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Customer Engagement Data Strategy

Stop counting likes, start measuring results: Vanity metrics vs. actionable metrics

decorative image representing vanity metrics

There’s no denying that a little external validation feels good, even for experienced marketers. A like on Instagram, a retweet, or maybe even a viral blog post with a million views — those are the warm fuzzies we chase, right? 

But here’s the thing: while those metrics might look good in a presentation, they are vanity metrics. And vanity metrics don’t pay the bills. It’s time to stop counting likes and start measuring results. Because, friends, your bottom line can’t cash in on pageviews alone.

When it comes to business success, you need to focus on the metrics that actually impact your business at the end of the day. So, let’s break down the difference between vanity metrics and actionable metrics, and figure out how you can invest in actionable metrics to start making meaningful decisions that are data-driven and goal-oriented. 

What’s the difference between vanity and actionable metrics?

Think of actionable metrics like the root of a plant. They are essential for growth and sustainability. Vanity metrics are like petals; they make the plant look beautiful, but are not the foundation of its strength. 

Introducing vanity metrics (and why you should be careful)

Vanity metrics are superficial metrics that may seem impressive but don’t provide meaningful insights into your business performance.

Metrics like page views, social media likes, and impressions fall into this category, and while they can signal channel effectiveness and provide indicators of growth, these metrics are simply directional and should not inform decision making that is linked to business goals and outcomes.

Take your page views, for instance. It’s not enough to know that 10,000 people organically viewed your new page yesterday. That may sound great, but what if your ultimate goal is to have those users download a brochure and only 2 of those 10,000 visitors took that high-value action? Not looking so great anymore. Perhaps poor UX design or a technical bug is preventing users from taking that action and if you’re just focusing on pageviews, you may not recognize that there is actually a problem on-site.

What matters most is how many of your website visitors are staying, engaging, and converting. Focusing on vanity metrics like pageviews means you might miss an opportunity to identify what is truly driving (or hindering) growth and performance. 

Introducing actionable metrics (and why you should care)

Actionable metrics are meaningful metrics that directly impact your business goals and can be used to make informed decisions. These metrics are tied directly to your business objectives, such as increasing revenue, improving customer retention, or enhancing lead generation, and they drive decision making. 

We’re talking conversion rates, cost per conversion, and retention rate. You know, the good stuff.

Actionable metrics provide insights that can be used to adjust strategies and tactics. For example, in reviewing your organic search traffic — an actionable metric — you notice that traffic for a specific set of high-intent keywords has plateaued or dropped, even though those keywords are highly relevant to your business. As a result, you might revise content on existing pages targeting those keywords to better match user intent. You might reoptimize the title tags, meta descriptions, and headers to better align with search engine algorithms. You might strengthen internal linking to the underperforming pages from higher-traffic, authoritative pages within your website to improve their ranking. Using the insights from this actionable metric, you can make targeted adjustments, potentially increasing both keyword rankings and organic traffic for high-intent queries, leading to better visibility, more qualified leads, and increased conversions. 

In another example, you might review your cost per acquisition (CPA) for a specific ad campaign and notice it is significantly exceeding your target CPA and making the campaign less cost-effective. As a result, you might refine your audience targeting by narrowing down segments or implementing lookalike audiences to reach a more qualified audience. You might run some A/B tests on different versions of ad copy, headlines, and visuals to see which combinations drive more conversions at a lower cost. Perhaps you might even consider improving the user experience on the landing page. By acting on these insights, you can lower your CPA, increasing the cost-effectiveness of the campaign and maximizing ROAS.

How to set up an actionable measurement strategy

Now that we’ve thrown down some definitions, let’s get to the meat of it: how do you turn these actionable metrics into something your team can rally behind? The secret is in your measurement strategy. Without a well-defined plan, you’re like a football team without a playbook—you might make some progress, but you’ll never get to the end zone.

Step one? Identify your goals. Whether it’s organic search growth, increased conversions, or happier customers, start by defining what success looks like for your business. Next, choose KPIs that align with these goals. For example, if your goal is to increase sales, actionable metrics like conversion rates and cost per acquisition (CPA) should be at the top of your list.

Need help identifying KPIs and making sense of metrics? Learn more about Tallwave’s data strategy and analytics services.

Once you’ve defined your goals and KPIs, select the tools that will help you track and report on these metrics. Tools like Google Analytics, Hotjar, and SEMrush can provide the in-depth data you need to make informed decisions. But remember, tracking the right metrics is just the beginning. You’ll need to analyze, test, and iterate on your strategies continuously.

See how Tallwave was able to transform an e-commerce company’s strategic decision making through an enhanced measurement strategy and data visualization.

Actionable metrics in action! Important things you might track

Here are some actionable metrics that you should be paying attention to:

Actionable engagement metrics:

  • Click-through rate (CTR): The CTR reflects how compelling your content is on the surface. Are people intrigued enough by the messaging and/or creative to click through? A high CTR can indicate strong relevance, but without conversions, it might be time to refine your targeting.
  • Cost per click: Getting clicks on your ads is important, but how much are you paying for each one? CPC measures the amount you spend each time someone clicks on your ad, giving you insight into the efficiency of your campaigns. Monitoring this metric helps you optimize ad spend, refine targeting, and improve the overall return on investment.
  • Keyword rankings: Driving traffic to your site starts with visibility, but how well are your target keywords performing? Keyword rankings track where your site appears in search engine results for specific terms, giving insight into your SEO efforts. Monitoring this metric helps you adjust strategies to improve search visibility, attract more qualified traffic, and increase conversions.
  • Core Web Vitals: Google’s key metrics for website performance — make sure your site is lightning fast, responsive, and user-friendly.

