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The future is now: Leveraging AI prompt engineering

Ever felt like you’ve squeezed every creative drop out of your content calendar? Struggling to brainstorm fresh campaign ideas? Need help turning numbers into actionable insight? Welcome to the age of AI prompt engineering, a technique that empowers you to unlock the potential of powerful generative AI tools.

Think of tools like ChatGPT, Bard, or Copilot as the next evolution of search engines. Instead of simply returning relevant web pages, they can generate creative text formats, translate languages, write different kinds of creative content, complete complex math equations, and even assist with coding tasks — all based on the prompts you provide.

What is prompt engineering?

Imagine having a powerful conversation partner with access to a vast library of information. This is essentially what you get with AI-powered language models. However, just like any good conversation, the quality of the interaction hinges on how you initiate it. This is where prompt engineering comes in.

Prompts are the instructions you provide to the AI, guiding it toward the specific outcome you desire. The more precise and well-crafted your prompt is, the more helpful and relevant the AI’s response will be.

Think of it like searching a library. A vague query like “find something interesting” will yield a mountain of irrelevant results. However, a focused prompt like “find books on the history of artificial intelligence published in the last year that are also available in ebook format” leads you directly to the information you seek.

These language models are trained on massive amounts of text data, giving them the ability to predict the next word in a sequence and understand the nuances of human conversation. They learn and adapt as you interact with them. That’s why providing the right prompt from the outset is crucial for unlocking the full potential of this powerful technology.

How are content marketers using prompt engineering in 2024?

One of generative AI’s most relied-on (and perhaps most controversial) uses in 2024 is content creation. Simple AI tools like Grammarly can run spelling and copy-editing checks, but a handful of tools take things a huge step further. Consider the following benefits of using AI for content creation, along with some engineered AI prompts that might help inspire content creators when using generative tools like ChatGPT:

Streamline content creation

Prompt-based AI can help you brainstorm new blog posts, generate product descriptions, craft engaging social media captions, and even write website copy in different tones and styles. 

Example prompts for content creation: 

  • “Create a listicle blog post with 10 engaging tips for businesses to leverage social media marketing in 2024.”
  • “Write a personalized email campaign for customers who haven’t purchased in the past three months, offering exclusive deals and re-engaging them with our brand.”
AI prompts can support content creation.

Banish writer’s block

Hit a creative wall? AI can help you jumpstart your writing process. Use it to generate outlines, suggest alternative phrasing, simplify technical terms, or even draft entire sections based on your initial ideas.

Example prompts to get the creative juices flowing: 

  • “Continue this paragraph about the benefits of using explainer videos in marketing campaigns, focusing on increased engagement and improved conversion rates.”
  • “Explain the concept of blockchain technology clearly and concisely, suitable for a general audience with no prior technical knowledge. Use simple language and relatable examples.”
AP prompts can inspire creativity

Diversify content formats

Expand your content repertoire beyond traditional text. Use generative AI prompts to create scripts for explainer videos, product descriptions in a conversational tone, or even catchy email subject lines.

Example prompts to expand your content’s range:

  • “Write a 2-minute script for an explainer video that introduces the key features and benefits of our new productivity software, targeting busy entrepreneurs.”
  • “Generate five engaging email subject lines promoting our upcoming webinar on social media marketing best practices, using a sense of urgency and highlighting the potential benefits for subscribers.”
AI prompt can help you diversify content creation.

A word on risk: Responsible use of AI-generated content

It’s important to remember that AI-generated content is a powerful tool, but it’s not a replacement for human expertise and is often easily detectable to human readers. Here are some key considerations:

  • Fact-check and edit: AI-generated content may not always be factually accurate. Always double-check the information and edit for clarity, tone, and brand consistency.
  • Prioritize ethical considerations: Be mindful of potential biases in the training data of AI models. Use prompts that promote inclusivity and avoid reinforcing stereotypes.
  • Maintain human editorial oversight: The human touch remains essential. Use AI as a springboard for creativity, not a crutch for critical thinking.

Another essential risk of AI content creation: sinking your site in the SERPs. Google’s most recent core algorithm update in March 2024 has caused significant volatility for sites with AI-created content. Proceed with caution and ensure you are using AI responsibly to avoid being penalized in the SERPs.

Beyond content creation: Using AI as your technical teammate

While content creation support can be a benefit of prompt engineering, AI offers all kinds of technical applications for marketers and growth leaders. Let’s explore how leveraging AI can go far beyond catchy headlines and blog posts:

Unveil customer insights with statistical modeling

Gone are the days of gut feeling or intuition driving marketing decisions. AI tools can analyze vast amounts of customer data to identify trends, predict customer behavior, and inform targeted marketing strategies.

Example prompts for uncovering actionable insights:

  • “Using historical sales data and customer demographics, develop a statistical model to identify which customer segments are most likely to respond positively to our upcoming discount promotion.”
  • “Analyze customer sentiment from social media conversations and online reviews to identify areas of customer satisfaction and dissatisfaction with our brand, focusing on recurring themes and potential areas for improvement.”
Using the right AI prompts can help draw insight from collected data.

Forecast future success (and slowdowns) with sales prediction

Predicting future sales performance is crucial for strategic planning. Marketers can utilize AI to analyze market trends, competitor activity, and historical sales data to generate accurate sales forecasts.

Example prompts to help you look ahead and see what might be around the corner:

  • “Based on current market conditions, competitor analysis, and historical sales trends, predict our sales figures for the upcoming quarter, incorporating a best-case, mid-range, and worst-case scenario.”
  • “Develop a machine learning model that forecasts customer churn based on past purchase history, website activity, and demographic information. Identify customers at risk of churning and implement targeted retention strategies.”
AI prompt engineering can inform future predictions and modeling.

Automate routine tasks

Marketers often spend valuable time on repetitive tasks like creating basic website landing pages or email marketing templates. AI can streamline these processes by generating basic code snippets based on your specifications.

Example prompts for automating routine tasks:

  • “Generate the HTML code for a simple landing page promoting our new e-book, featuring a signup form and a product description with a clear call to action button.”
  • “Create a Python script that automates the process of generating email marketing campaigns based on pre-defined templates, dynamically inserting personalized customer data and product recommendations.”
The right AI prompts can help you automate routine tasks.

Leave the numbers up to the experts. Learn more about Data Strategy services from Tallwave.

The art of the effective AI prompt

Now that you’re armed with the knowledge of prompt engineering potential, here are some key tips to ensure you get the most out of your AI collaborator:

  • Clarity is key: The more specific and clear your prompts are, the better the AI will understand your intent and deliver the desired output. Avoid ambiguity and focus on providing all the necessary context.
  • Start simple, refine gradually: Don’t overwhelm yourself with overly complex prompts initially. Start with basic instructions and gradually refine them based on the AI’s response.
  • Embrace iteration: The beauty of prompt engineering lies in its iterative nature. Experiment with different prompts for the same task and see what generates the best results.
  • Leverage examples and templates: Many AI tools offer built-in templates or successful prompt examples from other users. Utilize these resources as a starting point for your own prompts.
  • Remember context is king: The more context you provide about your target audience, brand voice, and desired outcome, the better the AI can tailor its response.
  • Think like a teacher: Imagine you’re instructing a student on how to complete a task. Frame your prompts in a clear, concise, and easy-to-understand manner.

Balancing efficiency and ethics in AI

AI has become a powerful tool in the marketer’s arsenal, promising to streamline tasks and unlock new levels of efficiency with perfectly engineered prompts. However, this exciting new frontier comes with a responsibility to ensure its ethical and responsible use. 

At its core, AI ethics is a framework of moral principles that guide the development and use of artificial intelligence. Organizations like UNESCO are actively creating guidelines to address potential issues like bias and misuse.  

