Unlock Brand Loyalty’s Secret Weapon: Automated Personalization

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In today’s ultra-competitive marketplace, the brands that consistently deliver tailored experiences to their users stand out and command unwavering loyalty. The digital age has revolutionized the customer-brand relationship. In this new paradigm, personalization isn’t just a perk—it’s an expectation. But how do we deliver experiences that feel tailor-made for every individual, every time? Herein lies the art and science of modern personalization, woven with the threads of manual and automated strategies.

While brands can employ multiple personalization strategies, automated personalization has become the gold standard. Here’s a closer look at this innovative approach and its unparalleled power in solidifying brand loyalty.

The two tactics for Personalization

Targeted Personalization:

Definition: Directly targets a specific user segment, leveraging data insights to establish a hypothesis. The subsequent experiment either validates or refutes the set hypothesis.
Real-World Application: The explicit messaging throughout the conversion funnel urging unauthenticated users to recognize the perks of registering or signing up.

Black Box Personalization:

Definition: This technique is all about experimentation. It’s akin to casting a wide net, gathering data, and then refining a hypothesis based on outcomes.
Real-World Application: Netflix’s recommendation algorithm offers a splendid illustration. By understanding user behaviors and preferences, it curates a personalized list of shows and movies.

Manual vs. Automated Diving Deeper

Manual Personalization:

Overview: Manual personalization revolves around hypothesis-driven experiments, where targeting is rooted in prior knowledge and insights about customer segments. This method, being more hands-on, allows for an acute specificity in reaching the right audience with the right message.
Functionality: The journey begins with defining a pivotal Key Performance Indicator (KPI), often focusing on conversions. From there, hypotheses are formulated about the targeted segments, informed by existing data and research. The design process leans on conditional logic— for instance, “if a user exhibits behavior X, display content A.” This provides a tailored experience for each user type. Once the design is rolled out, the subsequent step is observing outcomes, validating results against the hypothesis, and then refining strategies based on the insights gleaned, ensuring continuous improvement and relevance.

Automated Personalization:

Overview: Automated personalization moves beyond traditional segmentation, creating various content variants without predefining the target audience. It harnesses the power of machine learning (ML) to intuitively determine which segments will respond best to each variant, making the process dynamic and responsive.
Functionality: Initiating with the specification of a Key Performance Indicator (KPI), such as clicks on a given space, the system then progresses into the ‘learning phase.’ During this phase, machine learning algorithms delve into understanding audience interactions with the content variants, picking up patterns and preferences. This is followed by the ‘test phase,’ where ML critically assesses if the personalized content versions have superior performance compared to their randomized counterparts. The beauty of this approach lies in its ability to evolve and refine without manual intervention, ensuring the content remains fresh, relevant, and engaging.

Strengthening the Loyalty Chain through Mastery of Customer Data

To truly harness the potency of automated personalization in cultivating brand loyalty, an in-depth and meticulous understanding of customer data is paramount.

Crafting Comprehensive Profiles: With accurate identity resolution, brands can curate exhaustive prospect and customer profiles. This, in turn, assists in targeting non-members effectively, propelling both engagement and acquisition.
Unearthing Crucial Insights: A robust Customer 360 perspective, grounded in dependable customer identity, brings to light invaluable insights. Such insights and predictions shape enrollment strategies and foster increased program adoption.
Facilitating Omnichannel Loyalty Experiences: An omnichannel approach ensures customers encounter relevant touchpoints, campaigns, and journeys across platforms. Such cohesive experiences amplify the uptake of loyalty rewards and offers.

Why Automated Personalization is a Loyalty Game Changer

In the age of information overload, consumers yearn for experiences tailored to their unique preferences, and automated personalization is the beacon lighting the way. Moving beyond the confines of traditional segmentation, it offers a dynamic approach, delivering deeply customized interactions at every touchpoint. Let’s delve into the transformative powers of automated personalization:

Fluid and Evolving Experiences: Traditional methods often operate on fixed strategies which, over time, can become obsolete and fail to resonate. Automated personalization, on the other hand, functions like a living entity—constantly learning, evolving, and reshaping based on real-time user behaviors and preferences. This ensures that users always encounter fresh and relevant content.
Heightened Engagement and Conversion: The beauty of automation lies in its precision. With algorithms curating content that mirrors users’ interests, engagement levels skyrocket. It’s simple—when users see what aligns with their interests, they’re more inclined to interact, leading to higher conversion rates.
Fostering Genuine Trust: The path to brand loyalty is paved with trust. Automated personalization is not just about serving tailored content; it’s about demonstrating a brand’s genuine effort to understand and cater to its audience. When customers sense this level of attention to their needs, their trust in the brand solidifies, nurturing a bond that goes beyond transactional interactions and blooms into unwavering loyalty.

In Conclusion

Brand loyalty is no longer just about quality products or top-tier services. In this age, it’s about how well a brand understands and caters to its users. Automated personalization, when executed with a fine understanding of customer data, can work wonders in not just attracting but retaining loyal customers. Embracing this strategy, brands can set themselves on a trajectory of sustained growth and unparalleled customer fidelity.

Originally Published on LinkedIn

AI’s Lustrous Promise: A Double-Edged Sword?

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The allure of AI is powerful, drawing in organizations eager for progress and efficiency. Top executives sing its praises at industry events, and the largest companies in the world are at the forefront of utilizing the revolutionary new capabilities in their work. Yet there remains an underlying question beneath the surface for the rest of us: Can AI truly deliver on the lofty expectations, or will teams chasing these benefits be left scrambling to fill in the gaps?

While everyone’s been briefed about AI’s benefits, its associated risks sometimes fly under the radar. While not an exhaustive list, here are three potential blindspots and/or underestimated risks:

Transparency and Accountability
Replacing Experts with AI Administrators
Understand AI Tools and Their Limitations

Transparency and Accountability

In a research setting, issues of replication and reproducibility have been a well-known issue over the past decade. Advances in computational power and advanced tools have only raised the stakes of using complex analytics at scale. In a business setting, the promise of these capabilities comes with a hidden, often unstated implication: businesses are using these analytics to make complex decisions at scale.

