3 Things Demand Gen Teams Can Learn from Sales

Guest blog post from our friends at Clearbit

A demand generation manager must meld several operational roles (marketing, automation, analytics) into one core competency—increasing conversions, and ultimately, revenue. They’re the “growth hackers” of B2B marketing, working across disciplines to solve problems.

Just as growth hackers seek the viral loops that will skyrocket their growth, demand generation teams look for opportunities to automate and systematize the customer life cycle.

In practice, that’s meant that demand generation teams have been much more similar to marketing teams than sales teams. But from tools that automate formerly clunky processes to prospecting methods, there’s quite a bit that demand generation teams could take away from the techniques that sales teams are using to great effect today.

1. Prospect like Socrates

Asking questions is the most fundamental skill that salespeople need to be great. Steli Efti at Close.io has more than a half-dozen points he asks himself when talking to a new potential buyer — if he doesn’t know the answers, he figures it out by questioning the prospect:

  • How much value can I add to this person or organization?
  • How will the value I add be quantified?
  • How important will supporting this prospect be?
  • What are their overall wants and needs for my product?
  • What size is this deal and what is their budget?
  • How much additional usage will our product see as a result?

If you don’t know all of these angles on the deal you’re trying to close, then “there are all kinds of ways” that “quoting them a price too early can backfire,” according to Steli.

Extend this logic a bit further back in the customer life cycle. Imagine if your sales reps didn’t have to think through these kinds of questions at all.

Your job in demand generation is not limited to bringing in more traffic to your website. You have to make sure you make that traffic count. To do that, you want to have a clear sense of who exactly you’re generating this demand in.

There are many ways to understand your target customer base, but the old-fashioned way is still the best and most reliable. Pick up the phone, ask questions, listen, and learn. Don’t treat the Socratic method of prospecting as a one-off process that your sales reps have to do to ink deals. Understand your potential customers, their needs, and the amount of value you can bring their organization before the sales call, and the leads you take to your sales team will blow their minds.

2. Use Tools To Reach Peak Efficiency

Smart sales teams have fully embraced automation. Those doing it right are consistently reaping great prospects and warm leads while doing a fraction of the work they used to do.

Today, sales teams with developers are going all-in on Clearbit and MadKudu, who make a series of APIs designed to generate customized lists of leads, qualify them, score them, enrich them, and send them into CRMs.

However, if you don’t have development resources, the easiest way to get started is with Clearbit Connect, a Gmail extension that works like Rapportive used to. It lets you look up the contact information for virtually any employee at any company, segment by role, and see a large swath of company information at a glance.

Here’s an example of the kind of information you can get—name, address, company, role, title, site, social following, and more all from one request:

flowc

To use Connect, just install the Chrome extension and it’ll appear as an option inside your Gmail dashboard:

Demand generation isn’t just about bringing in more leads or helping your sales team succeed—it’s about improving results at all stages of the funnel and driving growth. And there’s no better way to do that than with data.

Find your best customers. Understand who they are, inside and out. Then clone them.

3. Automate & Customize

Cold emailing may seem like a foreign concept if your job is demand generation, but with a refreshed technique it can be a hugely powerful tool for testing new markets and validating hypotheses about who your customers are.

First, you need the target markets you’re trying to reach, the roles and titles of the people that can get value out of your product, and a list of emails.

With a tool like Customer.io, you can take that information and turn it into personalized emails built on liquid tags—little snippets of code that, like variables, let you input whatever information you want.

That means rather than force yourself to rewrite new emails all the time, trying desperately to make them sound fresh, you can create personalized, evergreen emails that actually work. Here’s an example of an email that Customer.io posted on their blog:

This automated-yet-customized approach to the welcome email is how top sales teams are heating up their cold leads and optimizing their pipelines. The power of this technique is even greater, however, when taken in the broader context of a demand generation team’s job.

You’re not just going to get good click-through rates—you’re going to bring in qualified leads who understand the value of what you’re offering.

Top, Middle, And Bottom Of Funnel

Demand generation teams are responsible for one of the B2B startup’s most important objectives—growing inbound awareness, conversions, revenue, and growth.

To reach peak performance, they need to look across all departments and disciplines for the latest and greatest methods. These are three that I think are super valuable—let me know what you think in the comments below. What demand generation processes do you use at your company? What processes do you wish you used?

Segment for CMOs

SaaS marketers are not usually engaged in the day-to-day engineering work. I’m no exception.

A few days ago I asked Sam Levan, our CEO, “what exactly are we doing with Segment?” I had visited Segment’s landing page but didn’t really get the value – it reminded me of the “middleware” concept IT departments have been talking about for years. Sam I talked about Segment and I finally understand it.

Since other CMOs might have similar questions I decided to write up our conversation as an interview. Hope it helps you!

Me: Pretend I’m a CFO, CSO, CMO, or CEO and not involved in day-to-day product development. What value does Segment provide to our business?

Sam: Engineering is the scarcest resource we have. Segment makes our developers more efficient and allows us to deliver value faster. Saves us time and money.

Me: Ok. That’s simple enough. How?

Sam: Like every business we need a set of third-party tools like email marketing, sales, help desk, and analytics to name a few. Sending our data to each service individually takes a lot of time to setup and support. By sending our data to Segment we can re-distribute it to ANY of these services almost instantly.

Segment maps data between our database and these applications.

Me: Can you give me an example?

Sam: Sure. Suppose you want to test out a new tool like Mixpanel.

Without Segment you have to ask the engineering team to integrate with Mixpanel’s API. This starts with a scoping meeting … which leads to a priority discussion … which leads to acceptance testing … and updates when we change our product … or they change their API.

As the CEO I don’t want our developers to have to spend time on this type of work – we need to be improving our product and delivering value for customers, not messing around with APIs.

With Segment you just flip a switch and you can start testing Mixpanel. It usually isn’t quite that simple but much, much easier than the alternative.

Me: Ok, I get it. So what is the downside of using Segment?

Sam: Well obviously Segment isn’t free, so I guess if you only need 1 or 2 tools it may not be worthwhile.

But to be perfectly honest we would probably use Segment even if we only used 1 tool in their platform. All they do is APIs and data mapping and they do it better than anyone.

Me: I’ve used Zapier before to automate rules and share data between apps like Wufoo and MailChimp. How is this different from Segment?

Sam: We don’t use Zapier and I’m not as familiar with their product. But my understanding is that Zapier is more popular with small and non-tech businesses and makes it easy to map fields between products like email, forms, file-sharing, etc.

It seems to be more geared towards solving a specific data-sharing problem between specific applications. Using your example, sharing the data you have on Wufoo with your MailChimp account.

