Reimagining Data as a Service for Enterprise

In 2018, we crafted a post sharing what enterprise Data as a Service (DaaS) meant at the time and how it was helping organizations stay competitive. A lot has changed since that last post, yet enterprise DaaS has skyrocketed as companies lean into data initiatives strategically. To help tame the uncertainties and unpack the many nuances, we’ve modernized our original 2018 post on the who, what, when, where, and why of Data as a Service to be more relevant to where we’re headed in 2025 and beyond.

Welcome to Enterprise Data as a Service 2.0

A lot has changed over a very short amount of time, which is why we want to define what modern Data as a Service companies look like and what you should expect when you bring one on board. Over the last few years, teams have felt the immense weight of knowing insights were available yet not being sure where or how to tap into those insights from the organization’s data effectively. This lack of access to the wealth of data available has eroded both the employee experience and the customer experience and kept teams from excelling at their work. Customers feel the weight of that with watered-down versions of experiences that would otherwise have the potential to be personalized and engaging.

Everyone we talk to wants to build out data engines to draw out the core findings from the wealth of collected data but isn’t sure where to start. Enterprise teams have asked questions, such as:

• How do we collect data properly?
• Are we answering the right questions?
• Can we do anything without a full-time data science team?
• Who will take ownership of our data pipelines?
• How do we pick from the plethora of available data management platforms?
• How do we keep our teams informed of new insights?
• How do we use our data to access the total addressable market and new verticals?
• Do we even have the infrastructure to support this?
• Who do we hire?
• How do we do it?

It’s a lot…and the steep influx of generative artificial intelligence (AI) platforms being infused into enterprises today makes it even more overwhelming.

Ultimately, teams are desperate for a clearer idea of how to strategically adopt generative AI to tame big data, infuse human behavior into those big data sets, and adequately disseminate the top insights to all teams. Of course, the strategy that works for a B2C company won’t work for a B2B or a member-based organization.

Because there isn’t a one-size-fits-all approach, the need for a modern DaaS provider has never been more critical. Tapping into a humanized and personalized approach to data collection and analysis for your organization, especially in an age of AI, opens the door to insightful data practices, clean analysis, and actionable takeaways.

The approach outlined above is how we work with our Data as a Service clients — collection, analysis, and actionable strategy. In this post, we’re lifting the veil by sharing the journey we walk our clients through when they reach out to us for Data as a Service so you can tame the overwhelm and shepherd your organization into a more modern, insights-fueled era.

What Modern Enterprise Data as a Service Looks Like

You don’t have to look far to understand we live in an era of data and automation. Every year, new generative AI tools arrive on the scene to tackle and automate another task faster and more accurately than ever, making the questions above feel even more daunting to tackle alone.

This era of automation can’t be ignored, especially as the amount of data continues to surge in growth. The IDC predicts that the global data sphere will grow from 33 zettabytes (ZB) during our previous writing in 2018 to 175 ZB by 2025. With the amount of data increasing rapidly, more organizations are wondering how to bring that data to their teams and extract actionable insights from all available.

Even more overwhelming is the need for personalization when extracting these insights. Forbes’s survey found that customers want you to know them.

That personalization starts with being able to leverage data effectively and efficiently—but not just any data. To offer a truly sublime personalized experience, organizations must go beyond the high-level quantitative data and dig deeper below the surface by tapping into qualitative data.

Still, many teams need help to deploy an effective qualitative data practice, which means they need help to showcase the humanity behind the data points. There’s a distinct problem with that lack of understanding:

We’ve reached the point where we can run through intelligence models and analyze what’s happening in the customer’s mind and purchase behavior to get those insights. The problem is that too many organizations still use surface-level data and lagging indicators, while qualitative data points and behavioral economics are needed to take a predictive stance.

If your organization wants to go after what’s new and innovative, it’s imperative that you’re solving for the needs, wants, and desires of your customers — all of which are data points that stem from what you’ll find through a strong qualitative data practice. With those needs, wants, and desires in clearer focus, your team will be equipped to cast a wider net by reaching a larger Total Addressable Market (TAM) with predictive analytics and propensity models and scoring.

And that’s the key to any modern enterprise DaaS approach:

While DaaS can and should lean on generative AI to help comb through data, starting with AI is risky. Enterprises cannot deliver a sublime customer experience without understanding empathy-based insights at every single one of the critical moments of the client’s journey. Modern DaaS providers know how to collect behavioral insights and tap into generative AI to unpack the human experience behind the data points so that you can move more strategically and effectively toward growth.

It’s Never Been More Important to Strengthen Your Data Practices

Data goes well beyond website visitors and repeat buyers. It goes to the heart of everyone spending their hard-earned money with you. Without a strong data practice, you’re missing out on those insights, which also means you’re missing out on serious cashflow opportunities.

According to a McKinsey report, companies tapping into data-driven B2B commercial growth report above-market growth with EBITDA (earnings before interest, taxes, depreciation, and amortization) increases between 15% and 25%.

If you’re tapping into your data but haven’t seen those same EBITDA increases, you might wonder what’s going wrong. How do you tap into data to personalize your customer and employee experience? How do you leverage insights to deploy a better accounts-based management system? How do you make your data practice actionable?

One way organizations have started to use data and AI to increase revenues is through targeted pricing, a practice that the Federal Trade Commission (FTC) is currently investigating.

The FTC is investigating how consumer personal data and generative AI have shifted the prices individual customers pay. More specifically, the FTC wants to know if organizations are adjusting prices based on consumer behavior so that someone more inclined to buy ends up paying more than someone newly introduced to the brand or product line.

This isn’t a new concern, either. Airlines and ride-share companies have long used dynamic pricing, increasing rates when market demands increase. You’ve likely experienced this adjustment if you ever tried to catch an Uber from an airport during rush hour after many flights landed versus trying to catch an Uber on an off-time at the same location.

Dynamic pricing isn’t the only way organizations tap into data to make strategic decisions. DaaS providers tap into data to personalize the buying experience through a different lens.

