Editor’s Note: This post originally appeared on Medium. The below is an excerpt. You can read the article in its entirety here.
In my previous article, How To Track Customer Acquisitions: Customer Lifecycle, Sales Funnel, and Content Strategy, I described how companies attract, engage, and convert customers, and how this process can be optimized and improved.
In this guide, I will make an attempt to describe how SaaS/enterprise companies design lead nurturing campaigns and how lead scoring can help prioritize leads and improve conversions, as well as how lead nurturing strategy affects the creation of drip email campaigns.
The primary goal of this article is to help you design or improve your current lead nurturing strategy. But the ultimate goal is to provide marketers and founders with some core principles on the topics of lead nurturing, and lead scoring. My intention isn’t to provide a cookie cutter solution, but instead to stimulate ideas and encourage a new way of thinking around these topics.
If you’re reading this article and have things to add, or see that I’ve missed a crucial point, please leave a comment. Contributing to this guide will make it a more useful resource for everyone in the startup and marketing community. To quote a principle of Ray Dalio:
“I’m stress-testing my opinions by having the smartest people I could find challenge them so I could find out where I was wrong.”
Table of Contents
Summary / Advisories
PART 1: Nurturing: Lead Nurturing and Customer Nurturing
1.1. What is nurturing?: lead nurturing, and customer nurturing?
1.2. Why is a nurturing strategy essential for growth?
PART 2: Lead Scoring Strategy and Design
2.1. How a company’s growth stage impacts leads scoring
2.2. Why a prospect’s FIT and PAIN are at the core of any lead scoring strategy
2.3. How to design a lead scoring system
2.4. What is a target (or SDR-ready) score and how do you select one?
2.5. What are the factors that drive lead scoring?
2.6. Reaching an SDR-ready Score
2.7. Static vs. Predictive lead scoring
2.8. What are the biggest mistakes when it comes to lead scoring?
PART 3: Drip Emails As A Primary Lead Nurturing Mechanism
3.1. What are the primary categories of drip email campaigns?
3.2. Segmentation can increase the effectiveness of your drip email campaigns
Summary
Acknowledgements
Resources
Advisories
1. Why write another guide on lead nurturing, scoring and drip emails when so much has already been written by others?
Most of the materials online on this topic are either too basic or overcomplicated. And even if you find a great source, such as Marketo’s Definitive Guide to Lead Scoring, there are still some important gaps that need to be filled. In this article I attempt to contribute to the overall conversation on lead nurturing, lead scoring, and drip email campaigns.
2. Why is this article so long? And why are you combining Lead Nurturing, Lead Scoring, and Drip Emails into one topic?
The reason why we are combining Lead Nurturing, Lead Scoring, and Drip Emails into one paper/article is that these topics are all part of a continuous marketing effort to attract, engage and convert customers. Lead nurturing is your communication process. Lead scoring is your strategy for prioritization. And drip email campaigns are one of the most effective ways to talk to your customers — for now at least. (Maybe chatbots will change this but for now email is still one of the most effective communications channels).
3. Why is this article missing a case study?
While I have tried to add examples from real companies, my initial intention was to include a more detailed case study to this article. But since this piece is already long, I decided to leave a detailed case for another post, and focus here on the bigger picture.
Part 1 : Nurturing: Lead Nurturing and Customer Nurturing
1.1. What is nurturing?: lead nurturing, and customer nurturing?
Before we jump into describing lead nurturing, let’s start by defining the term ‘nurturing’ as it is used in marketing. Let’s make it clear that I’m not attempting to introduce a new term for the sake of it here. New terminology can come across as noise when it’s simply about adding another meaningless term. However, in my view, using consistent and clear definitions helps bring clarity to marketing, sales, and customer success departments.
Here is the dictionary definition of Nurture: verb
Care for and encourage the growth or development of.
To support and encourage, as during the period of training or development; foster.
Nurturing (as a marketing term) is a process of continual communication with a company’s target audience across all stages of the customer lifecycle.