This is very different from exposing all of our internal business data to be shared across different applications.

Me: Ok. Switching gears a bit … MadKudu is also an Integration partner with Segment. What is the value for us of this partnership?

Sam: A no-brainer. Segment makes it easier for potential customers to try MadKudu and send us their data.

Me: I guess that seems obvious … but doesn’t this also mean that MadKudu’s customers could quickly switch to a competitor? Isn’t this how companies like Microsoft became dominant? By building proprietary interfaces and locking-in customers?

Sam: It doesn’t really work that way anymore. Every SaaS company like MadKudu needs to constantly re-sell its value every billing cycle – otherwise customers cancel. I don’t know a single SaaS founder who thinks she can lock-in customers.

Even if that were the case we also benefit from lower support costs by being on Segment since it reduces the costs of supporting our own API.

Me: Ok. Now just to add to the confusion … Segment is also a MadKudu customer?

Sam: That’s right. We use data science to help Segment’s sales team identify their most promising leads and turn them into customers.

Me: Good grief now I know why I find our status meetings so confusing. Let me see if I get this straight:

MadKudu is a Segment customer.
MadKudu is also a Segment Integration partner.
Segment is a MadKudu customer.

How the heck do you guys keep track of all of this?

Sam: (laughs) Yeah, it makes for some confusing conversations. Usually it is obvious from context.

Me: Ok, final topic. You were a data scientist for years before starting MadKudu and have consulted for hundreds of companies. Can you explain the value of Segment’s new data Warehouses solution?

Sam: Data warehouses have been around for decades but have struggled to live up to their promise. One major reason is that getting all business data into one place is a huge PITA.

Moving the data and building APIs is part of the problem – basically the same challenge we discussed earlier about building and maintaining multiple APIs. An additional challenge is understanding what each field means and mapping it to the same logical entity in another application.

This is why a lot of “data science” projects have traditionally struggled to get off the ground – it just takes too much effort to get everything setup and organized in the data warehouse.

Me: Ok, I’ve been a part of projects like this. I can recall months of meetings with analysts and business owners building data dictionaries, mapping fields. It basically sucked.

Sam: Exactly. This is the power and promise of Segment’s data Warehouse. It allows an analyst to quickly run cross-application SQL queries to get answers to critical business questions.

Of course you still have to know what the fields and data means – it just helps overcome a main impediment from getting these type of projects started.

Me: Thanks Sam, I think I get it. Suppose another SaaS company CXO is thinking about using Segment – how can he or she contact you?

Sam: Just email me at sam@madkudu.com. I’ll be happy to jump on a call.

Why are your job descriptions so boring?

We’re not that picky about who we hire – we just need a qualified warm body to fill the chair.
— Said by no startup CEO … ever

Things are going great here at MadKudu. We hit our growth milestone and our recent pivot allowed us to sign up awesome customers like Segment, Contactually, and Codeship.

But … alas … there is little time for celebrating in a fast-growing startup because success just creates new problems. We now have the #1 challenge facing every startup with traction: hiring initial team members.

We decided to get serious about building our team and invest time attracting the right people. We created a “careers” page and and started writing job descriptions.

I’m tasked with hiring our first full-time marketer. Before writing the job description I did – what you do – read a few dozen open job recs for ideas.

I’ve got > 2901 competitors for the right candidate

Shocker! We’re not the only startup trying to fill a marketing role – actually there are open marketing positions at 2,901 other startups on Angel List alone.

My challenge is getting the attention of the most talented people – those who could compete for jobs at companies like Medium or Slack.

At first I found this prospect intimidating – that is, until I started reading the job descriptions.

Most startup job descriptions are horrible

Generic. Repetitive. Uninspiring. BOOOORING.

Most look like they were written by Dilbert & edited by Catbert.

Don’t believe me? Spend a few minutes browsing jobs on Angel List. Go ahead, I’ll wait.

See what I mean?

“need … excellent communications skills … a team player … “

If “hiring” is a startup CEO’s biggest challenge why do so many put no effort into writing job descriptions?

I attempted to do better

I stopped looking to job descriptions on Angel List for inspiration. Instead I imagined myself talking to an incredible person – someone who has lot of choices – and trying to help her decide if the job is for her.

After a few hours I finished Want to be our Head of Marketing? It could be a lot better but it was the best I could do in the time I had.

How to write a job description that attracts the candidates you want

Sell. The. Job.

The people you want to hire have options. They are probably not even looking for a job. You need to sell the opportunity to work at your amazing company.

Write TO your ideal candidate. Tell her about your company and why you need her help. Make the opportunity sound so compelling that a friend would forward the job description to her.

Suppose your company keeps growing and she does amazingly well. What would be her opportunities? Sell a prospect on your vision for her awesome future.

Skip the superfluous bs

Everyone thinks they are …

… good at communication,
… a team player,
… a self-starter,
… and on … and on.

So don’t include these words in your job description – they just fill up space and make you sound like everyone else. In the history of job searching nobody has ever read a job description and thought …

“hmmmmm… this job sounds good but they don’t mention teamwork … guess I’ll pass since I won’t get to improve my teamwork skills”

good communication skills? Well that’s not me – I’m boring and ramble a lot. Better not apply for this job.”

Say what the job ISN’T – and who will HATE it

One of the easiest ways to attract the right candidates is to say what a job ISN’T. Talk about the pros and cons of your company.

For instance, Silicon Valley is full of legendary companies that “started in a garage.” Sounds cool AFTER the company is successful – but what is it like actually working in a garage? No windows … or air conditioning … plenty of dead crickets … not exactly an inspiring workplace for most people.

Except WE’RE NOT most people – and we want to attract people who share our values. We’re currently working in a shack we affectionally call “le donjon”. (yes, that’s French for “dungeon”).

donjon - 1

It has a whiteboard, a few desks, and lots of coffee stains. In the center is a little bell we ring whenever we close a new deal. I fell in love with the donjon the first time I saw it.

The right candidate will feel the same way.

Give examples. Tell stories.

Why are you hiring? What problem are you trying to solve? Can you describe a recent problem you need the ideal candidate to solve? Can you give a real example?

The right candidate will see the problem and think “I can do that!”

Yes, this takes more time – up front

Hiring can feel like a big distraction when you’re trying to do “real” work. It is much quicker to cut-and-paste someone else’s job description and hope it works out.

Invest the time selling your opportunity to the right candidate – you’ll get better results and save money in the long run.

Want to work in our beloved donjon?

Sweet – you’re one of us. I hope you’ll check out our open positions and apply.

