I'm Suyog Gandhi, Director of Customer Success at Salesforce, and it's my pleasure to delve into an exciting and essential topic - personalization. 

As an AI enthusiast and a staunch advocate for customer success, I've had the opportunity to advise small companies and startups, primarily focusing on strategy and go-to-market planning.

In this article, I aim to unravel the concept of personalization. We'll explore what personalization truly means, understand why it's necessary, and dive into the strategies and approaches for achieving effective personalization. I'll also highlight the significant role of AI in crafting tailored customer journeys.

Emotional connections with brands through personalization

As we delve further into personalization, let's consider some familiar brands that we all know and love. 

These brands span across various sectors - retail, enterprise tech, and more. What truly sets them apart isn't just the high quality of their products, but the emotional connection they establish with consumers through personalized experiences.

Take Google, for instance. It's far more than just a search engine. It's a custom-built highway, a gateway that leads you to a world of information, entertainment, and productivity tools, all designed to enhance your day-to-day life. 

Similarly, Starbucks transcends the identity of just being a coffee shop. It offers a personalized experience for socializing, relaxation, and enjoying high-quality beverages in a cozy atmosphere. 

Amazon, too, isn't just an online store; it's a personalized shopping haven, crafted to reduce your search time and bring products and services right to your fingertips.

Understanding customer psychology through cognitive biases

So, how did these companies manage to offer such exemplary personalized experiences? And more importantly, how can we emulate their success? The answer lies in understanding what customers want. 

To achieve this, we must first dive deep into customer psychology. Let's explore the fundamentals of what our customers seek and how we can fulfill their desires through personalized experiences.

The psychology of our customers involves understanding cognitive biases. These biases are systemic errors in thinking that affect judgment and decision-making. While often perceived as harmful, they can be incredibly useful tools in personalizing customer experiences.

Cognitive biases in personalization

Let's explore a few cognitive biases and how they can enhance personalization:

  • Cognitive load reduction: Personalization can significantly reduce cognitive load, making it easier for consumers to find what they're looking for. For example, consider the Netflix recommendation engine. It simplifies the process of finding movies, saving customers from browsing through the entire library.
  • Confirmation bias: People tend to pay more attention to information that aligns with their existing beliefs. Personalization can leverage this bias effectively. Look at Amazon's recommendation engine – it suggests products based on consumers' past purchase history, tapping into their existing interests.

Emotional engagement through personalization

The key to successful personalization is forging an emotional connection with the customer. By showing consumers products and information that resonate with their interests and values, we can deepen their emotional engagement. This approach goes beyond just meeting needs; it's about aligning with the customer's identity and values, creating a more meaningful and lasting relationship.

The approach to effective personalization

The foundation of personalization lies in thoroughly understanding your customer. This is the first and most critical step. The next step is segmentation. By segmenting your customers, you can tailor your approach to meet their unique needs and preferences.

Utilizing appropriate tools and technology is essential for effective personalization. Aim to personalize each interaction with the customer to enhance their experience. Regularly monitor the effectiveness of your strategies and integrate user feedback into your process for continuous improvement.

Always keep customer privacy and security at the forefront of your personalization efforts. Understand that perfection is not achievable in the first attempt. Implement, learn, optimize, and repeat – this cycle is key to continuous improvement in personalization.

Evolution of personalization

Personalization has evolved significantly over time:

  • Single message: We moved from a single message for everyone to segment-specific rule-based personalization.
  • Rule based segmentation: Then we progress towards segmentation, rule based segmentation, and personalization for that segment
  • Behavioral recommendations: We began tailoring recommendations based on individual customer behavior within each segment.
  • Omni-channel optimization: Personalization extended across various channels – mobile, web, and others, offering a unified experience.
  • Predictive personalization: Now, we're predicting customer behavior and preferences.

Looking ahead, the next step could be knowing what customers want before they do. This level of personalization aims to reduce customer cognitive load and anticipates their needs, driven by the right intentions. Achieving this requires strategic planning and innovative approaches across people, processes, and technology.

Operationalizing personalization

Operationalizing this level of personalization is not automatic. It demands a comprehensive strategy that effectively integrates all three elements: people, process, and technology. Let's explore how we can operationalize personalization to its fullest potential.

Data collection

In our quest to achieve sophisticated personalization, data plays a central role. It's the bedrock upon which all our strategies and implementations are built.

