You might wonder what we think we’re doing, going for Salesforce.
The instantly recognizable blue cloud is the bread and butter of the common sales rep and a universally reliable marketing automation, sales activity, and customer service powerhouse.
Salesforce is proud of its place at the top of the customer relationship management (CRM) pile, comes in fourth on Fortune 100’s list of employers, and is maintained by around 73,000 staff members, not to mention its 150,000 international customers worldwide.
With hundreds of thousands of people in Salesforce’s corner, why are we complaining?
From a lead scoring perspective, the CRM platform is too demanding, inaccurate, and slow.
Using the American cloud-based software company for your lead scoring efforts takes far too much time. Even when you think you’re finished designing your scoring model, there’s a good chance you won’t have accurate lead scores.
Salesforce’s formula fields, demographic attributes, and scoring formulas are too limited for the product-led growth (PLG) era.
Simply put, Salesforce’s lead scoring doesn’t cater to the modern SaaS sales process. Salesforce launched years before digital transformation really took off.
Despite having done its best to modernize its lead scoring with services like the Einstein Lead Scoring system, the CRM still doesn’t cut it for product-focused sales reps and marketing teams.
We might be preaching to the choir here, and if you use Salesforce for lead scoring, you probably have your own pain points laundry list.
SaaS lead scoring requires a variety of data sources and integrations, with a focus on product data. Getting this complex method right through Salesforce can be a headache.
We’ll investigate the issues with Salesforce’s lead scoring and provide a more efficient, tailored, and current alternative.
How does Salesforce’s lead scoring work?
If you aren’t familiar with Salesforce’s lead scoring systems, they fall into two broad categories: predictive and manual scoring.
Predictive lead scoring
Salesforce’s predictive scoring anticipates the conversion rates of potential leads. This automated model relies on artificial intelligence (AI) and machine learning to source data from your potential customers, accounts, and inbound leads.
This data hoarding model is part of Salesforce Einstein, the CRM’s flagship AI meant to compensate for thousands of customers’ human biases and errors. Sales Cloud Einstein and its predictive models can warn you of potential sales funnel dropouts and provides sales predictions.
Salesforce’s digital homage to the German theoretical physicist also logs your sales activities and interactions with hot leads.
The AI can perform much of the heavy lifting for marketing departments, but the Salesforce Einstein lead scoring scheme mainly focuses on your custom field selections.
Sales Cloud Einstein fusses over your database (regardless of company size) to thrash out a scoring model.
You don’t have to settle for the average lead score in the Einstein lead scoring dashboard, as this automated lead scoring and grading system lets you customize its settings and add new score field parameters.
Salesforce’s manual scoring service focuses on optimizing your current leads. It’s a far more self-reliant (if not laborious) way to score leads for your current sales funnel. Here, you create your preferred field filters.
Once you’ve nailed your score field, you need to pull properties from your Salesforce account to hammer your scoring mechanism into shape. These potential prospect properties include requested demos, opened emails, and firmographic attributes.
Regarding marketing activities, handpicking the right CRM data to build your own scoring property (Salesforce lacks a native one) takes a high marketing alignment. Even then, knowing if you’re on the right track is difficult.
Why Einstein Lead Scoring isn’t as clever as Salesforce wants you to believe
We’re happy for any sales manager that has found success with the Salesforce sales cloud and its AI. Salesforce provides a comprehensive solution to digital marketing’s more demanding tasks and a supposed appreciation of the behavior of leads.
The problem with complete solutions is that they use general methods to deal with niche issues. Qualifying leads with a PLG approach requires a tailored commitment to each prospect’s usage, pain points, and needs.
This attention to usage detail is something engineers might not be suited for. Einstein and its “AI-driven predictive lead scoring algorithm” is a highly technical service that the average salesperson might struggle to get comfortable with.
Unlike the real Einstein, Salesforce’s version can’t quickly crunch large figures. You might wait nearly 48 hours for Salesforce Einstein to finish raking your data and activate detailed lead engagement tracking.
Demanding delays abound
These slow response times are worsened if you can’t feed Salesforce Einstein enough key lead score metrics. Companies that don’t meet the AI’s data quotas must rely on Salesforce’s global model.
You’re then limited to other customers’ data as a grading solution to setting your scoring rules.
Einstein can’t draw up a unique scoring model without a large amount of product and lead data. Boilerplate scoring models won’t cut it for tracking hot prospects that don’t follow sequential buying journeys.
Product-led growth (PLG) startups need highly personalized scoring to identify properly qualified prospects with a proven interest in their products.
The tricky, expensive, and potentially wasteful genius
As far as lead scoring services go, Salesforce Einstein is pretty expensive. Startups must be as light as possible with a cheap but powerful tech stack.
Einstein Predictions currently charges $75 per user/ month. Adding Salesforce’s analytics platforms ranges from $125 to $200 per user/month.