Why should marketers care about ethics in AI? While these tools can be performance game-changers, neglecting ethical considerations can lead to brand damage, legal problems, and even product failures.  

Understanding key concerns like data privacy, fairness, and potential biases within algorithms is crucial for navigating the ethical landscape of AI marketing. By acknowledging these challenges and taking proactive steps, marketers can leverage the power of AI responsibly, safeguarding their brand reputation and building trust with customers in the process.

Embrace the future of digital marketing with Tallwave

While AI offers undeniable power and efficiency gains, it’s crucial to remember that marketing is ultimately a human endeavor. The ability to understand customer needs, craft compelling narratives, and build genuine connections remains irreplaceable. The key lies in striking a harmonious balance between cutting-edge AI tools and the irreplaceable human touch.

This is where Tallwave comes in. We can help you navigate the exciting world of prompt engineering, empowering you to leverage the strengths of AI while ensuring your marketing efforts resonate with a human audience. By working together, we can unlock the full potential of AI marketing, achieving success that’s both ethical and impactful. Let’s talk.

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Customer Engagement Highlights Uncategorized

Unlocking the power of data storytelling

Once upon a time, interpreting data was as simple as processing numbers. But with the volume of data collected multiplying exponentially every day, simply being able to analyze and interpret that data is no longer sufficient. Quickly providing data points and metrics without additional context and a story around what happened to produce the numbers and what to do about it is meaningless to business users who need to make decisions for their organization. 

Enter the world of data storytelling, the art of communicating data-driven insights effectively. This approach embraces narrative analytics and weaves facts and numbers into actionable insights.

The prologue: Why data storytelling matters

Data-driven storytelling is the art of transforming complex data sets into a compelling narrative. This narrative uses context, visuals, and insights to engage a specific audience and ultimately influence their decisions or understanding. It should be an essential part of any organization’s data strategy for two main reasons:

1. Data storytelling fosters engagement

In business, engagement is not just a buzzword; it’s a strategic imperative. While raw or straightforward data might be the backbone of decision-making, it’s the narrative around that data that provides qualitative context for quantitative information and mobilizes teams to action. Combining both straightforward data with an illustrative narrative via data storytelling transforms abstract figures into a compelling story, fostering engagement and understanding at every level of your organization.

2. Data storytelling enables understanding across teams

Bridging the gap between technical experts and non-technical stakeholders is a challenge that data storytelling helps to address. It presents the data in a way that is accessible to business users regardless of their technical or analytical expertise. There is an increasing need across numerous industries to bring these two groups together via data storytelling. Read our blog about bridging that gap here and how we have been successful at bringing teams together for the benefit of the overall organization. 

The plot: Elements of compelling data stories

The elements of compelling storytelling with data are like characters coming together to set the scene in your favorite novel. Consider your approach to the data story, how you’ll illustrate the story, and the potential impacts of your story with the following elements:

Audience-centric approach

In order to be effective, a data story must be crafted for the audience at hand. The storyteller must ensure they understand the motivating factors and perspectives of the intended audience. This oftentimes requires stakeholder discussions to build a clear understanding of metrics and KPIs that are relevant to respective audiences and/or persona groups. It also requires a solid partnership and mutual understanding of how stakeholders are using their data to make informed decisions. Without this context, storytelling around the data is much less effective.

Visualizing data for clarity

Data visualization is the brushstroke that brings your data story to life. Data without visuals is like a story without illustrations—less engaging and prone to misinterpretation. However, it is important to explore the art and science of visualizing data for clarity. Certain data points might be best visualized in a scatter plot versus a bar chart and it is important to think through the best and most straightforward visualization for stakeholders. Visual literacy is a superpower. It is also critical that data visualizations do not leave anything to be misinterpreted. While it is important not to clutter and overly complicate visualizations, clear and succinct titles and labels can make or break a visualization. Take a look at our example below.

Psychological power of storytelling

The human brain craves stories. There is neuroscience behind storytelling and why stories stick. Engaging multiple parts of the brain enhances the memorability of your data narrative, creating lasting impressions that transcend the numbers. Doing so is akin to creating a symphony of cognitive responses. By triggering various brain regions simultaneously, a well-crafted narrative becomes an immersive experience, leaving a lasting imprint on the audience’s memory.

Giving data a narrative engages multiple parts of the brain for emotional and empathetic processing. This is where it becomes more about the story than the raw numbers. The story becomes an experience, a journey that the audience embarks upon, making the data more than just information—it becomes a memorable and impactful narrative. The data narrative becomes a part of the audience’s cognitive landscape, ready to be recalled and reflected upon.

Learn more about Data Strategy & Analytics Services at Tallwave.

Intermission: Tallwave’s data storytelling in action

Narrating the full sales picture: A real example

Let’s dive into a real-world application of data storytelling. During a typical monthly reporting cycle, one of our clients, an e-commerce company, identified declines in sales and revenue across multiple digital channels on their owned website. Initially focused solely on e-commerce sales, the team was alarmed by the decline in organic and paid search sales. However, a more comprehensive understanding of the full story, including sales through brick-and-mortar partners, 3rd party marketplaces, in-store, and phone orders revealed a different story—increases in sales with partners resulted in greater TOTAL sales, and naturally cannibalized some of the sales from other sources.

In order to better tell this story, Tallwave created customized, purpose-built, actionable dashboards in Google Looker Studio. These dashboards helped to mitigate risk of misinterpretation, presenting a clear and concise representation of the sales landscape. It is not uncommon for individual stakeholders to misinterpret data that they might have a personal or departmental bias toward and inadvertently lead other stakeholders astray. Nor is it uncommon for individual stakeholders to exert their own bias in a way that tells the story they believe to be true. This is why it is important to include cross-functional stakeholders involved in rounding out the data story or dashboard visuals to ensure consistent KPI understanding and consensus.

A purpose built dashboard identifying products with a low add to cart to view ratio that the organization should focus on improving this ratio by comparing competitor price points, inventory availability, lack of useful information listed on the product page, etc..

The resolution: Practical tips for effective data storytelling

Reducing complexity

In an era where information inundates every corner of our professional landscape, simplicity emerges as a guiding principle in effective data storytelling. The call is clear: advocate for simplicity in both language and visuals, ensuring your data story is accessible to all members of your audience.

Language Simplicity: Complex jargon and convoluted terminology can act as barriers to understanding. Embrace clear and concise language, choosing words that resonate with a broad audience. Your goal is not to showcase your vocabulary but to convey the essence of your data story in a way that everyone can grasp.

Visual Simplicity: Complexity in data visualizations often leads to confusion. You do not gain style points for making data visualizations complex and difficult to interpret. Instead, opt for visual simplicity. Choose charts and graphs that convey the message without overwhelming the viewer. Consider the power of minimalist design, where each visual element serves a clear purpose.

The art of simplicity in data storytelling lies in finding the delicate balance between conveying intricate insights and ensuring comprehensibility. Simplicity does not mean sacrificing depth; rather, it involves distilling complexity into a form that enlightens rather than perplexes.

The theme: Emotion and impact

In the realm of data, numbers tell a story, but it’s the human connection that makes it memorable. Data storytelling is not just about numbers; it’s about people.

Humanizing Data: Every data point represents a real-world scenario, a decision, or an outcome that impacts individuals. Infuse life into your data by humanizing it. Share anecdotes, testimonials, or real-life examples that resonate with your audience. By connecting data to real people, you create a narrative that goes beyond statistical significance.

The true power of emotion in data storytelling lies in its ability to inspire action. An emotionally resonant story is more than a set of charts; it’s a call to action. 