 

photo credit: star5112

Startup vs bigco: the best career option in data science

Our fearless leader and CEO Sam Levan recently spoke at Galvanize in San Francisco about data science careers. A common student question is, “is a job at a startup or a big company better for data scientists”?

At MadKudu we’re in a good position to answer it – the 5 of us have more than 25 years experience in data science. We’ve built everything from the world’s largest fraud detection system to quick hacks in Google sheets.

So what is the best career option for an aspiring data scientist? Google or HotNewStartupWithCuteBlueDoggyMascot.com?

Before answering I’m going to share a secret about being a data scientist. Whether you work at a 2-person startup or CapitalOne, there is one attribute which best predicts your probability of success.

The super-duper-secret to being a great data scientist is …

… wait for it …
…… wait for it ……
……… WAIT FOR IT!!! ………

Data science is a SOCIAL skill

That’s it. That’s the big secret. Your career as a data scientist will be defined by how well you can communicate, write, listen, organize, lead, and empathize.

Do you need hard skills? Of course. You can’t do the job if you don’t know how to use R, Python, or MatLab. You have to know how to measure the statistical significance of your results.

But unless you fancy yourself the next Will Hunting, being a brilliant Python coder won’t make you any more effective than a good one if you can’t work well with others.

Data scientists don’t work alone

Data scientists try to solve business problems with data – an iterative activity which requires working with cross-functional teams.

Suppose you’re a knowledge engineer working at a bank. On any given day you will need to:

  • Talk to regulators about the riskiest type of criminal activity.
  • Help analysts understand customer data and what behavior the bank can track.
  • Ask (beg?) the operations to pull new data sources for you.
  • Testify in court.

The most effective data scientists are team players who make everyone else more effective.

If you want to be a lone hero data science isn’t for you.

Communication beats code

Every morning I read Nate Silver’s analysis on FiveThirtyEight. Is Nate the world’s greatest statistician?

Of course not. Nate Silver’s brilliance is his ability to help us understand how data answers important questions in politics, sports and life.

We try to deliver the same value in our work.

This is – by far – our most popular blog post. Why was it featured on Growth Hackers, Hacker News, and Growth Hacking digest? It wasn’t a great study – just 9 sample companies. We didn’t build any amazing models – everything was done in Google Sheets.

As an example of data science it is … meh. Our customers loved it because we helped them understand what the data means and how they can use it to solve business problems.

Any idiot can have an opinion. Lots of smart people can compile numbers.

Few people can help others understand why data matters and what they should do – be one of them and you’ve got the world at your feet.

Data science careers: Startups vs Bigco

Back to the students’ questions:

“What is the difference between being a data scientist at a startup vs a Bigco?”

Specialist vs generalist

The biggest difference between being a data scientist at bigco vs a startup is your degree of specialization. At a bigco you have the opportunity to work for months and years on the same problem.

Are you excited about spending 2 years creating the world’s greatest recommendation engine for Facebook? Do you like doing primary research? Becoming an expert in building models to solve 1 problem? Becoming a master in R, Python, or MatLab?

That’s life in a bigco. I know Knowledge Engineers who spend a career detecting violations of stock market wash sale rules.

Startups? Ha ha ha!

You won’t know what you’re working on next week, much less next year. You’ll be spending your time helping marketing, sales, and product teams answer basic questions. Since you’re constrained by data and time you’ll do much of your work in spreadsheets or SQL.

It isn’t uncommon for a data scientist at a startup to be juggling 5 different problems at the same time. Your expertise will be your ability to quickly acquire and apply new skills – fortunately this is a great skill to have.

Support system

Unless you work at MadKudu, you may be the only data scientist at your startup. Your colleagues may not understand what you do or how you can help them. You may have to define your own objectives. On your first day you might be told to “go help the sales team find the best leads”. Does this terrify or excite you?

At bigco you will have a support system. Your boss will tell you which project you’re working on. More experienced data scientists can answer your questions. Have a problem? Ask your boss – that’s what she’s for.

Getting dirty

Data science textbook examples are fairy tales. In 20 years I’ve never encountered such simple problems. In the real world:

  • Simply getting the data is HARD.
  • People don’t agree on what columns actually mean.
  • Everything changes while you’re doing analysis.

Bigcos have teams of people to help solve these problems: server-side developers to populate the data warehouse and business analysts who write data dictionaries.

At startups … well … it is probably up to you. The developers are all too busy finishing the next release and supporting customers to run SQL queries. You have look in the code to see how the product generates the account_activated event in Mixpanel.

Risk

At bigco you’ll have a nice salary, 401(k) plan, and benefits. You’ll work a little harder the 3 months before bonus time so you can get that new car. It feels safe – but is it?

Life at a startup is the opposite. Part of your compensation will potential, unknown upside from stock options. Will you have a job next quarter? It depends on whether the CEO can close the B round. It feels risky – but is it?

I’ve worked for the world’s biggest, most stable employer and at any-day-we’re-dead-lets-start-stealing-office-supplies startups. I’ve had friends lose $500K starting a company and others struggle for years to find a job after being laid off. Here is how I think about risk.

Working for startups is very risky in the short run but incredibly stable in the long run.

The stability of bigcos comes at a price – you develop fewer skills, build fewer relationships, and don’t get regular experience marketing yourself.

This risk is particularly true for a data scientist who can get stuck working on the same problem … with the same tools… and the same people … for years. A major industry downturn can be economically devastating when all companies in a sector are laying off employees.

Both bigco and startup careers have risks – you just need to understand the risks you’re taking and be smart about managing them.

What’s best for you – bigco or a startup?

After reading this post you’re probably more confused than ever – because there is no one answer.

My #1 piece of advice is to go out an interview with big and large companies. Meet the teams and ask lots of questions.

What is the #1 problem you would be solving? Why? Who else is on your team? What do they say about the problem? What tools would you be using?

It’s the only way you’re going to see what is best for you.

Best of all it will give you an opportunity to work on those social and communication skills – the most critical ones for your career in data science.

Photo credit: goingfar.org

How to accelerate SaaS sales with behavior-based conversions (part 2/3)

This is Part 2 of a 3-part series on how to build a Behavior-based conversion strategy for your SaaS product. Here is Part 1.

This post was a lot of fun to write – I hope you enjoy learning from the insights as much as I did.

In a previous post, we analyzed conversion data from 9 SaaS companies and concluded that optimizing conversions based on behavior is more effective than using an X-day trial for every customer.

IOW, starting the same 30-day trial for every new customer isn’t as effective as varying the buying incentives based on what the customer has done – what we call a behavior-based conversion strategy.