What’s happening today with parents who are back-to-school shopping is a prime example of modern enterprise DaaS in action.

Back-to-school season generates an average of $31.3 billion in sales, so brands that create pencils, markers, backpacks, and other essentials rely on late summer for a sales surge. However, in a recent survey by Deloitte, parents said that they prioritized price over brand preference. Without a DaaS provider to investigate this macroeconomic trend, brands might lower their prices to compete. However, enterprise DaaS companies take a different approach by tapping into the personalized experience that parents and students have when shopping.

Looking closer, the survey found that most parents (85%) were willing to splurge on must-have products. Also, when analyzing shopper behaviors, the data showed that more shoppers were turning to social media to make purchase decisions — (33%) compared to 21% in 2023. Rather than defaulting to discounts, there’s a distinct opportunity to keep profit margins higher by leveraging fresh platforms, messaging, and experiences to communicate value, reach customers where it matters, and influence decision-making beyond the price tag.

However, these insights aren’t as easily extracted as they seem. Instead, to get to know your customers and your brand’s competitive advantage and spot revenue-generating opportunities, you must ditch legacy data practices that you may still be leaning on in your business.

Challenges and Limitations of Legacy Enterprise Data Practices

Talking about legacy data strategies might feel funny when you zoom out on a modern timeline. After all, data science as we know it is relatively new. Still, because this practice has moved at such a breakneck pace through digital transformation, it’s caused many organizations to fall into three critical traps:

• Siloed based thinking
• Lack of actionable insights
• Surface-level takeaways that send teams in the wrong direction.

With the speed of data entering the picture and forcing immense business transformation, enterprises have struggled to manage attribution errors internally, adjust strategies to embrace this new trend, and, as a result, make off-the-cuff decisions that could send teams slightly off course. Let’s break down each of these challenges in more detail.

Breaking Data Out of Silos

Too often, teams work independently of one another. Even when an organization has a clear North Star Metric, siloed-based communications often lead one team to work toward a different goal than another.

Exasperating this issue is the fact that each of those teams might be seeking recognition for any individual wins achieved. For example, think about a marketer versus a salesperson’s worldview. Marketers don’t get paid on commissions. Instead, they see the world through attribution. On the other hand, sales often believe they don’t need marketing to succeed. Their mindset is more individualistic, so they want 100% credit for their own wins. This desire for acknowledgment often changes how teams gather, analyze, and present data, leading to a bigger divide and disparate strategy.

Having an enterprise DaaS company as a third-party voice can help mitigate these internal conflicts and break down silo walls. As a result, teams will work in closer harmony while playing from the same song sheet.

Making Data Actionable

Data governance is crucial. Without it, teams risk falling victim to the dirty data in, dirty data out rule. With just one error, teams can be sent in the wrong direction due to inaccurate insights. Because of this risk, teams are often left in the dark, unsure what insights they should leverage or how to apply them to their existing roadmap. More troubling is that many organizations are holding back from investing in deeper analysis tools, such as artificial intelligence (AI), because of uncertainty. A recent AI business survey found that:

When data isn’t actionable, teams aren’t sure how to reduce the cost of acquisition (CAC), increase lifetime value (LTV), or build retention internally or externally. Having a Data as a Service company on board can help you tap into new technologies strategically, responsibly, and in a way that aligns with your business goals. As a result, you can maintain strong data governance while extracting critical insights to empower teams to make better decisions.

Applying Empathy to Insights

Too often, data is taken at face value. This is especially true when it comes to survey results. Take the Net Promoter Score (NPS) as a prime example of a survey result that, when looked at in isolation, doesn’t offer a clear overview of what’s happening below the surface. In our post on Journey Analytics, we’ve broken down why this popular survey can be detrimental if the results aren’t approached and analyzed correctly. The high-level summary is that the net promoter score fails to consider who the customer would share their opinions with, how their opinion might change based on when they’re asked about the restaurant, and their motivations for sharing the recommendation.

Legacy enterprise data practices often don’t account for the people behind the responses. That’s where empathy mapping comes into play.

In our empathy map, you can see how we explore the people behind the data points. Rather than focusing on one singular response, like the Net Promoter Scores, we dig deeper. By peeling back the layers, you can listen to your customers’ thoughts, feelings, and actions to solve their problems.

Surveys certainly have their place, and DaaS companies know how to set them up properly for those deeper insights. For example, we don’t take high-level answers when we approach surveys. Instead, we ask follow-up questions, deepening the qualitative data we gather to understand better the solutions customers seek and their motivations for pursuing them. Rather than trusting that a customer is leaving because the price of a service is high, we dig deeper into the data to apply empathy that will answer whether that decision is related to perceived value, the competitive landscape, changed macroeconomic conditions, reduced budgets, or something different.

For years, a data-driven approach was admirable. This approach has been deemed riskier because it can lead teams to use data to inform the wrong things and think of data as absolute.

The type of data used to make strategic business decisions matters. With the depth of data available, teams are empowered to be insights-centric. This approach allows organizations to dig below the surface and uncover why customers behave the way they do, why market shifts happen, why employee retention drops, and why competitors are grabbing ahold of market share.

One company that recently realized the risks of taking an outdated data-driven approach was Nike, which reported 25 billion dollars lost in one day and 130 million shares exchanged in the stock market at the lowest share price since 2018.

Although this challenging season happened in 2024, the problem started in 2020 when Nike began a data-driven transformation initiative. Rather than digging into the insights behind the data points captured, which is admittedly a more challenging task, Nike invested billions of dollars in strategies that were easier to measure by eliminating categories that were harder to measure, such as relationships with elite athletes and a wide variety of retailers. Instead, they moved to a direct-to-consumer dominant strategy, reorganized the company, and reduced wholesale in exchange for direct-to-consumer models. This strategic choice turned out to be a mistake.