Lead nurturing is therefore a process of continual communication (or interaction) with a prospect that improves a company’s chances of converting that prospect into a customer at some time in the future. Lead nurturing is communication that happens between ‘Visitor’ and ‘Customer’ stages in the customer lifecycle funnel. Marketing teams are responsible for managing lead nurturing.
Moving on from this communication process that we’ve called lead nurturing, comes customer nurturing (or Customer Success). This part of the process refers to the communication that happens after someone becomes a ‘Customer’, in order to move them along to the ‘Loyal Customer’ stage. Customer success teams are in-charge of customer nurturing. Customer nurturing should be a never ending process of communicating with your customers.
Nurturing = Lead Nurturing + Customer Nurturing
LEARN MORE: Customer nurturing is critical to reduce churn. If churn is a concern for your business and you want to learn more about differentiating lead nurturing and customer nurturing, and why customer nurturing is an essential part of success in SaaS businesses, I’d recommend you read this article by David Skok on churn rate: Why Churn Is Critical In SaaS.
1.2. Why is a nurturing strategy essential for growth?
When we use or hear the term nurturing, everyone in the organization should understand that we are talking about communication with customers and prospects across all stages of the customer lifecycle. Every interaction with a target audience is nurturing. In this context, lead/prospect nurturing, and customer nurturing become self-evident terms.
Nurturing, like many other things in marketing, is not a new concept. One of the best examples of nurturing is a story of the greatest car salesman in the world — Joe Girard. Joe employed two assistants to work on his secret weapon that helped him sell over 13,000 vehicles over 15 years (between 1963–78). What was Joe’s secret weapon? Greeting cards. Yes, he and his assistants sent over 13,000 greeting cards per month.
“Joe sent out nearly 13,000 greeting cards a month to his customers, celebrating everything from Halloween to Groundhog Day,”
Way back in the 1960’s and 70’s Joe wasn’t just nurturing leads, he was also sending cards to customers, so he was nurturing customers too. He realized that the best way to build loyalty and increase referrals was to have continual communication with his target audience. In his case, the target audience was segmented primarily based on location. He wanted to target people in proximity to the dealership he worked at. Since social media and email weren’t an option, greeting cards did enough to keep him at the forefront of people’s minds and associate the car buying experience with Joe himself. Essentially, Joe created a simple, but very effective nurturing process that included both lead, and customer nurturing.
Granted, SaaS/enterprise software company sales processes are somewhat different from car selling but we still interact with human beings, so it can be argued that the concept of nurturing has not changed since the 1960’s when Joe started his greeting card campaign (I’m by no means implying that Joe invented “nurturing”).
ASIDE: I would like to point out that measuring and attributing exact sales numbers to the success of a greeting card campaign is extremely difficult. Was Joe calculating ROI on each greeting card? Was he able to say exactly how many sales came from his greetings cards? Can he attribute whether it was the last greeting card or the first one that brought customer into the dealership?
I highly doubt he had a clear answer to any of these questions, but he had a feeling that this nurturing campaign was working. Of course, he could ask customers why they came to him and to his dealership, but how often would customers misjudge the fact that Joe was probably the only car salesman in the area that they could remember by name?
Hopefully, it is clear by now that defining nurturing as 3 standalone terms brings more clarity and order to an organization’s marketing activities. Another reason why nurturing is an important term to define and use is because it changes organizational perspective from just lead nurturing — what marketing teams are doing to help sales close deals — and customer nurturing — what customer success teams are doing to create loyal customers; to become a notion of creating broader nurturing campaigns to target audiences across all stages of the customer lifecycle. Examples of such broad nurturing campaigns are sending birthday greeting cards, hosting community events, and surveying target audiences to identify industry trends. All of these are nurturing campaigns that target your audience across any stage of the customer lifecycle.
Part 2: Lead Scoring Strategy and Design
Lead Nurturing, Lead Scoring, and Drip Emails
Now let’s zoom in on lead scoring.
