Every organization wants to identify the right sales leads at the right time to optimize conversions. Lead scoring is a popular method for ranking prospects through an assessment of perceived value and sales-readiness. Scores are used to determine the order in which high-value leads are contacted, thus ensuring the best use of a salesperson’s time.
Of course, lead scoring is only as good as the information supplied. Data from a multitude of sources can help build a more objective view of each lead, as well as uncover customer insights and enable organizations to better understand target markets.
But data is only the first step. The process matters, too, and many of today’s lead-scoring methodologies require manual inputs. With human involvement comes a natural slowdown in results, not to mention the risk of inaccurate scores due to data collection issues or lack of data access due to data silos.
With growing volumes of data and limited sales resources, automation is becoming more critical for lead scoring, which points to the need for machine learning. In addition, by enhancing this one aspect of marketing analytics, you’ll also help improve your customer 360 views, and enable data-driven decisions by your Marketing and Sales organizations.
Exchange human learning for machine learning
Today, lead scoring is plagued by four common challenges, all of which revolve around data:
- Data collection issues. Ingesting large quantities of data from multiple sources can be challenging, especially for growing organizations that may lack an experienced data engineer or data scientist.
- Disconnected systems. First-, second-, and third-party data often lives in different applications or data repositories, which means these data sources exist as data silos and are not readily available for analysis.
- Data quality. Depending on how digital channels are set up and how each touchpoint in the prospect’s digital journey is captured, data quality may be bad or inconsistent.
- Go-to-market alignment. It can be challenging to unify data sources from Sales and Marketing teams’ activities to ensure all data is in one place and available. In addition, teams must work together to ensure the lead-scoring results and logic are embedded in the operational platforms used by both teams.
While some organizations do not use lead scoring due to limited data, insufficient resources, or lack of awareness, most use either rules-based scoring or points-based scoring, both of which rely on marketing automation platforms.
For rules-based scoring, a marketing operations team is tasked with defining the important profiles or campaign activities within the marketing automation platform, and setting up rules around how to promote or demote leads. Similarly, the point-based model requires points to be assigned to different campaigns or profile types, and then those points are added together to deliver a cumulative score that indicates the value of the lead.
Multiple challenges exist with both rules-based and points-based lead scoring. First and foremost, these models require human effort. Someone must identify the rules or points and maintain them, which is a manual, subjective process that requires time and effort. Not surprisingly, these requirements also mean the processes aren’t scalable, nor do they deliver near real-time results, and the data used for scoring is often limited to what’s captured in the marketing automation platform.
All of these issues point to why machine learning is a superior method for lead scoring. Machine learning empowers you to deliver an automated system that learns over time and updates automatically, using a constant input of data from a multitude of sources.
- Technical perspective. Machine learning enables teams to build a scoring system that takes into account as much information as you want. ML models can handle hundreds of features (attributes), such as lead profile, demographic features, and engagement behaviors, all of which result in stronger analysis and more accurate lead scores. In addition, ML models are powered by daily training algorithms that produce near real-time predictions. Not only do you learn what kind of leads have the highest likelihood to convert, but these scores become more accurate over time.
- Operational perspective. ML makes it possible to minimize human efforts and help stakeholders optimize data workloads. Because time is the most precious resource for sales, ML lead scoring is a gift that helps improve conversion rates and close more meetings with high-value leads. By removing data barriers and manual work, sales productivity improves, and marketing can better target its efforts.
With near real-time visibility into lead conversion rates, you can monitor model performance and business operations to ensure you’re seizing the best sales opportunities. You can define key metrics to monitor how your regional sales teams follow up with high-score leads, which can surface opportunities for improvement in operational processes. You can also compare actual conversion rates with predicted conversion to see how strong your lead scoring is and tweak the model as necessary.
In short, you are able to take control of lead scoring and make it work for your teams, rather than making your teams work hard to produce lead scores.
How to deliver lead scoring with machine learning
To get started with predictive ML, there are three key stages involved in execution, namely:
- Preparation. All relevant data must be made instantly accessible from all relevant data sources that can help build a holistic understanding of your leads.
- Modeling. Raw data must be processed to create new features, and the model must be trained.
- Operations. Recommendations must be delivered to business users, which requires model results published into operational applications for easy consumption.
Let’s walk through each of these three stages in more detail to look at what’s required and why each is important for lead scoring.
Data preparation requires you to bring together all relevant data sources into a single data platform. Data collection is the critical first step. The goal is to build an overall picture of the lead journey, which will likely require you to map hundreds of data points in the lead generation ecosystem. Examples of data you might decide to use include:
- Firmographic and technological information to determine what kind of companies the lead comes from, which can include company size, revenue, and industry. Information can also include what technology or other products they already use, or are currently inquiring about, that relate to you product and service offerings.
- Demographic and profile data such as job functions and personas to help understand the lead as a person.