By changing to a data-driven strategy instead of an insights-centric approach, Nike had a more challenging time aligning products to their customer’s needs, relying on lagging indicators to understand their customers rather than leading indicators to predict what their buyers needed and target their messaging around future purchases. Leaning too heavily on easily harvested data from their website, Nike missed out on market data, causing their product development teams to shift in the wrong direction. Ultimately, this led to:

• $25 billion of lost market capitalization in a single day when reporting their Q2 results
• $70 billion of market capitalization lost in 9 months
• 130 million shares exchanged in one day, resulting in the lowest share price since 2018

In addition, because they relied so heavily on data rather than infusing behavioral insights into their analysis, they fell into the trap of fixed mindset thinking. If the data said it was X, it was X. Their teams did not add color to the black-and-white picture they received.

Data-driven missteps like the one Nike experienced happen because they need to understand how to tame the proliferation of unstructured or semi-structured data. This lack of understanding caused them to rely on easily measured data points, missing a huge untapped opportunity with effective quantitative and qualitative data practices that are often implemented by enterprise DaaS companies.

Using Enterprise Data as a Service to Build Effective Quantitative and Qualitative Data Practices

Everyone we talk to wants to build out data engines to draw out the core findings from the wealth of collected data. Today, we’ve reached the point where we can run through intelligence models and analyze what’s happening in the customer’s mind and purchase behavior to get those insights. The problem is that too many organizations still use surface-level data and lagging indicators, while qualitative data points and behavioral economics are needed to take a predictive stance.

Companies today crush it when it comes to quantitative data models. One look at your Google Analytics 4 dashboard will tell you which pages see the highest bounce rates and how much time people spend on your pages. Customer Relationship Management (CRM) tools, like Salesforce or Oracle’s NetSuite, can give you an overview of who’s responding to your messages and how fast those replies come in. Quantitative data from many platforms clearly shows what is happening in your business. The challenge is this type of data doesn’t tell you why.

Modern organizations must marry quantitative and qualitative data to drive strategic decision-making. While quantitative data provides measurable metrics, having a qualitative data practice in place can help you go deeper to understand the psychological, emotional, and social factors that will help determine why people buy and, ultimately, why they become brand ambassadors.

In this graphic, you’ll notice the blend of data points needed to move beyond simply knowing what’s happening in your business instead of understanding why. Rather than seeing which platforms your audience uses to become aware of the problem at hand, you can go deeper and pair qualitative data to understand what’s going through their mind during that stage of the process. Likewise, you can dig into the cognitive associations of the brand and pair that data with the most effective paths to purchase so you know where you’ll see the highest ROI long-term.

To reach this level of analysis, Data as a Service companies must start with the humanity behind the action—an understanding that requires an effective qualitative data practice.

Implementing an Enterprise Qualitative Data Practice

In this post, we won’t go into depth about what effective qualitative data practices look like. If you want to get up to speed, it’s a good idea to read our post on the indisputable value of qualitative data. Let’s look closer at how you can implement a more insightful qualitative data practice in your organization.

Human beings are messy and unpredictable. That’s because our thoughts and actions are determined by how we feel. If you want to understand what motivates your customers to take action and move forward with your organization, you must unearth the motivations behind those emotions. Insightful qualitative data tells organizations this.

When a Data as a Service provider comes into your organization, you should be looking to tap into qualitative data to achieve these three things — (1) Understanding what motivates customers to buy and stay loyal, (2) How to align their wants, desires, and needs with your acquisition and retention strategies, and (3) How to effectively and constantly reinforce value. These three elements are the key to success.

Success, however, is a nebulous concept. When engaging with a Data as a Service partner, it’s important to define what a successful qualitative data practice looks like regarding the actionable outcomes you’re pursuing across the bow tie funnel.

In our opinion, success from insightful data looks a little something like this:

As you can see, qualitative data starts by better understanding the Total Addressable Market (TAM) via insights-driven cohorts and personas. TAM can span a variety of cohorts, which is why it’s crucial to know how each becomes aware of the brand and the path they take toward awareness of your organization in particular.

Qualitative data allows enterprises to go beyond demographics and understand where each cohort is in their self-awareness journey. The various journey stages of self-awareness are as follows:

If you’re in marketing, you may have seen similar frameworks to showcase how a customer moves through the purchase decision. These awareness stages showcase the acquisition side of the bow tie funnel specifically. Everyone starts at the stage, problem unaware. At some point, however, they hunger for something different but still aren’t aware of where to satisfy that desire or the consequences of not taking action. As that hunger deepens, it becomes a pain point that cannot be ignored. It’s at this point the customer becomes aware of the solutions available. After extensive research, they learn about the brands that can help them and the differences between the various companies.

What’s important about this framework is that each cohort in TAM will take a unique journey in their behavior to move through each stage.

For example, Cohort A might see an ad in a magazine and then visit the website, opt-in to a lead generation campaign, and learn about the brand through emails before buying. Cohort B might see an ad on YouTube and read reviews before visiting the brand’s website, seeing a retargeted ad, and ultimately making a purchase. Cohort C might move faster by quickly jumping in after seeing a social media post and a handful of ads.

Again, this data looks at what someone is doing. Compelling qualitative data also examines why each of these cohorts takes different purchasing paths. This requires a deeper understanding of what’s happening across each awareness stage.

We’ve changed the verbiage traditionally used on marketing funnels like this one to reflect the need for more profound qualitative research. At the top, you’ll see that a trigger must occur for someone to go from problem-unaware to problem-aware. Those triggers are key life moments that make a person realize something needs to be remedied. After that recognition, the person starts to look at the factors of the problem, emotionally and logically, causing the person to start researching. When in the discovery phase, the person will find solutions on platforms such as Google, social media, YouTube, and many others. Then, as the person learns more about the solutions available, they analyze everything through associations, meaning they try to determine if the solution aligns with their beliefs, values, or lifestyle. Finally, they enter the alternatives phase, weighing alternative solutions before deciding on the one to buy from.