Actionable conversion metrics:

  • Conversion rate: Are clicks translating into real business? If you’re seeing high engagement but low conversions, it could signal issues with the landing page or the offer. This metric connects clicks with actual outcomes, telling the full story of your ad’s performance.
  • Cost per acquisition (CPA): This metric shows the efficiency of your campaigns by measuring how much you spend to convert a user into a customer. Are you spending too much for each new customer? Optimizing CPA helps ensure your budget is working smarter, not harder.
  • Return on ad spend (ROAS): For every dollar you’re spending on ads, how much are you getting back? ROAS helps you understand the effectiveness of your ad spend, but it’s important to factor in long-term value, not just immediate returns.

Actionable customer affinity metrics:

  • Customer satisfaction (CSAT): It’s one thing for users to adopt your product, but are they happy with it? CSAT scores help you measure sentiment and reveal areas for improvement. Monitoring CSAT can help you prioritize fixes or new features.
  • Product adoption rate: Adoption isn’t just about signups; it’s about active, regular use. Are users finding long-term value in your product? Measuring this helps ensure that your product is not only attracting users but also retaining them for ongoing success.
  • Customer retention: Acquiring customers is crucial, but keeping them is even more important for long-term growth. Customer retention measures how well you maintain relationships with existing customers, providing insight into loyalty and the overall customer experience. Tracking this metric helps you refine strategies to reduce churn and boost lifetime value.
  • Customer lifetime value: Attracting new customers is vital, but how much value do they bring over time? CLV measures the total revenue a customer is expected to generate during their relationship with your business. Monitoring this metric helps you focus on increasing long-term profitability through retention, upsells, and better customer experiences.

Turning actionable metrics into success

Once you’ve got the right metrics in place, it’s time to use them to your advantage. While vanity metrics shouldn’t be the primary focus, they have value in contexts like brand awareness and credibility. Actionable metrics, on the other hand, provide valuable insights that can help you optimize your marketing strategies, making them more effective and efficient.

At the end of the day, the success of your marketing efforts hinges on which metrics you use to guide your decision making. Vanity metrics might make you feel good, but actionable metrics are what drive real business results. By implementing a strong measurement strategy and tracking the metrics that matter, you can ensure continuous improvement and growth.Want help developing a measurement strategy that focuses on what really matters? Let’s talk. We’re here to help you achieve measurable success through data-driven decision-making.

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CRO Customer Engagement Data Strategy

Talking the talk: Semantic layering removes the jargon from business data 

Decorative image representing semantic layering in data strategy.

Organizations are collecting information at an unprecedented rate. Website analytics, CRM data, mobile app data, social media engagement metrics, marketing automation data, and customer feedback – the list goes on and on. But stockpiling books doesn’t make you a scholar, and hoarding data doesn’t guarantee insights. The true value of data lies not just in its volume but in its usability.

For many businesses, extracting insights from data can be a complex and time-consuming process. Data silos, inconsistencies, ambiguous definitions, and overly-technical or inconsistent language can create all kinds of roadblocks and bottlenecks, hindering practical data analysis and utilization. Semantic layering offers a powerful solution. It creates a unified and consistent view and vernacular that is applied to source data from different platforms with different definitions. This creates a consistent data language, making it easier to find, understand, and apply data for better decision-making.

What is semantic layering?

Picture a cool carbonated beverage. Yes, it’s a delicious treat, but it’s also a linguistic minefield. In the South, it might be universally called a “Coke,” regardless of whether it’s actually Pepsi or Coca-Cola. Head to the Midwest and it’s a “pop,” while those on the coasts tend to opt for “soda.” 

Now imagine your internal teams talking about a new business prospect, which happens to be a new business unit with a huge, global company where you’ve previously worked with other business units. Your marketing team might describe them as a “prospect.” But they may be a “client” to your sales team or even a “counterparty” to your finance team, with all those different terms appearing in different systems to refer to the same company. Sound familiar?  

This vocabulary confusion is just like the chaos you might encounter in data without a semantic layer. Different data products and systems, like different regions, use their own definitions for what may be similar or related data elements. A semantic layer maps data with diverse definitions from a variety of sources into familiar business terms to create a single, unified, and usable view of data across an organization. This breaks down silos and allows everyone to speak the same language, fostering a more collaborative and insightful approach to analysis.

For example, a Google Analytics report output may use the metric “avg_session_duration_seconds,” but a HubSpot report of the same landing page engagement may refer to that metric as “time on page.” Semantic layering would recognize that this is the same metric with different labeling and deem both metrics “Average time spent on the site,” for example, to make it consistent for individual users.

Here’s what semantic layering aims to establish:

  • Standardization: Semantic layers apply easy-to-understand terms to complex and inconsistent metric nomenclature. This ensures everyone within the organization speaks the same “data language” no matter the source system, eliminating confusion and wasted time deciphering cryptic codes.
  • Business-friendly representation: Semantic layers translate technical data structures into business-friendly language. This allows users without a deep technical background to easily understand and interact with the data, which is especially important for data literacy across your organization.
  • Contextualization: Because semantic layering speaks to individuals in a common, understandable language, it enables individuals to draw connections across data and see the bigger picture in order to form a cohesive narrative across the data and unlock deeper insights from their analysis. It also helps to reduce the risk of confusion or misinterpretation of data, for more sound decision making.

Semantic layering takes complex data from your systems and translates it into clear business terms everyone in the organization can understand. This standardized data language makes it easier to analyze and gain valuable insights.

Why is semantic layering important?