3 steps to begin a SaaS Behavior-based conversion strategy

30-day trials are a great way to start a SaaS company. They are easy to setup and the trial expiration creates a “buy now” urgency. But using the same 30-day trial for every customer isn’t as effective as putting

… the right purchasing incentives to
…… the right customers at
……… the right time.

In Part 1 I described the 3 small changes required to get started.

  1. Don’t begin a timed trial until a customer completes Activation events – those a trial customer must complete or they won’t convert.
  2. During the trial use marketing and sales to get customers to complete Engagement events – the key value-creating used by most of your customers.
  3. Create early conversion and upsell incentives for customers who complete Acceleration events – “delight” events which indicate customers are getting tremendous value from your product.

Activation events are “NECESSARY but NOT SUFFICIENT” – customers who don’t complete them don’t buy but completing them doesn’t lead to a conversion.

Engagement events are “NECESSARY and SUFFICIENT” – customers who don’t complete them don’t buy and completing them increasing conversions.

Acceleration events are “SUFFICIENT but NOT NECESSARY” – customers who don’t complete them might buy anyway and completing increases conversions.

table

In this post I’ll cover how you can find Activation, Engagement, and Acceleration events and build your Behavior-based conversion strategy around them.

Use the Behavior conversion matrix to find your Activation, Engagement, and Acceleration events

The Behavior conversion matrix will help you instantly identify these events based on how your customers are actually behaving.

First let’s use your data to categorize events based the theoretical “Necessary” and “Sufficient”.

Categorizing “Necessary” and “Sufficient” events in your data

Identify ~50 relevant boolean customer events and calculate the following:

  1. The percentage of time the customer DID the behavior and converted to a paying customer. This is the probability of conversion if TRUE – the degree to which an event is “Sufficient”.
  2. The percentage of time the customer DID NOT do the behavior and converted to a paying customer. This is the probability of conversion if FALSE – the degree to which an event is “Necessary”.

(I’ll show you step-by-step how to calculate these in Part 3 of this series)

Here is a summary of how these conditions relate:

summary table

Create the Behavior conversion matrix by calculating event probabilities

By taking every customer event and calculating these probabilities we can now identify which ones are Activation, Engagement, and Acceleration events. This is most easily illustrated with an example.

Imagine a SaaS small business accounting application. Most customers use it for basic expenses tracking and reporting. The most valuable customers also find and hire a tax accountant through the application.

We can calculate the conversion probabilities for each event as follows:

tables

Which can be plotted in a bubble plot as follows:

On Average, 3.1% of all trial customers convert to paying customers. The Behavior conversion matrix allows us to see how different events relate to the Average.

signed_up is – not surprisingly – the simplest example of an Activation event. A customer who doesn’t sign up can’t possibly pay, but since every customer signs up the event doesn’t improve our chances of converting. Thus the probability of converting if signed_up == FALSE is 0, while probability of converting if signed_up == TRUE is 3.1% – the same as the Average user.

connected_bank is a more interesting Activation event. Very few customers who don’t connect their bank to the application convert – perhaps they love entering data by hand for some sadistic reason – but taking this step doesn’t improve their conversion rates.

entered_expense is an Engagement event. A customer who doesn’t complete has <1% chance of converting while those who do are 4x likely to convert (12% / 3.1%).

hired_accountant is an Acceleration event. Customers who find an accountant through the app will almost definitely buy, but those who don’t still often will.

Accelerating SaaS Sales with the Behavior Conversion Matrix

Identifying your most important conversion

After plotting dozens of events your Behavior conversion matrix will start to look like this:

graph2

The most important events are those distant from the Average User Baseline – you will get the most impact by organizing your onboarding around them.

In 3 Steps – from 30-day trial to Behavior-based conversions

Suppose our simple SaaS accounting app currently starts all customers on a 30-day trial at signup. Here are a few simple changes to implement Behavior-based conversions.

  1. Begin the trial after the customer completes Activation events

Continue to nurture a trial customer until connected_bank is TRUE and then start the 30-day trial – until he takes that step he isn’t in a position to buy. He doesn’t know the value of the product and a trial expiration won’t create a “buy now” incentive. Connecting a bank account to the app is a Necessary but NOT Sufficient event for conversions.

  1. Create incentives to complete Engagement events during the trial

During the trial use marketing automation and inside sales to get the customer to complete entered_expense. Offer workshops on cost accounting or have Customer Success reps help them choose expense categories. Offer trial extensions and continue to convert these customers for up to 6 months following trial expiration. Entering an expense is a Necessary AND Sufficient event for conversions.

  1. Create early conversion and upsell incentives for customers who complete Acceleration events

Upsell customers to an annual plan or offer early-conversion discounts after the event hired_accountant. These are the most promising prospects and many will convert early. Hiring an accountant is NOT Necessary but is a Sufficient event for conversions.

In Part 3 of this series I’ll walk through a complete end-to-end example showing how derive the probabilities above. We plan on releasing it in mid-April – if you would like to see it sooner please tell us on Twitter or comment below.

Like our writing? You’ll love our product. Try MadKudu for free.

 

Photo credit: Timothy Neesam

How to accelerate SaaS sales with behavior-based conversions (part 1/3)

In a previous post, we analyzed conversion data from 9 SaaS companies and concluded that optimizing conversions based on behavior is more effective than using an X-day trial for every customer.

We call this approach “behavior-based conversions”. In this 3-part series I’ll explain the strategy and show you how to implement it.

Subscribe to our newsletter I’ll send each to you as I write them.

The pros & cons of your free 30-day trial

Easy to setup and manage

X-day free trials are effective because they reduce the cost of sales and create an artificial purchasing incentive. Customers try your products risk-free using their own data and you have the opportunity to delight them.

The trial deadline creates an artificial “buy now” incentive that drives conversions.

X-day trials are easy to create and manage with payment APIs like Stripe and Recurly. Just start the trial when customers sign up and try to get them to pay before it ends.

But not optimal if you treat every customer the same

X-day trials are not optimal because customers will convert at different rates. An expiring trial isn’t the best purchasing incentive for all of them. Some would pay right away, some need a few days, and some take 6 months.

Looking for the “optimal” trial length is a fool’s errand – there isn’t anything magical about a 14-day, 30-day, π-day, or 65.5890987899875476-day trial.

Your customers are all different.

Behavior-based conversions accelerate sales by treating customers differently

A customer may need 2 months of nurturing before buying a product – if so, giving him only 14 days risks losing a sale. Contrarily, if a customer is ready to buy at signup we lose 30 days of revenue by asking him to pay at the end of a trial.

Behavior-based conversions get

… the right purchasing incentives to
…… the right customers at
……… the right time.

