversion rates?

Purveyors of AI, or more accurately Machine Learning (ML), would tell us that the machine doesn’t make mistakes like this. Our 100% genuine intelligence just doesn’t stack up to their Artificial Intelligence.

The problem is that machines will make exactly the same mistake if we don’t give them lots of data.

Just as machines need data, we know that we need more data before we start an ad campaign targeting Firefox users. We’ll ask our analytics person to pull together all website visits for the last year, and calculate the conversion rate for each. This increases the size of our dataset from three to many.

If this analysis goes the way of most analyses, we’ll find that there’s not a meaningful difference in conversion rates among browsers. Most experiments end up being inconclusive. That’s just the way it is.

In this scenario, we “wasted” an hour of our data scientist’s time, an hour of our time, and another twenty minutes explaining to our boss why we were so unproductive today.

“What if,” the AI crowd says, “you could get a machine to sort through your data looking for clues and figuring out who’s more likely buy. You don’t have to waste your time. Let the machine do it.”

This is an exciting proposition. The machine wouldn’t just look at the browser. It would look at the time of day, day of week, and week of the year that visitors converted. It could consider the device being used, screen size and operating system. It could add in the source of the visit, the number of times a visitor has been to the website, and whether the visitor has bought before.

After crunching through all of your analytics data, the machine would give you a percentage chance that the next visitor to your website will convert. And here comes a person with a Safari browser on a Mac computer at 3:30pm EST on a warm Tuesday afternoon who’s never been to the site before.

The machine might spit out, “There is a 51% chance this person will complete the lead form.” Actually, the machine will just say, “0.51”. Machines are so boring.

It’s amazing that a machine can so accurately predict a human being’s behavior. This is incredible.

But, is 51% good? And if this is true, what should my website do differently to make this Safari visitor more likely to buy? Do I reduce the price by 49%? Do I flatter this visitor for being above average? Do I ignore them?

This is “the rub” with machine learning. The machine can’t tell us what to do with the data it gives us. There are systems that will tell us if a visitor is “at the top of the funnel” or “in the consideration phase.” Still, what do we do with that? A price-sensitive buyer may want to see a discount when “at the top” of their purchase process. A relational buyer may not care about discounts until they’re “at the bottom,” ready to buy.

The machine won’t tell us, “Target Internet Explorer visitors coming late at night on a Windows computer during the springtime months with a picture of a cat.” It spits out the probability for each visit: “0.51, 0.34, 0.71, 0.92”.

Wait! A 92% probability? Is that important!? Well, no. They’re probably going to buy no matter what we do. “As if.”

AI-Driven Results

Scoring customers in a customer relationship management (CRM) platform has required that marketers hand-code the algorithm. We decide which actions indicate that a prospect is moving closer to buying. We decide how to value each action. It can work, but it isn’t rocket science – or AI.

Alternatively, we can dump sales data into a machine learning algorithm and let it calculate the probability that each prospect will turn into a customer. The sales force can focus on those high-probability clients and disregard the low-scoring leads. It’s using past performance to predict the future, and should be more accurate than arbitrary assignment of values to actions.

This is how machine learning is entering your life as a marketer.

Can AI Marketing Tools Increase Your Website’s Conversion Rates?

Amazon famously introduced product suggestions to the eCommerce world. “People who bought this also bought that and that and that.”

It’s not an easy problem to solve. There are a lot of variables to crunch and it has to be done quickly. This is a prime area for AI.

Mailchimp launched a similar tool to add product suggestions to the emails of its eCommerce clients. Every time you send an email to someone, Mailchimp will include a few product suggestions at the bottom of the email. The machine learning algorithm will compute the probability that one or more products will appeal to a subscriber, based on the behavior of all email recipients. Those products with the highest probability get added to the email. This prompts the visitor to buy.

As if.

It’s hard to know how well the machine has learned what your visitors buy collectively. This is the limitation of AI. We can’t really see what is inside the box. All we get is a number.

If you implement a suggestion engine on your website, we recommend running an A/B test to measure its effectiveness. This is done by adding the “Also bought” suggestions for half the visitors and hiding it for the other half. This will give us some conclusive data about how the suggestion AI is performing. Is it increasing the order size on average, or reducing it?