Your business relies on data to drive informed decision-making and fuel growth so being able to access, interpret, and leverage that data in a meaningful language is key. Semantic layering is a cornerstone of your data strategy for several key reasons:

Data jargon made familiar

A semantic layer removes technical barriers by translating complex jargon into relatable terms, allowing everyone from marketing specialists to financial analysts to recognize and act on the data they need, regardless of their technical experience.

Enhanced data governance

Semantic layering promotes consistency and quality with data nomenclature. By establishing clear definitions and rules it minimizes the risk of errors and inconsistencies that can plague traditional data analysis. It removes ambiguity and the need for individual interpretation of metrics as they are defined from an original source. This further reduces errors and increases the accuracy of the findings and their usability.

Efficient data onboarding

With a standardized language in place, businesses can apply semantic layers to new data sources, ensuring consistency from the beginning. This allows you to adopt and take action on new data quickly.

Deeper insights unlocked

Semantic layering ensures consistency and understanding by establishing a common language, which empowers us to more efficiently uncover meaningful patterns and trends within the data. This facilitates data-driven decision-making across all levels of the organization.

Learn more about Tallwave’s data strategy and analytics services.

Semantic layering offers a strategic investment in data understanding

Semantic layering isn’t just a technical solution – it’s a strategic investment in the future of your business. By making your data terminology consistent and understandable, you empower your team, improve decision-making, and position yourself for long-term success in the data-driven world.

Take the next step: get in touch with us today to learn more about how semantic layering can help your organization unlock the power of your data!

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Data Strategy Strategy

3 building blocks for fostering data literacy in your organization

A decorative image representing the benefits of organizational data literacy.

From understanding customer preferences to optimizing operations, data-driven decision-making has become the cornerstone of organizational success. However, the true value of data lies not just in its collection, but in the ability for individuals across the organization to access, successfully interpret, and effectively communicate its insights. This is where data literacy comes into play. It’s an essential competency that every member of an organization needs to create a data-driven culture.

Foundations of a data-literate culture

The current pace of business waits for no one, and decisions need to be made swiftly and with confidence. Data literacy positions businesses to harness the power of data to gain valuable insights into market trends, customer behavior, and operational efficiencies and leverage those insights to make data-informed decisions. Whether it’s identifying new opportunities for growth or mitigating risks, data literacy provides a solid foundation upon which businesses can thrive.

But data literacy isn’t just the responsibility of data scientists and analysts. Achieving a truly data-driven culture requires every member of an organization—from the C-suite to frontline employees—to be equipped with the ability to access critical data and the skills to read, interpret, understand, and communicate insights effectively. Without this data literacy skills up and down their ranks, businesses risk making decisions based on intuition rather than evidence, leading to missed opportunities and costly mistakes.

At its simplest, achieving a data-driven culture boils down to three core building blocks: broad access to high-quality data; the ability to not only read, but to effectively interpret and understand the data to glean actionable insights; and the ability to successfully communicate data-driven insights and recommendations through data storytelling. 

Building block #1: Broad access to high-quality data

When it comes to data-driven strategies, the quality of the output depends on the quality of the input. So data quality management is critical for ensuring that business decisions are fueled by high-quality data. Data quality is a function of collecting the right kind of data and applying good data hygiene practices to the data collected to preserve data integrity. With eight out of every ten business leaders surveyed by Braze in 2023 admitting to collecting more data than they can realistically use, most organizations have plenty of the wrong kind of data. And a lack of data standardization across departments and data sources, sound data validation and verification processes, and data cleansing and enrichment can lead to inconsistencies, holes, and an overall lack of confidence in data quality.

But quality aside, just getting access to the data needed to drive strategies and measure impact is a challenge for many organizations. With digital ecosystems that consist of a wide range of platforms that hold bits and pieces of data in isolated pockets, data fracturing and siloing prevents the kind of democratized data access that underpins a data-literate culture.

Building block #2: Data interpretation

The power in having access to the right kind of data lies in what you do with it. And making data actionable requires interpretation. Effective data interpretation is critical for understanding the current state of affairs, identifying opportunities for improvement, and driving strategic decision making within organizations. Without proper data interpretation, organizations run the risk of making decisions based on incomplete or misleading information, leading to misguided strategies and missed opportunities. Survivorship bias is a classic example of the dangers of data misinterpretation. 

During World War II, military analysts faced the challenge of how to better protect aircraft from enemy fire. Initially, the method involved examining returning planes for bullet holes, leading to the straightforward strategy of reinforcing these frequently damaged areas. The prevailing assumption was that the most bullet holes indicated the most hit sections of the aircraft. See image below:

An image of an airplane representing Ward’s data theory.

However, Abraham Wald, a skilled statistician, was tasked with reevaluating this data.

Rather than simply adding armor where damage was evident, Wald took a deeper dive into the data’s significance. He questioned the conventional wisdom of considering only the damage that was visible on returning planes and stressed the importance of considering what was absent in the data.  Wald recognized a fundamental flaw in the existing approach—survivorship bias. This bias had skewed the analysis, focusing only on aircraft that had returned from missions and ignoring those that had not, which might have suffered hits in different areas. This reflection on the broader implications of the data led to a critical shift in perspective.

Wald’s interpretation proposed that the undamaged areas on returning planes were, in fact, the most vulnerable. His counterintuitive insight suggested that these sections did not show damage because aircraft hit in these parts likely did not survive to return for evaluation. By shifting the focus to reinforcing these critical yet previously overlooked areas, Wald revolutionized the military’s strategy for aircraft armor, enhancing the survival rates of future missions. 