Within a compelling data story each element contributes to the overall harmony. It is also important to maintain consistency in your data narrative.

The twist: Can AI effectively assist in data storytelling?

AI in data storytelling: A double-edged sword

Data and analytics are always evolving and changing. Along with many other use cases, Artificial Intelligence (AI) emerges as a powerful tool to aid in crafting compelling narratives. However, this opportunity also comes with its complexities and challenges. Let’s explore how AI can effectively assist in data storytelling, its responsible usage, and the potential pitfalls that counteract efforts to tell a story with data.

AI’s role in enhancing data storytelling

AI offers the ability to sift through vast datasets, identifying patterns and trends that a human might overlook. Automation from AI can also assist in uncovering key narratives without as much of an exhaustive manual effort. With the help of AI, data stories can be tailored for specific audience segments, considering individual preferences and comprehension levels. This personalization ensures that the narrative resonates with diverse stakeholders, enhancing engagement and understanding.

Using predictive analytics can also be a big factor in forecasting future trends based on historical data. Integrating these predictions into data stories provides a forward-looking dimension, empowering decision-makers with strategic insights.

Responsible use of AI in data storytelling

It is important to be transparent about how you use AI algorithms to contribute to data storytelling. Specifically, outlining how AI is being used to process data ensures that stakeholders understand the methodology behind automated insights. This is critical for building trust with your audience.

While AI assists in streamlining the data analysis process, human oversight is absolutely imperative and neglecting the need for human oversight when building a data story could result in a less effective narrative. Human intuition and contextual understanding add a nuanced layer to storytelling that AI may lack. Striking a balance between AI assistance and human interpretation is key to responsible usage.

There is also a risk of AI identifying correlations without establishing causation. This can lead to misinterpretation of data relationships, potentially distorting the narrative and steering decision-makers in the wrong direction.

If you endeavor to use AI as a tool in your toolkit, to enhance your story, rather than using it as a crutch to tell your story, you will be exponentially more effective. 

The resolution: Navigating success in data storytelling

By acknowledging and addressing complex datasets, avoiding unnecessary complexity to prevent misinterpretation, and encouraging data literacy across various teams, organizations can transform the potential impediments into catalysts for success in data storytelling. At Tallwave, our data experts are equipped to support your data storytelling needs. We can help you embrace technical advancements, foster a culture of collaboration, and prioritize education to bridge the knowledge gap. Let us help you tell a comprehensive data story today!

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Strategy

The analytics evolution: Embracing the power of GA4 metrics

Now that we’ve all had a few months of working exclusively within Google Analytics 4 (GA4), it’s worth taking a moment to explicitly define some of the new metrics within the platform and how they compare to Universal Analytics metrics.

As clients and marketers navigate this transition and consider these metrics, understanding their distinct functionalities and how they diverge from their Universal Analytics counterparts becomes paramount for harnessing the true potential of GA4. This is especially true when it comes to metrics related to average duration.

Decoding the differences between Universal Analytics and GA4 metrics

In Universal Analytics (sometimes called GA3), metrics like Average Time on Page and Average Session Duration were widely used to measure user engagement. However, with GA4, there’s a shift in how engagement and user behavior analytics are measured. GA4 introduces Average Engagement Time, which is an entirely different way of measuring user engagement. Let’s compare the differences with the new metrics in GA4.

1. Average Time on Page vs. Average Engagement Time

In Universal Analytics, Average Time on Page measured how long users spent on specific pages. It was calculated by measuring the time between consecutive pageviews, assuming that the last page of a session didn’t require a subsequent view. However, GA4’s Average Engagement Time takes a more nuanced, user-centric approach. This metric assesses the actual time a user actively engages with the page, disregarding instances where the tab loses focus. For instance, if a user switches to another tab or app, GA4 doesn’t consider this time in the calculation, providing a more accurate depiction of user interaction and true engagement duration. Let’s take a look at more examples below:

Average Time on Page (Universal Analytics):

Calculation method:

Utilizes the time between two-page hits to compute the average time on a specific page within a session.

Measurement Scenarios:

  • Sequential Page View Scenario:
    • Scenario: User visits “Page A” for 10 minutes, moves to “Page B” for 25 minutes, and leaves.
    • Calculation: “Page A” registers a time on page of 10 minutes, but with no next page to feed into the model, no time on page data is captured for “Page B.”
  • Interruption In Session Scenario:
    • Scenario: User spends 5 minutes on “Page A,” switches to another site for 5 minutes, then returns to spend 25 minutes on “Page B.”
    • Calculation: “Page A” registers a time on page of 10 minutes, but “Page B” remains unmeasured due to interruption.
  • Bounce Scenario:
    • Scenario: User bounces from “Page A” after spending 30 minutes, with no subsequent page views.
    • Calculation: “Page A” shows no recorded time due to the absence of subsequent page visits.

Average Engagement Time (GA4):

Calculation method:

Measures the average length of time the website remains in focus in the browser, excluding time when the tab loses focus.

Accurate measurement scenarios:

  • Sequential Page Views:
    • Scenario: User spends 10 minutes on “Page A,” then 25 minutes on “Page B.”
    • Calculation: “Page A” registers 10 minutes and “Page B” registers 25 minutes.
  • Interruption in Session:
    • Scenario: User spends 5 minutes on “Page A,” loses focus for 5 minutes, returns to spend 25 minutes on “Page B.”
    • Calculation: “Page A” accounts for 5 minutes, while “Page B” registers 25 minutes.
  • Bounce Scenario:
    • Scenario: User spend 30 minutes on “Page A” then bounces.
    • Calculation: “Page A” registers 30 minutes.

2. Average Session Duration in Universal Analytics vs. GA4

The Average Session Duration in Universal Analytics was a fundamental metric used to gauge overall session length. It calculated the total duration of a session from the first to the last hit, including the time spent on exits or bounces. Conversely, GA4 approaches this with a subtle yet crucial difference. Instead of calculating the entire session time, it focuses on active engagement within the session, excluding periods of inactivity or when the browser tab loses focus. This shift emphasizes active engagement, providing insights that are more indicative of genuine user interest and intent.

3. Implications of transitioning metrics

The transition from Average Time on Page/Average Session Duration to be more focused on Average Engagement Time results in some implications for Marketers who are trying to interpret user behavior. The new methodology in GA4 aligns more closely with actual user engagement, offering a more precise view of user interaction on the website. This transition necessitates a shift in perspective, especially for those accustomed to Universal Analytics metrics. Embracing this change unlocks the potential for more accurate insights into user behavior, ultimately empowering businesses to tailor their content strategies more effectively based on genuine user engagement patterns.

This shift in perspective empowers marketers and businesses to ditch vanity metrics like average pageviews and prioritize meaningful interactions. They can craft targeted campaigns based on engagement patterns, identify conversion pathways hidden in passive metrics, and ultimately, drive growth based on genuine user interest.

Learn more about Data Strategy & Analytics services from Tallwave.

So long Universal Analytics, it’s time to embrace GA4 and all that comes with it

As we bid adieu to the familiar metrics of Universal Analytics and embrace the increased customer centricity of GA4, it’s like saying goodbye to an old friend and welcoming a more insightful companion. The shift from both Average Time on Page and Average Session Duration to Average Engagement Time empowers marketers to better understand true user behavior.

By equipping yourself with the right tools and knowledge, you can leverage GA4’s advanced capabilities to gain a deeper understanding of user behavior to uncover hidden conversion paths and personalize experiences for targeted segments. Embracing GA4 and its new measures will also let you prepare for the future of digital analytics with a platform built for flexibility and adaptability. And you don’t have to go at it alone. We’re just a click away and can help you build a meaningful data strategy that enables actionable insight.

