Make the right “build versus buy” decision with 3 simple steps

A couple weeks ago I attended a Point Nine and Algolia happy hour in Dublin. The premise was on point with a recurring question we deal with on a daily basis when it comes to software: “Should you buy versus build internal solutions?”

Many at the event shared the story of an in-house solution turning into a big costly distraction for their team. The main culprit? The decision to build in house was taken lightly without the hypotheses behind this decision being written down and communicated.

 

I’d like to share here a simple framework I’ve used and that I’ve seen work in this form or another at some of the SaaS rising stars (Algolia, Intercom, Mention…).

This framework helps support data driven, thus dispassionate, decisions on the topic of building vs buying.

The high level structure is:

Step 1: Validate the business need
Step 2: Get a rough but realistic estimate of the cost for the “build” option
Step 3: Decide and review your hypotheses in a given timeframe.

Step 1: Validate the business need

Even Chris Doig in his analysis of the problem writes that everything starts with well-defined requirements. However, as most founders know only too well, every decision to even think about doing something starts with a hypothesis of much positive impact the company can get from a new set of functionality.

Make sure to always go through the exercise of determining how much value you will get from this feature/product you’re considering.

Let’s take the example of building a lead scoring mechanism to help the sales team know which leads they should de-prioritize. The assumption is that the sales team is wasting time on leads that are unlikely to purchase your product at a high-enough price point. Seems fair. But how much value can we expect from implementing such a solution? Keep it simple. Let’s assume you have 10 SDRs, each at a base salary of $50k. If 20% (1 out of 5) of the leads they are reaching out to are unqualified, you are essentially wasting ~$100k annually. And this is without considering the opportunity cost from not spending that time on higher value leads.

With this rough estimate in mind, let’s proceed with evaluating the cost.

Step 2: Define basic requirements and compute an estimate of the cost for the “build” option

Whenever we consider building a solution internally, I like to approach it as I would if I were to write a RFI. This is a great forcing function to decompose the problem and identify the different required functionalities along with their impact on the expected value (aka their criticality). The individual costs are always higher than you initially thought, and the estimates for each item add up quickly!

For example, using the example of lead scoring, decomposing the problem could bring us to the following set of critical features:

– Build a programmatic way to fetch information about new leads from LinkedIn
– Define a heuristic to score leads based on the data obtained
– Build a scoring mechanism
– Build a pipeline to feed this score back into your CRM
– Add the score in a workflow to route leads appropriately
– Set up reports to measure performance in order to make adjustments if necessary

Once you have those listed, get an estimate from the engineering team for building each feature. This will enable you to have an idea of the cost of the “build” option.

You can use a simple spreadsheet to estimate the annual cost of building and maintaining a solution based on your team’s size, current MRR…

Download this calculator here.

For an early young company (6 engineers, $100k MRR), the cost of such a solution over the course of a year would be about $80,000.

This may seem high and the truth and that we all have a hard time estimating opportunity cost, maintenance cost (we are typically twice that of initial development)…

In parallel, look around to see what SaaS solutions are available to solve your problem and how much they would cost. A lot of them offer free-trials and/or demos. I recommend going through at least a demo as you will be able to get some valuable information about others who have worked on solving the problem you’re addressing. On the pricing aspect, if no pricing is shown and the product requires a demo, you can be fairly certain the cost will be at least $999/month.

Step 3: Decide and review your hypotheses in a given timeframe

You are now armed with all the data points to take a data driven decision! If you’ve decided to build in house, set a “gate” with your team to revisit the hypotheses you’ve made. For example, decide with your team to have a quick check-in in 90 days to discuss the learnings from building the solution in house and decide whether or not to continue or re-evaluate.

 

Notes
I want to emphasize that no answer can be right without context. What is initially true might very well become wrong. Therefore we’ve built a lot of software to help us determine what are the critical components we would be looking for when shopping around. In these cases it was always essential to timebox the build phase and to constantly remind ourselves that the objective was to reduce uncertainty and unknowns.

Secondly, there is a hidden cost in buying that can come from the rigidity and inadequacy of the SaaS product you buy with your problem. This is why trials and POCs are so popular nowadays (which is why we offer one).

Lastly, the example picked seems like a no-brainer as the solution is for the “business” team. The level of rigour required to go through this exercise for tools used by dev teams is much greater. The main fallacy lying in the illusion that an internal tool will always be a better fit for all the company-specific requirements. This is not only a highly inaccurate; it also leads to ill-defined features. Going through step 1 can save hours of wasted time and headaches.

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.

Closing mid-market deals faster is the key to SaaS sales velocity

Guest post from MadKudu.