Lead scoring is a process of assigning scores to prospects based on their profile and behavioral data in order to prioritize leads, improve close rates, and decrease buying cycles.
If lead nurturing is a process of continual communication (or interaction) with a prospect that improves a company’s chances of converting a prospect to a customer at some time in the future, then it makes sense to combine this with lead scoring. Lead scoring helps prioritize leads for sales teams, and enables you to evaluate the effectiveness and efficiency of your nurturing campaigns.
2.1. How a company’s growth stage impacts leads scoring
The structure and strategy for lead nurturing and lead scoring depends on a company’s growth stage. Lead scoring isn’t equally important for every stage of the company’s development (bear in mind that while this high level strategy can be applicable in many markets, we are focusing on SaaS/enterprise companies in this discussion).
For the purpose of simplicity, let’s divide companies into three stages of evolution:
Early Stage: seeking product-market fit.
Growth Stage: expanding markets, growing demand, and scaling processes.
Maturity Stage: focusing on optimization and efficiency.
Lead scoring at an early stage company
For companies in the early stage of seeking product-market fit, lead scoring isn’t usually a top priority. If you generate just a few leads daily and you have capacity to follow up on each and every lead, the importance of prioritization is greatly reduced. However, even at early stage, it is highly recommended to implement some very basic lead and user tracking system. By doing this, you can collect valuable information on what journeys your prospects take before they buy. This means that when your company moves to the growth stage you have a few months/years of data ready to optimize your lead scoring.
At the beginning, you can just ensure that you track as many user actions inside the product as possible. This will enable you to run statistical correlation analysis on what action or patterns of user interaction with your company lead to higher close rates. Starting with a few months of product usage data will mean that your team doesn’t have to make wild guesses when starting lead scoring.
Lead scoring at a growth stage company
Often lead scoring is fully implemented and tested at growth stage. A company starts to grow and generate higher inbound demand, and at this point the goal is to identify prospects that have an urgent pain from those that perhaps need to be educated on problems and solutions; in other words, need to be nurtured. Lead scoring at this stage becomes critical to ensure that as the company scales, it is doing so using its resources most optimally.
Lead nurturing in a growing company can evolve from default communication with their audience to a more segmented and targeted approach, such as designing drip emails for each role in the buying process, or for each specific vertical. At this stage, communication becomes much more precise in lead nurturing campaigns.
For example, in an early stage company, when a prospect signs up, the company might have one drip email campaign for everyone. At growth stage, the same company may have a few drip email campaigns: one targeted to decision makers, one towards influencers, one communicating a specific value proposition for a financial institution, and so on. The goal is to divide your communications into meaningful segments based on buyer’s roles, verticals, or specific pains.
Lead scoring at a mature stage company
Mature companies should have well-designed, and smoothly working lead nurturing, and lead scoring systems. The primary difference with earlier stages is that a mature company will have more data to conduct robust pattern analysis, and further segment communication with its audience by vertical, by pain, by buyer’s role and so on.
2.2. Why a prospect’s FIT and PAIN are at the core of any lead scoring strategy
Before we discuss different lead scoring systems, let’s remind ourselves about the goal of lead scoring. The goal of lead scoring is to help a company prioritize leads, improve close rates, and decrease the sales cycle.
Lead scoring in terms of FIT and PAIN
According to State of Sales Productivity Report by Docurated, sales reps spend only 32% of their time selling, which seems inefficient. Since reps spend only about ⅓ of their time interacting with customers and prospects there is no surprise that “sales productivity is a top driver for hitting new revenue targets.”
Lead scoring is a primary strategy to improve the productivity of your sales organization by prioritizing prospects with higher potential Customer Lifetime Value (CLV). Prioritization allows companies to use resources more effectively, meaning that salespeople are spending most of their time with prospects that are closer to a buying decision.
