Predictive Lead Scoring Software: Benefits, Features, and Examples
Overview:
Predictive lead scoring software (PLS) helps businesses identify the most valuable leads that are most likely to convert into paying customers. PLS uses a variety of data points to predict which leads are most likely to make a purchase. This software offers several benefits such as improving accuracy in identifying leads, predicting customer behavior, and increasing revenue by turning leads into customers. This article will delve into who uses the software, benefits of the software, features of the software, and provide examples of relevant software products.
Who Uses Predictive Lead Scoring Software?
Businesses that are looking to gain a competitive edge in the market can benefit from PLS. Sales and marketing teams primarily use PLS to analyze customer behavior, reviewing the customer profiles that are most likely to make a purchase, and utilizing that data to improve their lead nurturing process. Small businesses, mid-sized enterprises, and start-ups all require this software to predict customer behavior and increase revenue. PLS is also used by marketers, researchers, and sales teams to run effective campaigns, identify qualified leads, and improve customer relationships.
Benefits of Predictive Lead Scoring Software:
Improved accuracy: Traditional lead scoring software uses only demographic and firmographic data to score leads. PLS uses predictive analytics to consider multiple data points such as web behavior, social engagement, and other factors that help predict a lead’s likelihood of converting to a customer. This approach increases accuracy in identifying leads that are more likely to convert.
Predicting customer behavior: PLS software can analyze customer behavior such as their likes and dislikes, preferences, location, and demographic characteristics. Using this data helps marketers to segment customers by behavioral patterns and identify the leads that are more likely to convert to customers.
Increased revenue: PLS helps businesses identify sales-ready leads faster, which helps them save time and resources in closing deals. Higher-quality leads and higher conversion rates contribute to an increase in revenue.
Features of Predictive Lead Scoring Software:
Predictive Analytics: This feature predicts which leads are the most qualified, based on the information and patterns in their data profiles, and is often used in conjunction with machine learning algorithms.
Web Analytics: This feature analyzes website traffic to measure lead behavior, their interaction with site content, and calculate web scores that help users focus their resources on the right leads.
CRM Integration: PLS integrates with CRMs to help align the information and resources between marketing and sales teams.
Scalability: This feature allows businesses to adapt and grow their sales pipeline while using the software.
Examples of Relevant Predictive Lead Scoring Software:
1. Infer (infer.com): Infer uses data from a business’s CRM to predict which leads are most likely to buy. Infer uses machine learning algorithms to score leads based on data points such as website activity, social media engagement, and firmographic data, which helps businesses understand which leads are the most qualified.
2. Lattice Engines (lattice-engines.com): Lattice Engines uses big data analytics to predict which leads are most likely to convert by analyzing patterns in firmographic, behavioral, and third-party data sources. This software’s proprietary algorithm helps marketers to prioritize leads and improve their understanding of customer triggers.
3. Leadspace (leadspace.com): Leadspace uses predictive analytics to score leads and assists marketers around strategy and campaign planning. This software combines proprietary algorithms with several data sources, including social media and content consumption, to predict which leads are most likely to convert.
4. 6sense (6sense.com): 6sense helps sales and marketing teams identify potential customers at every stage of the sales cycle based on data points such as contacts, account-level data, and buyer intent data. The software also provides recommendations on what information to share with the target audience and through which channel.
5. Conversica (conversica.com): Conversica’s suite of AI-powered lead conversion tools helps project management teams identify the hot leads in their pipeline and engage potential customers in human-like interactions. Conversica’s automation tools help businesses increase sales efficiency by automating lead follow-ups and other time-consuming processes.
Drawbacks and Limitations of Predictive Lead Scoring Software:
PLS software relies entirely on data analysis, which may have limitations due to inaccurate, outdated, or incomplete data. The results depend on the quality of data that the software considers to prioritize leads. These systems may also be challenging to set up and maintain due to the need for constant calibration and adjustment as a company’s customer base changes.
Conclusion:
In today’s highly competitive market, PLS software is essential to organizations aiming to predict customer behavior effectively. The software helps businesses save money and time by identifying the leads that are most likely to purchase, and focusing marketing teams’ efforts. PLS software, combined with CRM integration, enhances your understanding of individual leads, provides opportunities, and better targets customers at the right time. While limitations within predictive analytics may exist, businesses worldwide have improved its capacity to solve problems and increase revenue generation initiatives. Through PLS software, businesses have the data-driven insights to grow and thrive as they progress through the business cycle.