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How to use decision intelligence to tackle complex business challenges



How to use decision intelligence to tackle complex business challenges

Complex decision-making has become increasingly challenging as strong operational excellence and productivity, especially within marketing organizations, become vital competitive advantages. Across the board, the most successful companies and investors depend on fast and accurate decision-making, ranging from lead nurturing to recruiting and investment decisions.

Research shows that businesses make up to three billion decisions annually and a recent survey by Gartner reported that 65% of decisions are more complex (involving more stakeholders or choices) than they were two years ago.

Many businesses today, and the marketers that serve them, need better insight to bridge the gap between massive amounts of data and business decisions. Only 24% of companies say they are “data-driven,” whereas others face missed opportunities, inefficiencies, and increased business risks. The average S&P company loses $250 million annually due to poor decision-making.

Decision intelligence is a framework that bridges the gap between insights and decisions. It empowers organizations to make better, consistent, and data-driven decisions. Leaders and teams can make informed decisions at every level of the company!

What is decision intelligence?

Decision intelligence (DI) is an evolving discipline that combines data, analysis, AI, automation, and experience to make better decisions. DI helps guide decision-makers with actionable insights using optimization, simulation, and decision-analysis techniques.

In contrast to traditional decision-making approaches, which rely heavily on intuition and experience, DI incorporates methodical, analytical, and data-driven approaches.

The focus of DI is not just on the technology but on how it augments human decision-making processes. It is a multidisciplinary field drawing on expertise from various arenas, including computer science, statistics, psychology, economics, and business.

According to Dr. Loren Pratt, chief science officer and co-founder of DI software provider Quantellia, and author of “How Decision Intelligence Connects Data, Actions, and Outcomes for a Better World,” another key concept of DI is designing decisions like organizations design homes, buildings, and airplanes — by creating a blueprint first.

Much like a blueprint, a decision design helps align everyone involved in that decision — including stakeholders — around its rationale. She found that by treating decisions like a design problem, you can bring many design best practices to bear, such as ideation, documentation, rendering, refinement, QA, and design thinking.

In 2019, Google’s first Chief Decision Officer, Cassie Kozyrkov, established a new decision intelligence engineering discipline to augment data science with behavioral science, economics, and managerial science to focus on the next business advantage beyond the data.

Intelligent decisions are designed, simulated, automated, monitored, and tuned. 

Dig deeper: Why data-driven decision-making is the foundation of successful CX

What decision intelligence is not

Decision science. Decision science has usually been associated with the qualitative side of data. DS is the overarching term, while “decision intelligence” is the operational side. 

Strategic intelligence. Broadly, strategic intelligence means using BI insights to drive and support strategy. We also call this market intelligence which provides businesses with current industry trends and makes sense of consumer behavior to navigate a future course of action.

Calculated decisions. Not every output or recommendation is a decision, Kozyrkov says. In decision analysis terminology, a decision is only made after an irrevocable allocation of resources takes place. If you can change your mind for free, no decision has yet been made.

Applications of decision intelligence

DI applies to various decision-making problems, such as resource allocation, risk management, strategic planning, and, yes, marketing. I’ve used it in developing systems and platforms for complex energy, finance, policy, and marketing decisions.

Our last startup platform supported DI for go-to-market executives reducing the decision-making process from nine months to a fraction of time with greater visibility, training, and impacts.

DI has been applied in credit applications or fraud detection in financial services.  It has been used in retail to determine how much inventory to purchase, optimal stock levels, or price forecasts. According to Dr. Loren Pratt, employing decision intelligence can positively impact evidence-based decisions in a healthcare crisis.

Other use cases include customer satisfaction, marketing attribution, and competitive and go-to-market strategies. Designs of the framework of these decisions were standard for GTM; however, implementation required building an enterprise platform, training, and data support. But in the end, this decision-making time dropped from nine to one-to-three months. The average impact was over $10 million, including an apparel company discovering a new $90 million revenue stream embracing the platform. 

Dig deeper: Automating decisions with real-time situational context

Benefits of decision intelligence

McKinsey senior partner Kate Smaje states that organizations are now accomplishing in 10 days what used to take 10 months. Having DI supports the continually increasing pace of decisions required to stay competitive.

The first benefit is DI aids leaders in navigating complex decisions with more focused and comprehensive information. As you design the decisions, you can structure cross-organizational information toward specific goals or objectives. Having this kind of visibility facilitates navigating trade-offs between competing objectives. It eliminates more of the analysis paralysis found in most strategic and high-level tactical decisions. 

Next, DI reduces risk and uncertainty. Decision-makers with real-time data and insights can leverage DI to identify and proactively mitigate potential risks. With the visibility in trade-offs, organizations can better apply risk/reward plans to avoid costly mistakes hindering a competitive edge.

Decision Intelligence enhances efficiency and productivity. By automating specific decision-making processes and providing decision-makers with real-time data and insights, DI can help streamline decision-making and improve productivity. You are reducing decision latency. These processes can be built or programmed into systems to free up time and resources to explore more options or allocate to other important tasks and initiatives.

Finally, organizations leveraging DI gain a more potent competitive edge by leveraging data and technology by evaluating, then acting on, more intelligent and faster complex decisions which typically cripple momentum or transformation.

Limits and challenges of decision intelligence

With data, AI, and automation involved, it’s not surprising that there are some challenges and limitations that are also present with DI.