In his book “The Sales Acceleration Formula”, Mark Roberge points out that leads, both inbound and outbound, can be analyzed from the perspectives of fit and pain. The difference between outbound sales and inbound sales approaches is that the first follows the pattern of finding a good fit and then identifying pain, where an inbound sales approach attracts leads that have a certain pain and then qualifies whether or not these leads are a good fit for the company.
The higher the number of inbound leads, the more significant lead scoring and prioritization becomes. Otherwise, your team will run the risk of spending time with leads that aren’t as valuable, or missing out on really great opportunities that are buried in a pile of not very valuable leads.
Lead scoring is essentially a more scientific approach for marketing to assess fit and pain. Lead scoring provides a quantifiable answer to following questions:
PAIN
Does this prospect experience the pain that our product solves? How big is this pain relative to other pains and challenges that this prospect faces daily?
FIT
Is this prospect a good fit for us? Does he/she have the budget to pay for our product? Does he/she have enough resource to implement our product?
The level (concept) of FIT and PAIN is what drives lead scoring. Lead score shows how well a prospect FITs your ideal customer profile, and how much PAIN the prospect has based on behavioral data.
Lead Score = FIT Score + PAIN Score
TAKEAWAYS: First, if you are serious about significantly increasing the productivity of your sales team by putting a successful lead scoring strategy in place, now is the time to make sure you fully understand how FIT and PAIN influence your inbound and outbound actions. Second, you need to make sure you have a system in place for assessing FIT and PAIN.
2.3. How to design a lead scoring system
The most common way to set up a lead scoring system is to use numerical values to assign a score to each lead and then categorize leads into 3–4 lead buckets according to their score.
For example, a company will assign numerical scores to leads based on their profile and how they interacted with both product and website. Then based on the lead score, it will rank or categorize those prospects using a college-like grade system A, B, C, D or using labels — ‘hot’, ‘warm’, ‘cold’. In this setup, lead scores and categories can be as follows:
A leads (100+ score) -> SDR-ready
B leads (75–99 score)
C leads (50–74 score)
D leads (below 50 score)
The problem with this approach is that while everyone understands that leads in A category are SDR-ready, there is a little agreement in how conceptually (not only numerically) leads in categories B, C, and D are different. Except from the fact that they still aren’t SDR-ready. Grasping the difference between C and D leads, for example, can cause a great deal of confusion in marketing and sales teams.
In The Definitive Guide to Lead Scoring, Marketo’s team recommends creating separate scores for behavioral and profile metrics and then merging these scores into a table to see where each lead falls in prioritization.
LEARN MORE: You can read more about this lead scoring system in The Definitive Guide to Lead Scoring by Marketo’s team. While this guide provides some helpful information, it overly complicates lead scoring implementation by suggesting that companies need to track vanity metrics such as open emails, clicks on the links in them, and broad web page visits. Also, breaking lead scoring into A, B, C, D categories doesn’t provide much benefit for teams, as I will try to explain below.
In my view, this approach to lead scoring brings unnecessary complexity. The goal of lead scoring is prioritization. So instead of complicating lead scoring by creating artificial lead categories, why not just use a numeric score to identify how close a lead is to an SDR-ready score?
Every lead entering your marketing system has one desired next step — reaching an SDR-ready score. Therefore, every lead that wasn’t touched by your SDR team has only two meaningful states:
1) not SDR-ready; and
2) SDR-ready.
And how ‘hot’, or close the lead is to SDR-ready score is best represented by a numerical value. There is almost no practical value in breaking down two lead statuses (SDR-ready and not SDR-ready) into four categories (A, B, C, D), or worse yet using more subjective labels (‘hot’, ‘warm’, ‘cold’).
A counter argument is that breaking down leads into A, B, C, D categories helps companies analyze how prospects progress towards their target score. However, finding and testing optimal SDR-ready scores is difficult enough already. Creating and testing score ranges for each category rarely has any meaningful ROI attached to it.
Quite often companies end up with large variations of leads in each category that don’t really show problems in lead nurturing, but are rather the result of lead score ranges being far from optimal.