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Customer Engagement Strategy Uncategorized

Data-centricity: Takeaways from the Snowflake Data Cloud World Tour 

We recently had the privilege of attending the Snowflake Data Cloud World Tour event in Austin, Texas. It was a full day of presentations, demos, and customer breakout sessions dedicated to discussing the technical and cultural challenges that organizations face as they strive to become more data-driven. Industry leaders who have harnessed the power of Snowflake’s data processing technologies and platform experts convened to shed light on the evolving landscape of data utilization and the critical need for businesses to adapt.

In today’s business environment, where access to data has reached unprecedented levels, success hinges not on the sheer volume of data you have access to but on how effectively you can leverage it to make informed decisions on an ongoing basis. In fact, research by Mckinsey & Company found that insight-driven companies report above-market growth and EBITDA (earnings before interest, taxes, depreciation, and amortization) increases of up to 25%.

Our most significant takeaway from the event was the pervasive sense of urgency, coupled with encouragement, that resonated throughout the sessions we attended. In the fast-paced world of business, staying ahead of the curve is imperative. The lifeblood of modern organizations is data, and if your company hasn’t already placed your first-party data at the forefront of your decision-making process, you risk falling behind. While it’s one thing for decision-makers to prioritize data, it’s another to instill a data-centric culture throughout your entire organization.

During the event, heard from business leaders who recounted their early efforts to get their data houses in order. Some of these efforts date back to 2017 and 2018 when these visionaries recognized the transformative power of data and embarked on a strategic journey. Fast forward, as 2024 approaches, data isn’t merely a choice—it’s a necessity. If your organization hasn’t embraced a data-centric approach yet, the time to dive in is now.

Right after returning from the event, we received a timely report from Experian Research, focusing on the “Data Quality Revolution.” The message was crystal clear: if your business isn’t placing a strong emphasis on access to high-quality data, you should be, and the time to act is now. Continue reading as we explore the key takeaways from both the Snowflake event and the complementary Experian report.

The shift towards data-centricity: Where we stand

In the realm of data-driven decision-making, businesses are no longer tentatively testing the waters; they’re taking a deep dive. As highlighted in the Experian research report, “Over a third of business leaders say that better and faster decisions using data is a top priority to respond to market pressures. A continuous influx of accurate data enables team members—technical or not—to act with confidence. This is a claim that we see year after year and is vital in a market that is moving faster than ever.” 

This sentiment echoes the progressive strides made by forward-thinking companies showcased at the Snowflake event. For instance, the Senior Director of Data Architecture, Engineering, and Platforms at a Fortune 500 athletic retailer shared insights into their innovative use of real-time data. By monitoring inventory levels and analyzing optimal pricing strategies in real-time, they’ve effectively maximized space utilization and ensured optimal profitability without compromising margins. This sophisticated approach underscores how organizations at advanced stages of data maturity leverage their data reservoirs to tackle genuine business challenges. Experian defines data maturity as “the extent to which your business can collect valuable data, derive meaning from it, and leverage this information in the decision-making process.” 

Successful companies are often able to point to a mature data strategy that is disseminated throughout the organization that lends them a competitive edge. Consider Netflix or Amazon, for example. Both companies utilize their data to personalize content and provide product recommendations that increase customer satisfaction and ultimately drive greater customer engagement, retention, and overall revenue. 

However, this level of sophistication isn’t universal. For numerous organizations, the journey along the data maturity curve is just beginning. Bridging the gap between recognizing the potential of data-centricity and effectively implementing it remains a common challenge encountered across various industries.

A graph going the data maturity curve

The challenges: Technical and cultural hurdles

One of the key challenges emphasized at the event was the demand for tools that can expedite the transition to data-centricity without subjecting organizations to extended development timelines. In today’s fast-paced business landscape, waiting months for development to design and implement complex systems is simply not feasible and leads to frustration throughout the organization. 

What businesses need are solutions that are agile, efficient, and user-friendly. Experian “[s]urveyed businesses are looking at their technology to plan for scaling, expanding, and innovating data quality initiatives including easy-to-use tools for business users (50%).”  The emphasis on user-friendly tools highlights a critical aspect of overcoming technical hurdles—providing accessible platforms that empower business users, regardless of their technical backgrounds, to harness the full potential of data, ensuring that the journey towards data-centricity is smooth and collaborative.

Learn more about building bridges between business and technology.

Embracing the potential, feeling the pain

Many organizations now find themselves at a crossroads—they’re acutely aware of the immense potential that a data-centric approach offers, but they are equally familiar with the growing pains that accompany this transformative journey. The heightened awareness of the benefits is juxtaposed with the acknowledgment of the challenges. This duality can be both motivating and overwhelming. The Experian research report echoes this sentiment, revealing a profound truth: “Year after year, we find that data investment equates to business growth. Our study shows that 95% of super performers—these high-achieving and data-mature leaders—believe that data quality is fundamental to business operations going forward.”

This statistic underscores the critical importance of data quality in the contemporary business landscape. It’s not merely a matter of investing in data; it’s about investing in high-quality, accurate data that can fuel informed decision-making and drive business growth. The realization that data quality is intrinsically linked to future success is a powerful motivator for organizations navigating the complexities of the data-centric journey. It signifies a shift in mindset from viewing data as a mere asset to recognizing it as a cornerstone upon which robust business operations are built.

While the challenges are palpable, so are the rewards. Embracing the potential of a data-driven approach means not only understanding the significance of data quality, but also taking proactive steps to address it. As organizations grapple with the intricacies of data utilization, this awareness becomes a guiding light, illuminating the path toward transformative change. By investing in data quality, businesses not only mitigate risks but also position themselves for sustained growth and innovation.

In this landscape of shifting paradigms, Tallwave stands as a strategic partner, ready to navigate the complexities of the data revolution alongside your organization. We offer tailored solutions designed to guide you and your teams to think through what data matters to your organization and build a culture that ensures your business is not just prepared for the future but actively shaping it. Let’s embark on this transformative journey together—where challenges become opportunities and data becomes the cornerstone of your success.

Tallwave: Your partner in the data journey

If your organization is ready to embark on the journey of embracing data-centricity but you’re uncertain about where to start, Tallwave is here to provide expert guidance. We know the intricacies of this transformation, offering expertise in both technical solutions and cultural adaptations across various teams in your organization. Our approach is tailored to your specific needs, ensuring a seamless integration of data-centric practices into your existing framework.

Ready to make the shift?

Don’t wait until you’re left further behind—take action now. Discover the business benefits, navigate the challenges, and transform your data potential into tangible results. Your journey toward a data-centric future starts today. We’re ready to lead the way.

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Strategy

Building bridges between business and technology

There’s often a substantial divide between business and technology teams. It’s more than mere miscommunication; it’s a foundational misunderstanding of each other’s domains and a lack of shared context for each other’s needs and goals. Imagine architects striving to convey their visions to builders without comprehending the construction process, or builders attempting to decipher architectural blueprints without a sense of the broader design. This disconnect between business and technical realms can lead to frustration, costly errors, and missed opportunities. This is especially true for marketers and developers or technology partners.

There’s a unique power that comes with aligning technical teams with marketers to ensure a seamless and productive collaboration. When these two crucial functions get on the same page, the results can be transformative. Clear communication and a shared context for business goals, needs, and priorities eliminate confusion, accelerate project timelines, and enhance innovation. By bridging the gap between tech and marketing, you unlock the potential for more creative and data-driven strategies, resulting in a competitive edge. In case you don’t happen to have a magical marketing-to-tech translator, here are some tips for achieving shared understanding.

