B2B Sales Lead - Best Practices 1
Lead scoring system : Overview
- For both sales and marketing, lead scoring is the methodology that helps to ascertain the worthiness of leads or potential customers by attaching values to them based on their behavior relating to their interest in products or services.
- In both sales and marketing the procedure to score a lead is primarily dependent upon information gathered around the lead's occupation and role in that industry which helps to determine whether they're appropriate to sell to.
- Additional information that comes into play while deciding whether that lead would be interested in a company's products or services include the lead's activities, demographics, or areas of interest.
- At a more functional level, some of the aspects that are judged to measure a lead's interest include metrics such as i) which email messages leads respond to; ii) which pages they visit on the company website; iii) how long they visited, iv) any forms they filled or downloaded; v) whether they clicked on a blog post or connected via social media.
- However, the significance of the metrics depends on whether the company is selling a product or a service and the industry they are selling to.
Sales qualifying lead scoring system or SQL:
- Once a lead is transferred to sales and is considered to be actionable, sales representatives are required to further scrutinize the lead before assigning it to a dedicated account manager.
- This process includes a series of conversations in which the Sales Development Representative clarifies the needs of the lead and provides the lead with any supplemental information that they might need to confirm their decision.
- The types of supplemental information include case studies, ROI calculators, and free trials.
SQL implementation in a US commercial bank :
- U.S. Bank is the 5th largest commercial bank in the United States.
- The company Schermer, as a vendor of the U.S. Bank, established lead scoring criteria for the bank and also groomed the qualified leads using email retargeting and additional thought leadership content.
- The US bank also used Salesforce’s AI-powered Einstein lead scoring on top of the forty teams of wealth managers in Minneapolis, Washington and Milwaukee.
- In this system, machine learning was used to measure the leads' propensity to buy.
- The outcome of this effort lead to a 2.34 times higher conversion rate.
Best practices for a Successful Lead Scoring Model by Salesforce:
As mentioned above, Salesforce played a key role in implementing successful lead scoring models for US Bank.
- According to Salesforce, using the following best practices, a B2B (not specific to commercial bank) could really help improve sales productivity and the health of the company's sales funnel.
--Using Negative Scoring and Score Degradation:
According to Spear Marketing, 50% of companies could still benefit from putting a scoring reduction model in place.
--Setting up a separate lead scoring model :
It is advisable to set up different scoring models, for separate product lines.
--Model customization based on high-value actions and web pages
Pricing page or contact us pages are considered more “high value". As such, these pages should be assigned with higher point values.
--Not to assign points for every opened email:
Assigning points to every opened email often causes inflated lead scores. Therefore, submissions or page views generated from the email are often seen as a more appropriate metric.
- Reports and publications of commercial banks in the US:
To find out how the leading commercial banks are setting up a sales qualifying lead scoring system, we looked into the websites of several reputable commercial banks such as JPMC, US Bank, Bank of America, and Citibank. We wanted to find out whether any of the banks talked about any rules or procedures they follow in order to finalize sales leads. However, presumably owing to the fact that these types of information are part of competitive business strategy for the banks, the information was not publicly available.
- Reports by vendor companies :
We were able to locate some of the vendor companies that implement predictive analysis technologies for determining SQL scores. These companies included Salesforce, Schermer, DecisionCrm, and Agilecrm. With this we wanted to find whether the companies implemented their systems in any of the big commercial banks. Examples were found for Salesforce and Schermer. However, the implementation did not make a clear distinction between SQL and MQL and used lead scoring as an umbrella term. However, we provided the best practices outlined by Salesforce, even though they were not specific to SQL.
- Banking federation studies and reports :
We also looked into reports and publications by reputed US federations such as the American Bankers Association, the Savings and Loan Association, and the National Bankers Association. Our aim was to find relevant benchmarking or best practices studies about the SQL scoring system in commercial banks. Unfortunately, we were not able to find any relevant studies related to SQL in commercial banks.