The journey starts with the collection and cleansing of data. The quality of our final results hinges on how well we gather and organize this data. This involves leveraging various elements of our tech stack, such as CRM systems (like Salesforce), marketing tools (like Marketo), issue tracking systems (like JIRA), product management platforms (like Productboard), and customer success tools (like Gainsight, Tango, or Vitalii).

Once we've collected data from these diverse systems, we begin with straightforward reporting. These reports are created for different audiences, including individual customers, customer leadership, internal customer success managers (CSMs), organizational overviews, and cross-functional teams. The dual purposes of these dashboards are to assess the current health of our customers and our organization and to serve as a trigger point for further analytics.

By analyzing these reports, we can spot outliers and initiate lines of investigation. This is where our journey into deeper analysis begins.

Analysis

Having laid the groundwork with data collection and preliminary reporting, the next crucial step is analysis. It's here where we begin to unearth valuable insights, understand trends, and identify opportunities for further personalization. This analysis is not just about looking at the data but interpreting it in a way that reveals the deeper needs and preferences of our customers.

Analytics is the stage where our personalization journey becomes particularly intriguing. Here, we uncover areas ripe for opportunity and improvement.

Through analytics, we can pinpoint problem areas and aspects that need enhancement. With a clear understanding of these areas, we can start devising strategies to address them.

As we implement these strategies, they feed into the cycle of value for customer success. This involves creating segment-based personalization plans that lead to significant and recurring value for customers. The goal is to achieve increased stickiness, higher loyalty, and better customer and product adoption. And then, we repeat the cycle.

Throughout this process, the emphasis is always on highlighting value to the customer. This approach is not just about solving problems but enhancing the overall experience and relationship with the customer.

Addressing challenges in personalization

In the midst of this journey, we encounter various challenges:

  • Data overload: The sheer amount of data can be overwhelming.
  • Data quality: Ensuring the cleanliness and accuracy of data is a continual task.
  • Tool integration: Managing multiple tools and their integrations adds complexity.
  • Need for domain expertise: Expert knowledge is required, sometimes necessitating manual processes or specialized integrations.

The role of AI in personalization

As we navigate these challenges, it's clear that some require rigorous approaches. However, for many others, AI offers a powerful solution. AI can streamline processes, offer predictive insights, and automate aspects of personalization, making it a critical component in our toolkit. Let's explore how AI can be leveraged to enhance our personalization efforts and address the challenges we face.

AI vs. traditional approaches

AI provides highly contextual and dynamic personalization, delivering real-time results. This capability is crucial for achieving scalability in personalization efforts.

However, the transition to AI-driven personalization isn't instantaneous. It requires a well-thought-out strategy and a step-by-step plan.

The strategy for implementing AI in personalization

  • Foundation: Initially, assess your existing infrastructure and conduct controlled AI experiments to ensure you're moving in the right direction.
  • Growth: Next, develop an overarching customer data platform and begin testing initial models. This phase involves putting preliminary models into production and closely monitoring their performance.
  • Scaling: The most exciting stage is scaling. Launch a comprehensive customer data platform and focus on expanding the use of AI across the organization. Collaboration between Customer Success Operations (CS Ops) and the AI team is crucial here.

With these steps in place, the key question becomes: What kind of output can we expect? Yes, implementing these strategies is important, but understanding the end goal is equally crucial. 

We're aiming for a level of personalization that not only meets but anticipates customer needs, creating a more intuitive and satisfying customer experience. This outcome isn't just about leveraging technology; it's about transforming the way we understand and interact with our customers.

The impact of AI on personalization across customer segments

AI is set to revolutionize the way we approach personalization, particularly in how we cater to different customer segments.

AI can offer personalized onboarding recommendations to ensure a smooth and engaging start. For each customer, AI can generate a personalized playbook, enhancing their experience and engagement with our products or services.

AI can create highly tailored, impactful content, such as newsletters that consider their quarterly goals, financial performance, and potential opportunities. And for those at risk, AI can serve as an early warning system and automate risk retention campaigns.

As we consider the breadth of AI's capabilities in personalization, the question arises: How do we integrate AI into our organization effectively?

The landscape of AI architecture, although complex, is essential to understand. It ranges from CRM and data storage to building AI models and enhancing customer journey and engagement channels.

It's important to note that while the technology architecture might seem daunting, you don't need to be an expert in every tool. The goal is to understand the baseline and implement AI-driven strategies effectively.