Speaking in Tongues: Bridging the Understanding Gap

There are many reasons communication breakdowns occur between business and technology teams. In many cases, divergent communication styles and thought processes are responsible for misunderstandings, but there’s often more to the story. Here are three common gaps and possible solutions to align teams and come together.

1. Language and Jargon: Standardizing Terminology for Clarity

One thing business and technical teams have in common is they love their jargon and acronyms. Ironically, that’s also the root of many communication breakdowns that occur between them. In many organizations, seemingly straightforward terms can have very different meanings when used in different ways or by different teams. For example, will terms like “lead” or “account” be interpreted the exact same way to your sales, marketing, and engineering teams? Odds are they won’t.

Standardization of terminology: Bridging this language gap requires standardizing terminology across organizational departments where possible, especially for terms that apply to the business as a whole. Ensuring a shared understanding of standard terms within the organization can eliminate a significant hurdle to effective communication and collaboration between business and technical teams.

2. Lack of Context: Fostering Alignment with Business Goals

Miscommunication often springs from a lack of context. This can happen when team members occasionally lose sight of overarching business objectives. But more often, the challenge is that different teams don’t understand one another’s roles in supporting shared business goals and the intersection points between them. This context deficit can lead to misinterpretations and misaligned efforts and expectations, impeding collaboration between business and technical teams.

Clarity in business goals: Shared context begins with a shared understanding of business goals. Whether achieving growth, reducing costs, enhancing customer satisfaction, or another objective entirely, each team needs to have a clear line of sight into business goals. Connecting actions to goals: Besides clarifying the business goals, teams need to understand their roles in supporting them. For example, a developer or technical SEO optimizing website performance may need help to grasp how their work directly impacts customer satisfaction and revenue growth. 

Transparent communication: Transparent and consistent communication on progress toward and contributions to business goals can be a potent tool for bridging the context gap. Regularly sharing updates on progress toward goals and highlighting how various teams’ contributions fit into the broader strategy can help drive alignment across groups and foster shared understanding and ownership over goals. 

3. Communicative Friction: Creating a Safe Space to Negotiate Meaning

Miscommunication is typically unintentional. Individuals introduce their own mental models, biases, and past experiences into what they say, hear, and interpret. People also tend to prioritize their own perspectives, needs, and priorities without considering the viewpoint of others, often without realizing it. This can lead to misunderstandings, even when the message seems clear. For example, a statement about cost-cutting measures may be perceived positively by one team and negatively by another, depending on their prior experiences.

Fostering a culture of open communication: Encouraging empathy, active listening, and open dialogue can help individuals become more aware of their biases and better understand the perspectives of others. Organizations should strive to create a culture of open communication where employees feel comfortable seeking clarification and providing feedback. This can help prevent misunderstandings from festering and becoming more significant issues foster a more positive work environment, and enhance overall productivity and collaboration.

Come Together / Connect / Bridge the Gap

Understanding the three common challenges outlined above can help you avoid some of the most frequent culprits of the divide between tech and business teams. Here are a few actionable steps you can take across your organization to further bridge the gap, prevent miscommunication, and drive shared understanding.

Set shared and aligned goals

They say what gets measured gets done. If that adage is true, what gets measured collectively gets done collaboratively. In setting goals, ensuring clear connections between individual goals, team/department goals, and company goals can help get disparate teams rowing in the same direction. And when appropriate, establishing shared goals that require collaboration to achieve can incentivize teams to work together toward a common objective. 

Establish clear communication protocols

Develop and document communication protocols that outline the preferred methods of communication, frequency of updates, and responsible parties for different types of projects or initiatives. This will help ensure everyone understands the expectations and processes for sharing information.

Implement a project management tool 

Invest in a robust project management tool to centralize project-related information, tasks, and progress updates. This tool should be accessible to both technical and business teams, making it easier for everyone to stay informed and track project status.

Cross-train team members 

Encourage cross-training between technical and business team members to enhance mutual understanding. When team members understand each other’s roles and responsibilities, they are better equipped to communicate effectively and anticipate each other’s needs.

Foster a culture of transparency 

Promote a culture of openness and transparency within your organization. Encourage employees to share information, ask questions, and provide feedback without fear of repercussions. When information flows freely, it reduces the chances of misunderstandings and miscommunications.

Conduct regular feedback sessions 

Organize periodic feedback sessions where team members can discuss their experiences and challenges related to communication. Use this feedback to identify areas for improvement and implement necessary changes.

Use visual aids and documentation 

Encourage the use of visual aids, diagrams, and well-structured documentation to convey complex technical information more efficiently for non-technical team members.

Monitor and adapt 

Regularly assess the effectiveness of your communication strategies and adjust them as needed. Solicit feedback from team members and stakeholders to ensure that the solutions you implement address the specific challenges in your organization.

By implementing these solutions, you can foster better communication and collaboration between technical and business teams, ultimately bridging the gap and reducing the chances of miscommunication that can hinder your organization’s success.​​

Let a strategic partner facilitate clear communication

Just like a therapist can provide a trained, empathetic ear to overcome discord in personal relationships, enlisting a neutral third party can help break through the silos between business and tech teams to achieve mutual understanding. With a team of experts proficient in technical, marketing, and business strategy, our ability to translate between business stakeholders and technical teams is just as valuable to our clients as the work we do as data, marketing, and business strategists and practitioners.

By partnering with Tallwave, our clients gain more than an intermediary; they acquire a strategic ally capable of fostering collaboration and alignment across cross-functional teams and projects to create real impact for their businesses. Just as architects and builders seamlessly collaborate based on a shared blueprint, Tallwave facilitates the fusion of business and technology worlds, ensuring that your organization’s goals are not just spoken but realized.

Ready to bridge the gap? Let’s talk.

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Strategy

From chaos to clarity: Data quality management for actionable insight

Data quality management empowers business success

Data quality plays a crucial role in the business landscape when it comes to informing strategy and enabling growth. As organizations strive to do more (or at least the same) with less, mastering data quality management is imperative. Collecting and analyzing the right data points can help you retain customers, enhance customer experiences, optimize campaigns, boost acquisition, and achieve sustainable growth even as the economy shifts.

But in today’s data-driven environment with evolving tracking technology, compliance demands, and AI disrupting the status quo, problems with data quality and quantity problems are amplified. And these issues come with a steep price: misinterpreted insights, wasted resources, and even concerns with ethics, transparency, and consumer privacy.

Effective data quality management is complex, but it doesn’t have to be. Understanding the causes and consequences of poor data quality, finding a source of truth, building a data-driven culture, and working with the right data enablement partner all come together to empower informed decisions that lead to outstanding experiences. 

Missed opportunities: Causes and consequences of poor data quality

Once upon a time, CMOs and growth leaders spent their days thinking about brand strategy with creative license and assumed success came from stellar messaging. But today, these roles hinge on emerging technology, marketing agility, and driving ROI while expecting immediate results — all of which depend on quality data.

Statistics reported in the 2023 Braze Customer Engagement Review indicate that more than one-third of marketing leaders cite the collection, integration, management, and accessibility of data as their top challenges when it comes to customer engagement.

These data challenges come in many different forms. Here are a few common problems in marketing data quality management:

More data, more problems

Organizations often find themselves drowning in a sea of information in the era of big data. According to the Braze survey mentioned above, a staggering eight out of every ten leaders surveyed admitted to collecting more data than they can realistically use.

Graphic image displaying that 8 out of 10 marketing leaders believe they are over-collecting data.

Data down the drain

With so much data at hand, so much goes to waste. Experian’s most recent data experience research report stated that an estimated 73% of all collected marketing data goes unused. Overcollected and underutilized data can come with high costs, ranging from consumer privacy risks to wasted resources.

Have you stored your historical data from Universal Analytics? The clock is ticking! Learn more about GA4 migration.