This is a key component of how complex tools like AI can inadvertently create unanticipated risks from a business perspective. This is not simply fear-mongering about hallucinations (although negligent use of AI tools without considering this possibility can be extremely harmful). This is an issue that is already known in the field of machine learning; the risk of over-reliance on algorithmic decision-making without proper guardrails in place (or, as discussed later, the proper expertise) can lead to misleading results or, in some cases, even unintended social harm.

Without a deliberate, transparent, and focused effort to develop strong processes around transparency and accountability surrounding these new tools, businesses risk outsourcing decision-making to tools, while shouldering the burden of the consequences. This is not a sustainable model for most companies; one of the most important parts of implementing AI tools into a company will be alignment between stakeholders, executives, and practitioners into the capabilities and boundaries of what a given tool will (and will not) be expected to do.

More importantly, once these capabilities and boundaries are understood, there must be alignment at all levels on how to structure accountability for the decisions made by an AI tool. The potential negative impact of a toxic work environment, where workers are punished (even indirectly, such as requiring engineers to work unreasonable hours) for an AI failure they were never assigned responsibility for, cannot be overstated. As Artificial Intelligence becomes increasingly integrated into the assets (e.g., a website) of a business, the stakes involved with transparent accountability structures only increase alongside it.

Replacing Experts with AI Administrators

It seems natural that implementing these tools will allow an organization to replace highly paid Subject Matter Experts (SMEs) with lower-paid administrators. But it would be a monumental misstep and could have serious consequences for an organization. SMEs are not just overpaid machines spitting out statistical reports or lines of code–they are reservoirs of knowledge, validation, and expertise (as the namesake suggests). While administering prompts and managing processes will be important for utilizing AI in a business environment, SMEs will likely remain a critical part of executing the complex vision of leadership into practice.

The actual role of SMEs, however, may change considerably. Rather than utilizing their expertise in their subject language to generate outputs (e.g., reports, code, etc.), these roles will likely serve a crucial role of translator between leaders, administrators, and tools. Data scientists may become more valuable for their scientific expertise than their data expertise; computer engineers may become more valuable for their engineering capability (interfacing between a human’s vision of a tool and the actual capabilities of a machine to execute it) than for their coding expertise alone. As machine learning approaches machine intelligence, which is ultimately the vision of AI technology, the human element of human capital is also likely to become more valuable.

Before sidelining SMEs for AI and administrators, consider the long-term implications. Utilizing AI technology doesn’t mean sidestepping difficult business questions. Quite the contrary: by implementing machine intelligence in more and more advanced roles, the work of translating between human intelligence and machine intelligence will also increase with corresponding complexity. Validating, learning from, and building human intelligence from machine learning models is already a complex process–subject to extensive debate across all academic fields. This will only continue to grow as an issue as AI continues to become a more normalized part of industry and research.

Ultimately, as industry and research continue to implement more and more complex methods into their work, we cannot neglect to build a corresponding process of validation and learning alongside it–likely of equal or even greater complexity. Failing to take this process seriously can widen the gulf between the humans and machines involved in workflows–increasing the potential risk to both when the two parties are not on the same page.

Understand AI Tools and Their Limitations

A key issue that could fly under the radar is that, as the promise and expectations grow exponentially surrounding new AI tools, stakeholders and leaders quickly risk losing touch with the on-the-ground reality of exactly what new tools are capable of. Without careful consideration of the tools and technologies that actually exist, the rush to implement glossy new technology to ride the marketing wave of an exciting new technology can quickly drown out the voices of those actually in the know about what these technologies can (and can’t) do.

Leaders and stakeholders must understand that now more than ever, the value of actually listening to subject matter experts is at a high point. Marketing materials never have, and never will, sufficiently capture the details of what a given technology is capable of–and the often opaque marketing around new AI tools should raise a caution flag for stakeholders and leaders who are considering investing not only capital but also potential control (for example, the flexibility to move between different proprietary ecosystems), into these new technologies.

Organizations must take seriously the process of piercing the veil of what these tools could do and executing a vision of what these tools can actually do for that organization. This requires investment and planning. It likely requires implementing an AI tool within a narrow scope of an organization’s workflow (e.g., an AI chatbot on a digital site), and more importantly, it requires putting a truly critical lens over whether these small experiments actually provide value. Measuring and validating this performance goes beyond short-term revenue, but (to draw on the previous section) should also rely on expert analysis of where long-term risks lie. Each organization, industry, and organizational culture will have unique problems that should be anticipated prior to a full-scale integration of the technology.

Final Thoughts

Simply put, revolutionary changes cannot be sustained without a revolutionary-scale investment to sustain it. Integrating complex technologies deep into existing workflows requires an equally deep examination of those workflows, in order to ensure transparency for the workers and managers who will ultimately be responsible for that integration. Utilizing AI to pursue higher stakes business goals at scale requires more investment into the human experts who can understand and translate these goals (whether through decades of experience with your organization/industry or through expertise in the affected areas)–not less investment. Finally, any successful business knows that the promise of lofty returns without corresponding levels of risk is no promise at all. AI technology promises high returns. Respect the risk.

Originally published on LinkedIn

Is AI up to No Good? What Research, Insights and CX Leaders Must Know

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There have been tremendous advancements in CX technologies, but this one may be the biggest yet. You guessed it, I am talking about AI. At Brooks Bell, I lead programs to help organizations get the most value possible from insights throughout their organizations. There’s no question that AI is going to play a large role as teams onboard AI

Let me begin by saying I am excited to see how tools like ChatGPT, Google’s Bard and Wevo will enhance our processes and insights. On the other hand, I do have concerns about AI meeting the soaring expectations that have been set. I fear companies may make mistakes that will hurt their teams and long-term success.

In this article, I’ll be covering some of the considerations, benefits, and challenges that Research, Insights, and CX teams can expect. I’m also going to share some tips for getting started.

You are the People-Connector, Not AI

Leaders in Research, Insights, CX and related positions are people-connectors. We have the expertise and resources to get answers to burning questions, we can add the human-element to the huge amounts of quantitative data in our organizations, and we connect insights in one part of the organization to another.