Creating your behavior-based conversion strategy

Don’t begin timed trials until a customer is able to make a buying decision

Timed trials are great for creating artificial purchasing urgency – otherwise customers don’t have an incentive to “buy now”. But they only work if customers understand the value they get from your product.

Every customer needs to take a few early, critical actions before she can even begin using the product – signing up, connecting her Gmail account, etc. Before taking these actions she isn’t in a position to buy because she doesn’t understand the value your product would create for her.

These are Activation events – the critical steps that must happen before your customer begins to see the value of your product. Customers who don’t complete Activation events rarely buy – more importantly the deadline of your timed-trial won’t motivate them to complete Activation events. (this is easily provable as you’ll soon see).

Nurture your customers through marketing automation and sales until they complete Activation events – THEN start your timed trials.

Activation events are “NECESSARY but NOT SUFFICIENT” – customers who don’t complete them don’t buy but completing them alone doesn’t creating enough value for customers.

During the trial focus on creating initial value

Your product performs a few key value-added activities for customers. These are usually the core features of your product, those most of your paying customers use. You can identify key events in the customer lifecycle that indicate a customer is getting basic value from your product – these are Engagement events, or what Lincoln Murphy calls First Value Delivered (FVD).

Engagement events are NOT “delight” events – those associated with the few customers who are getting the most possible value from your product.

Try to get customers to complete Engagement events during your timed trial and use the trial deadline to create an artificial purchasing incentive.

Continue trying to get customers to complete Engagement events past the trial deadline. Offer trial extensions, workshops, sales calls, etc. for up to a year. Don’t simply toss these customers back into the same “nurture” bucket as those who haven’t completed Activation events.

Engagement events are “NECESSARY and SUFFICIENT” – customers who don’t complete them won’t buy but those who do are getting value from your product.

Accelerate conversions for delighted customers

Some customers instantly understand your product and quickly get tremendous value from it. These customers are ready to buy now and you can accelerate your SaaS sales by trying to get them to convert before a trial ends or upsell them.

You can identify these customers by those who complete Acceleration events – activities associated with your most delighted customers.

Offer additional purchasing incentives to customers who complete Acceleration events to get them to convert early.

Examples of purchasing incentives are coupons, annual contracts, or special offers. Or simply tell them to enter a credit card now so they don’t lose service.

Acceleration events are “SUFFICIENT but NOT NECESSARY” – customers who complete them are getting tremendous value but those who don’t might buy anyway.

So … are any real SaaS companies doing this?

Absolutely. In fact, you’re probably already doing some behavior-based conversions by extending trials or using your inside sales teams to close customers faster.

You will find every one of these tactics used by SaaS companies – we just put them together into a unified strategy we call behavior-based conversions.

Summary: Activation, Engagement, and Acceleration events

Here is a summary of the 3 events and how to use them.

table

How to find your events

You’re now wondering, “how do I identify Activation, Engagement, and Acceleration events?”

Your sales funnel diagram won’t help – it only shows what you think your customers should do, not what they actually are doing.

In Part 2 of this post I’ll show you how to identify Activation, Engagement, and Acceleration events by looking at your data.

You don’t need to be a data scientist. You won’t have to learn what “entropy” is and how it differs from a canopy.

You just need Google Sheets, 3rd grade math, and an open mind.

Part 2 of this post

Now the real fun begins – read part 2 of this series.

 

Photo credit: Mark Freeth

Be a hero to your sales team with this Slack hack

Let’s face it – marketing and sales operations can be thankless work. When we generate quality leads we’re “doing our jobs”. We toil away making slow, methodical progress – progress often unseen by the rest of the company.

Would you like to be the hero for a change?  To make a, quick, high-visibility impact with your sales team?

Of course you do! If so, give this hack a try.

With a bit of coding you can create a “hot” lead notification using Clearbit and Slack.

It’s a fun project for a Friday afternoon or a hackathon – we know from experience that sales teams love it.

Use Clearbit’s sweet API to learn more about your leads

What does your sales team do when gophillyeagles998@hotmail.com signs up for a 30-day trial?

Nothing! Your lead-scoring system ignores hotmail accounts.

Now suppose gophillyeagles998@hotmail.com is CEO of a 100-person company in Chicago (where he is the only Eagles fan) and he’s using a personal email account to test our your app.

ooooooops! Your sales team just missed an opportunity to engage a white-hot lead.

Clearbit helps solve this problem. Send Clearbit’s API an email address and it returns information about a lead such as:

  • Name
  • Location
  • Title
  • Company size
  • Industry

With a bit of hacking you can use the API to build a lead notification system in Slack.

Why you’ll be a hero for the VP of Sales

Slack “interrupts” sales reps to call immediately

Most sales teams know an ideal time to call a qualified lead – usually within minutes of signup or after a specific event. Since we all live in Slack they can get an instant notification to take action.

Miss fewer good leads who use a free email account

Some qualified leads will test your product with a personal email account. Identify and give them some extra love to make sure they have a good experience.

Qualify leads based on company size, role, etc.

Sometimes you only have an email address. Sure, you can manually look up businesses based on email domains or ask for additional information but this is a hassle.

With Clearbit’s API you can quickly qualify leads on simple metrics like “send me a Slack notification if company size is greater than 30”.

Add “conversation starters” context

Good sales reps look for anything relevant to get closer to a lead or start a conversation. With a Slack notification it is all there at their fingertips.

“You’re an Eagles fan too? Man, sure glad Chip Kelly is gone. Must be lonely there in Chicago”.

“Since you’re in manufacturing I’m guess you signed up to take advantage of our partnerships in Asia, correct?”

How it works

Send Clearbit an email address from your app (or a separate code hack), determine if the lead is qualified and post to Slack

slack_notify

How to setup

Some custom dev work – but don’t be afraid

Ok, ok, I know. Developer time is the scarcest resource at your company and they’re already overworked. This hack does take some custom dev work but:

  • The Slack and Clearbit APIs are well-documented and written by developers for developers – speaking as one myself, we like working with these type of tools.
  • Even junior level server-side developers can do it. It makes for a fun Friday project or hackathon.
  • You can give developers some very specific requirements about what you want by following the steps below – this will save them a ton of time.

If all else fails just find a bored developer and learn her favorite Starbucks drink – you’d be surprised what you can get done with a nice word and a $5 Latte.

Step 1 – Sign up for a Clearbit account.

They have a free version with limited API calls. The paid plans will pay for itself if you get 1-2 new deals/month from doing this.

Step 2 – Document which fields you want from Clearbit

Login and visit https://dashboard.clearbit.com/docs#enrichment-api and identify what Person and Company information you want in Slack.