Standardization struggles

A lack of standardization and guidelines in your data strategy leads to all kinds of complications. Inconsistencies across systems and departments create confusion, and inaccurate data can mislead decision-making processes. Incomplete data leads to gaps in insights, while outdated data fails to reflect the current reality. This results in unreliable data and an inability to inform strategy.

Disparate times, disparate measures

Fragmented data is a major challenge for many (if not all) organizations. According to a study conducted by Wakefield Research, 441 of the 450 senior data leaders surveyed indicated that data silos exist within their organization. In addition, 311 of the same leaders report they have “trapped” data they cannot access. Rescuing trapped data can open opportunities for actionable insights.

What happens when organizations unlock trapped data? You’ll find untapped insights into user interactions that allow you to create outstanding customer experiences. Learn more about Tallwave’s success with data unification and enablement strategy.

Left brain, right brain

What causes all the data debacles? It could be that marketing people and data people don’t always speak the same language. Braze found that 42% of respondents reported that their top data management challenge stems from working with internal data scientists and IT departments who don’t understand marketing priorities. The second biggest challenge is that marketing talent lacks data skills.

Infographic stating that 42% of respondents reported that their top data management challenge stems from working with internal data scientists and IT departments who don’t understand marketing priorities.

Data mismanagement comes with consequences that can be summarized in two words: missed opportunities. Without a data-driven strategy, you’ll likely end up with wasted resources and miss out on what matters: customer retention, acquisition, and growth.

Seizing opportunities: 4 considerations for collecting quality data

Access to high-quality data enables business leaders to better understand their customers: their needs, preferences, expectations, and buying habits. This understanding helps companies successfully satisfy and engage customers, increase brand awareness, and drive sales conversions.

While ongoing data quality management might feel like an uphill battle, there are a few best practices you can implement to harness the power of accessible analytics. 

Here are four actionable steps to consider:

  1. Align data collection with business goals: Start by aligning your data collection efforts with your organization’s goals and objectives. By focusing on the data that truly matters, you can avoid the trap of over-collection and instead gather the insights necessary to drive meaningful actions.
  2. Standardize data across systems and departments: Establishing data standardization protocols is vital to ensure consistency and accuracy. Implementing standardized data models, formats, and definitions across systems and departments fosters a unified view of the data and enhances its reliability.
  3. Implement data validation and verification processes: Introduce robust data validation and verification processes to maintain data integrity. These processes involve checking for completeness, identifying and resolving inconsistencies, and ensuring data accuracy through various validation techniques.
  4. Invest in data cleansing, enrichment, and visualization tools: Leverage data cleansing and enrichment tools to improve the quality of your data. These tools can help identify and rectify errors, fill in missing information, and enhance the overall value of your data. Additionally, data visualization tools and dashboards give stakeholders the context they need to gain actionable insights from complex data sets.

Understanding and acting upon data compliance requirements are also significant considerations, especially in healthcare. Learn more about HIPAA-compliant web analytics.

Data-driven culture: Collaboration and metrics that matter

To truly master data quality management, organizations need to foster a data-driven culture. This kind of environment empowers leaders to put facts before instincts and take valuable action with each decision.

Unified team, unified data strategy

A data-driven culture is fueled by connection. The symbiotic relationship between data scientists and marketing specialists enables realizing your analytics tools’ full potential, allowing your organization to make data-driven decisions and achieve greater results. When marketing teams and data teams combine forces and truly understand each other, priorities are aligned. 

Prioritizing data quality lets you unlock valuable insights, reach your target audience more effectively, and ultimately enhance customer experiences while maximizing the return on your marketing investment.

The first step in creating a culture defined by data is finding a data strategy and analytics partner who truly understands the metrics that matter. A true enablement partner, like Tallwave, can help you narrow down the most relevant data points to avoid overcollection and support unification. You’ll have access to meaningful insights that drive positive outcomes.


Ready to embrace data enablement? Let’s chat. We can work together to create a unified data strategy that gives you the information needed to implement outstanding experiences. Reach out to Tallwave now.

Categories
Strategy

Healthcare Web Analytics in 2023: Get Your Data In Order

On December 1, 2022, the U.S. Department of Health and Human Services’ (HHS) Office of Civil Rights (OCR) issued a bulletin stating that the use of third-party cookies, pixels, and other tracking technology by healthcare companies may be violating the Health Insurance Portability and Accountability Act (HIPAA). This is in the wake of a year of unprecedented data breaches involving business associates, or third-party vendors, throughout the healthcare industry. 

Bar chart showing a steep increase in healthcare data breaches since 2016
Source: www.hipaajournal.com/healthcare-data-breach-statistics

2022 saw over 700 healthcare data breaches impacting more than 50 million individuals. And nearly a third of the ten most significant breaches were due to third-party tracking pixels from companies like Google and Meta (Facebook). While Google and Meta help companies understand their website and other owned properties’ usage, users of the platform have inadvertently also exposed data ranging from personally identifiable information such as Social Security numbers, driver’s license numbers, and financial account information to medical record numbers, insurance account numbers, and more.

Chart showing healthcare analytics data breaches by entity
Source: www.hipaajournal.com/healthcare-data-breach-statistics

Such breaches come with hefty financial penalties, including fines, settlements, and other repercussions for the entities involved. But a more significant impact is felt by the consumer whose data has been compromised, as stolen personal information can result in identity theft. And recovery from identity theft is often a long and burdensome process.  

Graph showing a steep increase in the number of individuals impacted by healthcare analytics breaches since 2016
Source: www.hipaajournal.com/healthcare-data-breach-statistics

Up until last December when HHS issued its bulletin, it had not provided formal guidelines regarding sensitive healthcare data and HIPAA relative to online tracking technologies. So what does this announcement mean and how can healthcare organizations stay HIPAA compliant?

What do the HHS changes mean for healthcare organizations?

A good starting point is an understanding of the technologies involved and the risks they pose. The HHS announcement specifically speaks to tracking technologies, often third-party, which are generally anonymized. Tracking cookies, specifically pixels, are tiny bits of embedded code used to track a site visitor’s online activity. The data collected from the pixels provides insights that allow the site owner to develop marketing strategies, such as on-site personalized experiences and off-site retargeting campaigns, specific to each site visitor’s behaviors and interactions.

The problem? Many healthcare organizations are using third-party pixels to gain a better understanding of how they can optimize the digital experiences within their public-facing websites and patient portals. And these pixels may be sharing protected health information (PHI) inadvertently with third parties. Most often, the concern lies with pixels on the patient portal, a secure website or application where patients can access and interact with their health data. But PHI can also be collected from the public website and mobile apps in the form of cookies, web beacons, fingerprinting scripts, and other scripts. 

So what constitutes PHI? 

Protected health information is any information related to an individual’s past, present, or future health, healthcare, or payment for healthcare. This includes, but is not limited to:

  • Medical records, be they physical, electronic, or spoken
  • Information pertaining to billing, insurance, or of any financial aspect of an individual’s health or healthcare
  • Demographic information
  • Mental health conditions
  • Tests and laboratory results 
  • All information related to an individual’s diagnosis, treatment, or prognosis
  • Anonymous session user ID

As of December 1, 2022, anonymous session user ID is considered PHI.

Anonymous user identification allows the website to anonymously identify unique site visitors without the user having to log in or consent to a tracking cookie. Anonymous sessions are captured and aggregated and can include data such as (but not limited to) the user’s IP address, geographic location, language, device, and mobile carrier, but is generally, as the name suggests, anonymous. However, HHS has deemed that these data points connect the individual to the entity and therefore can be related to the individual’s past, present, or future health, healthcare, or payment for healthcare.