Ethics/bias. DI can methodically help reduce bias and reinforce ethical decisions. At the same time, with any data-driven and automated system, decisions leveraging DI built by humans still risk being developed based on biased or discriminatory data or algorithms. Awareness training, along with all other organizational data-driven efforts, is a must.

Data availability. Leaders and project managers must be aware of data access and availability limitations. Decision effectiveness is often challenging to find on smaller datasets. Sometimes things go wrong, but it’s more based on luck than data. For complex and infrequent decisions, an organization may need help to define an approach for measuring decisions. In such cases, technology limitations may prevent a solution. Organizations need to formalize such decision-making processes and can only use technology. Also, it’s worth highlighting what could be missing or the scope of what’s possible.

Resistance. An important part of DI is ensuring more transparency, consistency, and training in the decision-making process. The traditional culture of decision-makers will initially be resistant as it feels that it dismisses their experience or instinct or runs against their specific agendas. Those in charge of DI efforts need to communicate how DI benefits their efforts and leads to better outcomes for individuals and organizations.

Leaders can overcome these challenges and limitations through clear communication and a well-defined scope of its application. Each new initiative can grow and enhance an organization’s decision-making culture.

Tips and factors

  • Choose a focused decision. Begin by implementing DI in functions where business-critical decision-making needs improvement (e.g., data-driven, AI-powered). Alternatives include large complex decisions or ones that can be scaled and accelerated through automation.
  • Begin with outcomes. There’s a flood of data in your organization, but you should only gather relevant data to that outcome to design a decision model. Add additional data or test theories of additional information once you’ve started with your early set.
  • Map out decisions. Document assumptions, thoughts, emotions, concerns, and fears involved in your decisions. Review them quarterly or semi-annually. It will increase your organization’s decision-making muscle.
  • Don’t automate everything. Humans, especially when it comes to complex and sensitive decisions, are necessary.
  • Authority should be to the decision. Provide authority to make decisions to the people closest to the point of impact of that decision. Ownership will incentivize effective decision-making.
  • Develop new decision-making habits. Teach decision-makers to apply systematic best practices, such as critical thinking, trade-off analysis, recognizing bias, and listening to opposing views.
  • Beware narrow framing. In the book “Decisive” by Chip and Dan Heath, the authors explain that a straightforward way to improve decision-making is to avoid limiting the scope of the frame. A decision is rarely just a “yes” or “no.” There are always multiple options, so have at least three available for any decision.

Decision-makers frequently need more information, time, and experience to make complex decisions. A study by Bain found that business performance seems 95% correlated to the effectiveness of decisions. Decision intelligence systems improve efficacy by explaining and justifying the decisions, learning from past decisions’ feedback, and comparing the impact to improve decision effectiveness.

Decision intelligence is a crucial tool that can help you make better decisions. By combining data science, AI, and human expertise, DI can help reduce uncertainty and improve effectiveness. However, DI has its challenges and limitations. You must be aware of these risks and take steps to mitigate them.

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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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The Complete Guide to Becoming an Authentic Thought Leader



The Complete Guide to Becoming an Authentic Thought Leader

Introduce your processes: If you’ve streamlined a particular process, share it. It could be the solution someone else is looking for.

Jump on trends and news: If there’s a hot topic or emerging trend, offer your unique perspective.

Share industry insights: Attended a webinar or podcast that offered valuable insights. Summarize the key takeaways and how they can be applied.

Share your successes: Write about strategies that have worked exceptionally well for you. Your audience will appreciate the proven advice. For example, I shared the process I used to help a former client rank for a keyword with over 2.2 million monthly searches.

Question outdated strategies: If you see a strategy that’s losing steam, suggest alternatives based on your experience and data.

5. Establish communication channels (How)

Once you know who your audience is and what they want to hear, the next step is figuring out how to reach them. Here’s how:

Choose the right platforms: You don’t need to have a presence on every social media platform. Pick two platforms where your audience hangs out and create content for that platform. For example, I’m active on LinkedIn and X because my target audience (SEOs, B2B SaaS, and marketers) is active on these platforms.

Repurpose content: Don’t limit yourself to just one type of content. Consider repurposing your content on Quora, Reddit, or even in webinars and podcasts. This increases your reach and reinforces your message.

Follow Your audience: Go where your audience goes. If they’re active on X, that’s where you should be posting. If they frequent industry webinars, consider becoming a guest on these webinars.

Daily vs. In-depth content: Balance is key. Use social media for daily tips and insights, and reserve your blog for more comprehensive guides and articles.

Network with influencers: Your audience is likely following other experts in the field. Engaging with these influencers puts your content in front of a like-minded audience. I try to spend 30 minutes to an hour daily engaging with content on X and LinkedIn. This is the best way to build a relationship so you’re not a complete stranger when you DM privately.

6. Think of thought leadership as part of your content marketing efforts

As with other content efforts, thought leadership doesn’t exist in a vacuum. It thrives when woven into a cohesive content marketing strategy. By aligning individual authority with your brand, you amplify the credibility of both.

Think of it as top-of-the-funnel content to:

  • Build awareness about your brand

  • Highlight the problems you solve

  • Demonstrate expertise by platforming experts within the company who deliver solutions

Consider the user journey. An individual enters at the top through a social media post, podcast, or blog post. Intrigued, they want to learn more about you and either search your name on Google or social media. If they like what they see, they might visit your website, and if the information fits their needs, they move from passive readers to active prospects in your sales pipeline.

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How to Increase Survey Completion Rate With 5 Top Tips



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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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 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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