While funnels like the one above might seem nice on the surface, it’s not until you put them into practice that you see the full strength of the possibilities available in each stage. As a DaaS partner for a client, we had the opportunity to break out of this funnel and show them the opportunities at each stage. Here’s what that looked like:

While we obviously can’t give away specifics because we respect our client’s privacy—, we can share with you our side of how we approached moving through each stage of this modern funnel using data:

Triggers: We gathered data from third-party neutral sources around the various cohorts and uncovered the most likely trigger moments in the buyer journey for this particular niche. With those triggers in mind, we mapped out the content that would apply to that journey. Once done, we set up a data loop to test the content to find the highest qualified buyers, streamlining employee efforts and improving employee experience.

Factors: We collected data from various cohorts, including customers and our consumers who buy from competitors, using strategic surveys with intentional questioning to understand the factors that each cohort was researching when making their purchase decision. These strategic surveys go well beyond surface-level responses. We went far deeper, looking at it from a more human perspective to uncover the behavioral economics of what customers do in this particular stage.

Discovery: Once we knew what factors each cohort considers, we looked across various platforms to find specific action steps taken when customers discover solutions and brands to support their desires. In these areas, we gathered large amounts of qualitative data and combed it using generative AI to understand better what was happening at this stage.

Associations: In this stage, we sought to understand what happens at every stage of the rhetorical triangle, where we measure pathos (emotional appeal), logos (logical reasoning), and ethos (character appeal). By designing an advanced survey and funnel system, we were able to understand better how the customer perceived the brand and pinpoint specific areas the brand could do better. Through this strategy, we aligned the messaging and marketing with the cohort’s real-world experiences so they could move seamlessly forward in their journey with the brand.

Alternatives: We know that competition exists, so, at this stage, enterprises must know what other brands are offering and how the value and messaging stack up. Equally important is analyzing how effective competitor brand messaging is for certain cohorts. This isn’t easy research and requires strategic analysis to draw logical conclusions—something a DaaS partner can help brands accomplish.

Each of these steps moves toward the same goal. Just as we gather qualitative data to analyze each stage of the acquisition funnel, we must also do the same for the various stages of the retention funnel. That’s because we believe in the relentless pursuit of customers realizing the value of every brand we work with. When they see that value, brands can maintain that high retention rate.

Here’s what the retention side of the funnel looks like:

Customers continue to move through the retention funnel to deepen their connection with the brand. Rather than treating every purchase as a transaction, modern organizations realize the power of treating each person as a possible brand ambassador, which requires forming a deeper relationship with them through multiple communication layers.

When someone first enters the purchase stage, they often battle feelings of remorse and excitement. It’s up to the brand to steer them toward excitement from the get-go. During onboarding, they can reinforce the emotional and logical reasons the customer made the right choice to trust them with their hard-earned money. As the experience unfolds, brands can build a stronger foundation with their buyers and reinforce brand values. That leads to community building with the brand and making the customers feel like they belong to something bigger than themselves. Ultimately, it’s up to the brand to help the customer self-identify with its value, building retention and making them brand ambassadors.

Once again, here’s how this the retention side of the bow tie funnel works in action when implemented with the help of an enterprise DaaS company:

Purchase: To understand buyers’ thoughts at the point of purchase, we, as an enterprise DaaS company, implement data collection practices directly on the thank you page to gather qualitative data about how customers think and feel when they swipe their credit card. While this process is best suited for digital transactions, we can also gather data with in-person transactions by surveying buyers as they finalize their transactions.

Onboarding: Within the first few days, we implement another qualitative data loop to reinforce the value of the product or program by using the survey questions to remind the buyer why they signed up while simultaneously gathering data about what customers want to have a sublime brand experience. This might look like a phone call or an email series that asks questions and seeks to understand the initial expectations of the customer and, if applicable, the action steps they take in the first week after purchasing.

Experience: No matter what type of business you have, whether a membership-based business, B2B, or B2C, it’s a good idea to check in on the experience your team is delivering and see how the customer has integrated the brand or product into their lives. To do this, we again create a survey to reinforce the brand’s value and better understand how the buyer feels about the various parts of the product offering.

Community: We want to turn the buyers who are having a positive experience with the brand into active users and advocates of the organization. In this stage, we’re analyzing the customer’s behavior beyond the organization to find new opportunities to integrate the brand experience into other areas of the buyer’s life, such as hobbies, conversations, and more. For example, if you run a large coffee chain, you could look at how your loyal customers consume your food and beverage products weekly. If you run a membership-based program around a specific lifestyle, you could look at these customers’ conversations in online forums.

Retention: Ultimately, the culmination of these various data loops lets us understand how the customer perceives the product’s value and brand. We can then integrate those insights into communications around retention, boosting the value and profitability of our clients.

This process is how a DaaS companies integrate insights-driven ideas at every journey stage. By putting the findings of these multiple data loops into strategic motion, we can define the best qualitative data practices, quantify the qualitative insights, and tap into the insights results faster.

Data Looping by Enterprise DaaS Companies Leads to Faster Time to Insights

Let’s face it. Modern data science has shifted seismically over the last decade, and with that, so have the steps of data looping. With many data science practices available, methods for visualizing data, and technologies for analyzing insights, choosing the right methodology for extracting actionable insights is paramount. This all starts with how you approach the data looping process.

Modern data looping aims to understand better what’s happening across the bow tie funnel, as outlined above. With that understanding derived from qualitative and quantitative data, teams are better equipped to automate data analysis, identify patterns, and generate predictions. Ultimately, with a modern data looping practice in place:

At a high level, there are five steps we walk through when designing a modern data loop and analyzing the results.

When an organization partners with a DaaS company to deploy the iterative process of a modern data loop, its teams remain agile and quickly adapt new strategies based on real-time insights.

Step 1: Select Data Sources and Apply Context

Everything in the data loop starts with step one: data collection, definition, and contextualizing. Without assessing your data limitations, you’ll have a much harder time deriving accurate insights from what’s collected. Likewise, your team will struggle to define what each data source provides and the context behind the emotional moments when the data points were captured.

Step 2: Choose Conversion Methodologies

Modern DaaS companies can come in, analyze your full data loop, and pinpoint those limitations, allowing for cleaner and more contextually clearer analysis. With that understanding and context, teams can then move to step two—leveraging various conversion methodologies and platforms to convert and interpret the data points.

