How to Do Lead Scoring and Account Scoring in Salesforce
June 8, 2022
In today’s ever-changing digital world, your leads and customers quickly navigate from product to product in search of finding the answer to their immediate needs. This creates an endless cycle of lead management for your sales and marketing teams who struggle to prioritize the right customers. Generating MQLs (marketing qualified leads) is difficult because it requires someone to actually opt in and fill out personal information. In most cases, this is done through a form submission, the creation of an account, or a checkout process.
Once your marketing team has generated a new lead, that contact is automatically added to Salesforce as an MQL. The problem is your marketing team needs a way to move those leads through the funnel so they can be converted to SQLs (sales qualified leads) and your sales team needs a way to prioritize the leads coming in. However, when all of your leads and accounts look the exact same, many of them inevitably fall through the cracks and this is often the difference between closing a deal and losing a deal. This is exactly why lead scoring and account scoring are so important for sales and marketing alignment.
What is Lead Scoring?
Lead scoring is focused on optimizing and prioritizing every lead you generate in your business. At its core, lead scoring is the process of automatically assigning a numeric score to all of your inbound leads. Lead scoring looks at the existing fields within your CRM and creates a score for each of your current leads based on the fields that you define. In most cases, this includes demographic data (job title, industry, company size, department, location, revenue, etc.) and behavioral data (email opens, pages visited, content downloaded, webinar registrations, form submissions, etc.). Ultimately, the goal of lead scoring is to help your teams identify hot leads so they can shift MQLs to SQLs.
Defining a Lead Scoring Model
In almost every case a lead scoring model is based on a point range, where a higher score equals a better lead, but you can customize this as you see fit. Creating a lead score requires you to define your scoring range. To do this, you’ll first need to calculate your lead-to-conversion rate (e.g. the total number of conversions you generate from new leads). This can be done by dividing the total number of new customers you’ve generated by the total number of new leads you’ve acquired.
Once you’ve done this, you’ll want to identify exactly which traits are more likely to translate into a paying customer. You can do this by defining your scorning rules and examining your current and potential customers. It’s important to understand your ICP (ideal customer profile) and target audience. For example, is your target audience at the executive level or the manager level? Do you sell to B2B companies or B2C companies? Understanding exactly what actions your customers took to become paying customers is also extremely relevant (e.g. did they see an ad, request a demo, view your pricing page, submit a form, etc.)
You need to know exactly which marketing efforts are yielding the most conversions in your sales funnel. For instance, if you identified that leads who saw an ad are twice as likely to buy your product, you could assign a positive score to leads who meet that criteria. You can apply this same principle to filter out bad leads. If a job seeker or competitor downloads a resource from your website you could assign negative points to contacts who fall into that criteria. A proper lead scoring model gives your sales reps enhanced visibility so they can close more deals and generate more revenue.
What is Account Scoring?
Account scoring is focused on mapping user attributes and user-generated data to specific accounts so you can monitor their overall performance and health. With account scoring, the same data that is used to score your leads is also used to score your accounts. The difference is that your account score is based on the data from all of your leads, rather than at the individual level. The goal of account scoring is to provide your sales teams with real-time visibility so they can prioritize and take actions on accounts in the same way they would with leads.
Defining an Account Scoring Model
Instead of assigning positive and negative attributes at the individual level, with account scoring your model will need to be based on a summarization of all your data, so it’s important you define exactly which traits you want to be calculated.
For example, accounts that have reached a specific LTV (lifetime value), ARR (annual recurring revenue), or product usage threshold might be good targets for expansion. Likewise, accounts that have opened a certain number of marketing emails or viewed your product page X number of times might be good targets for your outbound team.
How Does Lead Scoring and Account Scoring Work in Salesforce?
Within Salesforce, you have two main options for lead and account scoring, predictive scoring, and manual scoring.
Predictive scoring is entirely focused on estimating when a potential lead will close. It uses artificial intelligence (AI) to analyze your data from customers, leads, and accounts to identify red flags and predict future outcomes. The goal of a predictive score is to remove human error and bias and rely solely on predictive modeling algorithms. For predictive scoring, Salesforce offers a feature known as Salesforce Einstein.
Sales Cloud Einstein does a lot of things, including automatically logging sales interactions (e.g. emails and phone calls). However, the main value that Einstein lead scoring and account scoring provides is that it automatically analyzes your existing customers, accounts, deals, and previously converted leads by looking at all of the custom fields and data across your entire database. This information is then used to create a scoring model.
To define more accurate lead scores, you can even choose to have Einstein exclude certain fields by choosing custom settings instead of default settings. It’s important to note that it can take up to 48 hours to analyze your data. If you don’t have enough data, Einstein will use a global model based on the anonymous data from thousands of customers who use Salesforce.
Only once you’ve accumulated enough lead or account data, will Einstein create a scoring model unique to you. Einstein can even be configured to show key metrics like average lead score, conversion rate, lost opportunities, etc. This predictive model is automatically updated every ten days to identify new trends based on your data.
Whereas predictive lead scoring is focused on estimating your likelihood of turning a lead or an account into a customer, manual lead scoring specializes in optimizing your leads and accounts. To do manual scoring in Salesforce you’ll need to first create a new score field (e.g. lead score or account score). After you’ve done this, you can build an actual scoring mechanism using your existing properties in Salesforce (e.g. item added to cart, job title, industry, emails opened, demo requested, etc.) Unfortunately, Salesforce doesn’t have a native property for scoring, so you’ll have to build it yourself and this can be somewhat difficult.
The Problem with Lead Scoring and Account Scoring in Salesforce
While Salesforce is incredibly powerful, one of the major problems with doing lead scoring or account scoring within Salesforce is that you’re always going to have important customer data that doesn’t natively live within the platform. This is because nearly all of the data has to be manually inputted by your sales team. If all your lead and account scoring models are solely based on the information your sales team is inputting they’re going to be innately flawed. Unless you’re using Salesforce Marketing Cloud to capture behavioral data and key events happening on your website or app, the important customer data you need is likely unavailable.
There are also always going fields that you don’t capture when you generate a lead (e.g. industry or job title). Likewise, your proprietary product usage data (e.g. workspaces created, last login date, subscription type, new users, etc.) won’t be available in Salesforce either. In reality, all of your customer data across all of your data sources (including Salesforce), already lives in your data warehouse.
Building Your Scoring Model in the Data Warehouse
Building a lead scoring model within the warehouse is more logical because you have access to the full range of your customer data. It’s also more efficient because you can transform and model the data as you seem fit. When it comes to building your lead scoring model in the warehouse, you generally have two options. You can either build a lead scoring model directly in your warehouse and then sync that data to a new property that you define in Salesforce, or you can sync your data directly to fields that you define in Salesforce and base your lead scoring model on those fields.
No matter what, there’s always going to be data that you need to get into Salesforce. Although it’s easy to create new properties/fields in Salesforce, it’s not easy to move the data from your warehouse to Salesforce. You could download manual CSV files or build custom integrations, but this is not scalable. Ultimately, you need a way to sync data from your warehouse directly to Salesforce, and this is exactly the problem that Hightouch addresses.
Activating Your Data in Salesforce
Hightouch simply queries against your warehouse. All you have to do is define the data columns in your data model and map them to the appropriate fields/properties in Salesforce.
You can schedule your syncs to run manually, on a set interval, using a cron expression, or after your transformation jobs have finished running in your warehouse via dbt.
Hightouch ensures that Salesforce is up to date with the same customer data that lives in your warehouse and establishes a single source of truth across your entire organization.
The first integration with Hightouch is completely free so you can actually start scoring your leads and accounts today!
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