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The myth of the single customer record

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Prepare for CDP implementation using a template for use cases

Sellers of Customer Data Platforms (CDP) promise their software will gather data from various applications, and assemble it into a single-source-of-truth “golden record” for each customer. 

It’s a lovely vision, but rarely achieved. And that’s perfectly okay. Most companies won’t achieve the goal of one record for each customer, but will find ways to cope with the limitations that prevent the creation of golden records.

Let’s use this common CDP use case to illustrate the complexity: Identifying customers among the hoards of anonymous visitors to your website. 

It’s a challenge. Anonymity was central to the internet’s design. And while there are lots of ways to identify anonymous website visitors, they all have their limitations. 

Imagine Robert Williams, our leading man and swing dance aficionado, interacts with Ella, publisher of (the fictitious, I believe) Ella’s Swing Dance Magazine.

Robert meets Ella on his commute to work. She tells him he ought to read her magazine. On his lunch break, Robert searches for the magazine website on the desktop he uses at the office. When Robert’s web browser makes a request to Ella’s Swing Dance Magazine website, Ella’s CDP puts a cookie on that device and creates a user profile. The profile includes the following information: 

Profile 1

IP address: 25.23.108.5
User-Agent: Mozilla/5.0 (Linux NT 10.0)
Referrer: https://www.google.com

The record might also include what pages were visited, and what type of content the visitor seems to prefer. The visitor is still anonymous to Ella’s CDP. The profile is one of the millions of unknown visitors.

When Robert gets home that evening, he types the URL of Ella’s website into his iPad. Her CDP dutifully puts a cookie on that device and creates a new profile. But on this visit, Robert decides to sign up for Ella’s free e-newsletter with one of his junk email addresses. The CDP captures the email address from the form submission and creates a second profile, which has more information than the first. 

Profile 2

IP address: 32.12.100.21
User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_6)
Referrer: [blank]Email: [email protected]
Name: Bob Williams

However, nothing in this second records enables Ella’s CDP to conclude the records are tied to the same individual. The records were created on different devices at different times, and share no information identifying Robert. 

Two weeks later, Robert and Ella are jitterbugging at Mobtown Ballroom. Ella has a few copies of her magazine, and Robert takes one home. He signs up for a print subscription using one of the blow-in cards. Ella’s fulfillment service dutifully records this new subscriber data, which is then imported into the CDP, creating Robert’s third profile with still more information:

Profile 3

Name: Robert Williams
Address: 123 Main Street
City: Bowie
State: Maryland
Zip: 20715
Phone: (301) 555-1212
Email: [email protected]

This profile has valuable information, including a new email address. But this profile has no data from online activity, so it doesn’t help with online ad targeting or customer journey data.  

Robert now has three profiles in Ella’s CDP. There’s no way to merge any of them. We know they’re all Robert. The CDP doesn’t.

Fortunately, Ella’s magazine has the good sense to include some special online content for print subscribers as a way to link offline and online behavior. A QR code printed in the magazine allows Robert to view a video on the website about the Travelling Charleston. Robert scans the QR code with his iPad. That takes him to the website, where the CDP recognizes the cookie it put on that device earlier.

Bingo! Now Ella’s CDP can merge the iPad profile (#2) with the subscription information (#3). Several good things happen as a result: 

  1. Robert’s three profiles have been consolidated into two
  2. Robert has become a known user in Ella’s CDP
  3. Ella’s CDP knows that Robert uses two different email addresses
  4. Robert’s subscription information (offline behavior) and the profile created when he accessed Ella’s site from his iPad (online behavior) are now linked. 

The record created from Robert’s desktop remains anonymous.

Read next: 90% of marketers say their CDP doesn’t meet current business needs

Note that, in this scenario, Ella’s CDP has been configured to accept multiple emails in a customer’s profile. Some companies designate the email address as a unique field – allowing only one per profile. In that case, the records would not merge, and Robert’s subscription information would remain in its own profile, not connected to any online activity.

Will Ella’s CDP ever be able to attach Robert’s work computer to his online profile? Maybe. For example, if Robert opens one of Ella’s e-newsletters on his work computer, the CDP might (depending on how strict it is about such things) recognize that as Robert and merge the profiles. 

Identifying individuals from their online and offline behaviors and creating single records may seem complicated, but it’s quite a bit less confusing than what happens in real life. Consider the complexity added when Robert’s smartphone and home desktop are added to the equation.