While essential elements to any data practice with these tools exist, many other ways exist to understand and interpret data for an even faster turnaround. It’s easy to fall into the trap of shiny object syndrome when reading headlines about emerging technologies, such as machine learning and generative AI.

This desire to move quickly can often cause whiplash away from equally robust existing platforms. For example, heat mapping and color coding can provide faster insights for teams to take action. Businesses caught chasing the newest silver bullet solution without strategically assessing how it fits into their bigger strategy are more likely to send teams off course by misunderstanding the data collected.

Step 3: Validating the Conversion Output

With the context, scope, and methodologies for DaaS companies to go after, many teams are eager to move forward with enthusiasm. Once those insights come in, the steps are clear, and teams should power forward confidently, right? Not quite. In taking swift action, teams need to pay more attention to validating the data and removing bias from the output. This third step in the modern data loop allows teams to add integrity to the insights collected.

• Preserve Variability: Ensure each category reflects the underlying variability of the findings
• Avoid Information Loss: Minimize the loss of important information during the conversion process
• Bias and Fairness: Ensure no new bias or unfairness gets introduced into the equation
• Transparency: Understand the conversion methods and potential biases or limitations
• Check Consistency: Ensure qualitative data represents quantitative data across the funnel
• Validate Against Objectives: Verify the data meets initial objectives and supports the intended analysis

Step 4: Document and Iterate the Process

At this point, many organizations feel good about their data practice. Their findings were in line with expectations, and they were able to extract critical information. There’s just one problem. There was no documentation about extracting that data, so they could not repeat or iterate upon the process.

In contrast, a Data as a Service company will record, track, and implement a continuous feedback loop. This involves recording the methodologies used, tracking changes during the conversion process, refining the conversion methods, and a feedback loop for continual iteration. In setting this up, you build trust among internal stakeholders and improve the overall employee experience by deploying a continual process rooted in clean data, consistent conversion methodologies, and conscientious integrity. With those value pillars in place, stakeholders will have more trust in the recommendations made from the insights provided, building organizational buy-in and dropping silo walls.

Step 5: Communicate Your Insightful Analysis

Finally, you’ll reach the “so what?” stage. When looking at the insights, teams sometimes ask, “So what comes next? So what do we do with this information?” Here, DaaS companies can add color to the black-and-white picture of plain data by visualizing and communicating the crucial findings.

Effective communication is a massive challenge among organizations, as they gather data from various sources and collection methodologies and combine them to deliver the correct information to the right people in the last mile of analysis. Here’s one way we approach visualizing this behavioral data for faster analysis and understanding:

RocketSource’s proprietary Customer Insights Map (CIM) extracts data from various sources and dashboards and brings the findings together. Here, teams can get a faster overview of what a customer is thinking, feeling, saying, and doing at every stage of the bow tie funnel by pulling from various data sources into one clear place. This map further breaks out the analysis to show what’s happening at each stage of the bow tie funnel listed above. Experience scores are mapped to touch points and platforms. The path to purchase data is outlined with each stage of the funnel. Ultimately, teams can get an overview of qualitative and quantitative insights in one place, painting a clearer picture of what’s happening along the buyer’s journey.

A data visualization methodology like the CIM ensures that various data visualizations don’t yield slightly different findings. Putting more intention behind the qualitative data practice, from soup to nuts, allows everything that comes through to be trusted. More importantly, it allows teams to take faster action and see growth results faster.

Creating a Clear Roadmap From Enterprise Data as a Service Analysis

Until now, we’ve covered using the modern bow tie funnel and data looping process to extract and understand critical insights. But DaaS companies should not stop here. True partnerships equip teams with a clear roadmap for how to take those findings and apply them across an organization’s three Ps: people, processes, and platforms.

These 3 Ps stem from our proprietary StoryVesting framework and are among the most critical elements in building an organization poised for consistent growth. That’s why any enterprise DaaS company you work with should effectively help you gather data and insights from these core areas. Let’s break down what that looks like, starting with putting the right people in the right seats on the right bus.

People: Onboarding a Supportive Team to Work Alongside Your Enterprise DaaS Partner

This is surprising to many people we talk to, but one of the most complex parts of data science isn’t data mining. It’s ensuring you have the right team assembled to get every puzzle piece right. To maximize your experience with a partner, your organization should have the right people leading the charge, and a DaaS provider can help you audit who’s on your team, which critical roles are missing, and how you can most efficiently fill those gaps.

A DaaS provider isn’t meant to take on all these roles alone. Many organizations aren’t operating with this ideal structure yet. Hiring a DaaS company is intended to step into your current organizational structure and train your internal team to become modern leaders. Knowing what that structure looks like can help you discern your organization’s needs and optimize your working relationship with the enterprise DaaS company you bring on board to support your team.

At the helm of any DaaS partnership should be one or several behavioral strategists, scientists, or economists driving the insights leadership. If you don’t have these people on your team right now, that’s okay. Still, it’s crucial DaaS companies are skilled in these areas and can train your team to work inside the organization to take ownership of the recommendations from the DaaS partner and then align the outputs to the key stakeholders and their needs. Having someone who understands the data science and the psychology behind why this is executed the way it is will help unpack all of the findings from the enterprise DaaS team.

Beyond the insights leadership, having someone oversee and manage the data processes is equally valuable. Qualitative data managers oversee the collection of qualitative data, and then they can collaborate with the artificial intelligence and machine learning (AI/ML) manager and private cloud managers.

Another layer you might want to add to your organizational structure is the boots-on-the-ground data collection team. Having specialists to design surveys, coordinators to manage logistics, survey interviewers, and CX/UX managers ensures clean and effective data collection practices.

Before you get overwhelmed thinking about hiring multiple people, rest assured that some of these competencies can be combined into one role. Outlining these categories helps you understand who needs to be onboarded to help the processes unfold smoothly alongside your DaaS partner.

To drive organizational buy-in, all key players must be aligned on every element of the DaaS partnership. This is what we call framework alignment.

