In marketing, there was a time when more = better. By casting a wide net, marketers hoped to pull in as many leads as possible. More leads, more traffic to the website, more content — what could be better?
The underlying idea was this: Increase the number of possible “attempts” to increase your chances of success.
With this “More of Everything” philosophy, marketing playbooks suddenly become over-abused, blogs became content farms, and companies started to over-measure and over-optimize commoditized best practices.
Not surprisingly, this strategy led to a breakdown between marketing and sales teams. Sales complained about the quality of the leads and marketing complained about the way salespeople engaged with those leads. And instead of working together, sales and marketing teams operated independently in their own silos. You see where this is going…
Account Based Marketing turned the whole “More of Everything” idea on its head by flipping the traditional marketing funnel upside down
Instead of ending with a target company that is a good fit for you to sell to (after you’ve filtered out all the bad leads), with ABM, you start with a bucket of target companies.
You identify the biggest opportunities at the beginning of the process and then proactively go after them.
ABM dramatically changed some of the best practices that marketing and sales teams have relied on and executed for years.
In this essay, we’re going to analyze three Account Based Marketing tactics. In them, we’ll work with several of the tools that you use every day including MadKudu, Clearbit, Slack, email, and more.
From the problem down to the details of their implementation, these recipes will save your marketing team time and help you provide a better end-to-end customer experience.
Reveal Loop – Discover new opportunities and save them in your CRM
For most SaaS companies, traffic is hardly the main issue. On average, the problem is about conversion rates: The percentage of traffic that actually converts into signups, trials, or paying customers.
In other words, what percentage of traffic translates into actual revenue at the end of the month?
The answer is very low. The conversion rate for most SaaS companies are in between 1 and 5%.
What if you could know who is visiting your website? What if you could automatically score those visitors? And, what if you could start engaging with them before they engage with you?
To implement the reveal loop we’re going to work with five different technologies, each of which will play a unique role in this process.
Let’s get started:
Plainflow → Create a trigger in Plainflow by specifying the criteria for users that should enroll in this journey. In this case, we’d like to take all of the users who made at least `1 pageview` in the last `hour`.
Clearbit Reveal → Clearbit Reveal will de-anonymize the enrolled users using their IP address and will return the company name to Plainflow.
MadKudu → Plainflow will route the company name to MadKudu as an input parameter, then MadKudu will automatically score the company name and will return the `customer-fit` score to Plainflow.
Slack → If the `customer-fit` is determined to be `good` or `very good`, it will trigger a Slack message in your #sales room.
Salesforce → And, it will open a new opportunity in your CRM.
We wrote an extensive review on how the reveal loop works on the Clearbit Blog: How to improve your outbound ABM performance with the Reveal Loop (via Plainflow)
Enrich new targets with Clearbit for new trial customers with a high likelihood to buy
Let’s say that your product provides a 14-day free individual trial. After the customer starts the free trial, there will be a moment when he realizes that your product is something he actually wants or better, needs for himself or his company.
MadKudu’s likelihood to buy parameter helps you predict that moment.
In this recipe, you’ll be able to enhance the user journey in-trial using a smart combination of MadKudu predictive signals with Clearbit data.
To implement this use case, we’re going to leverage five different technologies, each of which will play a unique role.
Plainflow → Create a trigger in Plainflow by specifying the criteria for a user that should enroll in this workflow. In this case, we will take all users who started a trial in the last `24 hours`.
Segment → Since MadKudu is connected to your Segment account, MadKudu signals will be piped into Segment, which will send them to Plainflow.
When the likelihood-to-buy of a trial user changes from low to high or very high, they will be processed ahead to the next step of the workflow.
Clearbit Enrichment → When the likelihood-to-by of a trial user is set as high or very high, Plainflow will call the Clearbit Enrichment API to get other relevant information about that customers.
In this step, output information from Clearbit and MadKudu are being saved and aggregated into a unified customer profile. This will be used to model the next steps of the journey.
Slack → Using the Clearbit estimated AnnualRevenue parameter of that company, you will be able to notify different people in your sales organization.
- If the ARR parameter is between 0 and 1M then you’ll send a Slack alert to SDRs that specialize in startups.
- If the MRR parameter is between 1M and 10M you’ll send a Slack alert to SDRs who takes care of mid-sized businesses.
- If the MRR parameter is higher than 10M the Slack notification will be sent directly to the VP of Sales or the team who takes care of large enterprise.
Here’s what the end implementation of this journey looks like in Plainflow:
Seamlessly coordinate low-touch, high-velocity sales and high-touch, lower-velocity sales based on the MadKudu score
Customer retention starts with onboarding. Understanding where customers encounter issues helps you improve processes and allows you to immediately engage to resolve problems.
Most SaaS companies have a great variety of daily signups. Therefore, it’s crucial that sales and marketing are able to immediately identify high-potential leads, to offer those leads best-fit onboarding consultation, and to deliver the right level of support during the onboarding process.
Many companies rely on their Customer Success team to manually identify high-potential leads. While this approach is good at identifying larger companies in your leads, it may also result in a large number of false positives, meaning that large companies were converting at a lower velocity.
Instead, what if you could offer white-glove onboarding to the customers who are ready to convert? In this recipe, we’ll use a smart combination of MadKudu signals with Clearbit data to enhance your understanding of your trial users’ journey.
Plan a different onboarding based on MadKudu segments. If the MadKudu Segment score is ‘good’ or ‘very good’ then enrich the company with additional Clearbit data and based on that data (ie. Number of employees, estimated ARR ranges, types of industry, countries or states where the company is located and more) plan different types of onboarding.
- Self-service signups will be guided through the product with an automated sequence of behavioural-driven onboarding emails
- Companies that require low-touch sales will activate the customer success team only when there’s a certain level product engagement
- Companies that require high-touch sales will activate the Sales team with a real-time alert on Slack and a new task on HubSpot immediately after signup
Gone are the days where marketers used one single monolith tool to do everything. Modern Marketing and Sales teams take the best-in-breed solutions in the market and creatively mix technology and workflow governance to optimize efficiency and deliver a world-class customer experience at scale.