Advanced Segmentation in Data-Driven CRM

Welcome back to our series on mastering customer relationships through the Data-Driven CRM Framework. In our journey, we've explored the Four Pillars of Data-Driven Communication, laying the groundwork for sophisticated customer engagement strategies. Today, we delve into a crucial component that significantly enhances the precision of customer interactions: Advanced Segmentation.

The Evolution from Traditional to Micro-Segmentation

Now, let's explore how this approach evolves from traditional methods to more refined techniques. While traditional segmentation divides customers into broad groups based on general characteristics such as demographics, purchasing behavior, or product preferences, micro-segmentation delves much deeper. It creates highly specific segments based on a detailed array of factors. This granular approach leverages intricate data points—from online browsing patterns and social media activity to transaction histories and customer feedback. For instance, instead of simply segmenting customers by age or location, micro-segmentation might identify a segment such as 'frequent online shoppers aged 20-35 with a high churn risk, identified by decreased engagement, who previously showed interest in eco-friendly products'

Let's delve deeper into how advanced segmentation leverages these insights to create a more holistic customer view. Advanced segmentation represents a holistic method that goes beyond mere categorization. It considers many factors, including the historical and predictive aspects of customer lifetime value, intricate behavioral, demographic, and transactional profiles, and even the identification of risks and specific communication preferences.?

This method ensures a truly comprehensive view of your customers by not just understanding the 'what' but also uncovering the 'why' behind their behaviors. Descriptive insights demonstrate current behaviors, while predictive analytics project future actions, empowering businesses to tailor their strategies proactively.

The benefits of micro-segmentation are manifold, enabling businesses to craft messaging and offers with remarkable precision. This significantly improves customer engagement and satisfaction. For example, by identifying 'high-value customers at risk of churn due to recent service issues,' a company can create personalized service recovery offers, leading to increased loyalty and reduced churn rates.

Moreover, micro-segmentation allows businesses to take a proactive stance in identifying emerging trends and customer needs, offering a competitive edge. Companies can swiftly adapt to market changes and customer dynamics by understanding the motivations and preferences of finely segmented customer groups, such as 'frequent business travelers interested in luxury travel experiences' versus 'budget-conscious family holiday planners.'

In summary, the strategic depth of advanced segmentation and micro-segmentation provides businesses with the tools to achieve unparalleled personalization in customer engagement.?

By anticipating future behaviors and preferences, businesses can respond proactively to customer needs, ensuring they remain ahead of the curve.

Embracing advanced segmentation and micro-segmentation transforms the traditional CRM model from a broad, one-size-fits-all approach to a finely tuned, highly personalized strategy.

The Engine Behind Advanced Segmentation

Now that we have established how embracing advanced segmentation revolutionizes CRM strategies, let's explore the technological foundation that makes such sophisticated segmentation possible.

At the core of this transformative approach are machine learning algorithms, which analyze historical data to identify patterns, predict future behaviors, and perform diagnostic analytics to understand the reasons behind these patterns. These algorithms can adapt and improve over time, continuously refining the segmentation process as more data becomes available.?

Predictive analytics, leveraging the power of machine learning, ?extends the capabilities of advanced segmentation by forecasting individual customer actions. For instance, predictive models can determine which customers are most likely to churn, enabling targeted interventions designed to retain them.

Data mining, a complex and sophisticated yet dispensable part of segmentation, efficiently processes extensive customer data to reveal hidden patterns and correlations. This critical technique uncovers customer groups with shared traits or behaviors, providing insights that are not immediately apparent, essential for crafting precise customer segments.

Having explored the technical underpinnings of advanced segmentation, we now turn our attention to a practical application.?

Case-study: Micro-Segmentation for Makeup Company?

The fashion e-commerce platform with 35,000 users set out to enhance the convenience of shopping and service quality by implementing new approaches to customer interaction. To achieve this, it needed to re-segment its target audience, as traditional segmentation principles based on purchase history were no longer effective. While the company possessed a large volume of data, it had to determine which of these data points were truly valuable for segmentation purposes.

BeInf offered a combined approach to micro-segmentation, leveraging descriptive (actual), predictive, and diagnostic customer attributes. By combining real-time customer data with predictive analytics and diagnostic insights, the company gains a comprehensive understanding of each customer segment, their needs, preferences, and potential effects of communication.?

The implementation of a customer attribute map has enabled the quick, easy, and flexible creation of micro-segments, which have been essential for a profitable customer communication strategy.

Above, we observe one of the micro-segmentation variants aimed at retaining customers. Each set of possible attributes forms unique micro-segments.

In this approach to micro-segmentation, attributes that describe historical customer behavior and contain forecasts of potential events and their causes are combined. The actual (descriptive) attributes include account type, predictive attributes include churn risk, selection of the optimal offer, and communication channel, while diagnostic attributes include the primary factor having the most negative impact on the high churn risk.

When selecting different types of attributes, the focus should not be on their quantity but on their quality - the function they perform in communication planning. In this case, there is no point in using attributes that do not carry any business value for retaining customers.

In the chain presented in the example, each attribute has its value in communication:

Churn risk: the higher the client's churn risk, the quicker and more "aggressive" the communication and interaction tools should be.

Account type: the more valuable the customer segment to the company, the more actively it needs to be re-engaged. Alternatives for such an attribute could be revenue segment, engagement stage both historical and predictive (in such a case, you will communicate primarily with those who are potentially the most profitable for the company in future periods, rather than relying solely on already formed results).

Top churn group of factors & top churn factor: depending on the main factors that influenced the customer's decision to leave, you can adjust the communication, use these factors for content personalization, and select personal offers.

Next best offer & channel: in this case, the company already had a working ML model for selecting the optimal offer (mechanics, discount, response probability, expected effect) and communication channel (response probability), so they used their results to select the interaction tool and reduce the churn risk.

In the chain depicted above, one of the micro-segments is highlighted, namely customers with a Platinum account type with a high churn risk due to a high percentage of product returns, for whom the greatest incremental effect can be expected after a call from a manager who will offer free delivery on the next order.

In our CRM saga, analytics turn potential 'Platinum-leavers' into 'Platinum-stayers,' proving that sometimes, the best retention tool is a well-timed free delivery offer.

Refinement and Personalization: Next Steps

In this segment, we've introduced the key concepts behind Advanced Segmentation and its vital role in refining your data-driven CRM strategies. By adopting a more nuanced, insightful approach to customer profiling, you'll set the stage for personalized, effective interactions. In our next article, we'll explore cutting-edge technological solutions to make these strategies both actionable and automated.

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