This example highlights the profound impact of effective data interpretation and the importance of considering all aspects of data to avoid misleading conclusions in strategic decision making processes. Data interpretation is not just about crunching numbers; it’s about turning raw data into actionable insights. This requires the ability to identify patterns and distill complex findings into an informed plan of action.

Building block #3: Data storytelling

For data-literate organizations, putting data to use to drive business performance and growth is a collaborative effort enabled by the ability to bring the entire organization along in understanding what the data is saying, what needs to be done about it, and the expected result of that action. Data storytelling is the art of weaving a narrative around data to communicate insights in a compelling and engaging manner. It goes beyond mere analysis by transforming raw data into a cohesive story that resonates with the audiences to drive shared understanding and strategic alignment. This may include using visualizations, anecdotes, and real-world examples to illustrate key points and highlight trends and make abstract numbers and statistics more relatable and easier to comprehend. 

The power of data storytelling lies in its ability to inspire action and drive change within organizations. By connecting data-driven insights with real-world implications, storytellers can motivate stakeholders to make informed decisions and drive strategic initiatives forward. Whether it’s convincing executives to invest in new technologies or persuading frontline employees to adopt new processes, effective data storytelling can be a catalyst for organizational transformation.

How do you foster data literacy?

So, how can businesses foster data literacy across their organizations? Here are a few key steps:

  • Break down data silos: Organizations increasingly possess the data needed to achieve a 360-degree view of their customers and by breaking down silos that separate data based on the source, organizations can achieve the “single source of truth” needed to unlock the holistic, actionable view of customer data required to drive business strategy.
  • Invest in training: Provide comprehensive training programs that equip employees across the organization with the necessary data interpretation and storytelling skills. This could include workshops, online courses, and certification programs tailored to different job roles and skill levels.
  • Promote a data-driven culture: Encourage a culture of curiosity and experimentation, where data is valued as a strategic asset. Recognize and reward employees who demonstrate proficiency in data analysis and decision-making.
  • Provide access to tools and resources: Ensure that employees have access to the right tools and resources needed to work with data effectively. This could include data visualization software, analytics platforms, and data libraries.
  • Lead by example: Senior leaders play a critical role in setting the tone for data literacy within an organization. By championing data-driven decision making and actively participating in data-centric initiatives and data storytelling, leaders can inspire others to follow suit.
  • Encourage collaboration: Foster cross-functional collaboration and knowledge sharing to break down silos and promote a holistic understanding of data across the organization. Encourage teams to work together on data projects and share best practices.

Unlocking the power of data through data literacy

In today’s data-driven world, organizations that prioritize data literacy will have a competitive edge. By empowering employees at all levels with the skills and knowledge to harness the power of data, businesses can drive innovation, make informed decisions, and ultimately, achieve their strategic objectives. Remember, data is not just about numbers; it’s about unlocking insights that drive meaningful action and create value for both customers and stakeholders alike. If you’re ready to unlock the power of your data by building a culture of data literacy, we’re here to help.

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Customer Engagement Reaching New Customers Strategy

Organizational growth strategies: Aligning purpose and practice

Decorative imagine of a garden representing organizational growth.

Organizational growth isn’t just about increasing revenue or market share. It’s about building a sustainable and purpose-driven organization. 

Sure, every company aspires to quickly grow and see revenue rise. However, understanding the “why” behind that growth is critical for attracting and retaining talent, authentically engaging customers, and achieving long-term success. Simply put, understanding your “why” and aligning your purpose with your operational practices can unlock true organizational growth.

Understanding the ‘why’ in business growth

Many companies define growth in terms of financial metrics. While these are important, focusing solely on profit and EBITA is like having a destination in mind without plotting a route to get there, which can leave business strategies directionless. A truly purpose-driven business clearly understands its “why”—its core reason for existence beyond making money. This purpose serves as a guiding star, rallying like-minded employees, inspiring customer loyalty, and fostering a genuine connection between your organization and the market you serve. When companies are guided by purpose, they’re more likely to find their way to financial success.

Consider Patagonia, a company renowned for its commitment to environmental sustainability. Their purpose isn’t just selling outdoor apparel but protecting the planet for future generations. This purpose informs everything they do, from product design to marketing campaigns, and has earned them a fiercely loyal customer base for good reason. Consumers get to feel as if they are playing a part in the health of our environment by purchasing Patagonia goods while having great gear to enjoy the great outdoors. This is a great example of how threading purpose all the way through an organization drives positive business outcomes.

The significance of authentic branding

Authentic branding is the outward expression of your company’s purpose. It’s about clearly communicating your values, mission, and the impact you strive to make. Customers today are savvy; they can sniff out inauthenticity a mile away. Remember when Kellogg’s CEO Gary Pilnick suggested on national television that American families consider “cereal for dinner” in response to rising food costs? Consumers certainly do, and an organized boycott is still underway.

When your branding authentically reflects your purpose, it fosters trust and creates a positive emotional connection with your audience. Here’s where crisp and consistent storytelling comes into your brand’s message and content strategy. Your company story should be woven into every facet of your marketing and sales efforts. This can include blog posts, social media content, website copy, and even customer case studies. By consistently telling your story, you reinforce your purpose and build brand loyalty.

Linking purpose with operational efficiency

Purpose is not just a feel-good slogan; it must be operationalized to fuel your company and ultimately growth. This means ensuring your business operations and processes are aligned with your purpose and your people are equipped to deliver on your promises, not just to customers but to one another. 

