When SaaS companies first start welcoming customers on board, HubSpot is a natural choice. Its pricing scales with your needs and its tools reach across the entire buyer journey: from lead generation to customer acquisition. By using HubSpot’s various engagement tools like landing pages, ad management, live chat & email campaigns, businesses generate valuable data about what turns prospects into customers.
As they grow, SaaS companies like InVision, Deputy, Front & AppCues complement HubSpot with best-in-breed tools for mission-critical tasks: product analytics data to track user onboarding & activation, data enrichment to learn more about user behavior and profile, and a unified platform for customer data generated by your product and leveraged by all teams.
As data and engagement tools spread, much of HubSpot’s core marketing & sales tools scale very well; however, SaaS companies struggle to leverage all of their customer data inside HubSpot. They want to feed MadKudu’s intelligence & signals into HubSpot to build better buyer journeys.
Up until today, there have been only two ways to score leads in HubSpot: building your own lead score manually using their point-based framework, or using their predictive lead scoring solution. Manual lead scoring requires constant upkeep, tweaking & analysis to make sure you’re identifying the 20% of the leads that will generate 80% of revenue. Manual scoring is most often based on preconceptions, which means it misses non-obvious leads that are actually a great fit in disguise.
HubSpot’s predictive lead scoring solution only leverages HubSpot data and misses out on the rest of your 1st party data and enrichment tools.
That’s why we’re super excited to release MadKudu for HubSpot, baking MadKudu into the entire HubSpot marketing & sales suite of tools. Out of the box, you will be able to:
Use all your customer data inside your scoring (Segment or Amplitude for in-app activity, Stripe for billing…)
Have the lead/account score available in all your other tools so you can optimize the entire funnel
Get account scores, for simple and powerful ABM execution
Understand why each lead is scored the way it is, thanks to our powerful signals and correlations.
We’ve been testing MadKudu for HubSpot in beta for the past few weeks with our great customers. We’re really excited to get it into more HubSpot users’ hands. You can learn more about MadKudu for HubSpot here
Ask a startup how they generate new customers and, chances are, they’ll talk in terms of marketing qualified leads and sales qualified leads.
Marketing qualified leads (or MQLs) are website visitors who have the potential to become great customers: they’ve actively engaged with your company, and they look like a good fit for getting value from your product. Sales qualified leads (or SQLs) have gone a step further, taking an action that suggests a willingness to actually buy from you.
But only 13% of marketing qualified leads ever become sales qualified, according to a B2B sales benchmark report. Those who do require 84 days of education and nurturing to be deemed “sales ready.” Even then, just 6% of those supposedly “sales-ready” prospects ever become paying customers.
Thankfully, there’s another way—a qualification framework that generates active, engaged leads that require less effort to close and make better long-term customers. Instead of MQLs and SQLs, we need to start talking about PQLs—product qualified leads.
What are product qualified leads?
When a lead becomes marketing or sales qualified, we’re using their interactions with our website—downloading an eGuide, viewing a pricing page, or submitting a contact form—to predict when they’re ready to buy.
We might reach out to try to arrange a sales call with somebody who just wanted to read a free whitepaper or cold email a website visitor who clicked onto a pricing page by mistake. This common issue is having someone from the right-fit company being captured as a lead, but not being hot enough to generate a conversation leads to a high volume of MQLs but a low volume of SQLs.
Instead, the PQL—product qualified lead—framework is radically different, using in-app behavior to work out exactly when a lead is ready to purchase.
The PQL model works because SaaS companies can be set up to allow users to get value from the product before making a serious commitment:
Freemium products, like Airtable, offer both free and paid packages.
Free trial products, like ProsperWorks, offer an introductory time- or feature-limited product experience.
Self-service products, like Zuora, allow users to spend as little (or as much) as they like.
This, in turn, allows you to identify when leads become sales-ready with much greater accuracy. Instead of relying on a proxy of sales readiness, it’s possible to see how users actually engage with your product. It works by identifying actions that show users have explored the product and received enough value that they’re ready for a sales conversation.
Product qualified leads are also much easier to turn into customers. The MQL/SQL framework puts the cart before the horse, selling to users before they’ve successfully adopted the product. Sales reps need to do more education and persuasion before a sale, and customer success reps have to work harder to ensure adoption afterwards.
By definition, product qualified leads have already adopted your product. They understand how it works, and they’re getting value from it. As a result, the sales process is little more than a formality. In fact, PQLs close customers at 6x the rate of SQLs, according to Redpoint Ventures partner Tomasz Tunguz.
How to find PQLs
In its simplest form, identifying product qualified leads requires a behavioral trigger: an in-app action or series of actions that correlates most often to users being sales-ready. When a prospect takes that action, they become product qualified and get passed on to the sales team.
