Capturing value at scale: How to move beyond AutoML

Companies know where they want to go. They want to be more agile, quicker to react, and more effective. They want to deliver great customer experiences, take advantage of new technologies to cut costs, improve quality and transparency, and build value.

The problem is that while most companies are trying to get better, the results tend to fall short: one-off initiatives in separate units don’t have a big enterprise-wide impact, adoption of the “improvement method of the day” almost invariably gives way to disappointing results, and programs that provide temporary gains aren’t sustainable. 

Despite the efforts of many companies to gain meaningful bottom-line benefits from advanced analytics, they’re still stuck in the “pilot trap.” These organizations are struggling to scale up from a portfolio of pilots and proofs of concept to a comprehensive digital transformation that fundamentally changes the entire business.

Working with many enterprises that span verticals and geographies, we’ve observed how leading companies best escape this pilot trap and capture and sustain the value from digital technologies: first off, they determine where to focus and how to scale.

Progressing from pilot projects to widespread deployment requires focus on an organization’s value drivers. The need is to be thinking value-backward rather than technology-forward. 

The most successful digital transformations happen when companies focus on technologies with the greatest potential impact on their strategic goals, such as improving customer services and operations. By keeping long-term goals at the forefront and avoiding quick fixes to isolated problems, organizations are more likely to get the senior commitment and enthusiasm needed for a large-scale transformation.

One of the approaches to narrowing this focus is applying ‘Root Cause Analysis’. Discovering root causes of business outcomes with interlinked KPIs is a crucial part of the continuous improvement process. Today’s technologies make it even easier—and more powerful.

Companies that use root-cause analysis to build a sustainable problem-solving culture can avoid continuous firefighting by effectively preventing fires from starting.

In parallel, the rise of advanced analytics has allowed these companies to detect many more problems than in the past, and more effectively—so long as they have sufficient internal support to interpret the output. This requires both AI technologies and cross-functional validation. 

For example, instead of working on separate initiatives inside organizational units, companies can think holistically about how their operations can contribute to delivering a distinctive customer experience. 

By focusing on customer journeys and the internal processes that support them, input naturally cuts across organizational siloes—marketing, operations, credit, IT—and ranges from customer-facing to end-to-end internal processes.

The vast stores of data collected across these units are often sparse and disparate, with a messy complexity that requires time and unique expertise. Using AI-driven analytics bridges the steep learning curves, connecting and exploring complex data in its raw, granular form for supervised machine learning. Such technology can act as a force multiplier in extracting every last drop of value from the long-tail of data that may have been previously out of reach.

Specifically, AI-powered driver discovery automatically runs millions of hypotheses on this data, building, testing and selecting composite features that drive business outcomes.

Delivering a great customer experience calls for disciplined execution and consistent service delivery. By analyzing customer journeys, companies can pinpoint the operational improvements that will have the biggest effect on customer experience.

EXAMPLE: CUSTOMER RETENTION

‍Using AI-powered root-cause analysis takes a holistic view, using multiple data types and sources, generating millions of hypotheses to reveal root causes of customer retention and its interlinked KPIs. It helps accurately identify customers at the highest risk of lapse, enabling effective, personalized retention campaigns.

SparkBeyond’s Hypothesis Engine helped a leading European media company reduce churn by 30% in 3 months. Read more to learn how AI-powered root cause analysis helped reveal thousands of clues from multiple organizational siloes that identified which customer was likely to churn, and why. 

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Forward-thinking enterprises are hallmarked by wide-sprawling teams of data engineers, data scientists and data governance professionals all working together with different specializations. Everything from the ETL pipeline, data integrity and privacy, analytics dashboards, and machine-learning optimization is expertly managed and maintained from a centralized department with clear data-strategy leadership.

Yet data science is not an elite realm reserved for those with deep pockets and infinite resources. With the right technologies and approach, most organizations can introduce and scale rich, actionable insights to business processes company-wide, and capitalize on the edge provided by AI-powered advanced analytics.

This edge is provided by dynamic microsegmentation: a granular, deep understanding of the market landscape.

Building and implementing machine learning models can be costly, and if the pandemic proved anything—can quickly become irrelevant. In order to capture sustainable value from advanced analytics, segmentation is a fast, granular approach that’s easy to scale across different business units.

Segmentation allows businesses to discover and explore the beliefs, attitudes, and motivations that drive customer behavior through their purchase decision journey. It helps generate a 360° view of their customers—one that sparks innovation, uncovers the most promising sources of growth, and helps develop successful products and brands.

The goal of creating microsegments is to identify specific groups of observations (e.g. customers, transactions, stores, locations, etc.) that have an especially high correlation with the target KPI (e.g. profitability, risk of churn, propensity to convert, etc.), which can be targeted with nuanced interventions. These segments also provide valuable business insight to guide decisions.

EXAMPLE: UP-SELL WITH PERSONALIZATION

‍AI-powered microsegmentation avoids cognitive bias, helping enable hyper-personalization at scale and across multiple channels. By leveraging advanced driver discovery techniques, organizations can test hundreds of millions of combinations in minutes, surfacing strong microsegments for operationalization.   

What’s more, drivers and segments are generated in natural language for high explainability, enabling cross-functional validation and business value.

SparkBeyond’s Hypothesis Engine helped India’s largest bank deliver cohesive personalized experiences across 10+ channels for its 45M customers. This has led to a 4-9x jump in conversion rates across all products. Stay tuned for an in-depth case study exploring how they rapidly scaled value across multiple units.