- Engagement data to learn how the lead engaged with your company’s marketing campaigns and content, or even engaged with your product before talking to Sales.
Some data might be internal and come from traditional SaaS applications, such as Salesforce CRM data and website traffic, while other data might come from external sources, such as digital ad platforms and firmographic information from third-party vendors.
The next step is to unify all of this data, which enables processing and including all relevant information you need. Whether the data comes through an API or any other type of ingestion, it’s crucial to ensure that all data sources are unified on a single platform, which greatly improves productivity for data scientists.
Ideally, you would use modern secure data sharing functionality to access live, governed data without an ETL process, which saves time and effort and ensures continuous data accuracy. Acquiring access to public data sources in a data marketplace via the same functionality is also beneficial, as it will provide additional data sets to consider for your model that are popular for Marketing and Sales organizations.
Rounding out the preparation stage is feature engineering, which is used to explore the unified data and create useful features for the model. Industry experience should be used to build the features that might influence whether a lead can be converted into a meeting. For example, you may believe that a certain marketing activity or engagement pattern impacts conversion, so you can create a feature in the model and see how important it actually is.
Pro tip: Because ML is a data-intensive endeavor, it’s best to use a platform that keeps track of your work so it’s easy to search for previous queries and reuse previous code from colleagues. Also, access to a UI that automatically calculates stats such as sums, averages, maximums, and minimums for profiling data is extremely helpful for doing quality checks, as is the ability to explore and visualize data in a chart or share with collaborators in a dashboard.
After the training data is ready, it’s time to train your model. Whether you use an internal tool or external programming tools such as Python for modeling, the idea is to query the relevant data, build a predictive model, deploy the model, and write back predictions to your data platform. These predictive scores can be pushed via an API into engagement platforms and CRMs to deliver the lead scoring recommendations directly to business users, with speed.
The beauty of building an ML model is that it’s trained every day to capture the latest patterns between conversions and different features. Depending on your business requirements, these predictions can be refreshed on an hourly or daily basis so your team is following up with the right leads as soon as possible.
Pro tip: There are tools that can train different models at the same time to find the best-performing model more quickly. Rather than build multiple models, you can explore and test anywhere between 15 and 30 classification models with different parameters. After comparing validation metrics, you can select the best baseline model and borrow learnings from it to fine-tune your internal customized model more effectively. This process will massively accelerate the model development lifecycle.
The final stage is to modernize the operational process and achieve near real-time scoring for your business users. With a predictive lead-scoring model, the best leads are served up to stakeholders in the applications they use every day.
This stage in the process is where the impact of ML really shines in comparison to rules-based or points-based scoring. The latter are linear processes, as illustrated on the left-hand side of the diagram. In this example, the marketing operations team sets up and maintains manual rules in Marketo’s rule-based model, and Salesforce identifies which leads should be assigned to the outreach sequence.
In contrast, machine learning enables an automated flow of data. While leads come in through Marketo, those leads get captured into the lead scoring model, which is trained every day. The lead is scored and then delivered with a near real-time prediction to the business user via an API, which pushes the score into Salesforce. At a regular interval, the scores are refreshed, and if a score is above a predetermined threshold, it will be added to a salesperson’s queue. This dynamic method ensures that each salesperson is spending time on the highest-value leads every day.
Invest in the right data platform to reap the benefits of lead scoring
In order to build ML models and automate the process, the most important action you can take is to invest in a modern cloud data platform.
Machine learning works best when you operate on one platform with one copy of data and many workloads. Secure and governed access to all data is the price of entry, and ML requires virtually unlimited performance and scale in order to fulfill its promise. Ideally, you want a near-zero maintenance cloud data platform that’s delivered as a service so your teams focus on their work, not on tuning, administering, and maintaining the platform.
The goal should be to move all modeling processes into your cloud data platform. That means your platform must provide:
- Data warehouse or data lake that delivers a single source for all data, instantly available to all users;
- Modern data sharing that allows access to live data from its original location in a controlled, secure, and governed manner;
- Data marketplace that enables the discovery and acquisition of third-party data via modern data sharing;
- Data engineering and feature engineering that power 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.
By adopting a cloud data platform, ML lead scoring can be built to support business goals and outcomes around growth and operational excellence. For example, Snowflake built and implemented an ML lead scoring model earlier this year, and our preliminary results speak to the power of machine learning. In the first couple months after implementation, our conversion rate from lead to meeting rose by 50%, and we scheduled and completed 2,700+ more meetings. In total, we estimated a savings of 27,000 hours in manual efforts by our Sales team—hours that have been repurposed for higher-value tasks.
It’s clear the future of lead scoring lies with machine learning. Isn’t it time to convert?
To learn how Snowflake uses the Data Cloud for ML-based lead scoring, view this webinar. To speak to someone from Snowflake about how the Data Cloud can transform your marketing intelligence and analytics, click here.