Merging records: deterministic vs. probabilistic method. Which is right for you? 

The “golden record” that the CDP salesman is waving in front of you assumes that all these different sources of information can be merged, but they need to have a field in the record to merge on. What’s that going to be?

Most companies opt for an email address as the best piece of personally identifiable information on which to merge records. But as we’ve seen, and as we all know, people have multiple email addresses. They also change over time. 

If you stick with a strictly deterministic matching method, you’ll need to match a unique field (like an email address or a social media account) across multiple profiles to create your “golden record,” and you’ll inevitably leave some information behind.

There are other options. Some CDPs use probabilistic methods to merge profiles. That method enables you to match records that might otherwise remain distinct. But you risk incorrectly merging profiles and creating a customer experience headache.

(Read this article for an in-depth comparison of deterministic and probabilistic matching.) 

You can’t create a single record for each customer that covers all the chaos and weird realities of how people behave. What you can do, and what you must do, is decide where that matters.

There are use cases where improperly merged profiles yield very bad customer experience outcomes. Stick with deterministic matching in those cases, even though you’re going to lose some of the data on interactions with that customer. You’ll have multiple profiles for some individuals, many of which will remain “unknown.”

Other use cases are far more forgiving. If you want to create a segment of people who share a particular interest, you don’t need to get down to the individual. In these cases, probabilistic methods are sufficient. 

In any event, recognize that “golden records” are a nice idea, but you’ll never actually get there.


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Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.


About The Author

Greg’s decades-long career in B2B and B2C publishing has included lengthy gigs in editorial, marketing, product development, web development, management, and operations. He’s an expert at bridging the intellectual and cultural divide between technical and creative staff. Working as a consultant, Greg solves technology, strategy, operations, and process problems for publishers. His expertise includes Customer Data Platforms, acquisition and retention, e-commerce, RFPs, fulfillment, and project management. Learn more at krehbielgroup.com.

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How to Increase Survey Completion Rate With 5 Top Tips

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How to Increase Survey Completion Rate With 5 Top Tips

Collecting high-quality data is crucial to making strategic observations about your customers. Researchers have to consider the best ways to design their surveys and then how to increase survey completion, because it makes the data more reliable.

→ Free Download: 5 Customer Survey Templates [Access Now]

I’m going to explain how survey completion plays into the reliability of data. Then, we’ll get into how to calculate your survey completion rate versus the number of questions you ask. Finally, I’ll offer some tips to help you increase survey completion rates.

My goal is to make your data-driven decisions more accurate and effective. And just for fun, I’ll use cats in the examples because mine won’t stop walking across my keyboard.

Why Measure Survey Completion

Let’s set the scene: We’re inside a laboratory with a group of cat researchers. They’re wearing little white coats and goggles — and they desperately want to know what other cats think of various fish.

They’ve written up a 10-question survey and invited 100 cats from all socioeconomic rungs — rough and hungry alley cats all the way up to the ones that thrice daily enjoy their Fancy Feast from a crystal dish.

Now, survey completion rates are measured with two metrics: response rate and completion rate. Combining those metrics determines what percentage, out of all 100 cats, finished the entire survey. If all 100 give their full report on how delicious fish is, you’d achieve 100% survey completion and know that your information is as accurate as possible.

But the truth is, nobody achieves 100% survey completion, not even golden retrievers.

With this in mind, here’s how it plays out:

  • Let’s say 10 cats never show up for the survey because they were sleeping.
  • Of the 90 cats that started the survey, only 25 got through a few questions. Then, they wandered off to knock over drinks.
  • Thus, 90 cats gave some level of response, and 65 completed the survey (90 – 25 = 65).
  • Unfortunately, those 25 cats who only partially completed the survey had important opinions — they like salmon way more than any other fish.

The cat researchers achieved 72% survey completion (65 divided by 90), but their survey will not reflect the 25% of cats — a full quarter! — that vastly prefer salmon. (The other 65 cats had no statistically significant preference, by the way. They just wanted to eat whatever fish they saw.)

Now, the Kitty Committee reviews the research and decides, well, if they like any old fish they see, then offer the least expensive ones so they get the highest profit margin.

CatCorp, their competitors, ran the same survey; however, they offered all 100 participants their own glass of water to knock over — with a fish inside, even!

