How To: Create your first Sales SLA Report

We’ve mentioned on countless occasions the importance of having a Smarketing SLA. In this article we provide instructions to create your first sales SLA report in Salesforce.

Pre-requisites

While other CRM will soon be documented, the focus of this “how to” is really on setting up Salesforce and building Sales SLA reports to measure the consistency of your SDR team’s follow up.

General considerations

To create the SLA Report, you’ll need to have the right information available at the Lead level. The overall idea is to create a “Time to Touch” field on the Lead object.

Whenever that field’s value is 0, the lead was never touched. This means that your reps never got to reaching out to that lead (or that it wasn’t tracked).

Whenever the field value is greater than 0, the lead was touched and the field value will is the time difference between the lead creation date and the date of the first touch. We’ll be considering activity completion dates to achieve this. Activities can either be Calls, Emails or Meetings (but you can easily customize this list).

This tutorial leverages Rollup Helper, which is a free app on the AppExchange marketplace and a great way to get started without building custom computations or ETL.

Steps to create your Sales SLA

Enable Aggregations in Salesforce

Install Rollup Helper from the AppExchange

Customize Salesforce fields

Create the following custom fields at the Lead Level:

  • Number of Touches is a number field with 0 decimal
  • Date of First Touch is a Date/Time field

Create aggregated fields with Rollup helper

  • Open Rollup Helper by opening the “App Launcher”
  • Create a new “Number of Touches” Rollup
    • Child Object = Task
    • Relationship Field = Name ID
    • Rollup Type = Minimum
    • Field = Created Date

 

Create a Custom Filter

Create a custom filter with the following criteria: Name = Sales Touches

  • Filter Criteria
    • You’ll need to click on “Show More” at the bottom of the Field List
    • Type = Call, Email, Meeting
    • Status = Completed

    • Here is what the filter should look like
    • Click on the “Save” button at the bottom of the page
    • Click on the “Save and Run” button at the bottom of the page
  1. Create a custom filter with the following criteria: Name =Number of First Touch
  • Create a “Number of First Touch” Rollup
    • Child Object = Task
    • Relationship Field = Name ID
    • Rollup Type = Count
    • Select the “Sales Touches” filter that we created earlier
    • Click on “Save” at the bottom of the page
  • Create a Formula Field at the Lead Level
    • Field Type = Formula
    • Field Name = Time to First Touch
    • Formula Return Type = Number
    • Decimal Place = 0
    • Formula: IF ( Number_of_Touches__c > 0,( Date_of_First_Touch__c – CreatedDate)*24, NULL )
    • Blank Field Handling: Treat blank fields as blanks

 

  • Create the report
    • Create a Bucket field on the “Time to First Touch” that looks like this
    • Group rows by “MK Customer Fit Segment” to look at your SLA based on the quality of leads
    • Group columns by “SLA”
    • Uncheck “Detail Rows” at the bottom of the report to hide the details
    • “Save & Run” and you’re all set

There you have it, you can now start measuring your average, min/max time to contact leads based on channels, reps… This is the first step to being able to identify the biggest areas of improvement for your SDR team.

Want to learn more or need help, please do reach out here

Why SDRs are at odds with Lead Scores

I don’t think I’m giving away any trade secrets by revealing that SDRs aren’t always the biggest fans of lead scores. Whether implementing a lead score built internally or a solution like MadKudu, SDRs are in the precarious position of being the primary users and having very little influence over the score itself.

SDRs carry a lot of intuition of what makes a lead good or bad. They aren’t surprised when a lead they perceive as good/bad is rated as such. And yet, they are viscerally frustrated when a lead they perceive as ‘bad’ is rated otherwise, and vice versa. Even if the lead score is scoring leads perfect, an SDR’s core metric – the number of demo’s booked – is often undermined by the lead score.

Lead scores are meant to filter out bad leads while surfacing lead with the highest probability to convert to customer. A poor-performing lead score might surface leads that are likely to get a phone call, but not likely to convert. These care called NiNas (No Intent. No Authority), and they are like grease in your funnel – they look like they should go down smooth, and then they dry up halfway down the funnel, slowing down everything else that should pass through easily.

NiNa’s are great for an SDR’s quota, and while we know that NiNa’s aren’t good for the overall business, this means that a good lead score is removing one of the easiest ways for an SDR to make quota.