Overview: what is the most effective way to increase SaaS MRR?

Regular readers of SaaScribe know increasing monthly recurring revenue (MRR) is the #1 challenge faced by SaaS founders. But what is the fastest way? Get more leads? Close bigger deals? Convert more trial users? Close deals faster?

That’s the question we tried to answer in this study. We analyzed the sales velocity of 45,000 qualified leads for 9 representative SaaS companies. Based on these results, the most immediate way for SaaS companies to increase MRR is by closing mid-market deals (deers) faster. We finish with some advice for creating a high-velocity sales closing workflow that targets mid-market leads.

Rabbits, deers, and elephants – oh my!

A few weeks ago we were chatting in MadKudu HQ about the sales practices at a few of our SaaS customers – how they identified the best leads, when they contacted them, etc.

We noticed that most sales reps focus almost exclusively on closing “elephants” (largest deals) and invest little time in “deers” (medium-sized deals). Traditionally this is how software sales has worked: since a rep can only manage a finite number of leads most sales teams will focus the largest deals.

But there are 2 drawbacks to closing elephants.

  1. They take longer to close.
  2. There are fewer of them.

Our hypothesis was that closing mid-market (deers) deals faster was the most actionable way to accelerate SaaS sales.

Sales velocity is our reference

We needed a way to make an apples-to-apples comparison between deers and elephants based on deal size, deal volume, and time to close. We decided to use Sales velocity since the equation considers all 3 variables.

 

 

Sales velocity measures how fast your team is making money. If normal velocity is “miles per hour” you can think of sales velocity as “money per month”. Thomasz Tunguz provides a detailed explanation of sales velocity in this post, but here are the basics.

The sales velocity variables

# The number of leads a sales team can work over a period of time. Deers have a higher inventory of available leads than elephants.

$ The average deal size. We expect elephants deals to be bigger than deer deals.

% Conversion rate, the percentage of leads that convert to paying customers. The rate of conversion for elephants could be bigger or smaller than deers depending on the amount of qualification.

T The average time for conversion, usually measured in days. We expect elephants to convert slower than deers since larger deals require more negotiation and touch points.

Thus the larger $ for elephants comes at a cost of larger T, smaller and possibly smaller %.

Methodology: how we calculated sales velocity for deers and elephants

We started by picking a representative sample of 9 SaaS companies. We then needed to categorize them into cohorts based on deal size.

Using number of employees as a proxy for deal size

Unfortunately we don’t often have the data we need to test our hypotheses. In this study we had no way of categorizing a lead based on deal size, so we used Clearbit to give us a best estimate.

We starting by identifying each company’s “good” leads based on domain, presence in Clearbit’s database, and behavior.

We broke each company’s good leads into 10 cohorts based on Clearbit’s employee_count data – this served as a basis for identifying elephants and deers. We ignored small deals – rabbits.

For each cohort we calculated the average time to convert, number of leads and conversions.

Example data for 1 company

Screen Shot 2016-04-12 at 8.12.14 AM

Results: what we learned from 45,000 qualified SaaS leads

Here are the results for the 9 companies we studied.

Screen Shot 2016-04-12 at 8.19.49 AM

Result 1: Deers are only closing 10% faster than elephants

We divided deer T by elephant T to see which is faster.

Surprisingly, deers are only closing 10% faster than elephants.

Screen Shot 2016-04-12 at 8.25.58 AM

Result 2: The conversion rate of deers is 3x more than elephants

It isn’t obvious why deer % should be so much higher than elephant %. Since sales teams invest more time selecting and engaging elephants you could argue that elephant conversions should be higher.

But deers convert at a much higher rate on average. There is also a large variance among these 9 sample companies.

Deer conversion rates are also high on an absolute basis. 7 out of 9 we studied are converting > 8% of their deers. (if this seems like b.s. remember we filtered these for “good” leads based on their presence in Clearbit’s database).

Screen Shot 2016-04-12 at 8.29.32 AM

Result 4: Deer deals can be 10x smaller and achieve the same Sales Velocity

Given these results for #, %, and T we can calculate the deer deal sizes needed to hit the same sales velocity. If you assume an equal SV for deers and elephants you can solve for relative $.

I’ll spare you the algebra – results are below.

Screen Shot 2016-04-12 at 8.35.48 AM

Thus on average elephant deal sizes need to be 10x bigger than deer deal sizes to achieve the same sales velocity.

Of course I’m assuming all deer leads are # – even given our filtering assumptions this is a stretch since reps can only work so many leads.