Only 10 of their 100 cats started, but did not finish the survey. And the same 10 lazy cats from the other survey didn’t show up to this one, either.

So, there were 90 respondents and 80 completed surveys. CatCorp achieved an 88% completion rate (80 divided by 90), which recorded that most cats don’t care, but some really want salmon. CatCorp made salmon available and enjoyed higher profits than the Kitty Committee.

So you see, the higher your survey completion rates, the more reliable your data is. From there, you can make solid, data-driven decisions that are more accurate and effective. That’s the goal.

We measure the completion rates to be able to say, “Here’s how sure we can feel that this information is accurate.”

And if there’s a Maine Coon tycoon looking to invest, will they be more likely to do business with a cat food company whose decision-making metrics are 72% accurate or 88%? I suppose it could depend on who’s serving salmon.

While math was not my strongest subject in school, I had the great opportunity to take several college-level research and statistics classes, and the software we used did the math for us. That’s why I used 100 cats — to keep the math easy so we could focus on the importance of building reliable data.

Now, we’re going to talk equations and use more realistic numbers. Here’s the formula:

Completion rate equals the # of completed surveys divided by the # of survey respondents.

So, we need to take the number of completed surveys and divide that by the number of people who responded to at least one of your survey questions. Even just one question answered qualifies them as a respondent (versus nonrespondent, i.e., the 10 lazy cats who never show up).

Now, you’re running an email survey for, let’s say, Patton Avenue Pet Company. We’ll guess that the email list has 5,000 unique addresses to contact. You send out your survey to all of them.

Your analytics data reports that 3,000 people responded to one or more of your survey questions. Then, 1,200 of those respondents actually completed the entire survey.

3,000/5000 = 0.6 = 60% — that’s your pool of survey respondents who answered at least one question. That sounds pretty good! But some of them didn’t finish the survey. You need to know the percentage of people who completed the entire survey. So here we go:

Completion rate equals the # of completed surveys divided by the # of survey respondents.

Completion rate = (1,200/3,000) = 0.40 = 40%

Voila, 40% of your respondents did the entire survey.

Response Rate vs. Completion Rate

Okay, so we know why the completion rate matters and how we find the right number. But did you also hear the term response rate? They are completely different figures based on separate equations, and I’ll show them side by side to highlight the differences.

  • Completion Rate = # of Completed Surveys divided by # of Respondents
  • Response Rate = # of Respondents divided by Total # of surveys sent out

Here are examples using the same numbers from above:

Completion Rate = (1200/3,000) = 0.40 = 40%

Response Rate = (3,000/5000) = 0.60 = 60%

So, they are different figures that describe different things:

  • Completion rate: The percentage of your respondents that completed the entire survey. As a result, it indicates how sure we are that the information we have is accurate.
  • Response rate: The percentage of people who responded in any way to our survey questions.

The follow-up question is: How can we make this number as high as possible in order to be closer to a truer and more complete data set from the population we surveyed?

There’s more to learn about response rates and how to bump them up as high as you can, but we’re going to keep trucking with completion rates!

What’s a good survey completion rate?

That is a heavily loaded question. People in our industry have to say, “It depends,” far more than anybody wants to hear it, but it depends. Sorry about that.

There are lots of factors at play, such as what kind of survey you’re doing, what industry you’re doing it in, if it’s an internal or external survey, the population or sample size, the confidence level you’d like to hit, the margin of error you’re willing to accept, etc.

But you can’t really get a high completion rate unless you increase response rates first.

So instead of focusing on what’s a good completion rate, I think it’s more important to understand what makes a good response rate. Aim high enough, and survey completions should follow.

I checked in with the Qualtrics community and found this discussion about survey response rates:

“Just wondering what are the average response rates we see for online B2B CX surveys? […]

Current response rates: 6%–8%… We are looking at boosting the response rates but would first like to understand what is the average.”

The best answer came from a government service provider that works with businesses. The poster notes that their service is free to use, so they get very high response rates.

“I would say around 30–40% response rates to transactional surveys,” they write. “Our annual pulse survey usually sits closer to 12%. I think the type of survey and how long it has been since you rendered services is a huge factor.”

