Efficient lead scoring is crucial for businesses to identify and prioritize high-value prospects for optimized conversions. This process involves assessing the perceived value and sales-readiness of each lead, which can be achieved by leveraging data from various sources to gain objective insights and a better understanding of target markets.
However, manual input in the lead-scoring process can slow down results and increase the risk of inaccuracies due to data collection issues or data silos. As businesses deal with increasing volumes of data and limited sales resources, automation through machine learning is becoming more important for effective lead scoring.
By adopting machine learning for lead scoring, businesses can improve their customer 360 views, enable data-driven decision-making, and enhance their overall marketing analytics capabilities. This way, businesses can efficiently rank their prospects, ensure the best use of sales resources, and increase their chances of success in the competitive market.
Overcoming these challenges requires businesses to invest in the right technology, processes, and expertise to optimize their lead-scoring efforts and ensure effective use of their sales resources.
While some organizations may not adopt lead scoring due to factors such as limited data or resources, many businesses leverage either rules-based or points-based scoring methodologies that depend on marketing automation platforms.
In rules-based scoring, marketing operations teams are responsible for defining important profiles or campaign activities within the marketing automation platform and setting up rules to promote or demote leads. Similarly, point-based scoring involves assigning points to different campaigns or profile types, which are then combined to generate a cumulative score that represents the lead's value.
However, both rules-based and points-based scoring models have their limitations. These approaches necessitate human effort to identify and maintain rules or points, which is a subjective, manual process that is time-consuming and not scalable. Moreover, these processes do not offer real-time results, and the data used for scoring is often restricted to what is captured in the marketing automation platform.
Lead Scoring using AI and ML has been proven a superior solution to the above problem. Machine learning provides an automated solution for lead scoring that constantly learns and updates itself based on data from multiple sources. This approach addresses the challenges posed by manual scoring methods and delivers accurate and near real-time results. By leveraging machine learning, businesses can optimize their sales resources and make data-driven decisions that enable them to stay ahead of the competition.
Executing predictive ML for lead scoring involves a series of essential steps that can guide organizations towards making accurate predictions. These steps are as follows:
The above steps can be classified into three phases:
The initial stage in developing a lead scoring model is to identify and collect all necessary data that can help in predicting lead conversion. This involves looking into various aspects of lead behavior, including their preferences, interests, and activities. Some examples of data that may be useful in developing a lead scoring model are:
Geographical data about the leads' location can help in understanding the market trends and behavior of customers in a particular region.
Data obtained from social media channels like Twitter, LinkedIn, and Facebook can provide insights into the interests and preferences of the leads.
Analyzing the purchase history of leads can help in identifying patterns and preferences that are useful in predicting future behavior.
Collecting data on how leads interact with your website, including pages visited, time spent, and click-through rates, can help in understanding their level of interest.
Data can be sourced from various internal and external sources, such as CRM systems, marketing automation platforms, and social media platforms. The key is to identify the right sources of data and ensure that the data is relevant, accurate, and up to date.
To prepare data for predictive ML, it's important to unify all relevant data sources into a single platform. This allows for more efficient processing and includes all necessary information. Accessing live, governed data without an ETL process through modern secure data sharing functionality saves time and effort and ensures continuous data accuracy. Additionally, feature engineering should be used to create useful features for the model. This involves exploring the unified data and building features that may impact lead conversion, based on industry experience.
The modeling stage involves creating and testing a predictive model that can accurately score leads based on various data points. This can be done using a combination of internal and external tools, with the end goal being to build a model that can deliver lead scoring recommendations to business users with speed and accuracy.
To ensure that the model stays up to date with the latest patterns between conversions and different features, it is important to refresh the predictions on a regular basis, either hourly or daily depending on business requirements.
One way to speed up the model development lifecycle is by using tools that can train multiple models simultaneously. By testing 15 to 30 classification models with different parameters and comparing validation metrics, you can quickly identify the best-performing model and use it as a baseline to finetune your internal customized model. This approach can save time and effort while delivering better results.
The operationalization and attainment of real-time scoring for business users is a critical aspect of the process outlined. To achieve this, it is imperative to construct an ML workflow that is capable of being trained on current data and incorporates a conditional deployment strategy. This may necessitate the use of a database, such as Snowflake, where the data to be trained is housed, a pipeline or mechanism to retrieve the data, and ultimately the data must be passed to the real-time scoring engine located within the MLOps platform. AIQ, an exclusive tool developed by Predera, is an excellent illustration of such an MLOps engine. With AIQ, business users are equipped with an array of capabilities to construct, deploy, and maintain an ML application featuring autonomous workflow functionality.
The diagram presented above is a clear illustration of the capabilities offered by AIQ. The architecture shown in the diagram was designed with an insurance client in mind, where the data used for training the model resides within the client's CRM applications, such as Salesforce and Neustar.
To facilitate data migration, Fivetran pipelines are employed to move the data from these applications into a database warehouse, in this case, Snowflake. Several integration layers, such as AWS Lambda and Amazon Kinesis, are also utilized to facilitate data exchange between AIQ, the MLOps platform, and the various other components of the architecture.
AIQ is equipped with a built-in Workflow Engine, Deployment Engine, and Monitoring Engine. The Workflow Engine is designed with various Model Training Workflows that can automatically retrieve data from the warehouse to train the model. Additionally, the platform also features retraining workflows, which can retrain the model in the event that its efficacy decreases due to data drift, concept drift, or other factors. The model with the highest efficacy is stored in the Model Catalog within AIQ, which allows for easy retrieval and deployment.
Upon deployment, the trained model is utilized for real-time scoring. As new leads are generated, the model scores each lead and returns the score to the client's selected CRM system. This process allows the client's sales team to focus on high value leads and ensures that they are contacted promptly, resulting in a more efficient lead generation process.
To effectively build and automate ML models, it is crucial to invest in a modern cloud data platform. ML performs best when operating on a single platform with one copy of data and numerous workloads. Access to all data must be secure and governed, and virtually unlimited performance and scale are necessary for ML to fulfill its potential. To achieve this, it is ideal to have a cloud data platform that requires minimal maintenance and is delivered as a service, allowing teams to focus on their work rather than platform tuning and maintenance.
The objective is to move all modeling processes into the cloud data platform, requiring a platform that delivers a data warehouse or data lake providing a single source for all data, instantly available to all users. Modern data sharing provides controlled, secure, and governed access to live data from its original location is also necessary. A data marketplace that facilitates the discovery and acquisition of third-party data via modern data sharing, data engineering and feature engineering that enable easy and fast data transformation, data science that empowers model training in the programming language of your choice, and data applications that capture data consumption and results are also required.
Adopting a cloud data platform for ML lead scoring can support business goals and outcomes related to growth and operational excellence. Snowflake recently implemented an ML lead scoring model, resulting in impressive preliminary results. In the first few months after implementation, their conversion rate from lead to meeting increased by 50%, and they scheduled and completed over 2,700 additional meetings. Overall, they estimated saving 27,000 hours (about 3 years) in manual efforts by their Sales team, allowing for higher-value tasks to be prioritized.