I, and many of my counterparts, are excited to see the role that AI can play in this space. White technical applications will shake out, I have one major piece of advice: remember the importance of being a people connector. AI isn’t going to build and foster relationships – only you can. So while you may find new, cool applications for AI – ensure you’re doing what you do best when it comes to helping people learn, share, and grow together.

AI as a Research Assistant: The Big Test

It only takes dropping a few sentences into ChatGPT and asking it to write a customer persona or summarize brand perception to see that AI can’t be ignored in our space. This exciting and scary. It’s exciting that we have advanced technology to quickly put our research study into words for us, but scary that some people may overuse it. By ensuring your team knows AI isn’t going to take their job as a researcher, they’ll feel more secure. And by integrating AI usage at the right time and in the right way, you’ll offload some time-consuming tasks so they can focus on more impactful things. It can be a win-win, but you have to do it right.

Here’s how I recommend getting started: First, look for opportunities in your existing process to automate. Determine what tools you’ll use, when you’ll use them, and what points of redundancy or validation are needed. Establish these rules up front and communicate clearly with the team on how you see AI complementing their expertise.

I also highly recommend doing an experiment. For example, take a research study that’s been completed. Only this time, use AI at those points in the process you identified to be good candidates. Document the pros, cons, what it got right, what it got wrong, and where human engagement is critical. Do a comparison evaluating things like quality of output, level of effort, and time needed. This real-life test will yield valuable learnings that you can apply to your future usage of AI.

It’s critical that your team knows how to work with AI – not against it or instead of it – so do what you can to help your team evaluate and establish the right boundaries.

Is AI a Con Artist?

The intricate workings and coded logic of AI remain largely shrouded. There’s limited visibility into its mechanisms, and implicit trust is very risky. That’s why I suggest treating AI as a con artist in your organization. If you assume AI is going to push boundaries and perhaps be out to get you, you’ll proceed with caution. And at this stage, I believe that’s the right approach.

This leads to governance. Deciding on accountability is very important. Where does the blame lie if a junior designer produces sub-par work via an AI tool? Is it the individual? The AI? The team leader? What checks and balances are built in to ensure only the right, intended outputs move forward?

Keep a watchful eye on AI and ensure you aren’t handing over control without proper governance. I predict we’ll see stories about this playing out in real life as AI adoption continues.

Top 5 Best Practices for Onboarding AI

Let’s quickly recap my tips for onboarding AI like a best-in-class organization:

Remain a people-connector, even though AI may pull you away
Evaluate your process to identify places to integrate AI and free up your teams for different types of work
Help your teams leverage AI as an assistant, embracing its efficiencies while valuing human expertise
Apply AI to an existing project to measure the quality, level of effort, and time needed for each. Use this comparison to help tailor your AI solution.
Treat AI as a con artist in your organization, trying to pull one over on you at every turn. This skepticism will protect you while teaching you where AI excels and lags.

While the AI wave is exhilarating, navigating with an informed compass is crucial. Its introduction promises a new era in research, insights, and CX – but the human touch remains irreplaceable. Balancing the two is the key to a brighter, more efficient future. If you or your organization needs help, I’d love to brainstorm with you.

Originally published on LinkedIn

Build an Insight Engine with an Insights Center of Excellence (iCOE)

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Uncovering customer insights and applying them at scale is critical. Understanding customer needs, preferences, and behaviors are core competencies at best in class organizations. As VP of Strategic Consulting & Solutions at Brooks Bell, I’d like to give you some tips to help you bring customer insights to life in your organization. I’ll introduce you to a concept that will optimize collaboration, facilitate sharing, and build accountability: an Insights Center of Excellence (iCOE).

Brooks Bell’s Insights Flywheel

At Brooks Bell, we specialize in creating teams capable of uncovering insights in all they do, sharing them across their organizations, and taking quick action. We help our clients gather insights through experimentation, advanced analytics, and customer research. Our consulting services ensure teams are equipped with the right people, processes, and governance to execute efficiently and drive value for their company. This customer insight flywheel builds momentum around insight-driven decisions across organizations and drives success. I lead a team of consultants to build this capability for top brands.

We’ve navigated many different types of organizations, seen common challenges, and have crafted customized solutions to overcome them.

Defining an iCOE

Centers of Excellence are used across industries – technology, data, business processes, security, innovation, even stretching into academia and the military. The concept of a COE is a known, proven one. And its primary function is to provide leadership, best practices, research, support or training for a specific area of focus. What I’d like to walk you through in this article is the application of a COE to insights.

An Insights Center of Excellence (iCOE) is a centralized team or function that focuses on generating, sharing, and applying customer insights across the organization. It acts as a hub that brings together experts, tools, and best practices, fostering a culture of data-driven decision-making.

Why Consider an iCOE

The expertise, governance, and collaborative nature of a COE lends itself to customer insight generation and sharing. Insights come from many places in an organization, and so bringing them together is a common obstacle to overcome. By employing a COE structure, organizations will bridge the gaps in insight creation and sharing.

The benefits of this include more insights, diversity of insights, wider application of insights, iteration of insights, and overall alignment. And from a culture perspective, there is enhanced collaboration that builds relationships alongside a greater appreciation for the insights and work being done by teams people may not often interface with.

Common Challenges of an iCOE

While implementing an iCOE can bring numerous benefits, it is common to experience headwinds when integrating with existing processes, gaining organizational buy-in, and ensuring the quality and consistency of insights. Proper planning and clear communication are key to overcoming these obstacles. We help clients with process transformation and organizational effectiveness, and I’ve seen tremendous benefit from working with an expert partner to guide this process. It can be quite disruptive if not approached correctly, so ensure you’re taking the time to align on strategy and build a communication plan.