Since this conversation, “Delighted” (the Qualtrics blog) reported some fresher data:

survey completion rate vs number of questions new data, qualtrics data

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The takeaway here is that response rates vary widely depending on the channel you use to reach respondents. On the upper end, the Qualtrics blog reports that customers had 85% response rates for employee email NPS surveys and 33% for email NPS surveys.

A good response rate, the blog writes, “ranges between 5% and 30%. An excellent response rate is 50% or higher.”

This echoes reports from Customer Thermometer, which marks a response rate of 50% or higher as excellent. Response rates between 5%-30% are much more typical, the report notes. High response rates are driven by a strong motivation to complete the survey or a personal relationship between the brand and the customer.

If your business does little person-to-person contact, you’re out of luck. Customer Thermometer says you should expect responses on the lower end of the scale. The same goes for surveys distributed from unknown senders, which typically yield the lowest level of responses.

According to SurveyMonkey, surveys where the sender has no prior relationship have response rates of 20% to 30% on the high end.

Whatever numbers you do get, keep making those efforts to bring response rates up. That way, you have a better chance of increasing your survey completion rate. How, you ask?

Tips to Increase Survey Completion

If you want to boost survey completions among your customers, try the following tips.

1. Keep your survey brief.

We shouldn’t cram lots of questions into one survey, even if it’s tempting. Sure, it’d be nice to have more data points, but random people will probably not hunker down for 100 questions when we catch them during their half-hour lunch break.

Keep it short. Pare it down in any way you can.

Survey completion rate versus number of questions is a correlative relationship — the more questions you ask, the fewer people will answer them all. If you have the budget to pay the respondents, it’s a different story — to a degree.

“If you’re paying for survey responses, you’re more likely to get completions of a decently-sized survey. You’ll just want to avoid survey lengths that might tire, confuse, or frustrate the user. You’ll want to aim for quality over quantity,” says Pamela Bump, Head of Content Growth at HubSpot.

2. Give your customers an incentive.

For instance, if they’re cats, you could give them a glass of water with a fish inside.

Offer incentives that make sense for your target audience. If they feel like they are being rewarded for giving their time, they will have more motivation to complete the survey.

This can even accomplish two things at once — if you offer promo codes, discounts on products, or free shipping, it encourages them to shop with you again.

3. Keep it smooth and easy.

Keep your survey easy to read. Simplifying your questions has at least two benefits: People will understand the question better and give you the information you need, and people won’t get confused or frustrated and just leave the survey.

4. Know your customers and how to meet them where they are.

Here’s an anecdote about understanding your customers and learning how best to meet them where they are.

Early on in her role, Pamela Bump, HubSpot’s Head of Content Growth, conducted a survey of HubSpot Blog readers to learn more about their expertise levels, interests, challenges, and opportunities. Once published, she shared the survey with the blog’s email subscribers and a top reader list she had developed, aiming to receive 150+ responses.

“When the 20-question survey was getting a low response rate, I realized that blog readers were on the blog to read — not to give feedback. I removed questions that wouldn’t serve actionable insights. When I reshared a shorter, 10-question survey, it passed 200 responses in one week,” Bump shares.

Tip 5. Gamify your survey.

Make it fun! Brands have started turning surveys into eye candy with entertaining interfaces so they’re enjoyable to interact with.

Your respondents could unlock micro incentives as they answer more questions. You can word your questions in a fun and exciting way so it feels more like a BuzzFeed quiz. Someone saw the opportunity to make surveys into entertainment, and your imagination — well, and your budget — is the limit!

Your Turn to Boost Survey Completion Rates

Now, it’s time to start surveying. Remember to keep your user at the heart of the experience. Value your respondents’ time, and they’re more likely to give you compelling information. Creating short, fun-to-take surveys can also boost your completion rates.

Editor’s note: This post was originally published in December 2010 and has been updated for comprehensiveness.

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Take back your ROI by owning your data

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Treasure Data 800x450

Treasure Data 800x450

Other brands can copy your style, tone and strategy — but they can’t copy your data.

Your data is your competitive advantage in an environment where enterprises are working to grab market share by designing can’t-miss, always-on customer experiences. Your marketing tech stack enables those experiences. 

Join ActionIQ and Snowplow to learn the value of composing your stack – decoupling the data collection and activation layers to drive more intelligent targeting.

Register and attend “Maximizing Marketing ROI With a Composable Stack: Separating Reality from Fallacy,” presented by Snowplow and ActionIQ.


