Lead Scoring in the CRM plays an important role in handling and prioritizing sales pipelines. Most CRMs come with automated lead scoring mechanisms in which with the help of AI and ML, they increase of decrease the score of the leads.
But, as every business is different, the businesses should add custom lead scoring workflows in the CRMs to get better visibility on the sales pipeline in the CRM.
Automated lead scoring is a crucial aspect of digital marketing that helps prioritize and identify high-quality leads for more targeted and effective campaigns. Here are some key rules to consider when setting up automated lead scoring for your digital marketing campaigns:
- Website Visits: Assign scores based on the frequency and duration of visits.
- Page Views: Give higher scores for pages indicating stronger interest, such as product or pricing pages.
- Content Downloads: Assign scores for downloading resources like eBooks, whitepapers, or case studies.
- Video Views: Score leads based on how much time they spend watching your videos.
- Email Opens: Assign scores based on the rate of email opens.
- Click-Through Rates (CTR): Higher CTRs could indicate stronger interest, leading to higher scores.
- Form Submissions: Score leads who submit forms through email campaigns.
Social Media Engagement:
- Likes, Shares, and Comments: Assign scores for engagement on social media platforms.
- Follows and Page Likes: Higher scores for leads who follow or like your brand on social media.
Demographic and Firmographic Data:
- Job Title: Assign scores based on the relevance of the lead’s job title to your target audience.
- Company Size: Larger companies might receive higher scores, depending on your target market.
- Industry: Score leads higher if they belong to industries you consider high-priority.
Lead Source and Channel:
- Assign scores based on the effectiveness and historical performance of different lead sources or marketing channels.
- Give higher scores to leads generated from channels with a higher conversion rate.
Lead Interaction Timing:
Assign scores based on the recency of interactions. Recent engagements may indicate a higher level of interest.
CRM Data Integration:
Incorporate data from your customer relationship management (CRM) system to include factors like past purchases, customer status, and customer feedback.
Implement scoring decay to reduce scores for leads who have not engaged for a certain period. This helps ensure that the scores remain reflective of current interests.
Deduct points for undesirable actions, such as unsubscribing from emails or repeatedly visiting the careers page.
Sales Feedback Loop:
Regularly gather feedback from your sales team to refine and optimize the scoring model based on the actual performance of leads.
Machine Learning and Predictive Analytics:
Utilize machine learning algorithms to analyze historical data and predict lead scores based on patterns and behaviors.
Regularly review and refine your automated lead scoring rules based on the performance of your campaigns and the evolving behavior of your audience. Adjustments may be necessary to ensure that your scoring accurately reflects the qualities of high-converting leads in your specific context.
Want to set up CRM automations for effective lead nurturing and better tracking of sales?
CrossNibble has helped several clients optimize their CRM operations and have also helped them nurturing their customers in a better way.