Here’s where data-driven accountability and data-centricity come into play. Leaders and decision-makers need access to the right metrics and data to assess operational efficiency. These metrics should measure progress toward company goals, client satisfaction, customer perception, retention, and growth. By holding leadership accountable for key performance indicators (KPIs) that connect to purpose-driven growth, you create a feedback loop that ensures alignment across all levels of the organization.

The critical role of sales and delivery synergy

Imagine a scenario where your sales team paints a rosy picture of your product or service to close a deal, but then the internal teams responsible for delivery lack the resources or capacity to fulfill those promises. This disconnect between sales and delivery creates a frustrating experience for customers and can damage your reputation (your brand). It’s important to remember that sales are just one step in the customer journey.

For true organizational growth, your sales and delivery need to be in lockstep. This requires clear communication between both teams, ensuring everyone understands the product or service capabilities and realistic timelines. Additionally, shared goals that prioritize customer satisfaction and advocacy are crucial. By aligning purpose with operational practices, you can foster strong collaboration between sales and delivery, ensuring a seamless and positive customer experience from initial contact to successful delivery.

When internal solutions fall short: Finding the right partner

Sometimes, organizations get stuck in their siloed workflows and struggle to create the internal alignment necessary for sustained growth. This is when partnering with a trusted external partner can be invaluable. Infusing fresh perspectives and outside expertise can help you refocus your strategies with your customer at the center and your purpose as the driver for greater resonance with your target audience and purpose-driven growth for your business.

Here’s why seeking external help can help bring purpose to your efforts:

  • Internal blind spots: Teams can be too close to the problem, losing perspective on the value your brand exists to create for your customers and missing potential solutions.
  • Leadership challenges: Leaders may lack the expertise or resources to implement necessary changes when purpose becomes disconnected from practice in their business operations and strategies.
  • Translation to execution: Even when the purpose is clear at the brand level, it’s not always easy to thread it through to execution at the hands of practitioners. From sales and marketing to product, IT, finance, operations, and every other business discipline, the subject matter experts across all these domains need to approach what they do from the context of why they do it, which stems from your purpose.

By partnering with an agency that aligns with your purpose, understands the digital landscape, and approaches marketing, product, and data strategies as tools to help you fulfill your brand’s purpose and create the kinds of experiences that your customers demand, you can overcome organizational inhibitors and achieve your growth goals.

At Tallwave, we’re passionate about helping businesses unlock their full growth potential by aligning purpose with practice. We believe that a well-defined purpose, operational efficiency, and authentic execution of the brand are the keys to sustainable growth in today’s competitive environment.

Ready to take your organization to the next level? Get in touch with us today to discuss how we can help you craft a purposeful and profitable growth strategy.

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Customer Engagement Uncategorized

Unlocking the power of data storytelling

A decorative image representing 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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Customer Engagement Reaching New Customers SEO Strategy

Microconversions: Unlocking the power of incremental steps in your conversion funnel

Image showing a piggy bank, referencing microconversions

Introduction: What is a microconversion?

In the dynamic world of digital marketing, where every click and interaction matters, understanding microconversions is crucial. But what exactly are they? Let’s start by demystifying this term.

What is a microconversion?

A microconversion is any incremental step a user takes to show initial interest in your brand or product. Unlike the grand finale of a macroconversion, like a product purchase or subscription that constitutes a final goal and often achieves a financial outcome, microconversions are the incremental steps along the way that lead up to those final actions. Imagine a visitor to your website as a curious explorer embarking on a journey. Along the way, they encounter various signposts, each representing a microconversion. These small actions might not lead immediately to a purchase, but they’re part of the breadcrumb trail that leads prospective customers to that final transaction.

Learn more about the power of the “micro-yes” in sales.

Why do microconversions matter?

1. Trust building and brand advocacy

Microconversions are like the first handshake between you and your potential customer. At the earlier stages of the buying journey, some common microconversions include:

  • Email newsletter sign-up: When a visitor subscribes to your newsletter, they express interest in staying connected. This small commitment builds trust and opens the door for further communication.
  • Social media sharing: When someone shares your content on social platforms, they vouch for your brand. Their endorsement reaches a wider audience, potentially attracting new visitors and signaling trust and confidence in your brand.

2. Insights into user behavior and intent

Microconversions provide valuable insights into user behavior. By tracking these smaller interactions, you gain a deeper understanding of what resonates with your audience and gain insights into the stage of the buyer’s journey they’re in and their needs at that stage. Examples include:

  • Page views: The number of pages a visitor views indicates their level of engagement. High page views suggest interest, while low views may signal disinterest. The nature of the content on the pages viewed can also illuminate stage and intent. For example, if a visitor navigates to specific product pages, adds products to a cart, or reviews a page on returns, those behaviors are all microconversions on the path to purchase that signal a higher degree of intent than a visitor that lands on your home page and then leaves.
  • Comments on blog articles: Engaged users often leave comments. These interactions reveal their preferences and pain points.

3. Optimization opportunities

Microconversions act as breadcrumbs leading you through the forest of user experience. They can also serve as a “canary in the coalmine” of your digital engagements, signaling friction that can then be resolved and highlighting areas for improvement. Consider:

  • Process milestones: These are linear steps toward the primary macroconversion. Analyzing them helps identify bottlenecks and UX pain points. For example, for one client, we pinpointed significant dropoff between the process milestones of viewing a product page and adding the product to a cart, particularly for mobile users. We discovered this was due to an issue causing the “add to cart” button to display much further down the page than intended, causing many users to overlook it and abandon the page. Addressing this issue allowed us to increase add-to-cart actions by 3.8x.
  • Secondary actions: These desirable but non-primary goals indicate potential future macroconversions. Examples include downloading an ebook, creating an account, or watching a video. Using these secondary actions as opportunities to deploy targeted outreach can be a great way to optimize the path to purchase with stage-specific content and messaging that nurtures prospective customers toward other high-value actions.