The addition of anonymous session user ID considered as PHI now adds additional complexity to an already confusing data security landscape. Furthermore, in order to protect themselves and their patients, the onus is on healthcare providers to ensure they and their partners are not improperly using tracking technology on the healthcare provider’s digital properties, mobile apps, etc.

How can healthcare organizations keep web analytics HIPAA compliant?

As there is no easy website or mobile app consent solution, it is best to develop a compliant strategy that will protect both the healthcare organization and its consumers. Developing a compliant strategy requires engaging all departments (marketing, marketing analytics, legal, IT, etc.) and ensuring organizational alignment around it. This starts with examining your current analytics tech stack to determine if it meets both the organization’s needs and HHS requirements.

Is Google Analytics HIPAA compliant?

Over 28 million websites worldwide currently use Google Analytics, over four million of which are in the United States. Of all U.S. industries that use Google Analytics, hospital and healthcare companies are the third most prevalent. Google Analytics isn’t the only option for tracking website data, but it has the largest market share, and for good reason. It is robust and intuitive. But Google Analytics has also faced challenges, having been banned in a few European countries due to General Data Protection Regulations (GDPR) violations. Google did take steps toward addressing the European Union’s GDPR requirements with its recent release of GA4.

So, does Google Analytics meet the new requirements outlined in the HHS bulletin? The simple answer is no. In basic and 360 configurations, GA3 and GA4 no longer meet the HHS compliance requirements. This is primarily due to specific attributes of the data sets, specifically the session and user ID dimensions. 

As a result, healthcare companies are expediting their searches for alternative platforms that will provide organizations with the information they need to measure their digital customer experiences and — more importantly — store that data securely.

After the Universal Analytics sunset on July 1, 2023, you will have a minimum of six months to access your previously processed data. Are you ready to transition to GA4?

What are the best next steps toward achieving compliance?

The first step is to identify and outline requirements for a cohesive transition to a new, compliant platform. The most important of these requirements is a HIPAA-compliant analytics platform provider, one that will be covered under a Business Associates Agreement (BAA). The good news is there are a handful of platforms available that fit this important need. 

Additionally, all businesses are unique and have priorities that must be considered when planning a transition to a new analytics platform. Some examples of priorities might include ease of implementation, tag management capabilities, user limits, integrations with other Google products, and interface complexity, among other things. 

Once requirements have been prioritized across internal teams, analytics owners will be able to guide a best-fit decision.

Whether your organization has been using Universal Analytics for years or you have recently migrated to GA4, Tallwave can help you organize around your requirements, gain internal alignment, and provide expertise on next best options all the way through the implementation and reporting transition. Reach out when you’re ready to learn more.

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Uncategorized

The What, Why & How of Customer Behavior Analysis

Case and point: COVID-19. The pandemic put physical experiences on an indefinite pause and demanded businesses accelerate digital transformations to meet consumer needs. As discovered in our “Data Driving Insights Into the Evolving Customer Experience“, many consumers have not only adopted, but have a new-found affinity for some of those digital-first experiences, including telehealth, mobile banking services, and retail subscription models.

 

Now, as pre-pandemic norms start to make their comeback, businesses must reassess how pandemic-purchasing behaviors and changes in media consumption will continue to shift customer expectations, and – as a result – demand new marketing behavior, as well.

 

We saw this first-hand while helping a client map out and navigate the shifting landscape of food supply and distribution. COVID-19 altered the way in which restaurants do business and created a highly changing and dynamic situation for the industry. But before they could alter the customer journey to meet new everyday business needs, they needed to complete a customer behavior analysis and reconsider their customer segmentations. By conducting a customer survey that included 580 decision-makers within the food/supply ordering chain, we were able to pinpoint specific ways COVID impacted the customer journey (specifically menu evaluation, product selection, and ordering). Additionally, attitudinal segmentation helped us uncover new strategies for supporting each of their core customer groups. At the end of the day, it’s a crucial exercise that can drive increased value realization, customer engagement, loyalty, and market share.

 

So, ready to conduct your own customer analysis? Let’s get started.

Customer Analysis and Customer Segmentation 101

Before we dive in, let’s drive alignment around the definitions of and differences between customer analysis and customer segmentation.

What is customer analysis and customer segmentation?

Customer analysis is the process of researching your customers, using both qualitative and quantitative methods, to develop insights and understanding. Customer segmentation involves using those insights to divide customers into groups centered on similar characteristics.

Why is customer analysis and segmentation important?

Customer analysis and segmentation helps you better tailor your offerings and messaging to add value for your customers, addressing their specific use cases. Survey data from McKinsey shows companies using customer analysis to improve their services consistently outperform their competitors. The results showed 93% of companies whose corporate decisions were driven by consumer analytics earned greater profits than their competitors, that number jumped to 112% for sales growth and 115% for return on investment.

 

Also read: How to Holistically Map Your Customer Experience

Businesses must reassess how pandemic-purchasing behaviors and changes in media consumption will continue to shift customer expectations, and demand new marketing behavior.

A Customer Analysis Framework to Increase Customer Engagement

There are four stages that any customer analysis project should follow:

Stage 1: Identify current customers

How much do you truly know about your customers today? Chances are you could have internal data you aren’t fully maximizing that can help you understand more about your customers. This proprietary data could help you form insightful survey questions to ask your customer base so you can get a strong understanding of what drives their purchases.

 

Remember to ask questions that help you understand:

  • Market-based trends influencing purchase decisions.
  • Disruptors, such as changes in technology, that are changing your industry.
  • Competitors that are gaining momentum.
  • Information about your target audience that is missing or inaccurate.
  • Where you can maximize return on investment throughout the customer journey.

Stage 2: Break customers into subgroups based on traits and motivations

After analyzing your internal and survey data, you’ll want to start segmenting groups based on what motivates their purchase decisions. In the case of the leading food service company we mentioned above, we were able to segment customer profiles based on:

  • The level of decision making authority
  • Job role
  • Location — urban, suburban, rural or other
  • Restaurant type
  • Price sensitivity

Although these probably won’t be the exact same segments you’ll use to understand your customers, they give you an idea on how to structure your approach. You want to create segments around realities that could influence what products or services customers want from you and how much they’re willing to spend.

Stage 3: Outline customer groups needs

Once you understand your customer groups, the next step is digging deep into their specific needs. For the example above, we asked questions such as “How long has your business had to close as a result of COVID-19?” or “How have your weekly supply needs changed as a result of the pandemic?” Questions like these help assess new trends for each use case and where customers are experiencing pain points.

 

Needs can develop through a number of avenues so be sure to understand the following when trying to get to the bottom of what customers are actually looking for:

  • Pain points
  • Values
  • Motivations
  • Influences
  • Benefits

Stage 4: Pinpoint solutions for each customer group

Once you know who your customers are and what they’re looking for, you can start to solve the problem! This is where your company will really shine and how you’ll differentiate yourselves from competitors.

 

Here are some solutions you can start to get creative with:

  • What improvements do you need to make to your core offerings, technology or customer service?
  • What resources can you create that would help your customers use your product or navigate their industry?
  • Is there a new product or offering that would highly differentiate your company from competitors?

Also read: How to Use Design Studios For Innovation

Techniques to Improve Customer Analysis

We have a lot of experience with customer analysis and over the years we have found these simple adjustments help create superior customer profiles and insights.

Combine data to create detailed buyer profiles

Using both qualitative and quantitative data can help you create a full picture when it comes to understanding your customers. Qualitative data refers to information you gather from first person interviews, focus groups or observations you make in the field. Quantitative data uses internal metrics and survey results to structure the themes that could be arising from your qualitative information. Using these in tandem can help you understand both functional and emotional drivers to create multi-dimensional profiles.