Start with just a few of the most important ones – and understand that Clearbit usually only has a subset of this data.

clearbit api

Step 3 – Identify your threshold for qualified leads

Write down the qualification rules for leads.

e.g. “only create a Slack notification if metrics.employees > 50

Step 4 – Create a dedicated Slack Channel for the notifications

slack channel

Step 5 – Send this to your developer

Give your developer the Clearbit login credentials and the requirements you created in Step 2 and Step 3.

(And we’ll be oh-so flattered if you send her the URL to this blog post as well.)

Developer resources

In addition to the Clearbit and Slack APIs there is clearbit-slack on Github. You can also ask us any questions or suggest other resources in the comments below.

Bonus: User predictive analytics to include in-app behavior

This a simple approach for quickly alerting sales teams about qualified leads through Slack. It is a great way to get started if your sales team isn’t identifying and contacting leads quickly.

Add your customer’s in-app behavior to qualify leads better and arm sales with more customer data

Adding predictive analytics based on user behavior is far more effective – especially if you get >10 leads/day. We can do this for you. The benefits of predictive analytics are:

  • Better qualification based on what your best customers have done in the past.
  • More effective “call now” notifications the moment customers are ready to buy.

Predictive analytics does this more effectively than building and managing a pile of lead scoring rules. Best of all we can do this for you so you don’t have to hire a data scientist. And we can do this for you.

 

Did I mention we can do this for you?

30-day trial? 14-day? Freemium? Here’s why it probably doesn’t matter

2/29/2016 update – We’ve had a number of requests to expand on this post and provide examples of behavior-based conversion incentives. We decided to write a 3-part series on this topic. You can read the first one here

Whenever I launch a new SaaS product I obsess about sales and onboarding details.

Should I offer a free trial? How long?
Or should I have a free version with no trial (freemium)?

The blogs and books have opinions but most are based on limited data or anecdotes from one SaaS marketing team. Here at MadKudu we try to answer these questions based on data – how our customers are selling.

This week Erik, Sam and I tried to learn how trial length affects SaaS conversions and revenue acceleration.

Here’s what we learned.

Data from 9 representative SaaS companies

We selected 9 companies with different models and relatively clean data. We then identified every trial user who converted to a paying customer and grouped them by of days it takes to convert.1

Example

Here is a simple example you can copy to illustrate the process.

Screen Shot 2016-02-12 at 4.36.16 PM

2 days after customers sign up for this fictitious SaaS company 142 (87+55) converted, 142/305 = 47% of the total who will eventually convert.

Results

After looking at the number of daily conversions we graphed their accumulation relative to starting a trial and came up with the following.

Screen Shot 2016-02-12 at 4.47.23 PM

How to read this graph

This graph shows the rate at which a SaaS company converts trial customers to sales. For example, here is how evaluate the results for Company C:

Screen Shot 2016-02-12 at 4.57.56 PM

Company C offers a 30-day free trial – not surprisingly, most customers who decide to pay do so at the end of their free trial. But customers also convert before and after the 30th day of the trial. This graph illustrates the rate at which that happens.2

Sales acceleration

You accelerate sales (and get a high-five from Tomasz Tunguz) by pushing this curve up and to the left without sacrificing top-line revenue.

Screen Shot 2016-02-12 at 5.00.04 PM

SaaS companies accelerate sales to hit profitability faster. VCs like Redpoint’s Tunguz look for companies who can get customers to start paying sooner.

Observations

We studied relationships between trial lengths, conversion rates and models – the results surprised us.3

Screen Shot 2016-02-12 at 5.19.57 PM

Observation 1: It takes about 40 days to get 80% of SaaS conversions

It takes most SaaS customers a little more than a month to test out and purchase a product. This general rule seems to hold true regardless of trial length or whether a product has a free version.

This is … well … surprising.

For instance, look what happens when we isolated a SaaS company Freemium (G), 14-day trial (E), and a 30-day trial (C).

Screen Shot 2016-02-12 at 5.29.57 PM

Not surprisingly, freemium conversions are faster since customers can quickly choose to purchase the premium versions. And, of course, a 14-day trial accelerates faster than a 30-day trial.

But these curves converge when 80% of customers convert, around 40 days after a trial starts.

Observation 2: Half of SaaS conversions happen AFTER the trial ends.

A free trial creates artificial purchasing urgency. But there isn’t anything magical about the last day of a trial – some customers continue to convert at their own rate based on incentives or their perceptions of value.

Every single company we studied had customers who converted more than 100 days after signing up – most had customers who converted 6 months after signing up.

Observation 3: The “S” curve is the $ curve

Ok – this is cool. When we first noticed these results we assumed it was an error. It isn’t. Check out how similar the curves are between company B and C:

Screen Shot 2016-02-12 at 5.48.12 PM

 

Identical curve … different businesses.

Why? Both companies have 30-day trial SaaS products and similar pricing. But that’s where the similarities end.

They sell completely different solutions to different types of customers. One seems like it would have a much faster adoption rate than the other.

After a little investigating we have a hypothesis: both companies rely primarily on the final day of the trial to drive conversions.

In other words, both companies may be missing opportunities to accelerate sales by:

  • creating additional incentives to convert sooner, and
  • continuing to drive conversion after the trial ends.

That’s why we call the “S” curve the $ curve – we see opportunities for using predictive analytics to grow revenue by developing unique conversion incentives for different customers.

All of your customers are exactly the same – so use the same conversion incentive for everyone

See how silly that sounds? Obviously we suggest you do the very opposite.

The trial period isn’t magic – whether it is 14 days, 30 days, 177.6 days, or π days. Your customers will convert at different rates based on who they are and what they do.

Which … drum roll please … takes us to our conclusions …

My advice for accelerating your SaaS sales

Running A/B tests looking for the 1-size-fits all trial period might be a waste of time – there probably isn’t a magic number for all of your customers. Instead, try to optimize your conversion rates based on qualification and behavior.

Pursue post-trial sales

If you simply toss all post-trial customers into your “nurture” campaign you are almost definitely missing opportunities. Predict those most likely to buy based on qualification and behavior – target and pursue them aggressively for at least 90 days after your trial ends.

Or ask us to do it for you.

Create during-trial conversion incentives

The lesson from Observation 1 above is that your customers will convert at different rates. Identify behavior predictors and create incentives to convert them as fast as possible. This is especially true if your conversion curve fits the “S” pattern above.

Yes, we can also do this for you.

Optimize conversions based on value creation – not time

The end of a free trial only serves one purpose – creating purchasing urgency. The best time to create this urgency is soon after the customer generates value from this service. This isn’t as hard as it sounds – and I can prove it to you: try MadKudu for free – we won’t ask you to pay until we start creating real value for you.