I have personally ventured into creating an AI-based model to analyze customer data and derive recommendations for enhancing customer experiences. This model is particularly geared towards enterprise customers, focusing on providing high-touch services to high-value clients.

Implementing AI-driven personalization in salesforce

In my journey of integrating AI into our personalization strategy, I primarily utilized Salesforce, focusing on two key components of its architecture.

I used Einstein AI, which is integrated with the Salesforce CRM platform. This integration allowed me to tap into Gainsight's capabilities, providing me with insightful information from these combined sources.

I designed a CDP with multi-dimensional segmentation, not just based on Annual Recurring Revenue (ARR) but also considering factors like industry, ARR, and others. This platform also included our existing playbooks, CSM team setup, product usage, and customer journey data.

The next step involved creating a model that brings together our domain knowledge and data. In my case, I took on both roles as I had been exploring AI for some months. For those less familiar, using easy-to-use tools for simpler use cases is feasible, though an AI consultant may be needed for more complex scenarios.

I developed an anti-churn model to identify potential customer churn, incorporating various parameters like product usage, multi-threaded relationships, and executive connections.

The model enabled me to perform micro-segmentation, revealing more at-risk customers than initially identified. This insight was crucial for redirecting our focus to mitigate churn risks.

The final phase is deploying the model in a live environment. This involves feeding more data into the model, learning continuously, and generating various reports and recommendations. Now, let's take a look at the kind of recommendations that emerged from this process and how they can be applied to enhance customer experiences.

Maximizing personalization through AI-driven recommendations

In my experience with implementing AI in personalization, I discovered how it can transform the way we understand and engage with our customer segments.

AI helped me predict renewal rates across different segments. This insight is critical in identifying areas where 100% renewal may not be achievable and requires attention.

AI can generate a list of customers not using the latest features. It can also identify customers at risk of churn. Part of the strategy is not just understanding problems but also automating solutions. Recommendations can be automated, forming an integral part of our proactive playbook.

AI enables us to create highly specific micro-segments and corresponding customer treatment strategies (CTS). For instance, for finance customers identified as churn risks, AI can provide detailed insights, such as changes in executive level or a tendency to focus on features rather than long-term value.

Implementing and automating playbooks

These insights enable us to create immediate, targeted actions and automated playbooks for various scenarios. This proactive approach is pivotal in addressing customer needs efficiently.

The quality of outputs is directly tied to the quality of the data fed into the AI models. Integrations like Slack can be used to feed unstructured data into our analysis, enriching our insights.

Through tools like Gainsight and API integrations, AI-generated actions can be immediately reflected on every customer success dashboard, offering real-time, actionable insights.

The journey of incorporating AI into personalization strategies is a continuous learning process. The insights gained not only improve our understanding of customer needs but also enhance our ability to respond proactively and effectively. This journey, while complex, is immensely rewarding and crucial for staying ahead in delivering exceptional customer experiences.

The future of AI in personalization and customer success

As we look ahead, the future of AI in personalization and customer success is indeed bright and full of potential.

AI's ability to predict churn is just the beginning. By identifying low-hanging fruits through analytics, we can achieve early wins in our AI journey.

AI should not only focus on churn prevention but also on enhancing renewals, upselling, and cross-selling. These efforts must complement human personalization efforts, as an automated-only approach may not suffice.

The future involves crafting playbooks that combine AI-centric or automated CTS with human interactions. This dual approach will undoubtedly lead to a more enriching customer experience.

Gradually, we will see more standardized and unified Customer Data Platforms (CDPs), paving the way for the development of specialized customer success AI tools.

As we advance, maintaining trust and transparency in our AI implementations will become increasingly important.With the evolving landscape, we must also be prepared to adapt to more stringent privacy regulations imposed by governments.

Begin by understanding customer segmentation and creating tailored playbooks for these segments. Put the strategy into action, focusing not just on deployment but also on learning and adapting.

Leverage AI-centric personalization strategies to scale your customer success organization and enhance the overall customer experience.

The intersection of AI and customer success is an exciting and relatively uncharted territory. As we embark on this journey, the potential to transform customer experiences and drive organizational success is immense. The key lies in balancing technology with a human touch, ensuring that we not only meet but exceed customer expectations.

This article is based on a presentation given by Suyog at our virtual Customer Success Festival in 2023.

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