For a company like Expensify, that trigger might be the submission of 10 invoices. For Slack, it was a team sending 2,000 messages.
“Based on experience of which companies stuck with us and which didn’t, we decided that any team that has exchanged 2,000 messages in its history has tried Slack—really tried it.” –Stewart Butterfield, Slack
At that 2,000 message milestone, a typical team of 10 people has used Slack for a week. They’ve experienced the product’s core features—quick collaboration, fewer email exchanges, rapid file sharing—and seen firsthand the impact Slack could have on their long-term productivity. Once a team has hit that milestone, they have a 93% retention rate.
Using a tool like MadKudu, these behavioral triggers can be combined with demographic data—looking both at customer fit and likelihood to buy—to fully automate the lead qualification process. Pushing the MadKudu score to sales tools like Salesforce allows sales reps to view a detailed breakdown of every lead’s current qualification status, while allowing Ops teams to prioritize lead routing as well.
Working out manually which actions correlate with sales-readiness can be a tedious process best left to MadKudu; product analytics tools like Amplitude play a valuable role in the modern marketing stack.
More than just a great way to feed precious behavioral data to MadKudu, products like Amplitude help product and marketing teams to dive in granularly into the customer journey—allowing you to drill-down into the last five actions taken by your users—which can help identify opportunities to improve the customer journey and inspiration for experiments.
By tracking conversion rates resulting from each of these actions, you can learn which behavioral signals are indicative of sales intent and adjust your definition of a product qualified lead to match.
How to prioritize PQLs
For a company that generates thousands of leads, it isn’t always enough to have a binary, qualified/unqualified system. You also need a way to prioritize following up with those leads.
This was a problem experienced by HubSpot’s VP of Product, Christopher O’Donnell. When he dug into the company’s acquisition strategy, he identified four distinct sub-types of product qualified leads:
Free users who have hit a given PQL criteria
Users who have requested sales assistance
Users who have reached a limit in their free plans
Users who have purchased without any sales involvement
At one end of the spectrum, we have a free user who has triggered our PQL criteria, completing an action like sending 2,000 messages or submitting 10 invoices. Though this user has experienced the core value of our product, there’s no guarantee they’re ready to buy: they might be happy sticking to our free plan.
Of all our PQLs, these users will require the most sales involvement to become customers: they need to be persuaded that a paid plan is significantly more valuable than the free plan they’re currently using.
Most companies define “Hand raisers” as PQLs who have filled out a contact form to explicitly ask for sales support. They’re more sales-ready than other free users but might still require a few sales touches to convert.
Next are users who have tripped a feature limit on their free plan, like a Slack user reaching their message history limit. These users have successfully adopted the product, so much so that their usage has outstripped their free plan, requiring very little persuasion to become paying customers.
At the far end of the spectrum are “touchless purchases,” free users who are so sold on the value of the product that they become fully-fledged customers without any sales action.
Each of these users is product qualified, but they differ in how much nurturing they need to become customers. By identifying multiple product qualification criteria, it becomes easier to recognize these differences and tailor the sales process to their exact needs.
How to turn PQLs into customers
Instead of trying to persuade someone to purchase a product they’ve never used, you’re helping them get more value from a product they already love. Instead of guessing their needs from a contact form submission or the pages they’ve visited on your website, you can actually see how they’re using your product and shape your sales process to match.
You can find a lead’s most-used product feature and talk through the extra functionality that comes with a paid plan.
You can dig into the limitations they’ve hit on their free package and help them choose a tailor-made plan that matches how they actually use your product.
You can work out which actions they need to take to get more value from the product and send personalized nurturing emails and in-app reminders.
By switching to PQLs, you’re no longer reliant on slick sales patter to close deals. You’re letting your product—the heart of your business, and the reason your customers part with their money each month—sell itself.
Putting customer experience before customer revenue
Product qualification isn’t just a way to speed up sales and improve conversion rates. By using in-app engagement as your primary qualification metric, you’re making a clear, unequivocal declaration: customer experience comes before customer revenue.
Instead of focusing your energy on selling a product to people who have never used it, you’re offering a great product experience, up front, and using the strength of your product to sell itself. You’re using your sales team to support the product and making product adoption your number one priority. You’re generating leads that are easier to close—and most importantly—creating customers who are guaranteed to get value from your product.
Identifying sales-ready accounts to reach out to is the heart of freemium sales acceleration. Freemium businesses rely largely on product adoption to trigger the a-ha moment that will ultimately lead to successful sales engagement, so quantifying that a-ha moment in the form of activity scoring is crucial.
For Sales SaaS reps with hundreds or thousands of accounts assigned to them, reaching out manually every three months yields little or no results. What are the odds that today is the day that an arbitrary account is ready to buy? Very little, right. Coupled with that is the fact that an account may have dozens of associated contacts to choose from and engage. Randomly picking based off of job titles and reaching out is a spray and pray strategy that yields equally unpredictable results.