Customer journey mapping

Customer needs and motivations might change throughout their lifecycle, so it’s important to map their journey so you can pinpoint areas where you might be of greatest assistance (and achieve maximum return on investment). Here’s what to remember when you start customer experience mapping:

  • Chart the important turning points in their journey using the qualitative and quantitative data you’ve collected.
  • Look internally, who are the stakeholders in your organization that are responsible at each part of the sales process?
  • Does your internal map follow the reality of the journey your customers are going on. If not, where do they diverge and how can you adjust your processes to better accommodate for your customer experience?

Tools to ingest, aggregate, and analyze datasets

Data can easily become overwhelming; trying to collate information manually is likely to take a lot of time and cause you to miss themes. Using data software can help eliminate these two problems.

At Tallwave, we empower proprietary software for our clients to help process data, pinpoint customer pain points, and map a digital journey from start to finish. Using a software like this that aggregates all data sources is essential to making sure your customer analysis project gets done on schedule, and is as accurate as possible.

Plan for the design studio and think outside the box

A design studio is a rapid iteration process that helps your team collaborate and align so you can creatively tackle inefficiencies and improve the user experience across your entire organization. This approach could help you understand the way your entire team is approaching the process and arrive at solidified decisions together.

 

If you take a design studio approach, we recommend setting some rules to encourage creativity. Make sure everyone participating is ready to create, not watch. This includes even those who are inexperienced or feel more comfortable watching! Reimagine possibilities together in a space where everyone’s ideas matter.

Create customer feedback system to power agile innovation

Once you’re ready to implement changes, you’ll want to make sure you have a method that allows you to listen to your customers and respond to their feedback fast. Your customers can drive your innovation, so make sure you’re consistently gathering qualitative and quantitative data about them to iterate and continue providing solutions that meet their ever-changing needs!

The Bottom Line

Customer behavior analysis helps businesses across all industries understand where they currently stand and where they need to go in order to improve the holistic customer experience, accelerate value realization, increase customer engagement, and develop new digital-first experiences. As a result of this process, we’ve managed to to improve the ways in which our clients leverage technology and integrate new features to not only understand evolving consumer behaviors – as seen during the pandemic – but plan for their future CX needs.

Want more information about evolving customer behaviors and needs? Read our latest research report or contact our team today.

Categories
Strategy

Qualitative vs. Quantitative Data In CX Design: Everything You Need to Know

This is a common issue for many organizations, big and small, but it’s not an impossible one to solve!

 

If you’re experiencing a similar situation, you need to invest in gathering persona data that will not only tell you who your customers are and what they care about, but why they care and how they expect brands to make them feel.

 

To get started, let’s define each set of data, how it’s gathered and what it’s used for.

 

What is qualitative data?

Qualitative data – or primary data – is commonly gathered by businesses and plays an important role in understanding target audience sentiment and informing customer journey design. By conducting unstructured or semi-structured first-person user interviews, discussions, on-site observations, in-house moderated user testing, web analytics, and focus groups, qualitative data-collecting techniques allow companies to interact directly with their key customers, see how they’re using their products or services and receive feedback in real-time. It helps define the customer journey and establish an initial foundation and understanding of all internal and external customer experiences.

 

There’s just one problem: Sometimes people don’t know what they want, don’t have the words to truly express how they feel, or simply, aren’t honest.

 

That’s when quantitative data comes in.

Quantitative data enhances primary research and design efforts by quantifying key problem areas.

What is quantitative data?

Quantitative data – which can be gathered through a variety of structured surveys, questionnaires, and polls – is essential secondary research. When transformed into statistics, it enhances primary research (qualitative data) and design efforts by quantifying key problem areas. It also allows marketers, developers, business leaders and customer experience drivers to peer into customer details, attitudes, and behaviors from a data-driven standpoint, and test hypotheses established from qualitative data.

Qualitative Data vs. Quantitative Data: When Should You Use Each & How?

Let’s start with the when. To craft the best customer experiences, companies should collect and analyze both data sources on an ongoing basis. Because – and this is a big one – audiences and their expectations are always changing. By executing primary and secondary research to gather qualitative and quantitative data, companies make themselves better equipped to not only identify, but truly understand their customer base – how they interact, experience, and feel about a website, application or overall brand.

 

And don’t forget to gather employee input, as well! Employees are often the first to know what’s working and what’s not. Most organizations shy away from gathering input from employees, but in our experience, leveraging this powerful knowledge base sooner rather than later helps identify root challenges and opportunities to improve faster and more effectively.

 

Also read: Crafting Employee Experiences That Improve Customer Experiences

 

Now, the how. After both qualitative and quantitative data has been collected, follow these steps:

Map your qualitative data

Example of mapping internal & external qualitative data

Using the qualitative data gathered, map internal perspectives around critical touch points and test it against customer feedback that was collected. This should reveal discrepancies between what internal teams believe is important, versus what customers assign value to. By taking this qualitative approach, teams can visually display opportunities and challenges within the current experience. Providing a picture that illuminates the differences between internal and external stakeholder perception makes achieving cross-functional alignment on future plans easier. There’s not a whole lot to argue about when the writing’s on the wall.

Pinpoint exact moments of friction and/or leverage in your customer journey

Utilize quantitative methods via surveys and other previously mentioned techniques to analyze customer sentiment – opinions and responses – as well as perspective at every stage of the journey. Keep in mind, each interaction a consumer can have with your brand, both passive and active, is a touchpoint and part of your overall brand journey. Therefore, every interaction must be diligently and continuously monitored, evaluated and iterated because one singular touchpoint can cultivate customer affinity or aversion.

Pair quantitative customer sentiment with a qualitative understanding of the user journey

Pairing qualitative and qualitative data

Quantifying the customer journey creates a data-driven understanding of the critical inflection points that drive loyalty and churn. This naturally illuminates root causes of friction (or conversion!) and enable teams to be data-driven in problem solving and planning for future CX initiatives and investments.

How To Use Qualitative & Quantitative Data To Decide on Next Steps

At Tallwave, we create an ‘Impact Matrix’ – this tool highlights opportunities for improvement and compares the impact they’ll each have against the level of effort and investment they’ll require. This helps create alignment and buy-in for low-risk, high impact initiatives that are critical to shaping and improving the customer experience.

 

Find a similar exercise or tool to visually demonstrate all the opportunities that lie ahead and inform the build of a new strategic roadmap that can take your teams and company into the future.

 

Perhaps most importantly, don’t let perfection get in the way of progress! Making big system changes to your end-to-end user experience may take time, but avoid trying to solve it all at once. Identifying the biggest opportunities and making incremental improvements over time, while learning along the way, will make a huge difference.

 

Last but not least, don’t stop. This isn’t a “set it and forget it” game. Customer behavior – be it with an existing website, application or with a brand across numerous touchpoints – must be closely monitored to ensure both user and business goals are consistently met. If they’re not, all teams – Content Strategy, Product Development, People & Culture, Performance Marketing – must align to identify solutions for evolution and continue growth.

 

Also read: Why Customer Experience Can’t Be All Data-Driven

Don’t let perfection get in the way of progress! Identifying the biggest opportunities and making incremental improvements over time, while learning along the way, will make a huge difference.

Bottom Line

Let data be your guide. Qualitative input is key, especially early on, but also leverage quantitative data as much as possible to make decisions. The combination of qualitative and quantitative helps you identify where there is friction, but also gives you the context you need to develop solutions that hit the mark. And if you don’t have the right data infrastructure set up today, that’s a good place to start.

If you need help collecting, comparing and mapping your qualitative and quantitative data to improve your customer journey and overall experience, contact our team today!

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