Lead scoring needs to be numerical and progressive
To simplify, let’s remind ourselves that the goal of lead scoring is to prioritize leads and improve how a company nurtures leads over time. Progressive scoring means that leads can be scored between 0 and ∞, with one target (SDR-ready) score somewhere on this continuum, that when reached, moves the lead to SDR qualification stage.
2.4. What is a target (or SDR-ready) score and how do you select one?
A target (or SDR-ready) score is a score that shows that a lead has reached a certain level of FIT and PAIN that qualifies that lead to be passed to the SDR team for further qualification.
If you are just starting out with lead scoring you will have to analyze what pattern of actions prospects are taking that lead customers to buy your product. Ideally, your target lead score has to be correlated with 10%-15% close rates. What this means is that 10%-15% of the leads that reach the SDR team have to close in the duration of your average sales cycle.
The best way to get the most from this process is to test and optimize your SDR-ready score for more efficiency. If your SDR team has sufficient resources, you can experiment by lowering your SDR-ready score to 8%-10% probability-to-close to test whether your sales team can close more deals by starting interaction with prospects earlier in the buying process. On the other hand, if your SDR team can’t effectively follow up with every lead that has reached an SDR-ready score within 48 hours, you might want to increase your SDR-ready score to correspond with a 15%-20% probability-to-close.
This relationship between target (SDR-ready) score and probability-to-close is important. If leads that reach your SDR team have a probability-to-close below 10% in the average sales cycle, the chances are that your SDR team is wasting their time; those leads weren’t nurtured enough. On the other hand, if your SDR-ready leads are closing with much higher probability, for example 20%, then you might be leaving money on the table and not growing as fast as you could. In this instance, one solution would be to hire another SDR.
Target (or SDR-ready) scores will be unique to every company. I doubt any company can get it right from the start. Like many other important things, getting it right is all about continuous optimization and testing.
SDR-ready = Lead Score with probability-to-close in the range of 10%-15%
ASIDE: Recently, I came across an interesting method of scoring leads used by the Engagio team. Engagio seems to have a system of assigning value to their prospects not based on the clicks in the email (by the way I believe no actions in the email should be scored) but based on the time prospects spend reading the email, watching the video, or on the website. This is an interesting concept to try, but I haven’t seen any company implement this model well, so it’s hard to make any valid analysis. One reason for my caution is that we can’t always assume that higher time spent in product leads to higher perceived value. For example, if a Facebook user spends more time on the platform, it’s very likely that he/she derives higher value from it. But what if your product delivers value on the fact that customers need to spend less time using the product in order to achieve their desired outcome?
In order to put this into context, let me share a real world example. I was buying a CA fishing license on one of those .gov websites recently and it took me 20 minutes to do so. Was my time spent on the website correlated with desired value? No! Quite the opposite. I wish I had achieved my desired outcome in 30 seconds or less.
2.5. What are the factors that drive lead scoring?
Lead Score = FIT Score + PAIN Score
Effective lead scoring strategy includes two categories of data on which any prospect is scored:
Profile / Demographics = FIT
Behavioral = PAIN
How a prospect’s ‘Profile’ data is selected and scored
Profile (or demographics) data is more static and gets scored early in the process when prospects enter (or sign up to) your marketing automation or CRM.
Profile data typically includes five of the most important characteristics of the prospect:
Email
Title
Company
Location
Size
Depending on the company and the product, the importance and weight of each profile characteristic can be higher or lower, and the difference in importance should be reflected in the lead score. Here are the questions you should be asking for your unique situation:
Email
Is this a personal or corporate email? Corporate emails should be scored higher since they usually mean that the prospect is okay with communication at a corporate level.
Title
Even if you don’t currently ask for title, for example if you just ask for email, you should consider a data enrichment solution to help you identify the title of a prospect. Obviously, if the prospect is a manager, VP, or C-level, a higher level of attention should be given to this prospect and a higher score should be assigned.