How to Build an iCOE

You may be thinking “Sounds great! How do I get started?” I can help! Our recommendations are tailored to the specific needs of each organization, so there’s no one-size-fits-all answer. However, I have some tips:

Formalize your vision for the iCOE. What is the vision and purpose of this group? What outcomes do you want the iCOE to achieve for your organization? Once you have set your vision, you can create your insights roadmap or plan. Determine how you’ll measure progress and what success looks like 3, 6, 12 months from now. You’ll find yourself coming back to this frequently, so use it as your true north.
Develop a shared definition of an insight. Everyone’s understanding of what an insight is may vary. This variability changes how insights are written and their perceived applicability in decision-making. Standardizing the terminology and definition, allows individuals to better understand insight value, generation and application, thus fueling the insight flywheel.
Start your iCOE with existing team members. It will be an additional responsibility on their plate, but this will be a faster and more successful way to get the ball rolling. You have experts in-house. Find them! You’re looking for people that know their craft well, love to talk about it, have influence in the organization, and are comfortable building something new. At this stage, it’s smarter to put a budget toward a partner that can help you stand up the structure and prove ROI. This foundation-setting will make a stronger business case for expansion later.
Map your insight centers and pull them in. These are teams within your organization that have access to customers and information that lead to great insights. Think about research, customer service, marketing, product, business intelligence, etc. Some of these teams may be great at generating, sharing and using insights already, but I assure you there are others that have the capability that aren’t using it. The iCOE and organization will get so much value from their participation, and they will too as part of the organization’s insight network.
Define roles, responsibilities and process. Once you’ve identified specific key players and teams in key areas, it’s time to put definition around roles, responsibilities and process structure that will set the foundation of your iCOE and will enable collaboration, accountability and more informed decision-making across your organization.

We’ve witnessed firsthand the transformative impact of iCOEs on our clients’ customer insights initiatives. By establishing a strong organizational structure and implementing effective COE practices, organizations can achieve remarkable results.

If you’re looking to establish an iCOE for your customer insights efforts, I’d love to chat more with you. Contact us via the contact button on the site or DM me on LinkedIn you can find my original post below.

Originally posted on LinkedIn

AI and Your Team: People, Process, and Governance

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As the digital landscape evolves, many organizations continue to grapple with the challenge of incorporating Artificial Intelligence (AI) into their team’s day-to-day practices. AI will reshape how we work, plan, strategize and launch experiences, services, and products, making it much more than simply a technological advancement; it’s a transformative shift that will impact teams at their core. In my role at Brooks Bell, I help our clients design the practices, processes, and systems that make an organization work efficiently and effectively. Because questions surrounding the integration of AI into organizational design continue to become priority agenda items in meetings, I thought it might be useful to centralize insights on how we’ve navigated the journey toward successful AI integration and team member onboarding, focusing on the people, processes, and governance it takes to bring this to life.

Setting the Vision

Expectations couldn’t be higher for the efficiencies, creativity, and innovation that AI will unleash for organizations. Still, those expectations will come crashing down if leadership across the organization lacks a defined vision and purpose for what AI is bringing to the team. Without a well-defined vision of the organization’s future state — including what AI should unlock and why — team members will jump into emerging platforms without clarity of intention and purpose. This will leave team members guessing as to what’s expected of them, how emerging platforms are contributing to their individual, team, and organizational goals, and where they should be looking to leverage AI within current practices. This is a waste of investment and a costly churn on the employees following leadership’s direction (or lack of direction).

To avoid this, I encourage leaders to identify the role they want AI to play in the organization and communicate that message clearly and frequently across the organization. Consider what steps in the process AI can automate, where there are opportunities to integrate within existing tools, how to measure the platform’s success, and how the measures of success for teams will evolve. This foundation will help your teams identify where/how to engage, bring clarity to what’s expected of them within their roles, and will ultimately incentivize the desired behaviors to invest in and integrate AI into their roles.

Building Trust with AI

Regardless of your seniority, tenure or position within an organization, there’s a lingering thought somewhere in your subconscious that AI will eventually replace you in your current role. This isn’t a far-fetched idea. if team members don’t evolve alongside AI, it can and likely should replace their day-to-day responsibilities. But, if AI were viewed as a partner—a virtual team member that tackles tasks that would typically take days or months to do, giving them the mental space to focus on bigger things — team members might be more receptive to the change that AI will certainly bring to their work. When first introducing AI to respective teams, help team members identify the situations where AI might be a natural complement to the work they’re already doing or the activities where AI could bring efficiencies and freedom to otherwise high-effort tasks. Consider platforms that teams already use to make the change less jarring. Introducing AI within their Slack or Teams instance will be easier to manage than introducing a net-new platform for those team members to work within. This helps reinforce the idea that AI works for them, not vice versa.

AI’s Biggest Risk

People can build systems, models, and neural networks, but at the end of the day, they’re only going to be as good as we make them. The more we hand over control to AI, the more socially, psychologically, and emotionally dangerous those platforms can become, perhaps unintentionally, for certain groups of people — especially those underrepresented in the tech industry. While some elements of AI may be governed in certain regulated industries (like healthcare and financial services), most organizations will be forced to accept the platform’s ethics, standards, and governance models.

How can organizations reduce their risk in doing so? In the same way we challenge many of our existing assumptions at Brooks Bell; we must continually define, refine, and validate AI outputs. Redundancies should be built into the process to ensure that accountability is clear and the human element is represented. Establishing clear Ethical Guidelines and Accessible Governance Models for AI is going to become one of the central challenges of this technological transformation.

AI’s Potential

Acknowledging the high degree of risk involved with AI, there are aspects that I couldn’t be more excited about. Largely, it’s the emerging opportunity to study and better understand the evolving dynamics between humans and tools like ChatGPT, Google Bard, and Microsoft Copilot. It’ll be fascinating to see how people adapt as AI becomes more prevalent and influences our work and behaviors. How innovators and early adopters will begin to answer critical questions and demonstrate business success that the rest of an organization might be most curious about. How we’ll evolve our workflows to build on AI’s strengths while we allow ourselves the time and space to do work that we love. And how organizations will measure the success of AI pilots and validate the ROI of their investments.

In Summary

AI integration isn’t just a tech upgrade – it’s a transformative journey for your entire organization. To navigate this journey successfully, embrace it with an open mind, define clear strategies, establish rigor and discipline, and a focus on how you can help your teams adopt emerging platforms that will benefit not only them, but the entire organization and the experiences you’re creating for your customers.