Here, you’ll see the framework for working with a DaaS partner, which we showcased above. If your team does not agree on what those stages are, the type of data being collected, or the insights you’re looking for, you’ll struggle to make headway with even the best DaaS provider. Likewise, suppose your team is not speaking the same language regarding the customer journey funnel. In that case, you’ll struggle to understand how to capture more leads in the acquisition funnel and how to boost perceived value in the retention funnel.

The same necessity for organizational framework alignment holds when considering how you collect behavioral data and the maturity of your data collection practices. When your team is aligned, DaaS companies can sequence the processes effectively and tap into emerging technologies and platforms to build the engines to speed your time to insights.

Processes: Intelligently Adding Generative AI Into Your Data Practices

When generative AI started becoming more mainstream in the enterprise world, many people approached us to ask how to integrate this emerging technology intelligently and properly into their processes. Perhaps you’ve wondered the same thing in your organization.

Before we discuss some of the ways we recommend using generative AI for business purposes, let us first state that there are many ways we are not using generative AI from a data perspective. Rather than relying on AI to do all of the analysis, we believe in methodically handing over parts of the data sequence for analysis and injecting a human analysis throughout the process. To this end, we can speed up certain elements and gain insights faster.

A sequential analysis allows us to work methodically as a DaaS provider without interrupting an entire organization with new processes. This intentional approach is crucial for driving organizational buy-in, mitigating risk, and keeping costs low while revenues increase steadily.

As outlined above, the process starts with the gathering and discovery phase. It’s here that DaaS partners work with you to ensure you’re gathering the correct information and practicing clean data collection methodologies. From there, your teams will work with a DaaS company to outline the strategy and scope for the project. This stage requires the insights leadership to clarify how the key stakeholders define success so the DaaS provider can focus on the organization’s top priorities. With those clarifications, a gorgeous and contextually clear data visualization can be generated, combining various data sources and showcasing how they correlate to the organization’s North Star Metric.

With strong data collection practices and visualization processes, the DaaS partner can take the findings and conduct a gap analysis to pinpoint where things are missing in the organization’s strategy. The partner can also build reports to showcase the proposed next steps. After the final checks and balances to QA the data findings, the DaaS partner will offer a formal release and training recommendation for the organization.

While this process seems straightforward, inevitable friction points appear as external data sources enter the equation.

Many moving parts get an organization to the lump sum goal. These parts include areas not always considered in team discussions, but always influential on the outcome. Key trends, industry, macroeconomics, and market forces impact data operations at large. Each element must fit into the equation at some point in your analysis to get a more accurate view of what’s happening and what’s possible for your organization. The timing will greatly depend on onboarding new talent, training your team, and working with your DaaS provider. As your team gets on the same page and adopts the right processes for your end goals, you can lean into updating or adopting new platforms to fuel those efforts.

Platforms for Strong Data Operations With DaaS Companies

The proliferation of new and emerging platforms makes even the savviest leaders’ heads spin. Take a look at MarTech’s 2024 platform map, a company famous for tracking the ever-growing landscape of marketing technology.

It’s hard to make sense of all these available tools. There’s no possible way every organization could use each of these tools. Nor is it possible that every tool is ideally suited for every business. That’s why, at RocketSource, we remain staunchly tech agnostic.

Perhaps the most compelling and confusing emerging technology from the Martech Map is Generative AI. With its proliferation, many teams look to DaaS companies to answer the question, “What will we do about it?” That question stems from posts like this one from Slack, which discuss the benefits of AI that are hard to ignore for your team, making it feel like you must adopt generative AI into your organization.

Saving a few minutes each day turns into hours and weeks over the course of a year. Slack makes it easy to bring AI automation into routine tasks, giving you time back every single workday. ⏰ pic.twitter.com/8yYd39lT7G

— Slack (@SlackHQ) October 1, 2024

These posts often tout the speed at which an organization has accomplished something with AI that would’ve taken a human 10x as long to complete, which could cause your team to worry about losing their job to bots. These posts also spark fear in your C Suite, who worry about being left behind, causing their job to be at stake.

According to a report by Coresight, the market for AI tools is set to explode, rising from $79.8 billion in 2024 for generative AI hardware to a predicted $235.5 billion in 2028. It’s no wonder that the increase in generative AI in organizations’ platforms and processes is being called the productivity frontier. McKinsey estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually, increasing the impact of productivity rooted in the adoption of AI by up to 40%.

Here’s the catch. You don’t necessarily need to know precisely where to infuse AI into your current processes and data operations. Many organizations feel overwhelmed about where to begin, and trying to get it perfect causes serious hesitation around generative AI in the corporate world. There’s a reason why Gartner found that 30% of generative AI projects are abandoned due to poor data quality, inadequate risk controls, escalating costs, and unclear business value. Bringing a DaaS provider into the equation allows you to adopt AI platforms correctly, alleviating complexity and increasing business value.

Take Procreate as a prime example. Procreate recently made the bold statement, “AI is not our future.”

Procreate software caters to creatives who are some of the top professionals expressing the most worry about AI stealing art from artists. That claim is evidenced by a class action lawsuit that has been filed against AI image generators Midjourney, Stable Diffusion, and DreamUp. Artists who are part of the class action lawsuit say that these platforms are guilty of copyright infringement, did not compensate original artists, and did not properly credit any artists for their artwork when using it to create AI images.

Artists aren’t alone. ADP, a payroll company, released their annual People at Work report that found 85% of workers believe AI will impact their jobs over the next two to three years. What we like about the Procreate statement on this matter is this phrase in particular:

You’ve heard us say something similar about generative AI in the past, and you’ll hear us say it again — we cannot afford to strip humanity out of data collection and analysis. That’s why we don’t go straight for the shiny new AI platforms when we partner with an organization as their DaaS company of choice. Instead, we take a systematic approach to decide which types of AI tools can be infused into their processes to speed time to insights without eliminating the human touch.