Lead Scores should serve SDRS

While not exactly a black box, Lead Scores have historically operated as such for SDRs. Their purpose is to help SDRs prioritize the highest-value leads, which should be great for helping them hit their quota; however, without knowing why a lead is good, lead scores provide little more than expectations for how the engagement should go.

At the same time, Lead Scores are calculated by measuring a lot of valuable information, most of which is not visible to the SDR. Beyond job title and employee count, lead scores evaluate the predicted revenue of each company, the size of specific teams, the tech stack & tools that a company uses, whether their solution is B2B or B2C, whether it has a free trial or not, whether they’ve raised venture capital, and much more. There can be thousands of signals that are weighed initially in order to figure out which ones are the best determiners of success, against which every lead will be measured.

MadKudu Signals sitting inside Salesforce
Sample MadKudu Signals sitting inside Salesforce

In the above example, we can see how valuable it is to know that the lead is performing 150K daily API calls, or that their company has multiple active users on the account , or that they are using Salesforce: these are indicators of the buyer persona, the use case, and therefore of the right message for the SDR to send.

For SDRs, these signals are context. Context for why a lead is a good lead, and that’s exactly how a Lead Score and serve an SDR. Constructing the right message, understanding where your lead is coming from, identifying what the tipping point is that made them sales-ready: SDRs and Lead Scores are trying to do the exact same thing.

With one customer, MadKudu was able to demonstrate a disproportionate ratio between Opportunities created and Opportunities won – another way to look at that is prospects that made it past an SDR vs. prospects that made it past an AE. What you can see above is that having ‘Manager’ or ‘Operations/HR’ in a prospects title negatively impacted their odds of getting through an SDR (or negatively impacted an SDR’s chances of getting them to an AE), while it greatly increased their chances of becoming a customer if they made it to an AE.

Knowing which kinds of titles are good for AE’s can help SDRs understand what to spend more time on, but it can also help SDR managers better train their SDRs on how to win with those personas.

Speaking with Francis on our weekly podcast, it was clear to me how important it is for SDRs to buy in to a Lead Score. If you’re in charge of implementing a lead score, you need to bring SDRs into the conversation early to understand how the lead score can serve them. Making a lead score actionable for SDRs means that your front line for feedback on how well your score is performing will be more incentivized to work with the score instead of working against the score.

How we use Zapier to score Mailchimp subscribers

There’s no better way to get your story out there than to create engaging content with which your target audience identifies. At MadKudu, we love sharing data-driven insights and learnings from our experience working with Marketing Operations professionals, which has allowed us to take the value we strive to bring our customers every day and make it available to the marketing ops community as a whole.

As interest in our content has grown, it was only natural that we leverage Zapier in order to quickly understand who was signing up and whether we should take the relationship to the next level.

Zapier is a great way for SaaS companies like us to quickly build automated workflows around the tools we already use to make sure our customers have a frictionless relevant journey. We don’t want to push every Mailchimp subscriber to Salesforce, because not only would that create a heap of contacts that aren’t sales-ready, but we may end up inadvertently reaching out to contacts who don’t need MadKudu yet, giving them a negative first impression of us as a potential customer.

Today we are able to see who is signing up for our newsletter that sales should be paying attention to, and let’s see how:

Step 1: Scoring new newsletter subscribers

The first step is to make sure you grab all new subscribers. Zapier makes that super easy with their Mailchimp integration

Next we want to send those new subscribers to MadKudu to be analyzed. While MadKudu customers have a dedicated MadKudu integration, Zapier users who aren’t a MadKudu customer can also leverage Zapier’s native Lead Score app, which is (you guessed it) powered by MadKudu.

Step 2: Filter by Lead Score

We’ve got our MadKudu score already configured so after I feed my new subscriber to MadKudu, I’m going to run a quick filter to make sure we only do something if the Lead Score is “good” or “very good.”

If you’re worried that the bar will filter out potentially interesting leads, consider this a confidence test of your lead score.

Zapier Filtering by Lead Score Quality

Step 3: Take Action, Communicate!

For MailChimp signups that pass our Lead Score filter, we next leverage the SalesForce integration in Zapier to either find the existing contact inside Salesforce (they may already be there) or create a new lead. SalesForce has made this very easy to do with the “Find or Create Lead” action in Zapier.