Originally published on LinkedIn

Improving Customer Experiences with Customer Journey Mapping at Brooks Bell

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In today’s customer-centric landscape, understanding the customer journey and pain points is crucial for delivering exceptional experiences. Our mission is to build insight-driven organizations. By creating a cycle where insights fuel activities and teams work together, we foster alignment, efficiency, and improved customer metrics for our clients. This customer-first, insight-led approach is what top brands strive for, and we’re proud to be a part of their journey. Allow us to guide you through the process and shed light on the value it brings.

You may be wondering how to embark on this transformational cycle or how to leverage your existing activities for change. That’s where Customer Journey Mapping comes in—the project that kickstarts the process and sets organizations on the path to success. You may be wondering how to begin this cycle. Or, how to take your existing activities and use them as catalysts for change. We’re going to tell you how!

We’ll want to begin with a project that we call Customer Journey Mapping. A Customer Journey Map layers quantitative analysis of the customer path with qualitative data from moderated task-based UX research to unearth and confirm the most prevalent customer journey pain points and opportunities. From there, you can strategically prioritize and target the areas that need attention.

Let’s dig into this integrated data approach. First, we consult the quantitative data to define the customer path. understanding their actions, behaviors, entry points, and exit points on the site. This quantitative analysis provides us with an accurate picture of customer activities. Secondly, we leverage qualitative data to understand the how and why behind customer engagement. Through moderated, task-based research studies, we gain valuable insights that go beyond surface-level observations. This holistic view allows us to put ourselves in the shoes of the customer and make informed decisions based on their needs. We want to know where most customers are spending their time on the site, what they are doing, where they came from, when they leave, etc. If we’re honing in on a journey not traveled by many, efforts to improve it won’t be seen at scale. The quantitative data allows us to ensure we’re getting an accurate picture of the places and activities taking place. Then, the qualitative data comes into play to help us understand how and why they’re engaging as the quantitative data indicated. This allows us to make sense of the quantitative data and put ourselves in the shoes of the customer. Through moderated, task-based research studies, we’re able to go beyond saying there’s a drop off from Page A to Page B, and we’re able to get feedback around why that may be. The use of these two data sources together ensures you’re focusing efforts in the right place at the right time for the right people.

Once we gather and analyze this data, we provide our clients with comprehensive reports that summarize pain points, highlight opportunities, map them to key performance indicators (KPIs), associate them with customer personas, and recommend actionable next steps for improving the customer experience. We may uncover things we can share with other teams, suggest specific experiments, additional customer research, or even advocate for quick resolution of bugs our clients weren’t aware of.

Implementing a Customer Journey Mapping project in your organization is an excellent step towards building an insight feedback loop. The collaboration across teams, the compelling data stories you’ll uncover, and the positive impact you’ll make after taking action will resonate throughout your organization. Contact us to learn more about Customer Journey Mapping and get some helpful pointers!

Originally posted on LinkedIn

How Brooks Bell Maximizes Impact with a Proprietary Opportunity Model

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At Brooks Bell, we build insight-driven organizations. We help teams uncover insights from their activities and share them across their organizations to be applied on a larger scale. This feedback loop of activity>insights>repeat is critical to success in today’s fast-moving environment. It enables insights from one team to be used to fuel activities from another in perpetuity. The positive impact on efficiency and alignment can’t be denied. But where should teams focus their time and attention to ensure they make the biggest impact?

A primary driver of customer insights is experimentation, so let’s start there. With a history steeped in A/B and multivariate testing, Brooks Bell knows the impact test insights can have when applied across an organization. As experts in analytics, we know the importance of focusing experimentation efforts in the areas that count. In order to help unlock the most powerful insights that we can have the most confidence in, we look to the most impactful customer journeys with the biggest room for improvement. To help us deliver consistent results, we created our proprietary Opportunity Model to help us prioritize when and where to allocate resources. While I can’t divulge our secrets, I’d like to introduce you to this general concept.

The Opportunity Model is based on two types of data: quantitative and qualitative. The quantitative data tells us where the most eyes are and when the critical activities are (or aren’t) happening, among many other things. It’s important that we know about traffic and conversion because those are important components to achieving statistically significant results in any test. The qualitative tells us the “why.” It is important to know the “why” to evaluate our ability to improve the experience. Our Analytics team is heavily involved in quantitative data storytelling, while our Research Insights team leads the charge on the qualitative side. When these two teams and data sources unite, you’re given the “what” and “why” data you need to make informed decisions.

Let’s first dig into some details of the quantitive data used to fuel the Opportunity Model. We’re looking for the data to tell us the 1) impact and 2) potential of the pages/areas along the customer journey. When considering the impact, we measure how much a page/area impacts the conversion funnel. For example, the percentage of people adding to their cart, starting to fill out a form, or completing a purchase. Then, we determine the potential impact if we were to make an improvement. This allows us to see which pages/areas have the largest revenue potential, alongside what type of lift would be required in order for that to happen. We also consider industry benchmarks, historical benchmarks, and diminishing returns in our calculations to determine how much-untapped potential there may be. We get this data from Brooks Bell’s proprietary dataset.

Regarding the qualitative component of the Opportunity Model, we’re looking for data to direct us toward the possible improvements we could make. For example, how optimized is the user interface (UI)? Is it clear and simple for the customer to complete what they came to do? Is there friction in the way of them completing their task and possibly reducing conversion? Challenges that can be resolved in the UI likely have a higher potential for improvement, while things more difficult to influence (motivation, brand, timing, etc) would have less potential.

By combining these two types of data, we can rank the pages/areas according to their Opportunity Score, which indicates how much value they can generate through testing. The higher the Opportunity Score, the more likely a page/area is to produce winning tests that will significantly impact the bottom line alongside the customer insights we’ll glean. We use the Opportunity Scores to inform the testing roadmap the team will execute, ensuring a scalable approach that acknowledges potential from any angle.

Sounds good conceptually, but let’s check out some real examples!