Ultimately, organizations can reach data maturity by leaning into AI rather than shying away from it, but only if it’s approached through the right lens. We approach this by helping our clients build their qualitative practice. With robust data practices, we can quantify the qualitative data and feed it into private large language models (LLM). By tapping into a private LLM for faster answers and insights, teams can leverage their own internal generative AI systems to make sound business decisions rooted in empathy, humanity, and behavioral science. Using platforms such as GPT 4.0, Gemini, and IBM Watson, we can design a logical sequence rooted in their data collection rather than data pulled from all corners of the Internet.

An additional warning: As impressive as some of these generative AI platforms are already, they are just a fraction of what’s needed in a modern enterprise tech stack. AI isn’t meant to be a silver bullet to replace all of an enterprise’s platforms and processes. Instead, it’s meant to be an add-on to the other data collection platforms that assist teams in gathering the qualitative insights needed. Here’s an example of what that could look like.

This graphic shows an example of what a recommended tech stack could look like for strong enterprise data looping. Qualtrics is a tool that could be used for survey analysis. Digioh is a good tool for generating easy forms and quizzes. Dedoose is a strong qualitative research tool. Hotjar looks at the behaviors on web pages, allowing for more over-the-shoulder research. SurveyCTO is a scalable approach to data collection.

While each tool is powerful, this is only a recommended product suite for some organizations. It’s important to note that we’re tech agnostic, meaning we don’t advocate or affiliate for one particular tool. Instead, we look at the enterprise’s needs and make platform recommendations based on what will support the employee experience and generate the best outcomes for the team. In this recommendation, we looked at what the organization currently had in place and what was working. From there, we made additional recommendations to their tech stack to support the team at every stage of the funnel shown above.

When you think about where you want to be as an organization, knowing that you do not have to start with AI to get good information today is important. Still, it’s wise to start thinking about where to fit AI into your roadmap so you can reach higher data maturation.

While your organization might understandably hesitate to use AI, much like Procreate, a DaaS company can help clear the muddy waters. A strong AI strategy is designed to fit into only some parts of your business. Instead, it’s meant to support and serve your customers and employees for a stronger experience. Partnering with a company that can help you infuse the emerging AI technology effectively with the right data collection platforms without losing the human touch is essential. Through these partnerships, you can attach stronger business value to your generative AI efforts.

Extracting Business Value From Enterprise Data as a Service Companies

The ultimate goal of partnering with a DaaS company isn’t simply to get your data in order. It’s to derive meaningful business value so you can continue to forge up the S Curve of Growth. To start identifying a clear roadmap rooted in data and insights, you must first understand your organization’s data maturity.

In working with a DaaS partner, you can understand what’s happening across the what/where, when/how, why, and will stages at all levels of digital maturity. No matter where your organization is currently or where you want to go, a DaaS provider can help you activate and automate the necessary symptoms to reduce human error and improve reliability.

There are three core scenarios that we see most often as an enterprise DaaS company:

• Trigger: High customer acquisition costs (CAC)
• Reactive: High churn rates reducing future sales and lower lifetime value (LTV)
• Proactive: Lack of team buy-in with insights locked in silos

These scenarios and many others require intervention rooted in data and insights over guesswork and siloed management. Let’s look at how this process unfolds for each scenario.

Trigger: Using Enterprise Data as a Service Findings to Refine Go-to-Market Strategies for Customer Acquisition and Retention

The first scenario is for teams who want to grow their revenues by acquiring new customers and building their current buyers’ lifetime value (LTV). This strategy requires everyone on the team to understand the various triggers that get their people to act.

As an organization, the desired action is often to move potential buyers from problem-unconscious to solution-aware, acquiring new customers as cost-effectively as possible. With data and insights, your team can pinpoint where customers are churning out of your bow tie funnel, which we outlined above, and how you can course correct to acquire and retain more people before they churn.

Perhaps the biggest concern many organizations have around creating go-to-market strategies for customer acquisition and retention is how to gather the data that will move the needle. We’re often asked questions like:

• How do I collect data when I can’t talk to people?
• How do I convert persona-based marketing into cohort-based marketing?
• How do I capture behavioral data with all the regulations on cookies?

To help answer these questions, let’s examine behavioral data collection and customer experiences in a post-ChatGPT world more closely.

Jack Morton, a brand experience agency, recently researched market attitudes toward organizations’ data collection practices. The goal of this study was to understand how the influx of AI platforms has impacted consumers’ willingness to offer up their personal data to organizations and brands. What they found was promising for companies.

48% of consumer respondents said they would exchange data for better brand experiences. The number of Americans who had previously insisted on keeping their data private (61%) dropped significantly to 52% after ChatGPT became more prolific.

But here’s the real takeaway from the study. Not only are consumers more open to sharing their data, but over half of consumers (63%) expect an AI-driven experience that’s personal (59%), relevant (57%), and environmentally friendly (57%) as a result of offering access to their behavioral preferences. Having a strong DaaS provider at the helm can help meet those expectations.

Best-in-class enterprise DaaS companies use data-driven insights to inform go-to-market strategies from problem-unaware to solution-aware at every stage. However, understanding customer behaviors and preferences through data analysis requires organizations to effectively tap into qualitative and quantitative data to derive more substantial insights. By harnessing modern data practices across the bow tie funnel, businesses are better equipped to tailor marketing campaigns to meet various target cohorts at just the right stage in their journey. Whether the customer is just starting their journey in navigating the competitive landscape for a solution or is already neck-deep in the organization’s ecosystem, understanding how to communicate effectively and create product-driven strategies for retention can bolster the organization’s revenues.

Perhaps more importantly, DaaS companies are also well-equipped to offer personalized customer experiences post-purchase, increasing the customer’s lifetime value. By optimizing product offerings to meet customer needs more directly, organizations can enhance the overall experience and improve retention.

Reactive: Improving Behavioral Data Collection from Digital Experiences with Insights from Enterprise Data as a Service Findings

The next scenario covers the middle part of the digital maturation index—companies that are ready to become more reactive with their data, personalizing and improving digital experiences for their buyers.