Title helps you identify the role of the prospect in the buying process
Title is not just for you to assign a higher score, it will also help you to identify the role of the person in the buying process. If your product is more technical and more expensive it is very likely that a few people in the organization will be involved in the buying process. They can have different needs and different agendas, so it is highly recommended to structure your Drip Email Campaigns for each participant in the buying process: decision maker, influencer, and end-user. But I’ll talk about this more later.
Company
The reason why it is helpful to know the exact name of the company is that you can check whether the company is on your target list, and if so, you can assign a higher score. By target list I’m referring to account-based sales strategies. If a prospect that you are targeting in your outbound campaign came through your inbound channel and registered, it is very likely that they were exposed to your message in outbound communication.
Location
Location matters, simply because not many companies are selling to customers all across the World, and even if they do, not every market has the same profit potential. This is true for countries, for example US vs. Eastern Europe, as well as states, for example California vs. Mississippi (I have nothing against Mississippi, I’m just making a point).
Size
The size of the prospect’s company is helpful in estimating their potential revenue or buying power. On the other hand, if you are selling your product for sales people on a per seat basis, then you could also look into more specific numbers, like the number of salespeople in the organization. SaaS/enterprise companies can use a data enrichment solution to help create a full picture of incoming prospects and score these prospects accordingly.
For example, Outreach, a sales communication platform focusing on outbound, sells its solution to sales reps. The size of sales organization is crucial in estimating potential customer’s LTV. In this case it makes more sense to ask about the size of a prospect’s sales team than the size of the company overall.
How to pick and score behavioral data
Profile data is easy to find and create rules and scoring around. With behavioral data things are far more challenging. First of all, there are many more behavioral factors to consider than profile factors. Second, profile data is more static and only gets scored once at signup stage; behavioral data is more dynamic and requires constant monitoring and regular checkups.
While SaaS companies can use a data enrichment solution to help create a full picture of incoming prospects and score their profile characteristics accordingly, no such 3rd party solution is available for your team to analyze what behavioral factors need to be scored. There are some good products that can help you track how users interact with your product, but only your team can make a connection between product and company interaction and closed deals. No single 3rd-party product will have the connection between product usage data and closed deals.
There are two categories of behavioral data:
Product engagement and
Non-product engagement.
Product engagement actions are actions that prospects perform inside of your product. This is by far the most important category compared to non-product engagement. It is almost always true that when behavioral data is correlated with closed deals, product engagement/usage data has a higher impact on probability-to-close compared to non-product related interactions.
Here are a couple of examples of product engagement actions:
User returns to your product.
User completes core action in the product (eg: creates proposals, signs proposals).
Non-product engagement actions are actions that the prospect performs outside of your product, such as:
Downloading a whitepaper or industry research.
Registering for a webinar.
Visiting your pricing information, FAQs, or documentation pages.
One temptation to avoid is giving a score for very trivial actions like visiting your website, opening drip emails, clicking on links in emails and so on. These actions require very little time investment from prospects and are more often than not, driven by curiosity.
The higher the PAIN (or interest), the higher the time investment a prospect is willing to make. When it comes to drip email campaigns, the primary goal for the drip email is to bring a prospect back to your product and entice him/her to take an action.
Track customer interactions with your product as early as possible
As we discussed in the previous section, lead scoring becomes more important when a company has already figured out product-market fit and is ready to find a scalable process to reach its target audience. However, tracking customer interactions and engagement early can pay handsomely in the future. When you finally decide to implement lead scoring and decide which score needs to be assigned to which engagement/action you will have some data on which to conduct correlation and regression analysis.
The speed with which leads are re-scored is critical for timely and targeted outreach from your sales reps.
For example, at RapidMiner lead scoring happens overnight so that the next morning sales reps can reach out and offer targeted resources like documentation, tutorials or webinars based on something that user has done in the product. For RapidMiner the short term goal is to get this overnight re-scoring process down to an hour.
What if you have enough data for machine learning using correlation and regression analysis?