Financial Services

For a financial services client, we identified a landing page as one of the highest opportunity areas on their website. This page greatly impacted the conversion funnel, as it was the first point of contact for most visitors and influenced their decision to explore the products and services offered. It also had a high potential for improvement, as it had a low conversion rate compared to industry standards and had several elements that could be optimized, such as headlines, images, copy, and calls to action. By applying the opportunity model, we prioritized this page for testing and generated several hypotheses based on our data. We then ran multiple tests on this page. We implemented several winning variations that collectively increased its conversion rate by over 20% and generated an estimated $1.5 million in incremental annualized revenue for the client.

Retail

For a retail client, we identified their Product Detail Page (PDP) as one of the highest opportunity areas on their website. This page had a high impact on the funnel, as it was the last step before adding a product to the cart and influenced the purchase decision of the visitors. It also had a high potential for improvement, as it had a low average order value compared to industry standards and had several elements that could be optimized, such as product images, descriptions, reviews, and cross-sell recommendations. By applying the opportunity model, we prioritized this page for testing and generated several hypotheses based on our data. We then ran multiple tests on this page. We implemented several winning variations that collectively increased its revenue per visit by over 12% and generated an estimated $3 million in incremental annualized revenue for the client.

We’ve used this approach for years with our clients. As you saw in the examples above, the ROI potential is huge. But let me share some of the other benefits we’ve observed:

Save time and resources by focusing on the most valuable pages or areas for testing
Increase testing velocity and throughput by running more quality tests that yield more insights
Enhance testing quality and validity by using data-driven hypotheses and prioritization
Boost testing culture and maturity by adopting a systematic and strategic approach

Feel free to reach out to learn more about our Opportunity Model or share your approach. I am happy to brainstorm or connect you with one of the experts on our team to help you maximize the impact of your program!

Originally published on LinkedIn

What I’m Still Thinking About Following Forrester CX NA

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I was fortunate to attend Forrester CX NA recently. It was the first time I traveled for work since the pandemic. And while I’m a decades-old Forrester fan, it was my first time attending their conference.

Chatting with the Forrester analysts and learning about their research was amazing. You know that feeling when you click with someone you just met over a topic you’re both obsessed with? That adrenaline boost you get when you know you’re both dialed in and could talk about it for hours? It was like that throughout the conference. I can’t say enough good things about the hospitality of the hosts and the expertise of the analysts, speakers, and attendees.

But there are a few topics I am still thinking about. You should be thinking about them, too.

1. AI is dominating conversations – and for good reason.

These days, it’s hard “not” to hear about AI. And that was the case at the conference. In a bold keynote speech by Forrester CEO George Colony, he shared that Forrester doesn’t typically promote new technology until they’ve had some time to see it used successfully, witness some mistakes, and observe competition coming online so that they have a complete viewpoint. Colony shared that he was breaking that precedent regarding generative AI and encouraged brands to act immediately. He shared that this may be the end of Google, websites, and paid advertising as we know them. He shared that brands that don’t act now will fall behind and that it’s critical that organizations devote time and resources to developing their AI strategy and execution capabilities. And while I think he took a, perhaps, provocative approach regarding the scale and impact, you can’t deny that AI is a game changer.

I remember personalization being THE game changer, and it took years and years to become a reality. The technology was there, but it took time for the adoption, team skillsets, and consumer demand for personalization to become part of daily corporate life. When AI was first discussed, I wondered if that “hurry up and wait” situation could similarly happen with AI? Since then, I’ve answered my own question and truly believe AI is different. AI is happening now. The barrier to entry is significantly lower and the use cases far broader, allowing it to spread like wildfire.

Any team member can hop on ChatGPT and begin using it. In fact, you probably have team members using it to support their work today. They may use it to inspire content, write code, generate ideas, do competitive research, etc. And unlike personalization, there is no contract or procurement red tape either. It’s available to everyone now. For these reasons, AI will follow a different trajectory than personalization did and we’ll see quicker, yet perhaps more haphazard, adoption of it in daily life.

So, what does this mean for you and me?

As brands determine how to bring generative AI to the forefront for their customers, we must not forget about the impact it will have on internal teams. The ways teams adopt AI will surely impact their day-to-day interactions and have the potential to cause significant disruptions (alongside the innovations). Change management is, and will continue to be, critical. In my daily work, I plan to be vigilant about the people, process, and governance challenges and support building best practices specific to onboarding and leveraging AI. You should keep this top of mind, too.

2. Quantifying CX ROI is still a real challenge.

CX experts want to do the right things for their customers. They want to do smart, interesting, innovative projects that they believe will improve CX. However, it often comes down to a conversation across the table from their CFO. And while CFOs may also share their desire to do right by customers, their role necessitates sufficient proof that the investment will result in monetary benefit to the organization.

The truth is it can be difficult to quantify the ROI of improved CX. What is the monetary value of improving Net Promoter Score (NPS) or Customer Effort Score (CES)? Or, how much will the insights gleaned from a customer research study impact the bottom line? It’s tricky, right? It’s not as straightforward as other digital metrics like Revenue per Visitor or Average Order Value that we rely on for experimentation. And while experimentation ROI is built on the solid footing of incremental annualized revenue that finance teams have come to trust, many qualitative CX metrics aren’t there yet. As a result, leaders are thinking creatively and working to bridge divides across data sets so they can uncover and share a more quantitatively-based narrative. We know the value is there, and it matters, but how can we put a number on it when that’s asked of us?

What does this mean for you and me?

I am continuing to work with our brilliant team at Brooks Bell to look at different ways to bring this to life, and we have some tips to help you get started today.

First, if you can quantify the impact – calculate it, share it, repeat it. Those A/B test lifts you uncovered that improved Revenue Per Visitor? That disastrous decision your customer research study helped to avoid? The process efficiencies you implemented that enable you to achieve more with the same headcount? Those are quantifiable, undeniable returns. Share them up and across your organization.