Monitoring and measuring microconversions: Enhancing your conversion insights

Understanding what microconversions are and the signals they represent is only half the battle. Unlocking their power to gain insights into the path to macroconversions and inform strategies for optimizing digital experiences to improve conversion requires ongoing monitoring and measurement. Both the types of data each microconversion produces and the methods for collecting and analyzing that data vary:

Qualitative data

Qualitative data can be invaluable for getting a sense for how effectively website visitors are navigating to and completing microconversions and where they may be encountering roadblocks in the path toward macroconversions. Here are some common approaches for gathering qualitative data on microconversions and examples of these measurement methodologies in action:

Heat mapping & scroll mapping

Heat mapping is like having a thermal camera for your website. It visually represents user behavior by highlighting the “hot” and “cold” areas of a webpage based on where users click, scroll, hover, and otherwise interact with the page (and where they don’t). Here’s how it works:

  • Heat maps: These colorful overlays show where users click, move their mouse, or spend the most time. Red and orange areas indicate high activity, while blue and green areas are less frequented.
  • Scroll maps: These reveal how far users scroll down a page. Understanding where visitors drop off helps optimize content placement.

Example: Imagine an e-commerce site. A heat map reveals that users consistently click on the “Add to Cart” button but rarely explore the footer links. This insight prompts you to enhance the checkout process and reposition critical links.

Session recording

Session recording is like a digital surveillance system for your website. It records user sessions, capturing every click, scroll, and interaction through the eyes of the user. Key points:

  • User behavior: Watch real users navigate your site. Understand their pain points, hesitations, and moments of delight.
  • Error identification: Spot usability issues, broken links, or confusing forms.

Example: You notice users repeatedly abandoning their cart during the payment step. Session recordings reveal that a confusing coupon code field is causing frustration. Fixing this leads to higher conversions.

Quantitative data

Quantitative data brings a numerical lens illuminating actions that can be counted, measured, or otherwise described in numbers. Where qualitative data can help you channel the perspectives and feelings of website visitors, quantitative can put that data into perspective in terms of its frequency and impact. Here’s how quantitative data on microconversions is often collected:

Basic analytics tools

  • Google Analytics (GA): The Swiss Army knife of web analytics, GA tracks user behavior, traffic sources, custom website conversion rates, and more. It’s free and essential for any website.
  • Built-in e-commerce analytics: Platforms like Shopify, WooCommerce, or Magento offer built-in analytics. They provide insights specific to e-commerce, such as product performance, revenue, and customer demographics.

Example: GA shows that your blog attracts high traffic, but few readers proceed to the product pages. You optimize the blog-to-product link placement, resulting in increased sales.

Funnel reports

Funnel reports visualize the user journey. They break down the conversion process into stages:

  1. Awareness: Visitors arrive on your site.
  2. Interest: They explore content, view products, or sign up.
  3. Consideration: Users add items to their carts or engage with your services.
  4. Conversion: The final purchase or desired action.

Example: An e-learning platform’s funnel report reveals that most users drop off during the “Interest” stage. You tweak the landing page content, leading to better engagement.

Remember, microconversions are the stepping stones that pave the way for macro success. By combining qualitative and quantitative insights, you’ll create a conversion funnel that’s both user-friendly and revenue-boosting! 

Making the most of microconversions: Optimizing for conversion

The final step is putting qualitative and quantitative data-driven insights to work to optimize the digital experience to increase the microconversions (and ultimately macroconversions) your audience is successfully completing. This can be done broadly to optimize the digital experience as a whole or more narrowly to optimize for a specific high-value action through two distinct but interrelated approaches: 

Digital Experience Optimization (DXO)

Digital Experience Optimization (DXO) is the strategic process of enhancing user interactions with digital technologies to drive superior customer experiences. It encompasses a holistic approach to improving every touchpoint where users engage with your brand online. DXO aims to create seamless, personalized, and delightful experiences across websites, mobile apps, social media, and other digital channels.

Why does DXO matter?

  • Customer expectations: In today’s digital landscape, customers expect smooth, relevant interactions. DXO ensures you meet these expectations.
  • Business impact: Positive digital experiences lead to increased customer loyalty, higher conversion rates, and improved brand perception.

We discovered this was due to an issue causing the “add to cart” button to display much further down the page than intended, causing many users to overlook it and abandon the page. Addressing this issue allowed us to increase add-to-cart actions by 3.8x.

Conversion Rate Optimization (CRO)

Conversion Rate Optimization (CRO) focuses on improving the percentage of website visitors who take a desired action, such as making a purchase, signing up, or downloading content. It involves data-driven experimentation to enhance user experience and drive conversions.

Core elements of CRO

CRO applies a systematic approach to increasing high-value action completion by identifying and testing solutions to resolve friction points along the path to conversion to continuously improve performance. This process includes:

  1. Setting expectations: Clearly define goals and success metrics for each conversion action.
  2. User insights: Understand user behavior through analytics, heatmaps, and session recordings.
  3. Hypothesis development: Formulate hypotheses about what changes will improve conversions.
  4. Testing velocity: Regularly test variations (A/B tests, multivariate tests) to validate hypotheses.
  5. Cross-device testing: Ensure consistent experiences across different devices.
  6. Pre-test prototypes: Validate ideas before full implementation.
  7. Limit changes: Focus on impactful modifications rather than overwhelming redesigns.