Analysis: your practical options for quickly increasing sales velocity

So how can you increase sales velocity? Conventional wisdom says “it depends” because these variables are codependent.

In the long run this is certainly true – you can adjust pricing or increase qualified leads. But you have fewer options in the short run because your team is already optimizing most of these variables.

You’re reading this because you’re not looking for a long-term theoretical plan – you’re looking for fast actionable, wins. Let’s consider your options in the context of this data.

SVblue

Increase $? Not easily.

Most SaaS companies have already tested pricing and are reasonably close to optimizing conversions and pricing. Unless your product is new there are probably no quick wins from price increases.

Increase elephant %? No way.

Your sales team is already calling every elephant – again … and again … and again. If there was an easy way to close more deals they would be doing it.

Increase deer %? Unlikely.

Based on this data the deers already have a high conversion rate. Increasing it dramatically is probably unrealistic.

Marketing automation and product are already doing a pretty good job at getting deers converted. The qualified deers who don’t convert are already pursued by sales after the trial ends.

Increase #? Yeah…right.

How about increasing your qualified leads? Maybe waive a magic wand so more wonderful customers suddenly show up?

Every SaaS company we know is already working hard to prospect for more leads. Any increases won’t come easily (or cheaply).

Decrease elephant T? Nope.

Elephants take multiple touch points to activate. They have customized workflows and often require purchase orders. Your sales team is already trying to close them yesterday.

Decrease deer T? Yes!!

The only remaining option is to close deers faster.

The 9 companies we studied have roughly the same average time to close for deers and elephants. The only logical conclusion is that these companies are not trying to close deers faster.

This is the key insight from this data.

Insight: The fastest way to increase your SaaS revenue is to close deers faster

Deers should close faster than elephants. Fewer people in the decision loop. Fewer meetings. Less negotiation. Deers also pay with credit cards – not purchase orders.

But according to this analysis deers are closing about as fast as elephants – too slow.

Why? Because SaaS companies are relying on the free trial conversion to close deers

These results are consistent with results from our previous study because most SaaS companies rely on trial expiration as the primary buying incentive.

From this previous study you can see how most SaaS trial conversions occur around the end of the trial period – 30 days in the graph below:

Screen Shot 2016-02-12 at 5.48.12 PM

IOW, deers sign up for your product, self-serve and you don’t try to get them to pay until the trial is about to expire.

Action: how you can close deers faster and accelerate sales velocity

You may be tempted to use marketing automation or product workflow to close deers faster – in our experience your inside sales team will be much more effective.

Here are few tips based on what we have implemented with our customers.

Don’t chase the whole herd – qualify your deers

1691205018_90bd181004_b

Flooding your sales reps with a pile of mid-market leads won’t work – you’ll probably decrease your sales velocity. If your sales reps start converting less than 15% of their leads they will become frustrated and less effective.

You need to identify the most qualified deers, engage them the moment they are ready to buy and developer a higher-velocity closing process.

Some tips for qualifying leads:

  1. Leads that are in Clearbit’s database are a good initial filter. You can also disqualify any free email accounts (gmail, hotmail, qq, etc.).
  2. Segment deers and elephants with simple rules – for instance, start with employee_count or plan.

Start with 1 dedicated “Deer Hunter” sales rep

We suggest starting with 1 dedicated sales rep to close deers. Let’s call her the “Deer Hunter”.

Start small and begin tracking the sales velocity of the Deer Hunter.

Build a higher-velocity engagement workflow

The Deer Hunter can’t simply manage list of deer leads and systematically work through them – this takes too much time. Instead, work with marketing to develop a sales automation workflow that gets a deer to take the first engagement step.

For example, an email campaign that asks a deer to reply to a question or schedule a call based on qualifying demographics or behavior.

Create a 1-touch deer closing script

The Deer Hunter needs a script that gets a deer to convert on a single call. The script should include any conversion incentives (i.e. discount, free feature) and minimize product education. Sales should be credit card only.

Target deers who are ready to buy now

Your customers can take specific steps that indicate a high likelihood of buying – invite a friend, add 5 projects, etc.

We call these “Acceleration” or “Delight” events and showed you how to find them in our series on Behavior-based Conversions.

These events are highly correlated with conversions and indicate a customer who is ready to buy now.

In this example hired_accountant is Acceleration event:

graph

Notify the Deer Hunter when a deer is ready to buy

Deers who complete Acceleration events are ready to buy now – these are the ones you want your Deer Hunter to target.

Send your sales teams notifications through Slack, email, or Salesforce when deers complete Acceleration events.