Not everything is quite as straightforward and quantifiable, though. And if you find that you’re struggling to find that type of data, I encourage you to zoom out to see the bigger picture. Align with your leaders on your company goals, work with them to prioritize them, and agree upon what activities you will be doing to support them. Establish the belief that the activities you agreed upon support those high-level goals. And as you make progress, share that up and across your organization. In this case, you’ll do less calculating of the individual activities because you’re aligned at a higher level. However, make it a goal to track how your findings impacted metrics further down the line so that you can tie those back in quantitatively. This will likely require working more closely with other teams and asking lots of questions to help define how your work is enabling them, and what value they have (or can) attribute to it.

3. Conferences are different post-pandemic.

As I mentioned, this was my first post-pandemic work travel. Many of our clients had travel restrictions and the conference scene has been hit or miss, so it was exciting to get back into it. Like many post-pandemic things, it was different. And it wasn’t due to masks or sanitizer stations, but because we’re all a little different on the other side of the pandemic.

In the past, I was used to a buzzing office M-F, 9 am-5 pm with many trips to see clients, deliver trainings, and lead workshops. I am a little shy, so I was always a little nervous beforehand but would find my stride. Fast forward just 3 years, but that feels like a lifetime ago! I am not as used to being around many people all at once or standing in the spotlight. It can be somewhat overwhelming and exhausting. And I don’t think I am the only one that feels that way!

I noticed more people finding a quiet workplace rather than networking, grabbing a meal from a local restaurant over the conference-provided meal, and steering clear of making eye contact at the sponsor booths. I get it. The pandemic may have helped some of us retreat into our bubbles even further. And as a result, gathering together isn’t quite the same.

This has me wondering… What will the future of conferences be? Virtual conferences feel like dawn-to-dusk webinars, and I rarely (if ever) hear good reviews. So that’s likely not the answer. But large conferences with thousands of people may be a little much in this post-pandemic world. I predict smaller, intimate gatherings that enable 1:1 conversations and less obligatory mingling will be preferred and likely lead to the most meaningful connections.

What does that mean for you and me?

We should encourage each other to get out of our comfort bubbles to build our networks and learn from each other. Let me know if you’d like to join our Click Community on LinkedIn. That’s one place we’re hoping to facilitate a sense of community and a culture of sharing. Let’s talk about what networking formats would best enable that in our world today. Hit the conference scenes, see what you like/don’t, and share your thoughts. There’s no user manual for what they should look like now, so let’s make our own!

Originally published on LinkedIn

4 pitfalls to watch out for as AI revolutionizes the financial services space

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Will they keep the momentum going? 4 warning signs can predict trouble ahead.

If there’s one thing we can count on, it’s advancements in technology. These innovations seem to effortlessly integrate into our individual daily lives, often without much ado. However, when it comes to organizations, embracing new technologies and tools is far from seamless. The introduction of these changes often disrupts teams, processes, and established ways of operating. To fully leverage new capabilities, organizations must strategically dedicate resources to evolving their people, processes, and governance in response. Those that do will witness the successful implementation, adoption, and benefits of their new capabilities. Those that neglect these components of change management find themselves endlessly calculating ROI, searching for reasons why things didn’t go as planned, or scouring contracts for an escape route.

New Capabilities

A recent report by Visa highlighted a notable surge in digital remittances, the transfer of money by a migrant worker to a family member or friend in their home country. As a result, they invested in valuable partnerships to make the transfer of money across the globe easier. In theory, this will improve the lives of the people using the service and be a great addition to Visa’s capabilities.

Research has consistently demonstrated the crucial role of streamlined application processes in driving growth, and we have witnessed this firsthand while optimizing application flows for our clients. Mastercard has recently launched its latest innovation, Open Banking for Account Opening, aimed at making the account opening experience more convenient and secure. The ultimate goal is to alleviate the anxiety and apprehensions that applicants may feel when completing complex and time-consuming applications that include their most private information. A win-win for the customer and Mastercard.

And more broadly speaking, the financial services industry is in the midst of a monumental transformation driven by the widespread adoption of artificial intelligence (AI). A recent global survey revealed that approximately 60% of companies have already integrated AI capabilities into their operations, often first using virtual assistants or conversational interfaces to enhance customer experience while keeping costs down. And we’ve only scratched the surface in terms of harnessing AI opportunities.

Big moves are happening! These advancements are truly exciting, but sustained progress can only be managed by carefully evaluating the impact on people, processes, and governance along the way. Achieving success in this dynamic landscape necessitates a comprehensive and interconnected approach.

To help you navigate these complexities and ensure you are embarking on new capabilities with an approach that can scale, we’re providing four things to watch out for along with tips for how to identify if this is happening in your organization.

4 Warning Signs

Whether you’ve recently rolled out a new capability or you are considering something new, here are the top four red flags to watch out for. Spotting these warning signs early will help you identify a problem quickly so that you can adjust course.

1. Misaligned Goals

When goals within an organization are misaligned, each department focuses on its own objectives rather than aligning to a common goal. This fragmentation can be catastrophic to the new capabilities you’re trying to build. Misaligned goals are a likely culprit if you observe communication silos, resistance to resource sharing and collaboration, and/or teams working on things that have the potential to undermine your transformation efforts. If you see misaligned goals in your organization, it’s a great opportunity to be a connector. Work with stakeholders to share your goals and objectives, listen to theirs, and collaborate to find a common ground that can set everyone up for success.

2. Information Silos

We mentioned information silos above, but what does that mean? Information silos occur when information remains confined within pockets and is not shared widely. This often leads to a lack of transparency, duplication of efforts, reduced efficiency, and diminished decision-making capabilities. You’re likely dealing with information silos if you don’t have the information you need at your fingertips if data access is a challenge, and if coordination among teams feels challenging. This is often a people- and process-related challenge, and so working with the people that have the influence to obtain and share information is a great first step. You may need a tool to capture and share insights, like illuminate®, if the challenge involves lacking a single source of truth.

3. Unscalable Process

New capabilities are shiny and exciting, but people aren’t always as thrilled about developing and documenting processes (or even change in general!). But, it’s so important. An unscalable process restricts the organization’s ability to adapt, innovate, and effectively respond to changing market dynamics. If left unchecked, it leads to missed opportunities, decreased competitiveness, and potential loss of business. Signs that you’re working with an unscalable process may include bottlenecks, increased errors or delays, overwhelmed resources, strained communications, and an inability to meet growing demands or customer expectations. This requires change management expertise. If you have this expertise in-house, engage with them to reevaluate your process and create a new one. If you don’t have this expertise readily available, our expert Solutions Consultants can step in and have your process defined and ready to grow.

