How predictive analytics fuels product-led-growth
Introduction to Predictive Analytics
Predictive analytics is a big buzzword today, especially with a lot of companies looking to fuel their growth by using analytics. But is predictive analytics really what we need? Here we will look at what predictive analytics is and isn't. We will also see how predictive analytics can be best used with product-led growth.
Businesses now have the infrastructure to measure and analyze data across channels and verticals. However, most of the current insights and business intelligence metrics are retrograde in nature and lack a forward thinking approach. Predictive analytics is the craft of leveraging machine learning to analyze the data while understanding the future outcomes.
Predictive analytics scores customer segments to understand user data and model the propensity of the user to make a purchase, churn or any other future behavior. This way business leaders can understand the impact of their decisions and leverage their data to make these decisions faster and efficient.
The scope of Big Data Analytics is traditionally limited to B2C companies as these approaches require a significant number of data points. The scope is also limited to large enterprises that have the data teams required to support such analytics. However, with the advances in Artificial Intelligence we at Clientell are helping Business Leaders leverage the power of machine learning.
Clientell is combining the world’s most innovative algorithms in a no-code platform that offers powerful predictive analytics and insights from data to report in a single click.
What is Product-Led-Growth?
GTM funnels for SaaS companies are relatively simple with “users” on top and “paying customers” at the bottom. Marketing and sales teams in SaaS companies generally tend to focus on increasing the number of “users” or improving the top funnel metrics. At the bottom of the funnel the conversions are led by the value added by the product. This client success, engineering and sales team aid this journey of “Product-Led-Growth”.
The goal of this exercise is to create opportunities and widen the top funnel at the same time facilitate bottom funnel activation through feedback. This feedback becomes important while defining user journey and taking the product from an exploratory to a “need-to-have” for the client. The sales teams often encounter problems in understanding, evaluating and communicating this feedback to the product teams. Thus, creating unwanted roadblocks in the middle funnel to conversion.
Traditionally the top funnel was driven by cold calling and emailing as main outbound marketing channels to driver user growth. However, the main challenge was finding the right users who need the product as well as building trust, so that users would be willing to pay to try the product.
As the cost of delivering product is decreasing product led growth solves the above problems by making product the center of attention in the sales funnel. Only 2-5% of users end up as paying customers. Thus with relevant feedback and efficient communication between the various stakeholders the middle funnel improvements can be achieved.
How do the most successful SaaS companies solve this?
The best-in-class SaaS companies leverage data at each stage of the funnel to personalize the user journey to a paid client. Pinpoint precision is required to offer the users the best pricing, value proposition and service hyper-personalized to their needs. The best GTM playbooks are built by segmenting the users in terms of personas, use cases, actions, time frames and usage metrics in the following manner:
How does Clientell help build a better experience for your customers?
Clientell is a no-code AI-powered platform that allows businesses to leverage historical data and the power of predictive analytics to drive conversions. Here’s how we do it:
Smart Segmentation: Breakdown users into micro-segments based on behavior, usage, activities and hundreds of metrics to improve their journey.
Predictive Scoring: Our state of the art AI chooses the best machine learning model based on the users industry and data points. The micro-segments are then scored based on similarities to choose the best actions required to drive a conversion.
Factor Analysis: The factors driving user behavior are analyzed and actionable insights sent to the relevant teams improving the efficiency of feedback communication.
360 view: Clientell uses dynamic algorithms to consolidate all relevant user information and insights into auto-generated reports. Thus empowering business teams to take real-time decisions and build a culture of iteratively improving the sales funnel.