How successful companies use machine learning and BI to intelligently maximize the value of their customers through augmented funnel analysis

Funnel Analysis is an essential tool enabling businesses to identify and address bottlenecks in the customer journey, both in streamlining their buying experience and increasing conversions. But central to any discussion of funnel analysis is a singular question: Why?

A simple online analysis may reveal 35% of visitors left once they reached the cart, but what core tenant of the customer journey caused this and how can an organization rectify it? Comprehensive business intelligence coupled with predictive analytics and machine learning allow data-driven businesses to gain deeper insights into customer behavior and make informed decisions about how to improve their customer journey.

An effective data-driven marketing pipeline can be split into three crucial steps for success: Data, Insight and Action.

Data

With a plethora of software products for inbound marketing, sales, and online analytics it has never been easier to capture and store online customer behavior. The key challenge in the data step of this process is bringing all this data together, then cleansing and integrating it for downstream analytics.

ibi^™ WebFOCUS^® is a strategic enterprise business intelligence and analytics solution equipped with tools and capabilities to gather and analyze trusted data. An integrated and flexible platform to unify and govern data across an enterprise is the first essential step to an effective data-driven marketing strategy.

Insight

Equipped with harmonized, clean data ready for analytics a BI team can confidently develop tailored dashboards and visualizations. Successful teams take this a step further, and extend those insights across the organization for consumption by all users, regardless of technical expertise. To achieve this, insights must be democratized and shareable on an immense scale and customizable to the needs of an executive as well as a technical marketing specialist.

Static reports and dashboards may fill a certain use case or niche, but to achieve re-usable and enterprise-scale operational intelligence, these insights need visual discovery tools, granular customizability and massive scalability.

Action

Once data has been married with insight, an organization has an effective pipeline for transforming online analytics to democratized business intelligence. However, once an organization has a comprehensive analysis of what is happening in their customer journey, the question remains of why?

To understand how to dynamically attract and convert customers, organizations increasingly incorporate machine learning and predictive analytics into their sales funnel. Not so long ago, this meant a siloed data science team working in a black box to the rest of the organization.

With tools like ibi^™ WebFOCUS^® ML Functions, the paradigm has shifted towards self-service predictive analytics and end-user driven modelling.

One example is customer segmentation, which arms an analyst with insight into why customers act the way they do according to their demographic group and purchasing trends. This can be achieved with no-code clustering algorithms to uncover latent patterns and groups within customer behavior and funnel analysis data.

Machine learning is also disrupting churn analysis, identifying and preventing at-risk customers. Combined with Automated Insights, a built-in function for discovering correlations, distributions and patterns on-the-fly, an organization can pinpoint certain factors that may influence a customer’s retention and path to conversion. Machine learning can then aid in analyzing a customer’s preferences, sensitivity to pricing and purchasing patterns to generate actionable insights for customer retention and engagement.