4. Limited Data Access

Data is sensitive and restricting access is part of keeping the data secure. But sometimes that results in unintentionally blocking data needed to make decisions. Without it, team members won’t have access to all necessary information and may miss out on opportunities for innovation and improvements. This one is pretty obvious when it’s happening in your organization. If you’ve been making a decision and said “I wish I knew ___” or “It would be great if I had data about _____,” then this issue is likely present in your organization. To resolve it, you’ll need to work with the people involved with data governance. From there, the challenge becomes combing through tons of data to extract actionable insights. This is even more challenging when your organization hasn’t optimized its data landscape to include the people, processes, and governance that support it.

In Summary

New capabilities enable brands to engage with customers in new ways. But, it’s not as simple as buying the technology. People, processes, and governance issues will arise. To ensure these capabilities keep functioning and even scale, beware of the four warning signs that indicate there’s trouble ahead. These warning signs are applicable to all organizations – even distinguished brands like Visa and Mastercard.

At Brooks Bell, we build insight-driven organizations that can scale and adapt quickly. If you are on a rocky journey or just beginning a new one, contact us to learn more about our approach.

Originally Published on LinkedIn

The Tool is Just the Beginning: Solving People, Process, and Governance Challenges Before they Start

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Will a new tool solve your problem? Probably not. Twenty years of experience have shown us that, while technology has the potential to solve challenges, the most critical components to success come down to people, process, and governance. If you’re not considering these alongside the tool itself, you are setting yourself up for a new set of problems. If you’re investigating a new addition to your stack, replacing a deprecating one (RIP Google Optimize and Maxymiser), or rethinking some old decisions – keep reading.

We help organizations become agile and insight-driven so they can keep pace with ever-changing customer expectations and adapt to any situation. Our approach involves the right people doing the right things at the right time, a process built for agility that is rooted in curiosity, alignment in department KPIs, and properly utilized technology and systems. As you can see, the tool selection is the beginning of a longer journey. Addressing the tooling in isolation will simply replace one set of challenges with another.

The decision to invest in a tool can be expensive and the contract process is often cumbersome, and we want to help you do it right. We’ve identified some key learnings that can set you up for success no matter where you are in your technology journey.

People

Successful tool adoption and usage in your organization are dependent on the skillset and willingness of the people using them. You’ve been operating in one way for a reason, and introducing this new variable is bound to come with a few hiccups. Here’s how to get ahead of them:

You need a team of curious problem-solvers. When something is in conflict with current operations (and it will be), you need people willing to create and execute a solution as quickly as possible. They need to be curious and nimble, allowing them to embrace the new direction and make it successful as quickly as possible. They need to be comfortable failing forward and failing fast.

Your team needs to have the right skillset to leverage all aspects of the tool. Regardless of the type of tool, having a team that understands how to use it and knows why it functions that way will allow your team to use it most effectively. This varies depending on the type of tool, which is a topic worthy of a separate article.

It may be tempting to utilize hours from a tool partner to fill some of these gaps. And that’s often what they’re hoping for, even providing financial incentives to do so. Beware that this may result in stalled progress if your contract isn’t scoped to fit your needs (you may hear “you don’t have hours for that,” “that’s handled by a different team,” “you didn’t select that option”). And perhaps more importantly, product companies’ expertise lies in just that – their product. The nuances and additional considerations along this journey require different expertise and perspective.

We have developed a framework called Keystone™ that defines the roles and characteristics of the most highly performing teams. We use this framework to help our clients ensure they have the right team composition to allow them to grow and scale successfully. We believe organizations should own their programs, so our efforts are directed at helping teams build their capabilities internally.

Process

Having a well-defined process ensures that you have the right people doing the right things at the right time. Without this in place, you’re at risk of bespoke processes killing your efficiency, causing confusion, and ultimately undermining their own efforts. Of course, this isn’t intentional, but we’ve seen it time and time again. Here’s how you can save yourself these growing pains:

Teams need a well-defined process that they can look to for guidance. It defines the rules of the game and eliminates ambiguity. It removes emotion and opinions, allowing teams to focus on ensuring consistent, quality work and finding opportunities to optimize the process over time.

This well-defined process is not “set it and forget it.” It’s critical that your process is built with agility in mind. Customer needs change, business expectations change, technology changes and you need a framework that can weather those storms and evolve to meet current needs. We view process documents as living artifacts that should be maintained and updated regularly.

These recommendations come from decades of experience. A consistent baseline for execution is critical, updates are a must, and your team should be empowered to own this process to meet business objectives.

Governance

If you’ve got a tool, a team of curious problem solvers, and a defined process to grease the wheels, you should be good to go, right? Not quite. Without disciplined governance, all of that forward momentum will inevitably stall.

KPIs across the organization myst align so that teams are rowing in the same direction. If this sounds utopian, begin with your own team. Ensure that everyones’ metrics are complementary and designed to achieve the goals of the business.

Develop a regular cadence of team check-ins to ensure they’re focused on the things that matter and that they are sticking to the agreed-upon rules of engagement. And from there, ensure you are building points of connection with other teams to help expand the reach and latitude of your work.

“If you take care of the small things, the big things take care of themselves.” By developing a regular cadence of oversight, making the right connections across the organization, and ensuring that teams don’t get distracted in the day-to-day grind – you’ll ensure you build a ship ready for the waves, that won’t suffer from neglect, or unintentionally veer off course.

In Summary

A shiny, new tool is just the beginning. Successful digital transformation is achieved when tools, people, process, and governance empower each other. We’ve provided you with some of the most common pitfalls along with how to avoid them. We also know that every organization is different and there are nuances that should be considered when building your action plan. If you need expert help, contact us. We build insight-driven organizations and can definitely help yours.

Originally published on LinkedIn

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