Correlation and regression analysis are the two most popular techniques that will help you identify engagements and actions that lead to a higher probability of your prospect becoming a customer. Once you have this data, you can create a list of actions/engagements from highest to lowest probability-to-close. Out of this list you can pick actions/engagements that increase probability for any prospect to become a customer.
Most accurate and targeted profile data can lead to an SDR-ready score with no or limited behavioral score. For example, if you are selling a SaaS product for HR and a VP of HR at Salesforce signed up for a trial but hasn’t done anything inside of the product, you want to follow up with this prospect as soon as possible based on potential customer LTV, and your lead score should reflect this. But tune this approach in the way that only about ~5% of all leads scored on profile data only automatically hit a SDR-ready score. The prospect has to be a 100% match from a profile perspective, and I would suggest giving a score if this prospect is also from a company/account that you are targeting in your outbound sales.
Let’s use Pandadoc to showcase examples of good behavioral scoring:
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Examples of bad behavioral scoring:
Keywords,
Email clicks,
Website visits.
ASIDE: I strongly believe that email engagements should not add to the score but drive to product where score can be improved with increased engagement.
How to select what score to assign for each engagement action:
Conduct simple correlation analysis
Put together a list of actions that you record in your product and website along with profile data. Conduct simple correlation analysis to identify what factors impact deal-closing the most.
Prioritize engagement actions based on probability to close
After correlation analysis, you should have a list of actions with corresponding probability.
Assign score to engagement actions
This is the easiest way is to turn probability percentages to numerical scores. For example, when your prospect creates a doc in your app, it increases probability to close by 25%. So, just assign 25 lead scoring points to every prospect who completed this action.
Why ‘Velocity’ and ‘Pattern Analysis’ are important factors in lead scoring
To help explain what we mean by velocity when it comes to lead scoring and nurturing let’s look at a couple of prospect examples:
Example 1: Prospect A
Prospect A has signed up for your product free trial. This prospect received a high profile lead score. But besides getting to the product dashboard, the prospect has not engaged with your product. The next time this prospect returned to your product was 2 weeks later, and while engaged with the product he/she reached an SDR-ready score.
Example 2: Prospect B
Prospect B on the other hand not only signed up to your free trial but also completed onboarding and became an ‘active user’ by completing actions that lead to delivery of the 1st unit of value (often described as the ‘Aha’ moment).
Both, Prospect A and Prospect B reached SDR-ready scores, but with one significant difference. Prospect B has completed the path from 0 to SDR-ready in one day and for Prospect A this took 2 weeks. Prospect B had a higher velocity and this usually indicates that this type of lead is experiencing higher pain and more interest in a product. This lead therefore requires, and justifies a more immediate response from your SDR team.
LEARN MORE: please do not confuse lead scoring velocity with what Jason Lemkin calls Lead Velocity rates: Why Lead Velocity Rate (LVR) Is The Most Important Metric in SaaS.
Velocity = (Profile + Behavior)/Time
Higher velocity leads to a higher score.
Attribution vs. Pattern Analysis (first-touch vs. last touch)
There is tons of information written on the first touch vs last touch attribution. But let’s take a minute to share a quick reminder about what attribution is. Attribution is the process of identifying a set of actions by a user/prospect that impacts desired outcome. In our particular example of lead nurturing, we can use the desired outcome of becoming SDR-ready or converting to a paid customer.
While we as marketers worry about how to attribute first and last touch, we forget that pattern is the most important aspect that needs to be analyzed when it comes to uncovering conclusions about how customers buy.
As an ex-athlete, I will give you a track and field analogy to illustrate my point. What has more impact on an athlete becoming an Olympic champion? Is it his or her first practice or the last? You could argue that people get into sports through inspiration, ie. the first race argument, or you may say that you can’t win the Olympics by skipping the last warm up before the race. While both these arguments have truths, it is the practice patterns over a long period of time that correlate most with successful performance. The same is true for Joe Girard’s greeting card campaign I highlighted earlier. It is a regular pattern that leads to success, rather than the first or last card received by his customers and prospects.
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