Wasn’t that simple?

Epilogue: YOUR business is special

You’re probably wondering … what’s up with Company A?

Screen Shot 2016-02-12 at 5.57.44 PM

Why does it appear to do a lousy job at sales acceleration? Did Company A hire a bunch of monkeys to answer support emails?

monkey (Mixed-Breed between Chimpanzee and Bonobo) playing with a laptop (20 years old) in front of a white background

Nope. No monkeys.

Company A has an awesome product and knows their customers well. Their product is complex and has a slower adoption rate because a customer needs to integrate it into their infrastructure over time – that’s why they have a slow-and-steady adoption rate powered by converting and up-selling freemium users.

What works for them probably won’t work for you.

Every SaaS business is different and special, especially yours. Predictive analytics isn’t magic or a robotic solution – just the math we use to amplify what is already special about you.

Want us to write more posts like this?

We love doing posts like this but it also takes us a HUGE amount of time to look through and interpret the data. If you want us to write more like these please share it on Twitter and vote for it on Growth Hackers.

photo credit: Thalo Porter

 

Use predictive analytics to reduce churn by 20% in 2 days – with 3rd-grade math

Most SaaS companies have 3 misconceptions about churn:

  1. They don’t realize how much churn is costing them.
  2. They think they know why customers churn.
  3. They think predicting churn with data is too hard.

If you’re not using predictive analytics to prevent churn this hack will help reduce your churn by about 20%. It takes about 2 days of work over a few weeks and you can do it in Microsoft Excel.

We used similar techniques to help Codeship retain 72% of their at-risk users.

 

Download the spreadsheet to follow the example below.

You need to predict churn with data

Your customers cancel for lots of different reasons. Projects get scrapped. Users get stuck and bail. The key user takes a sabbatical to breed champion goldfish.

Quite often you can intervene before this happens and prevent it – but the primary predictors of churn are not always obvious.

For instance many SaaS marketers assume last_login_at > 30 days ago predicts churn. We almost always identify better predictors such as changing patterns in user behavior.

Let me re-phrase this point a little stronger:

If you’re not looking at data to predict churn you are almost definitely missing the fastest, easiest way to increase your MRR.

Why this hack is effective

You don’t need a data scientist. Or developer time.

As long as you have access to metrics in Mixpanel, Intercom, etc. even junior members of your marketing team can do it.

Credit card companies invest massively in predicting churn because slight improvements generate millions of dollars. You’re not Capital One – you’re a SaaS company. You don’t need know what “entropy” is to start predicting churn.

You don’t need need statistics

Can you add? This the only math skill you need. There is one equation but we’ve already put it into the spreadsheet for you.

If addition is too complex consider outsourcing to a 3rd-grader. They’ll work for peanuts (or at least cookies).

The results are immediately actionable

We’re going to start with the data you already have in your analytics or marketing automation platform – so you can use the results to send churn-prevention emails or generate alerts for your sales team.

Step-by-Step: find the best predictors of customer churn

Download the spreadsheet

Click here to download.

The examples are easier to understand if you spend a few minutes looking at the spreadsheet. I break down each step below.

PR Power! – our example company

I’m going to walk you through each step using examples from a fictitious SaaS startup called PR Power! we introduced in a previous post.

PR Power! helps media managers in mid-sized businesses do better PR by generating targeted media lists. Customers pay $50-$5,000/month after a free trial. Marketing Mark, the CMO, is charged with reducing monthly churn from 5% to 4%.

Step 1 – Identify predictors of churn

Try to identify predictable reasons why customers cancel.

Mark’s team spent a few hours looking at the last 20 customers who canceled and identified a few predictors. He also interviewed the sales and customer success teams about these customers.

They came up with the following events that are likely to predict why a customer cancels an account with PR Power!

Champion departs – Usually PR manager leaves the customer’s company.

Project canceled – Customer signed up for a specific PR campaign and then decides not to run the campaign.

No journalists – Customer can’t find a good journalist in PR Power! to cover a story.

Support fails – Customer contacts support a few times and the problem isn’t solved – usually indicated by support tickets open a long time.

Stale list – Customer’s media list is less useful because journalists no longer available or active.

Step 2 – Translate the churn predictors to data rules – or eliminate them

Mark’s team took these qualitative events and tried to identify existing data in Mixpanel that might predict them. 3 were straightforward 2 took a bit of investigating.

No journalists required identifying customers who had searched for journalists but didn’t add them to the media list.

Support fails was simply too hard – the support desk data on tickets isn’t in Mixpanel so they decided to skip it.

Step 3 – Count the occurrences of each predictor

Mark put the predictors at the top of his spreadsheet and identified every customer who matched a data rule yesterday.

For instance, User 80374 last_login_at > 30 days ago is TRUE so he entered a 1 for Project canceled.

Step 4 – Track every customer who churns until you hit 100

Mark adds a “Canceled?” column to the spreadsheet. Each day he identifies every customer who cancels until 100 customers cancel. This takes 2 ½ weeks.

Step 5 – Count the matching events for each predictor

Now for the 3rd-grad math …

For each predictor, count every customer where the churn predictor is TRUE and the customer canceled.

matches

Mark starts with the Project canceled rule and counts the following

Number of times last_login > 30 days ago is TRUE and YES, the customer canceled.

For instance, customer 80374 and 89766 fit this criteria. He counts 22 instances.

Step 6 – Enter the results into the spreadsheet

Enter the total in the appropriate block of the 3×3 matrix to calculate the Prediction Score (This is implementation of the Phi coefficient).

Mark enters 22 and calculates Prediction Score for Project canceled at 0.009

Step 7 – Identify the biggest predictors of churn

Rules with the higher Prediction Score are better predictors of churn.

Mark compares the Prediction Score for each rule and sees an obvious pattern.

results

Two observations immediately jump out at Mark:

First, last_login_at > 30 days ago doesn’t tell him much about Project canceled. Since PR Power! has long-term customers who use the product periodically this isn’t surprising.

Second, No journalists is the clear winner. In hindsight, this makes sense – customers who try to find a journalist and can’t are getting no value from the product.

Step 8 – Take steps to prevent churn

Mark creates 2 rules in Mixpanel for the No journalists predictor.

Small accounts

When a customer has total_searches > 5 within last 30 days AND media_list_updated_at > 30 days ago Mark creates an auto-message inviting a customer to watch a webinar on “How to search for a journalist”.