Include information about the customer, actions completed, and anything else the sale rep needs for the deal-closing script. Here is an example of what we send to our customers:

About MadKudu

 

MadKudu helps B2B SaaS apps accelerate revenue by qualifying leads based on demographics and in-app behavior.

Sign up for a free trial of MadKudu now .

Photo credits: the_boglin

Close deers faster: your quickest win for increasing SaaS MRR

Summary: We analyzed the sales velocity of 45,000 qualified leads for 9 representative SaaS companies. Based on these results the most immediate way for SaaS companies to increase MRR is by closing mid-market deals faster. We advise setting up a separate, high-velocity sales closing workflow that targets mid-market leads who are ready to buy now.

A few weeks ago we were chatting in MadKudu HQ about the sales practices at a few of our customers – how they identified the best leads, when they contacted them, etc.

We noticed that most sales reps focus almost exclusively on closing “elephants” (largest deals) and invest little time in “deers” (medium-sized deals). Traditionally this is how software sales has worked: since a rep can only manage a finite number of leads most sales teams will focus the largest deals.

But there are 2 drawbacks to closing elephants.

  1. They take longer to close.
  2. There are fewer of them.

Our hypothesis: the most immediate opportunity to grow SaaS MRR is by closing mid-market (deers) deals faster

So … we quit there. It seemed easier to hold on to an opinion rather than actually look at the data.

Of course I jest. We decided to research the sales data of a sample of our customers to see if there was data to support our hypothesis.

Sales velocity is our reference

We needed a way to make an apples-to-apples comparison between deers and elephants based on deal size, deal volume, and time to close We decided to use Sales Velocity since the equation considers all 3 variables.

 

 

Sales velocity measures how fast your team is making money. If normal velocity is “miles per hour” you can think of sales velocity as “money per month”.

The sales velocity variables

# The number of leads a sales team can work over a period of time. Deers have a higher inventory of available leads than elephants (although acting on them places more pressure on qualification).

$ The average deal size. We expect elephants deals to be bigger than deer deals.

% Conversion rate, the percentage of leads that convert to paying customers. The rate of conversion for elephants could be bigger or smaller than deers depending on how much qualification is done for the lead pool.

T The average time to for conversion usually measured in days. We expect elephants to convert slower than deers since larger deals require more negotiation and touch points.

Thus the larger $ for elephants comes at a cost of larger T, smaller and possibly smaller %.

Methodology: how we calculated sales velocity for deers and elephants

We started by picking a representative sample of 9 SaaS companies. We then identified each company’s “good” leads based on domain, presence in Clearbit’s database, and behavior.

We broke each company’s “good” leads into 10 cohorts based on the employee count attribute in Clearbit. We decided to use employee_count as a basis for identifying elephants and deers. We ignored small deals – rabbits.

For each cohort we calculated the average time to convert, number of leads and conversions.

Example data for 1 company

Screen Shot 2016-04-12 at 8.12.14 AM

Results: what we learned from 45,000 qualified SaaS leads

Here are the results for the 9 companies we studied.

Screen Shot 2016-04-12 at 8.19.49 AM

Result 1: Deers are only closing 10% faster than elephants

We divided deers T by elephant T to see which is faster.

Surprisingly, deers are only closing 10% faster than elephants.

Screen Shot 2016-04-12 at 8.25.58 AM

Result 2: The conversion rate of deers is 3x more than elephants

It isn’t obvious why deer % should be so much higher than elephant %. Since sales teams invest more time selecting and engaging elephants you could argue that elephant conversions should be higher.

But deers convert at a much higher rate on average. There is also a large variance among these 9 sample companies.

Deer conversion rates are also high on an absolute basis. 7 out of 9 we studied are converting > 8% of their deers. (if this seems like b.s. remember we filtered these for “good” leads).

Screen Shot 2016-04-12 at 8.29.32 AM

Result 4: Deer deals can be 10x smaller and achieve the same Sales Velocity

Given these results for #, %, and T we can calculate the deer deal sizes needed to hit the same sales velocity. If you assume an equal SV for deers and elephants you can solve for relative $.

I’ll spare you the Algebra – results are below.

Screen Shot 2016-04-12 at 8.35.48 AM

Thus on average elephant deal sizes need to be 10x bigger than deer deal sizes to achieve the same sales velocity.

Of course I’m making an assumption that all deer leads are # – even given our filtering assumptions above this is probably a stretch since sales reps can only work so many leads.

Analysis: your practical options for quickly increasing sales velocity

So how can you increase sales velocity? Conventional wisdom says “it depends” because these variables are codependent.

In the long run this is certainly true – you can adjust pricing or increase qualified leads. But you have fewer options in the short run because your team is already optimizing most of these variables.

