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Segmenting Your Analytics and AI Demand, Part 2: Expressing Unrealized Value and Scoring Segment Dimensions

In part 1 of “Segmenting Your Analytics and AI Demand,” we discussed the demand side of the analytics equation and emphasized the shift from a supply-driven to a demand-oriented information economy, where meeting the needs and expectations of data consumers is paramount. When considering demand segmentation, different groups within an organization have varying needs, abilities to articulate those needs, and levels of advocacy. The goal is to help analytics leaders develop strategies that cater to these diverse demands, ultimately leading to more effective analytics implementations.

In part 2, we’ll explore the rest of IIA’s framework for evaluating critical dimensions of a demand segment and a scoring system to help you transition to higher levels of maturity within each segment.

Unrealized Commercial Value

To what extent could a segment’s unrealized commercial value be made through the application?

Mature analytics teams prioritize segments with significant untapped commercial value for immediate attention, aiming to extract maximum value before resource investment stabilizes or decreases. They continually evaluate segments for new value opportunities, offering independent perspectives on where value lies. This consultative approach helps identify projects with potential commercial value, as well as nonquantifiable benefits, fostering strong, lasting relationships. These teams understand that value can manifest in various ways, such as increased revenue, cost reduction, and operational improvements, and they focus on segments with the most untapped value.

Unrealized Competitive Value

In addition to unrealized commercial value, what is the unrealized competitive advantage for each segment?

This dimension is about the perceived value of analytics within a segment, often challenging to quantify in standard commercial metrics but evident in its positive impact on business partners. Analytics teams focus on capturing a segment's untapped competitive value through advanced analytics and AI, responding to a trend where organizations using such analytics are favored by partners. Unexpected segments, like human resources recruiting new graduates, can benefit significantly from visible and innovative use of analytics, enhancing their competitiveness in tight markets.

In-Production Demonstrative Use Cases

How many analytics projects are in production for a given segment?

A key measure of advanced analytics and AI success within a segment is the number of in-production analytical applications. Mature teams leverage these projects as success stories to showcase analytics transformation. In-production use cases should demonstrate the team's skills clearly to demand-side segments; for instance, a predictive churn model for a direct sales force highlights the team's ability to build accurate predictive models for complex issues like customer churn, making the team's capabilities easily understandable to business professionals.

Core Resource Drain

How much of the analytics team’s scarce analytics resources is each demand segment consuming?

Asked another way: To what extent is any given segment high maintenance?

An analytics team may have a fantastic relationship with a demand segment that has solid analytics capability and data autonomy, influential advocacy, great unrealized commercial/competitive value, and many successful in-production applications. This may seem like an ideal scenario, but if the segment consumes 90% of the team’s available resources, the team has a significant balance problem in its portfolio.

The trick to optimal segment portfolio management is to achieve good segment results as described above using only 5 to 15% of the available resources, per segment (depending on the number of segments). Encouraging self-sufficiency, and data and analytics autonomy, is a key component for successfully optimizing analytics and AI for the enterprise. Self-sufficiency and autonomy also factor into why many organizations employ hybrid analytics models with significant local analytics capability.

Segmenting Your Analytics Demand eBook

In transitioning to a demand orientation, analytics leaders will quickly become aware that the demand side is multifaceted and that no two demand constituent groups are the same, creating complexity when trying to develop demand-based analytics strategies. To ameliorate this, experts at IIA have identified the 10 critical dimensions to a segment and explained them in this eBook, allowing you to better understand where your current demand segments are and where your target demand segments should sit.

Unrealized Leverage

To what extent can a segment’s use cases and opportunities build an analytics team’s technical or methodological muscle?

Mature analytics teams sometimes engage with segments with problems that will help the team solve problems of their own, such as deficits in technical or procedural knowledge and capabilities. Analytics teams adopt this strategy because it’s increasingly difficult to secure funding for upskilling activities directly as educational and developmental activities. The teams focus on analytics projects that grow their capabilities and meet other threshold conditions, particularly for commercial and competitive value.

Scoring Current and Target States of a Segment

To effectively use the demand segmentation process, start by identifying and characterizing each segment within the demand side of the information economy. Then, score the current state of each segment across all 10 dimensions below. For each dimension, assess the segment on a scale from level 0 to level 4 and translate that into a numeric score, with level 0 being 0 and level 4 being 4. You can choose to use unweighted or weighted scores as desired.

Once you have scored the current state of a demand segment, use the same process to score the target state for the end of the period, typically one year. This target state represents the optimal state based on what is known about the demand segment. The difference between the current and target states indicates the relative workload compared to other segments, with a higher target score suggesting a greater potential impact on the organization. These scores help you focus your scarce resources on the segments that offer the most significant opportunities for improvement in the coming year.

Demand Segmentation Dimensions:

  1. Demand Pattern
  2. Analytics Capability
  3. Data Autonomy
  4. Analytics Autonomy
  5. Unrealized Commercial Value
  6. Unrealized Competitive Value
  7. In-Production Demonstrative Use Cases
  8. Core Resource Drain
  9. Unrealized Leverage

Mapping the Demand Change

After characterizing the current and target states and documenting the rationales for each segment, create a visual representation for each demand segment, using the demand pattern and analytics capability dimensions as the vertices. The goal is to move segments closer to the upper right over time. By including impact scores, you can prioritize efforts to support these shifts, both within individual segments and across segments. Impact scores help compare segments for resource allocation and action, even if they start and end in the same places, providing a clear path for improvement. IIA Research and Advisory clients have full access to IIA’s demand segmentation research brief with sample scoring grids and visualizations.

Final Thoughts

Segmenting your demand side is foundational for a demand-oriented analytics enterprise. Understanding your data consumers informs strategies, culture initiatives, and organizational models. This segmentation process is integral to running an analytics function, capturing current states, forecasting target states, assessing impact areas and resources, and planning segment movements to align with team goals. Integrating this knowledge into strategic initiatives ensures success in enterprise-wide analytics endeavors.