Large Accounts

When a customer has total_searches > 5 within last 30 days AND media_list_updated_at > 30 days ago Mark creates an alert for the sales team to notify them about a customer at risk for churning.

An easier way – ask us to do this for you

You don’t need even need 3rd grade math.

Just take a free trial of MadKudu and let us run these calculations for you.

Cancel anytime if you don’t like it – keep whatever you learn and all the money you make from reducing your churn.

 

Want to learn more? Sign up for our new course.

 

Photo credit: Rodger Evans

How I teach SaaS marketers to accelerate deals

Forbes just released a study confirming what we’re hearing from SaaS CMOs:

78% [of B2B Marketers] see B2B marketings’ role expanding from demand generation to deal acceleration.

In SaaS companies “deal acceleration” means arming the inside sales teams with better information about customers:

  • Improving Marketing Qualified Lead (MQL) quality
  • Predicting when customers are about to churn
  • Providing sales with real-time information about what customers are doing in the product

I’m covering all topics in our new course. In this post I’ll tackle MQLs.

Is your SaaS marketing team ready for this shift?

Do you measure the quality of Marketing Qualified Leads (MQLs)?

Don’t worry, you’re not alone.

Most SaaS CMOs don’t measure and track the effectiveness of their MQLs. In this post we’ll show you how to use a single metric – the MQL Performance Score – to track MQL quality and grow your SaaS revenue.

Why you should care about MQL “quality”

When we interview our SaaS customers about their marketing and sales workflow we usually find sophisticated marketing automation systems and very basic MQL generation systems.

For instance, a SaaS marketing team may “just tag every lead in Salesforce as ‘marketing qualified’ if the trial customer finishes signing up”. We usually discover the following problems:

CMOs have no visibility into how sales uses MQLs

The CMOs don’t know if sales treats MQL differently or even uses them at all. Some sales reps don’t even know what “marketing qualified” means – much less what to do about it.

Sales believes marketing leads “don’t convert”

Sales may use MQLs in ways marketing never expected.

For instance, a rep may tag every MQL as a “Sales Accepted Lead” under an incorrect assumption that someone in marketing already reviewed them. The rep engages many leads who never buy and concludes MQLs “don’t convert”.

CMOs have no feedback loop for improving sales support

Should marketing send sales more MQLs? Fewer? Should marketing supplement Salesforce with key actions the customer took in the product? Did our latest update to the MQL scoring rules improve or reduce MQL quality?

We suggest using a single metric – the MQL Performance Score – to track MQL quality.

Your MQL Performance Score

Every day you run a set of business rules that identifies “Marketing Qualified” leads in your CRM (e.g. Nutshell, Salesforce, or Pipedrive…). Your sales team identifies those most likely to buy and close them.

Your CRM also contains many other leads – what we call “non-MQL” leads – from trial customers, third-party sources, webinars, “contact” forms, etc.

A percentage MQLs convert to paying customers and percentage non-MQLs convert to paying customers.

In high-volume SaaS companies we expect (hope?) that MQLs convert at a higher percentage – if not, something is probably wrong.

The easiest way to measure MQL performance is to calculate your MQL Performance Score:

MQL Performance Score

Here’s how you do it.

Step-by-Step: How to calculate your MQL Performance Score

If you can use Excel and know 5th-grade math you have all of the tools you need. The practical challenge is getting and cleaning up the data – especially since the data is in your CRM and not the marketing stack.

Download a copy of the spreadsheet used in this post.

Step 1 – Break your leads into cohorts

Breaking your data into cohorts helps identify trends and reduces the impact of data anomalies. We suggest starting with monthly cohorts – that is, collect all leads who signed up in a given month and track their progress through the sales funnel over the next several months.

For each month gather the total number of MQL and non-MQL leads. Set up your spreadsheet as follows:

step1

In October 17,000 new leads were added to Salesforce. We broke them into 2,000 MQL leads and 15,000 non-MQL leads which we entered into Column C.

Step 2 – Count the leads in each sales workflow step

Create a column for each step in your sales workflow and plug in the number of leads.

Step 2

(click the image above to see a bigger one or download a copy)

Since your workflow is probably different I’ll walk through each column during October 2015 for the MQLs.

In October of 2015 2,000 MQLs were added to the CRM. Sales accepted (SALs) 440 of these leads (Column E). Sales contacted 396 (Column H) of these leads and 71 of them responded (Column K). Sales qualified (SQL) 66 (Column N) as likely buyers and 46 (Column Q) bought the product.

Step 3 – Calculate the percentage that converts in each step

Calculate the conversion rates for each column you created in Step 2.

Step 3

In October 22% (Column F) of MQLs were accepted by Sales. We calculated by dividing SAL count (Column E) by new MQLs (Column C).

Calculate this conversion percentage for Columns I, L, and O.

Step 4 – Calculate the MQL and non-MQL conversion percentage

Calculate the percentage of MQLs and non-MQLs that convert into paying customers.

Step 4

(Columns E-P are hidden)

In October 2.3% (Column R) of MQLs converted to paying customers (Column Q/Column C).

Step 5 – Calculate the MQL Performance Score for each cohort

Now calculate how much better MQLs performed relative to non-MQLs for each cohort.

Step 5

In October an MQL was 3.8 (Column T) times likely to convert than an non-MQL (2.3% / .6%)

How to use your MQL Performance Score

Getting insight into how sales uses MQLs

Looking at our complete spreadsheet above already raises some questions.

Analysis

What happened in December? Did the sales and marketing team drink too much egg nog at the Holiday party? Marketing only generated 400 MQLs and sales only accepted 300 non-MQLs. This looks suspiciously like a data problem.

Did November provide an example of how we can grow faster? It looks like the sales team paid more attention to MQLs in November. A higher percentage were accepted, contacted, and converted. Did we run a unique campaign? Did a particular sales rep choose to focus on MQLs? Further investigation is needed.

Measuring the impact of changes

Tracking MQL Performance Score allows you to systematically test and measure changes to your campaigns, products, and scoring rules.

Benchmarking your SaaS marketing team against competitors

Unfortunately we don’t yet have enough data to give you a good benchmark – obviously there are tons of variables. An expensive, enterprise SaaS product will have a lower MQL Performance Score than one that sells for $10/month.

For our high-volume SaaS customers we are seeing MQL Performance Scores of 3-6.

And … last but definitely not least … evaluating how much more $$$$$ MadKudu is making for you

Seriously – just sign up for a free trial of MadKudu – we’ll calculate your MQL Performance Score and show you how to improve it.

You have absolutely nothing to lose. You won’t have to pay us a dime until we prove how much more we can grow your SaaS revenue.

 

Want to learn more? Sign up for our new course.