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3 ways B2B marketers can use generative AI

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3 ways B2B marketers can use generative AI

As technology and automation evolve, B2B marketers can access tools and information faster than ever. With the rapid adoption of generative AI, that evolution is happening in real time. As B2B marketers, we must embrace and use this technology to our advantage. 

This article will cover three ways to use generative AI: keyword research, content creation and data analysis. Doing so will completely change your approach to how you market products and services across the digital ecosystem, leaving competitors who are not up to speed in the dust.

Unleashing the power of generative AI in keyword research

Traditional keyword research includes many methods, but they all have one thing in common: It’s a manual process. Some paid tools, free tools and plug-ins can help marketers analyze keywords, but this takes time and effort. It can also be costly when outsourcing this work to an agency. Even so, keyword research is an integral part of marketing. It should never be skipped or overlooked.

Some of the tools marketers use for keyword research include Google Keyword Planner, Google Search Console, Semrush and Surfer SEO which integrate AI into the platform. Browser plugins like MozBar and Keyword Research have also come a long way and continue to add value to B2B marketers.

Up to 44.5% of marketers use generative AI for keyword research. Platforms like ChatGPT can help marketers be more efficient with keyword research. SEO automation speeds up the process and makes it easier to find keywords, but humans are still required to ensure that generated keywords are relevant, make sense and fit the context. While AI outputs are improving daily, smart prompt engineering is now becoming a critical skill marketers need to learn to achieve better results.

Using generative AI for keyword research has many benefits, such as improving efficiency and accuracy and finding keywords that have yet to be used before. They speed up research and give users a competitive edge by letting them respond quickly to changes in search behavior.

These models also develop more specific and valuable keywords, ensuring marketing efforts reach the right people. Generative AI models can find low-volume or long-tail keywords that make it easier to rank content. 

Even though generative AI models for keyword research have plenty of potential, a few challenges should be addressed. For example, if you rely too much on AI, you might optimize content with keywords that could be taken out of context. The accidental spread of biases in the AI data could lead to keywords that could harm your brand reputation.

The biggest challenge with generative AI is that it lacks cultural context. Global multinational companies with markets everywhere could have an issue with using AI to optimize for local languages and ensure that all the content aligns culturally, considering slang and other local issues.

To overcome these challenges, finding a balance between AI-generated results and human oversight is essential.

Dig deeper: The end of marketing or a new beginning? The truth about AI

Integrating generative AI models into content development

The significance of content in digital marketing cannot be overstated. It enables B2B and technology companies to engage with target audiences, elevate brand recognition and establish an integrated marketing program deployed across all channels.

High-quality and relevant content that delivers value results in customer trust and loyalty. Companies must always prioritize content to thrive in the highly competitive digital landscape.

Like keyword research, content creation is a labor-intensive process. Marketers frequently invest considerable effort into writing long-form content like blogs, white papers, ebooks and reports. They also write short-form content for social media, headlines and other ad copy.

It’s also common for marketers to outsource content production to agencies, freelancers or copywriting platforms like Compose.ly. This increases expenses and complicates communication. Consequently, traditional content generation methods consume substantial time and resources.

ChatGPT and similar platforms offer marketers unprecedented opportunities to enhance all content creation and production. These models can generate content that appears to be handcrafted, ensuring consistency in the brand’s voice and simplifying the creation of diverse, engaging and contextually relevant content. 

However, marketers must always balance AI with an added layer of human supervision when employing generative AI in content development. While these models can expedite content production, human context remains necessary to ensure coherence, accuracy and cultural relevance. By incorporating feedback loops and refining procedures, marketers can achieve an equilibrium between AI-generated content and human expertise, ultimately enhancing content quality and efficacy.

The advantages of generative AI for content production include accelerated processes, increased precision and the capacity to generate substantial volumes of content. These models can rapidly create high-quality material, allowing marketers to respond to market fluctuations and seize real-time engagement opportunities.

Additionally, generative AI can generate accurate and relevant content tailored to specific audiences, ensuring the success of digital marketing campaigns. Producing high volumes of content allows marketers to think more strategically instead of writing a blog post.

Despite the transformative potential of generative AI, specific challenges exist. For instance, current AI technology can not fully grasp the cultural or business context, which could result in superficial or nonsensical content.

Ownership and copyright concerns may emerge as AI-generated content obscures the distinction between human and machine authorship. Transparency is vital in AI-generated content to preserve audience trust and mitigate misinformation.

Businesses must proceed cautiously when incorporating generative AI in content creation, ensuring that human oversight and transparency remain indispensable components.

Dig deeper: 5 AI writing assistants in action

Using generative AI in data analysis

Generative AI models bring in a new era of advanced data visualization. These methods enable real-time data tracking and dashboard creation, complex network visualization and various data display options. As a result, organizations may obtain the most up-to-date information, make informed decisions and quickly adjust to market shifts by leveraging real-time monitoring.

Detailed network visualization reveals the complicated connections between data points, providing crucial insights into the interactions between different data points. This multidimensional data representation allows businesses to understand each component of their marketing campaign performance.

AI models can likewise help marketers extract actionable insights from data. With the right prompts, AI outputs can find anomalies and outliers, assess feelings and emotions, segment markets and develop buyer personas. 

Anomaly detection identifies unusual variances that may indicate possible problems or possibilities. This is extremely helpful when managing large paid media campaigns across paid search and display ads. 

When analyzing large conversational data sets, AI outputs can find the emotional impact of the content through sentiment analysis and emotion recognition. Market segmentation and consumer profiling help organizations focus their marketing efforts by allowing them to modify their strategy accordingly.

Generative AI models can also improve predictive analytics. For example, time series forecasting uses historical data to predict future trends and events. Machine learning algorithms are critical in generating data-driven predictive models. Generative AI models lead to more accurate forecasts by developing these methodologies, which can help predict campaign performance.

Text analytics has also advanced significantly. Topic modeling and document clustering, network analysis, named entity recognition and relationship extraction, text summarization and content production are all tasks that use these models. 

Topic modeling identifies fundamental topics in large data sets like social media mentions, call center transcripts or media coverage. It can help find patterns of hidden context and narratives.

Network analysis reveals the connections between diverse communities, named entity identification and relationship extraction, on the other hand, reveal connections between separate entities. These text analyses can help marketers identify higher-authority influencers and content creators.

Generative AI is also making social media analysis more efficient. Social network analysis and community detection reveal the links between people in online communities, revealing user behavior and interests. 

Trend analysis and hashtag monitoring measure the popularity of specific subjects and discussions, allowing marketers to keep up with industry developments and trending topics. Influencer identification and interaction make finding notable industry individuals and future collaboration opportunities easier.

Making the most of generative AI in your B2B marketing efforts

As the digital marketing landscape changes, B2B marketers must use cutting-edge technologies to stay ahead of the curve. The good news is several generative AI statistics show marketers are starting to adopt this new technology, and for a good reason.

Generative AI can potentially change keyword research, content creation and data analysis in ways that have never been seen before. This will usher in a new era of data-driven and integrated marketing strategies. Even though there are still challenges and limits, generative AI models can lead to incredible results when used wisely and with human expertise and oversight.


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

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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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MARKETING

Take back your ROI by owning your data

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Treasure Data 800x450

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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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