You’re reading this because you’re not looking for a long-term theoretical plan – you’re looking for fast actionable, wins. Let’s consider your options in the context of this data.

SVblue

Increase $? Not easily.

Most SaaS companies have already tested pricing and are reasonably close to optimizing conversions and pricing. Unless your product is new there are probably no quick wins from price increases.

Increase elephant %? No way.

Your sales team is already calling every elephant – again .. and again … and again. If there was an easy way to close more deals they would be doing it.

Increase deer %? Unlikely.

Based on this data the deers already have a high conversion rate. Increasing it dramatically is probably unrealistic.

Marketing automation and product are already doing a pretty good job at getting deers converted. The qualified deers who don’t convert are already pursued by sales after the trial ends.

Increase #? Yeah…right.

How about increasing your qualified leads? Maybe waive a magic wand so more wonderful customers suddenly show up?

Every SaaS company we know is already working hard to prospect for more leads. Any increases won’t come easily (or cheaply).

Decrease elephant T? Nope.

Elephants take multiple touch points to activate. They have customized workflows and often require purchase orders. Your sales team is already trying to close them yesterday.

Decrease deer T? Yes!!

The only remaining option is to close deers faster.

The 9 companies we studied have roughly the same average time to close for deers and elephants. The only logical conclusion is that these companies are not trying to close deers faster.

This is the key insight from this data.

Insight: The fastest way to increase your SaaS revenue is to close deers faster

Deers should close faster than elephants. Fewer people in the decision loop. Fewer meetings. Less negotiation. Deers also pay with credit cards – not purchase orders.

But according to this analysis deers are closing about as fast as elephants – too slow.

Why? Because SaaS companies are relying on the free trial conversion to close deers

These results are consistent with results from our previous study because most SaaS companies rely on trial expiration as the primary buying incentive.

From this previous study you can see how most SaaS trial conversions occur around the end of the trial period – 30 days in the graph below:

Screen Shot 2016-02-12 at 5.48.12 PM

IOW, deers sign up for your product, self-serve and you don’t try to get them to pay until the trial is about to expire.

Action: how you can close deers faster and accelerate sales velocity

You may be tempted to use marketing automation or product workflow to kill deers faster – in our experience your inside sales team will be much more effective.

Here are few tips based on what we have implemented with our customers.

Don’t chase the whole herd

1691205018_90bd181004_b

Flooding your sales reps with a pile of mid-market leads won’t work – you’ll probably decrease your sales velocity. If your sales reps start converting less than 15% of their leads they will become frustrated and less effective.

You need to identify the most qualified deers, engage them the moment they are ready to buy and developer a higher-velocity closing process.

Start with 1 dedicated “Deer Hunter” sales rep

We suggest starting with 1 dedicated sales rep to close deers. Let’s call her the “Deer Hunter”.

Start small and begin tracking the sales velocity of the Deer Hunter.

Build a higher-velocity engagement workflow

The Deer Hunter can’t simply manage list of deer leads and systematically work through them – this takes too much time. Instead, work with marketing to develop a sales automation workflow that gets a deer to take the first engagement step.

For example, an email campaign that asks a deer to reply to a question or schedule a call based on qualifying demographics or behavior.

Create a 1-touch deer closing script

The Deer Hunter needs a script that gets a deer to convert on a single call. The script should include any conversion incentives (i.e. discount, free feature) and minimize product education. Sales should be credit card only.

Target deers who are ready to buy now

Your customers can take specific steps that indicate a high likelihood of buying – invite a friend, add 5 projects, etc.

We call these “Acceleration” or “Delight” events and showed you how to find them in our series on Behavior-based Conversions.

These events are highly correlated with conversions and indicate a customer who is ready to buy now.

In this example hired_accountant is Acceleration event:

graph

Notify the Deer Hunter when a deer is ready to buy

Deers who complete Acceleration events are ready to buy now – these are the ones you want your Deer Hunter to target.

Send your sales teams notifications through Slack, email, or Salesforce when deers complete Acceleration events.

Include information about the customer, actions completed, and anything else the sale rep needs for the deal-closing script. Here is an example of what we send to our customers:

Let us help you close deer faster

I can’t help you hunt deers – I’m a vegetarian and wouldn’t shoot poor little Bambi.

But MadKudu can help you increase your sales velocity by identifying the most qualified deers and telling you when they are ready to buy.

Sign up for a free trial of MadKudu now – you have nothing to lose.

P.S. Could you have written this post?

If so you should contact us – we’re hiring.

 

Photo credits: the_boglin, Kansas Tourism

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