Best practices for optimization

While CRO is focused on a specific digital experience, doing it effectively requires considerations that extend well beyond the specific microconversions you’re trying to improve, including:

  • Keyword research: Understand user intent and optimize content accordingly.
  • On-page SEO: Optimize meta tags, headings, and content for search engines.
  • User experience (UX): Prioritize intuitive navigation, fast loading times, and mobile responsiveness.
  • Content quality: Create valuable, relevant content that resonates with your audience.
  • Backlink building: Earn high-quality backlinks to improve authority.

Remember, DXO and CRO are ongoing processes. Continuously analyze, test, and optimize to create exceptional digital experiences and drive conversions. Let us show you how to incorporate this must-have continuous improvement cycle into your business!

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Customer Engagement Uncategorized

Cloud security management: Safeguarding your data

A decorative image showing a lock in the sky, representing cloud security management.

Over the past few years marketing and IT teams have been flying high with cloud-based innovations. These servers and software “in the sky” are aimed at improving flexibility, scalability, and efficiency of handling and accessing the data that empowers marketers to make the informed decisions they need to reach their target audiences and provide great consumer experiences. From collecting and storing paid media analytics to scheduling automated campaigns, the cloud seems to be the key fueling your brand’s digital ascent. 

But as you soar amidst the data-driven clouds, a question whispers in the wind: is your data safe?

Enter cloud security management, the vigilant guardian in this digital sky. It’s the framework, the set of tools, the sleepless protector ensuring your prized marketing data navigates the cloud with confidence and integrity.

The cloud security landscape: From seedling to towering tree

Cloud security’s journey began decades ago, as a tiny sapling sprouting from concerns about online data vulnerability. Today, it stands tall as a mighty oak, offering robust solutions across industries. But for marketers and IT teams who rely on high-quality data to drive actionable insight, understanding where you are in this forest is crucial. Are you just planting the seeds of cloud adoption, or do you have sprawling data ecosystems nestled within its branches? Identifying your stage sets the foundation for your cloud security management journey.

Learn more about Data Strategy and Analytics Services at Tallwave.

Understanding data protection: The roots of secure marketing

The heart of cloud security management is data protection. For many marketing teams, this translates to safeguarding customer information, campaign creatives, and brand-sensitive data. For others, like those who rely on healthcare web analytics data, the roots are even deeper. 

However, cloud security management isn’t without a few thorns. Today’s marketing landscape throws myriad security challenges our way:

  • Evolving threats: Hackers, malware, and data breaches constantly evolve, demanding dynamic, adaptable security measures.
  • Fragmented ecosystems: Multi-cloud environments and third-party integrations multiply data touchpoints, creating a complex security puzzle.
  • Human error: Accidental data leaks or inadequate employee training can unintentionally expose vulnerabilities.

To combat these growing threats, marketing and IT teams might consider an approach with two branches:

1. Data classification and encryption

Prioritize your data, classifying it based on sensitivity and implementing robust encryption measures for high-value information. Secure cloud storage solutions further solidify your digital fortress.

2. Cloud security standards

Adopt industry-standard practices like strong password policies, access controls, and regular security audits. Remember, prevention now is always better than breakfixes later.

So, having acknowledged the critical role of data protection, the question that begs to be asked is: how can we actively implement best practices and tools to build a data sanctuary within the cloud that protects and enables our marketing initiatives?

Best practices for cloud security management: Building your data a secure shelter

Let’s delve deeper and explore the practical tools and best practices marketers and IT teams can leverage to ensure data stays safe in the cloud.

Cloud security monitoring and threat detection

Invest in tools that continuously monitor your cloud environment for suspicious activity and potential threats. Early detection is key to swift and effective containment. Tools can vary by cloud provider. Those using AWS might look to GuardDuty or Amazon Inspector, while those on Azure might consider Microsoft Defender for Cloud or Log Analytics.

Future-proof your environment

Stay ahead of the curve by constantly evaluating and updating your cloud security measures. Remember, the digital landscape is ever-shifting, and so must your defense mechanisms.

Secure cloud storage

Choose reliable cloud storage solutions that offer robust security features, data redundancy, and disaster recovery options. Your marketing data deserves a digital vault, not a cardboard box.

Cloud security policy

Craft a comprehensive cloud security policy that outlines data handling procedures, employee training protocols, and incident response plans. Clear guidelines are your best friend in crisis mode.

Regulations like GDPR and HIPAA add another layer of complexity to the cloud security puzzle. Risk management in cloud security is key, by regularly assessing compliance and actively managing potential risks, you can chart a secure course through the regulatory waters. Other guidelines, like SOC 2 and ISO 27001, provide a roadmap for achieving and maintaining compliance, earning you precious trust and peace of mind.

Embarking on your secure cloud journey

Cloud security management may seem daunting and like a maze of risks and regulations. But remember, you don’t have to navigate it alone. Cloud security solutions abound, offering tools, expertise, and managed services to guide you every step of the way.


At Tallwave, we understand the intricacies of cloud security, especially in the dynamic world of digital marketing. Our team of marketing data strategy experts is ready to equip you with the knowledge, tools, and confidence to conquer the cloud’s highest peaks. Tallwave is just a cloud hop away; we’re here to help when you need us.

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Strategy

From chaos to clarity: Data quality management for actionable insight

Decorative image

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.

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Strategy

Healthcare Web Analytics in 2023: Get Your Data In Order

Stylized image of partners discussing healthcare analytics

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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Customer Engagement

Developing Nurture Strategies That Decrease Time to Value

Watering can
Photo Credit: Patricia Gortari/Tallwave

Whether you’re nurturing prospects or guiding product qualified leads through a free trial, intentionally crafting their journey allows you to coach potential buyers toward a purchase decision. Weak points in your nurture could be the cause of a low conversion rate.