Once we’ve communicated synced our Mailchimp lead to Salesforce, we use the Slack integration on Zapier to communicate everything we’ve created so far to a dedicated #notif-madkudu channel, which broadcasts all the quality leads coming from all of our lead generation channels.

Directly inside Slack, our team can get actionable insights:

  • The MadKudu score, represented as 3 Stars (normal stars for Good/ twinkling for Very Good)
  • The signals that MadKudu identified in this lead, both positive and negative
  • A link to the lead in Salesforce, for anyone who wants to take action/review

Actionable Lead Scoring applied to your Newsletter

Our goal here isn’t to reach out to newsletter subscribers – we want to build a long-term relationship with them, and we’re happy to keep delivering them quality content until their ready to talk about actionable lead scoring. What we’re able to do is see qualitatively & quantitatively the number of newsletter subscribers we have who are a good fit for MadKudu today.

This helps marketing & sales stay aligned on the same goal. Marketing is measuring newsletter growth with the same metric its using to measure SQL generation.

Segmenting Funnel Analysis by Customer Fit

Every week during our check-in, MadKudu Co-Founder & CRO Francis Brero & I talk about our current priorities. Our regular call also become an opportunity for Francis to download some knowledge from his time working with some of the top SaaS Sales & Marketing organizations – like applying lead scoring to funnel analysis. What started as an effort to onboard me with recordings & note-taking has turned into a series I call MadOps.

A lead score is the foundation for your marketing & sales alignment. It creates accountability for both teams and is the foundation of a strong Sales SLA. A foundation is only as useful as what you build on top of it, and that’s why we talk about Actionable Lead Scoring – leveraging your lead score to create a frictionless journey. Today we’re going to focus on how you can leverage your lead score in funnel analysis to see where your best leads are falling off.

Funnel Analysis & Actionable Intelligence

Understanding the customer journey’s inflection points and conversion rates is essential to scaling & maintaining success as a software company; however, the analysis you’re doing is just as important as the data you’re using to generate that analysis.

The goal of funnel analysis is to look at ways to remove friction from the customer journey, to improve activation & conversion, and to make sure that the users who should engage most with your product do. Accomplishing that goal without segmenting by lead score is like turning every lead into an opportunity in sales force and then trying to improve your deal won rate. You need to start with the right metric by answering the right question: what are my best leads doing and how can I make their journey better?

If you're not applying lead score to funnel analysis, you're making decisions based on flawed data.

Applying Lead Score to Funnel Analysis

Let’s imagine you want to look at the first 15 days of user activity in your self-service product, which corresponds to your 14-day free trial and immediate conversion. Of course, you already know that 50% of conversion on freemium occurs after the trial expires, but you’re looking to identify engagement drop-off before the trial even expires. After all, customers can’t convert if they don’t stay active.

A simple cohort analysis of all users who signed up over a two-week period would show that over 60% are dropping off in the first 24 hours, a smaller chunk 5 days out, and another group at the end of trial. You might conclude that you need to rework your onboarding drip campaign’s first emails in order to combat that big next-day dropoff. That would make sense, except are the people who are dropping off the prospects that matter most? Probably not.

Very good leads have a different funnel than very bad leads

One MadKudu came to this exact same conclusion, and despite various drip campaign tests, they didn’t see that 60% drop-off move. Then we segmented their  funnel analysis, looking at how very good, good, bad & very bad leads acted, and we found that most of that 60% drop-off was very bad leads: they had made their sign-up process so frictionless that they were getting spam sign-ups who were never going to actually use their product. As it turned out, that small dip after 5 days corresponding to the biggest area of drop-off for very good leads, who were dropping off at the end of their intense drip campaign which only lasted 5 days.

In this case, not segmenting by customer fit completely masked where their focus should be, and they spent time trying to get spam signups to stay engaged with their product instead of looking at how their highest value prospects were engaging with their product.

Our recommended Setup

If you’re looking to start segmenting funnel analysis by Customer Fit, our recommended MarTech stack is to feed MadKudu into product analytics solution Amplitude using Segment‘s customer data platform.

