Predictive lead scoring with AI takes sales insights to the next level by helping print service providers identify and prioritize high-value leads. By leveraging AI analytics, printers can analyze incoming customer data and historical data to determine which leads are more likely to convert. And as most print salespeople know, even the smallest uptick in conversion rates can give a significant boost to revenue.
Machine learning algorithms make it possible for printers to evaluate various factors, such as engagement levels, demographic information, and purchasing behavior, to assign a score to each lead.
The use of predictive lead scoring can increase conversion rates by up to 30%, as it helps sales teams focus on the most promising opportunities. AI-driven predictive models continuously learn and adapt from new data, allowing printing companies to refine their lead scoring criteria and accuracy over time.
A key point here is that AI improves lead scoring and analytics without the usual sales department constraints of time, staffing, and money.
By integrating predictive lead scoring into the sales process, print service providers can reduce the time spent on unqualified leads, and close more leads with a higher lifetime value.
There are two ways to get started with predictive lead scoring. One is through software that you might already be using. The other is through some of the new AI platforms.
Platforms and Software for Predictive Lead Scoring
There are several CRM platforms and software solutions that offer predictive lead scoring capabilities. Most have or will soon have built-in AI features to help productivity.
Here are some notable examples popular in the marketplace:
Salesforce Sales Cloud: This CRM platform integrates predictive lead scoring to help businesses prioritize leads based on their likelihood to convert.
HubSpot: Known for its robust marketing and sales tools, HubSpot offers predictive lead scoring features that use AI to continuously refine scoring models.
Zoho CRM: Zoho provides a comprehensive CRM solution with lead scoring capabilities that can be customized to fit various business needs, including the printing industry.
Pecan AI: This platform specializes in AI-driven insights for predictive lead scoring. It helps to analyze historical data to find common traits among converted leads, and to build an ideal customer profile.
Using AI Chat Platforms for Predictive Lead Scoring
Companies that don't have a CRM platform with AI lead scoring capability don't have to be left behind. They can leverage AI chat platforms like Perplexity, Claude, or ChatGPT to implement predictive lead scoring by using basic office tools like Excel or Google Sheets.
Here’s an example of how they might create a predictive lead scoring system.
1 - Data Preparation: Start by organizing historical sales data in an Excel file. This data should include customer interactions, sales revenue, and product details. Ensure the data is clean and well-structured since AI models rely on high-quality data for accurate predictions.
2 - AI Integration: Use an AI chat platform to analyze this data. For instance, by using ChatGPT, Perplexity, or Claude, salespeople can input data files and ask the AI to identify patterns or trends that correlate with successful sales conversions. This can be done by feeding the AI specific prompts or questions related to lead characteristics and historical outcomes.
The video below goes into detail about how to automate this process using Salesforce, ChatGPT, and Zapier. He also gives a balanced view of the pros and cons of setting it up.
3 - Lead Scoring Model: The AI can help develop a scoring model by assigning weights to different factors such as engagement level, purchase history, and demographic information. The AI can simulate various scenarios to determine which factors most significantly impact lead conversion.
4 - Real-Time Analysis: AI platforms can provide real-time insights by continuously analyzing new data as it becomes available. This dynamic scoring allows sales teams to adjust their strategies promptly, focusing on high-potential leads as they emerge.
5 - Automation and Integration: Tools like Zapier, Make, or Pabbly can be used to automate the process, integrating AI chat platforms with CRM systems like Salesforce or with internal data files set up on Google Sheets. This setup can automatically update lead scores based on the latest data, ensuring that sales teams always have the most current information at their fingertips.
Keep in mind that AI tools are far from perfect, whether it's within a CRM or the person is working directly with an AI platform. Results need to be checked. The do-it-yourself AI user also needs to spend some time learning how to properly prompt (instruct) an AI tool. Poor instructions lead to questionable results.
Yet the prep work and learning curve can quickly be worth it. Print salespeople can enhance their lead scoring processes to quickly become more efficient and effective. This approach not only saves time but also improves the accuracy of lead prioritization, ultimately driving higher conversion rates and revenue growth.