Understanding the mechanics of a great nurture hinges largely on the concept of time to value (TTV), which refers to the time between when a customer takes an action and when the value of that action becomes obvious to them. TTV can help you diagnose where your nurture might be weak. For example, if you’re seeing low conversions from your free trial, it could be the case that your TTV is actually longer than the trial itself. This concept could apply to many points in the customer journey. Marketo found that 96% of website visitors aren’t ready to buy based on their initial visit. That’s when nurturing strategies come into play. Your nurture strategy helps to move customers through the marketing funnel with touch points that help communicate the value your product or service provides.

Tweaks to the nurture strategy can improve the customer experience and increase customer engagement and conversions. We worked with a SaaS company to revamp their nurture strategy to do just this. Originally, their customer onboarding experience had an ambiguous timeline and the high value actions weren’t made clear. We recommended changes that pivoted to an action-based nurture that reduced friction and personalized touch points. By identifying three critical stages in the trial onboarding period, we divided actions between what we called work, play and commit. We then frontloaded the sign up friction in the work stage. That allowed us to reduce the TTV and move customers through the play stage and toward commitment.

Also read: Uncovering the Root Cause of Low Conversion Rates to Unlock Continual Growth

If you’re trying to improve your customer nurture journey, there are some key best practices to incorporate. Here’s what to know.

Best Practices

Statistics show that 74% of companies are prioritizing improving conversion rates over the next 12 months, indicating this is a more important business need than driving traffic to their websites or even increasing customer lifetime value.

Here’s what to keep in mind if you are looking to revamp your nurture strategy to optimize your conversions and increase customer engagement:

  • Personalize: No one wants to feel like they’re receiving a cookie-cutter message from you so take the time to personalize your messaging based on customer actions. This goes beyond simply addressing them by name and takes into consideration where they might be in the journey.
  • Segment your lists: You can’t personalize if you aren’t segmenting, so be sure to divide your list by specific data points. There are many ways you can do this beyond the basic demographics of age and gender. You can create segments such as location, transaction history, web browsing history, and even device type.
  • Get creative and specific. Create multiple touch points: You should think of your nurture as greater than just one email. Consider all the channels you can use to nurture your customers — email, text message, retargeting ads. Make your communication ecosystem work together to create a world that pulls your customer in.
  • Include a call-to-action: In all your messages there should be a clear call-to-action that helps your customers understand their next steps. Keep it short and compelling.
  • Split test: Develop the practice of being data-lead by A/B testing all of your messaging. It’s hard to be entirely sure which subject line, call-to-action or topics will resonate with your audience, so let the data lead the way.

No one wants to feel like they’re receiving a cookie-cutter message from you so take the time to personalize your messaging based on customer actions.


Validation Strategy Framework

Make your communication ecosystem work together to create a world that pulls your customer in.

Product-Focused Campaign

Educate potential customers on everything your product can help them achieve with a product-focused nurture that highlights your most important features.

 

Purpose: Become a trusted thought leader for your prospects as they advance through the sales cycle.

 

Strategy: Highlight features that solve pain points using case studies, white papers, and internal data.

 

Success metric: You’ll want to determine if customers are using the specific features you’re highlighting for them in your nurture. This can tell you if the features you’re explaining are resonating with them or if you need to find more relevant features for their goals.

Competitive Campaign

A competitive campaign is more aggressive than other nurtures on this list. For this type of a nurture you’ll get specific about what differentiates your product, and what users have to lose if they choose one of your competitors.

 

Purpose: If you have a main competitor that customers are constantly weighing against you, a competitive campaign can work to overcome their objections by educating them about how you are better positioned to help them achieve success.

 

Strategy: Use specific information gathered during sales calls to address main objections without coming across as negative. Any press you’ve obtained or industry intelligence that proves your worth can be helpful here.

 

Success metric: Count actions such as signing up for your trial or upgrades to determine the success of this campaign.

Promotional Nurturing

Promotional nurturing can help move prospects across the finish line with a limited time, exclusive offer that encourages them to act now.

 

Purpose: Promotional nurturing helps you close a sale when you are in the purchase stage of the cycle.

 

Strategy: If you’re working with a big account that could significantly impact your business, offer special pricing or access to upgraded features based on what you know their needs are. For smaller accounts, adding a discount to your email nurture toward the end of the trial stage can inspire users to upgrade.

 

Success metric: For account-based selling, assess how many times you are able to close the sale. For product qualified leads, review how often your discount code has been used.

 

Also read: Optimizing paid media strategies to continually increase leads year over year

Your goal should be to help your customer get the value they are seeking faster by sending the right message at the right time.

How to Assess & Redesign Your Nurture Strategy

Improving your nurture strategy starts with assessing user behavior to identify where you can aid with value realization. This might include collating more data on your users so you can do a better job segmenting your nurtures. It could also include competitive research that helps you identify other journeys your users might be experiencing as they compare your service.

 

Use this research to map your entire customer conversion experience to identify opportunities to increase customer engagement. Then, identify gaps in content and specific trigger points that could reduce the time to value. If you find quick wins, implement these immediately while preparing your campaign overhaul.

The Bottom Line

There are so many ways you can nurture your relationship with your customers to increase engagement, prove your value and turn trial users into paying clients. The key to it all is constantly iterating by using data to understand what your customers are experiencing at each step in the journey. Customizing your messaging to respond to their actions and experiences will help you personalize each nurture touch point, increase customer engagement and prove the value of your product or service.

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