Click here to view more MarTech webinars.


About the author

Cynthia RamsaranCynthia Ramsaran

Cynthia Ramsaran is director of custom content at Third Door Media, publishers of Search Engine Land and MarTech. A multi-channel storyteller with over two decades of editorial/content marketing experience, Cynthia’s expertise spans the marketing, technology, finance, manufacturing and gaming industries. She was a writer/producer for CNBC.com and produced thought leadership for KPMG. Cynthia hails from Queens, NY and earned her Bachelor’s and MBA from St. John’s University.

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Revolutionizing Auto Retail: The Game-Changing Partnership Between Amazon and Hyundai

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Revolutionizing Auto Retail: The Game-Changing Partnership Between Amazon and Hyundai

Revolutionizing Auto Retail The Game Changing Partnership Between Amazon and Hyundai

In a groundbreaking alliance, Amazon and Hyundai have joined forces to reshape the automotive landscape, promising a revolutionary shift in how we buy, drive, and experience cars.

Imagine browsing for your dream car on Amazon, with the option to seamlessly purchase, pick up, or have it delivered—all within the familiar confines of the world’s largest online marketplace. Buckle up as we explore the potential impact of this monumental partnership and the transformation it heralds for the future of auto retail.

Driving Change Through Amazon’s Auto Revolution

Consider “Josh”, a tech-savvy professional with an affinity for efficiency. Faced with the tedious process of purchasing a new car, he stumbled upon Amazon’s automotive section. Intrigued by the prospect of a one-stop shopping experience, Josh decided to explore the Amazon-Hyundai collaboration.

The result?

A hassle-free online car purchase, personalized to his preferences, and delivered to his doorstep. Josh’s story is just a glimpse into the real-world impact of this game-changing partnership.

Bridging the Gap Between Convenience and Complexity

Traditional car buying is often marred by complexities, from navigating dealership lots to negotiating prices. The disconnect between the convenience consumers seek and the cumbersome process they endure has long been a pain point in the automotive industry. The need for a streamlined, customer-centric solution has never been more pressing.

1701235578 44 Revolutionizing Auto Retail The Game Changing Partnership Between Amazon and Hyundai1701235578 44 Revolutionizing Auto Retail The Game Changing Partnership Between Amazon and Hyundai

Ecommerce Partnership Reshaping Auto Retail Dynamics

Enter Amazon and Hyundai’s new strategic partnership coming in 2024—an innovative solution poised to redefine the car-buying experience. The trio of key developments—Amazon becoming a virtual showroom, Hyundai embracing AWS for a digital makeover, and the integration of Alexa into next-gen vehicles—addresses the pain points with a holistic approach.

In 2024, auto dealers for the first time will be able to sell vehicles in Amazon’s U.S. store, and Hyundai will be the first brand available for customers to purchase.

Amazon and Hyundai launch a broad, strategic partnership—including vehicle sales on Amazon.com in 2024 – Amazon Staff

This collaboration promises not just a transaction but a transformation in the way customers interact with, purchase, and engage with their vehicles.

Pedal to the Metal

Seamless Online Purchase:

  • Complete the entire transaction within the trusted Amazon platform.
  • Utilize familiar payment and financing options.
  • Opt for convenient pick-up or doorstep delivery.
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Hyundai’s Cloud-First Transformation:

  • Experience a data-driven organization powered by AWS.
  • Benefit from enhanced production optimization, cost reduction, and improved security.

Alexa Integration in Next-Gen Vehicles:

  • Enjoy a hands-free, voice-controlled experience in Hyundai vehicles.
  • Access music, podcasts, reminders, and smart home controls effortlessly.
  • Stay connected with up-to-date traffic and weather information.

Driving into the Future

The Amazon-Hyundai collaboration is not just a partnership; it’s a revolution in motion. As we witness the fusion of e-commerce giant Amazon with automotive prowess of Hyundai, the potential impact on customer behavior is staggering.

The age-old challenges of car buying are met with a forward-thinking, customer-centric solution, paving the way for a new era in auto retail. From the comfort of your home to the driver’s seat, this partnership is set to redefine every step of the journey, promising a future where buying a car is as easy as ordering a package online.

Embrace the change, and witness the evolution of auto retail unfold before your eyes.


Revolutionizing Auto Retail The Game Changing Partnership Between Amazon and Hyundai

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