Effective data collection from digital experiences is crucial for a successful enterprise DaaS strategy, and in today’s tech-heavy ecosystem, there’s no shortage of tools available to capture behavioral data. Take a look at some of the most commonly used ones here.

Adobe Analytics and Google Analytics 4 offer advanced tracking capabilities and insights into user behavior across websites and applications. Google Tags facilitates streamlined tagging and tracking for comprehensive data collection. BigQuery provides robust data warehousing and analytics, allowing for efficient storage and querying of large datasets. Data visualization tools like Google Looker Studio and Tableau create interactive and insightful visualizations that make data accessible and actionable.

Many data collection tools exist, but DaaS companies aren’t employed to answer whether one tool is better. They’re there to help you find the right tools for your organization so that you can most effectively personalize customer and employee experiences and equip you to drive full Account-Based Marketing (ABM) strategies.

You can’t execute the final output if you don’t have solid data practice. Until recently, leveraging data was somewhat labor intensive. Sales and marketing were forced to sift through high- and low-quality leads to find the golden needles in the haystack. Proper data collection processes that tap into behavioral data and bring teams closer to deeper insights can help bring in higher-quality leads, build retention, and close more deals.

Here’s what that process looks like:

The first step is to use data to understand who your best cohorts are based on past performance. Who can your team successfully sell to? Who’s more likely to buy? What are the most profitable cohorts? A whopping 66% of marketers lack confidence in their data, signaling that organizations haven’t moved up on the data maturation index. Working with a DaaS partner can help build that confidence by modeling the customer journey based on previous behavior.

With the right data being collected and more confidence in which cohorts to target, analysts can then comb through the insights to better understand who’s behind the transaction. That analysis allows marketers and sales teams to reposition and personalize their engagement approach. Better yet, teams can pass those insights to the entire organization, bridging the gap between marketing and sales and closing more deals.

With the right system, teams can start executing at a higher level to understand the elements that impact deal velocity and pipeline acceleration. Collecting and tapping into the right behavioral data can show teams which personas engage most, opportunity rates, account win rates, and more. This collection and behavioral analysis level can open up new prospecting pools you might not have known existed before.

Proactive: Telling Data Stories through Data Visualization

This final scenario is for those organizations who might be further along the data maturation journey but are now looking for ways to translate their data collection to tell a story that gains organizational and C-Suite buy-in.

The reality is this: If you can’t tell stories rooted in data and insights, you’re going to have teams infuse their personal biases, which inevitably will lead to steering the boat in the wrong direction.

Effective data storytelling is essential for ensuring stakeholders understand and implement insights. Here are the best practices.

Impactful data visualization must avoid clutter and focus on critical messages through simple structures. It must provide context, background, and explanations to make data more meaningful. It must be visually appealing with colors, charts, and graphs to engage the viewer. It must offer interactivity so stakeholders can explore the data through interactive dashboards. Lastly, it must offer a compelling narrative that ties data points into a cohesive story.

With those elements in mind, it’s clear that traditional dashboards just aren’t cutting it anymore. For too long, organizations have looked at dashboards that have high-level insights, such as website visits, sales calls landed, and deals closed. While that information is nice, it doesn’t get to the heart of what’s happening in your customer or employee’s life throughout the bow tie funnel. There’s no behavioral data attached to those figures, making it harder to approach your growth strategy through an empathetic lens.

Those dashboards, and perhaps the dashboard you’re using today, look a little something like this.

As an enterprise DaaS company, we’ve had multiple companies bring us dashboards like the one you see above and ask us to help them tell a better story. While those data points are important, they fail to answer core questions, such as the performance of marketing campaigns and what organizations are getting out of their marketing spend.

Going into these organizations, we’ve dug deeper into their customer journey funnels to find more impactful insights, such as new lifetime values (LTV), new costs of acquisitions (CAC), and areas where we can improve internal rates of return (IRR). By pairing these insights alongside data, such as cohort analytics, we’re better able to understand why consumers make the choices they do. For example, why do they stay loyal to one brand over another? What messages are speeding the path-to-purchase versus which messages are slowing down the customer’s decision making process? How can we recoup our investments in marketing, sales, and product development faster?

While going deeper into the data, we also work to simplify the story being told so that the story being told is clear to every key player. Our finished version looks a little something like this.

In this simplified version, teams can see more clearly what’s happening across the funnel, from acquisition to retention and overall trending market performance. Teams can then distill each side of the funnel to learn more about their target and how well they performed in each area compared to their overall budget.

While we can drill down into the various capabilities of the departments and organization, all data orients back to the North Star Metric and key metrics at each funnel stage. Under the acquisition, those key metrics are the average cost per customer and time-to-purchase. On the retention side, you might look at overall churn and retention rates to spot any alarming new trends and address them before it’s too late.

Using the trending marketing performance section, teams can toggle between each area and know how to budget the best for upcoming campaign opportunities with more predictive detail. This level of advanced analysis and storytelling through simplified data visualizations can help organizations know what to do next.

What Innovative Enterprise Data as a Service Companies Offer

Data as a Service is so much more than buying customer information. It’s about helping you leverage your data to find modern collection practices that will tell a deeper story and offer a clear roadmap for how to proceed.

Too often, organizations stay in silos that are frequently run by strong personalities that interpret data in their way to make themselves look good. With an enterprise DaaS company working in tandem, teams get that outside voice to look at all areas of data — quantitative, qualitative, and external factors — to show the performance across all departments. In other words, no more siloed thinking. No more obfuscating the truth about performance levels. Effective data storytelling offers a clear direction.

When you partner with a Data as a Service company, you’ll be equipped to build a strong qualitative data practice and then learn how to train your machine learning and generative AI platforms to speed up the time it takes to extract insights from your data. Together, your DaaS provider will leave you empowered to stay competitive by spotting macroeconomic trends and disseminating that information across all departments, dropping silo walls, and improving the employee experience.

If you’re ready to uncover where you are on the data maturity index and find out the best next steps for your organization, schedule your complimentary consultation call. We’ll help you understand how an enterprise DaaS company can help and how your organization can benefit.