We’ll never dispute Salesforce’s capabilities, but power doesn’t always translate into finesse. Your vital customer and product data isn’t natively present in Salesforce.
Sure, many third-party integrations are available, but they don’t communicate with Salesforce in a product-driven way.
Sales teams looking for product-qualified leads (PQLs) then spend much of their time shoveling data into Salesforce Einstein’s engine. Significant changes to your properties mean regular updates to your scoring model.
Sure, lead scoring models change, but they shouldn’t take days to do so. You should also have more ownership of your customer data.
How to make your own lead scoring model
You have an excellent sales team, but no human can completely and correctly satisfy the complexity of Salesforce’s manual lead scoring. And why should you?
At the same time, we’re also wary of automated lead scoring systems. You need a completely tailored system that evaluates trimmed leads.
It really depends on the brand that we're working with. Some people have webinars where the next step is an activation, naturally. Some people have really dense and complex products where you have to engage with somebody 5, 6, or 7 times before asking. So, I don't think there is one kind of panacea, perfect lead scoring methodology.Sebastien van Heyningen, Revenue Operations Consultant
How to score product-qualified leads
It’s time for effective lead scoring solutions, and we’ve got plenty.
You don’t need to spoonfeed or carry Breyta. Our integration roadmap includes all the essential tools needed to keep your lead scoring model intelligent, concise, and updated.
The beauty of integrating with fantastic tools like Clearbit, Segment, and Stripe is that all the data you need to create a tailored lead scoring model is at your fingertips.
We identified the most vital data sources for modern selling and how to combine them in actionable ways.
If you’re a PLG company, your sales team is probably hungry for PQLs. Here’s how Breyta helps you score them:
- Pore over your Clearbit-sourced firmographic data to establish your ideal customer profile (ICP).
- Establish your power user criteria according to product usage benchmarks sourced from Segment.
- Differentiate between paying and free users with revenue data from Stripe.
You can quickly create lead scoring models this way for PQLs, SQLs, and MQLs. It’s a flexible system that lets you change your scoring track without delays or anything breaking.
We spoke to Sebastien van Heyningen, a Revenue Operations Consultant, about lead scoring flexibility.
It changes with the size of the companies that you're going after. How pricey is your product? How many departments will you hit? Are you just serving marketing, or are you serving marketing, finance, and human resources? I don't think there's one right way to do it. But every company has their own right way to do it.Sebastien van Heyningen, Revenue Operations Consultant
It’s a tough order that your prospects might be hesitant to fill. We know how difficult it can be to get enough firmographic data on hot leads, for example, to score them properly.
Our Clearbit integration ensures that your cagiest customers are enriched properly. We’ve covered every potential scoring pain point, and we owe this foolproofing to our integrations’ combined depth.
Make the most out of your accounts
Breyta evolves your scoring system to be a flexible and invaluable account scoring and customer success tool. Once you have your new and improved lead scoring method down, you can apply it to a broader context.
The model you use to score individual leads can naturally be applied to entire accounts. PLG companies often deal with numerous users within a single company, so it makes sense to rank their grouped importance.
You can use your ICP to evaluate these accounts, and the combined firmographic and product usage data of each lead within a company. You’ll often have many companies using your product and your ICP helps identify the accounts bound to enjoy the most practical value.
Scoring can also be used as a diagnostic resource for your existing customers. Our customer health score activates your product usage data and NPS’s capacity for identifying and helping at-risk customers before the alarm bells go off.
We’ll direct you to customers that haven’t logged into your product for a while whose subscription renewals are closing in. Uninspiring NPS surveys can be disheartening, but we like to see them as opportunities.
You can create a signal list based on these otherwise damaging customer events and conditions. Preempting a customer crisis lets you lend support before deactivation hits.
It’s not all doom and gloom, of course. We also want you to know when an account is ready for upselling.
You can create a signal list that shows the accounts that have maxed out their user limit and have shown an outstanding engagement with your product.
The future of lead scoring is independent
You and your team don’t have time to plow through Salesforce’s lead scoring models, daily. Yes, this system works, but the upkeep isn’t worth your time or money.
There are too many services, platforms, and tutorials within Salesforce to justify your productivity. Breyta provides a consolidated, single source of truth for lead and account scoring.
We filtered all the lead scoring benefits of a huge CRM into an efficient package. Complexity is the antithesis of PLG, and you need to own your scoring system.
Here you're developing a score based on activities that you can track, within your product. You build trust rather than trying to find what people are doing in their homes and what research they're doing privately, and building a score off of that.Sebastien van Heyningen, Revenue Operations Consultant
Many companies feel they couldn’t operate without Salesforce’s scoring framework. We’re here to show you that you are more than capable of championing every lead and customer touchpoint.