Account-Based Engagement and the Fallacy of Job Titles

Every week during our check-in, MadKudu Co-Founder & CRO Francis Brero & I talk about our current priorities. Our regular call also become an opportunity for Francis to download some knowledge from his time working with some of the top SaaS Sales & Marketing organizations, such as Account-Based Engagement. What started as an effort to onboard me with recordings & note-taking has turned into a series I call MadOps.

As we saw recently with the Sales SLA, the path to alignment often starts & ends with clear definitions of metrics. The leads marketing hands to sales need to have the same definition & measurement for success, which is where actionable lead scoring plays a key role in establishing lasting alignment.

If we step back from Sales & Marketing and look at aligning each department to business objectives, we can see that metric disjunction can result in each individual team being successful while ultimately failing to create a relevant customer journey at scale.

The fallacy of job titles

One area where we often observe this is when we run funnel analysis by customer fit and look at job titles as predictors of activation and conversion. On self-serve tools such as API-based products, we often see that someone with a developer title is more likely to activate but very unlikely to convert (that is, to hand over the credit card), whereas someone with a CEO/owner title is more likely to give a credit card, but less likely to convert.

One analysis we recently ran for a customer demonstrated that perfect:

How job title affects conversion | Account-Based Engagement

  • Developers convert 60% less than the average user
  • Founders, CEOs & marketing convert 70-80% than the average user.

When we look at conversion & activation side-by-side for this same customer, the number speak for themselves:

Conversion vs. Activation | Account-Based Engagement

  • Founders/CEOs don’t use the software that much but end up converting higly
  • Product & Project managers have a higher activation but lower conversion rate

Product teams are historically motivated by increasing activation by building an increasingly engaging product; however, a developer is unlikely to respond to marketing’s nurturing emails or jump on a first sales call no matter how active they are on the product.

Likewise with more sales-driven products like enterprise software, SDRs are often singularly focused on the number of meetings they can generate for their AEs; however, low-level team members are significantly more likely to jump on a phone call and significantly less likely to convert as compared to their director counterpart.

In both of these instances, we see that product & sales development are able to optimize for their metric without accomplishing the core business objective of creating a great customer journey.

How Account-Based Engagement changes the rules

What this comes back to is account-based engagement, a nascent terminology in the marketing space stemming from the principal of account-based marketing but extending it across the entire customer journey and to all customer-facing teams. Where account-based marketing encourages running campaigns to generate interest not at the individual lead level but the account level – especially important when you have multiple stakeholders in the decision-making process – account-based engagement extends that to all teams, meaning that:

  • Product teams should seek not only to make as many active users as possible, but to create active accounts: building features that encourage getting other stakeholders involved or making it easy for your hero to evangelize your product value to other stakeholders.
  • Marketing teams should not seek to generate marketing qualified leads but marketing qualified accounts, including nurturing existing accounts in order to get other stakeholders involved so as to set sales up for success
  • SDRs should not seek to generate meetings at the account level, not at the lead level, and shouldn’t be working on accounts where the necessary stakeholders are not already involved.

Account-Based Engagement | Identifying hidden opportunities

We’ve been recently working with two of our bigger customers who have a prosumer user base to identify marketing-qualified accounts that aren’t getting attention. We do this by looking not only at customer-fit at the account level – does the account look like the type of accounts that typically convert when sales engages – but also at behavioral-fit: are they engaging with the product the way paying customers typically do?

Sales reps who are qualifying leads as soon as the account is created aren’t going to be able to sift through the hundreds of warm accounts to identify which accounts have engaged properly (and been properly engaged) to be sales-ready; however, this is core to Account-Based Engagement. Just as our Sales SLA gives a common metric for marketing & sales to work towards, so Product, Customer Success, Sales & Marketing all need to have a common qualification criteria for an account in order to be aligned on how best to achieve business goals.

Remember: In B2B, you’re not selling to users, you’re selling to Accounts

The goal is not to reduce all teams to a single metric like revenue-generated, but rather to help reduce the natural tendency to game a metric by linking a common thread between the metrics that we use to measure success. That thread is Accounts.

It is all too easy to lose track of the fact that selling B2B software means that a company is going to buy your software, not a person. There are users, decision-makers, stakeholders and other advisors in the buying process, but at the end of the day a company is going to make a decision about whether to pay another company for their solutions. In this respect, every team should be focused on how to acquire, activate, convert & retain accounts, because at the end of the day it is not a user that will churn but an account.