Sales wants to know which accounts to reach out and who within that account to reach out. And we’ve got just the play for that.
Timing is everything: Surfacing sales-ready accounts & the right contacts to engage.
Our goal here is for sales reps to start every day with a list of accounts assigned to them that are ready to have a conversation. We’d also like to give that sales rep a filtered list of the contacts most likely to respond. That way sales reps spend all of their time crafting the most relevant message for their best leads.
This is a great play for freemium SaaS businesses with a large number of accounts where velocity & efficiency are key to success. This play is also good for products with a combination of low-touch and high-touch users – while you may have paying customers already, identifying when that paying account is sales-ready is key.
The bulk of this play is going to sit on MadKudu’s ability to build an accurate account-level behavioral scoring model. Structured data is going to be key – wecan’t build an account-level model if we don’t have account data. When we run this play with InVision, we use Segment for product data & HubSpot for marketing data. We’ll also need Salesforce for our sales data.
Once we have our data piping correctly, MadKudu is going to identify the features & activity that best predict sales readiness by looking at historical product & marketing engagement and the resulting sales outcome. Once the predictive model is build, MadKudu sucks down the latest activity data on a regular basis and looks for triggers.
When a sales-ready account is identified, MadKudu tags it in SalesForce and drops it into a daily report for sales reps. Within each sales-ready account, MadKudu’s models also look at the profile & activity of each contact in order to identify the contacts most likely to engage. Those contacts are recommended to the sales rep.
The Impact: +25% in Pipeline
InVision’s 25% increase in pipeline came from identifying accounts programmatically based on historical customer data. This came without any change to the product, just in optimizing for sales-readiness. If you’re looking at lead activity today, you may be leaving money on the table. With InVision, we identified accounts where no single lead achieved the activity threshold for an MQL, but the account as a whole hit the MQA threshold. It is the combined activity of several users that makes an account ready to talk to sales.
Once this MQA model is built, we can begin to add some of our other plays on top of it. Forcing the funnel by sales-ready accounts by customizing the app or website. Reducing friction on forms or triggering chat for enterprise prospects with a Fastlane play. We can score accounts throughout the buyer journey.
90% of your website traffic doesn’t convert, and there’s nothing worse than a missed opportunity. For b2b companies, retargeting is a no-brainer. It’s an easy way to make sure you’re always targeting an audience that has showed some intent to buy. However, the problem with retargeting anonymous website traffic broadly is that you don’t know who you are targeting and how qualified they are.
With just a high volume, many SaaS companies bid low on retargeting across their entire website traffic. They push their brand in front of their website traffic wherever they go. This spray and pray tactic means SaaS companies are only getting in front of traffic that other advertisers aren’t willing to pay more. Do you think your competitors may have a more focused strategy, outbidding you on your best leads and leaving the rest to you?
Click through rate is low because the quality filter is low. Conversion rate is low because most of your website traffic shouldn’t convert (candidates, low-quality leads, investors, analysts, perusers).
Our goal here is to focus our retargeting budget on the subsection of our website traffic that is worth the most to us. If we do that, we will be able to reallocate the budget we’re not spending on low-quality traffic to bidding more for our high-quality traffic.
We’ll need a few tools to Re-Get That Bread:
IP lookup: we’ll be using Clearbit Reveal for this.
Qualification: we’ll be using MadKudu for this.
Retargeting: we’ll be using Adroll for this.
As usual, we’ll be connecting this all through Segment.
Qualifying traffic has become pretty easy with the advent of IP Lookup APIs – the most popular being Clearbit Reveal. Feed Clearbit an IP address and it returns (among other things) the domain of the company or of the individual visiting your website. This is enough to score an account. We’ll be scoring with MadKudu, but you can also do it with your homegrown Lamb or Duck lead scoring model. We’ll send MadKudu the domain name provided by Clearbit, which will return a few important data points for this play:
Customer Fit segment: very good, good, medium, low
Predicted Spend: custom based on your specific pricing plans, predicting which
Topical Segmentation: custom based on your target segments (e.g for Algolia: ecommerce, media, SaaS).
With this data we’re able to feed AdRoll a custom audience of qualified traffic to target. This can be a bit tricky since AdRoll requires a static audience, but a quick script to update a static audience on a daily basis will get us around that hiccough.
Based on predicted spend, we can even build separate audiences for our various plans, each with different budgets. If we add in Topical Segmentation, we can run targeted messaging to our various ICPs based on their needs at various price points. If we know the predicted value of the qualified traffic, we can calculate our maximum budget as a function of our acceptable CAC.
The Impact: +300% click-through rate
When Chris Rodriguez at Gliffy first began building this play, he was looking to get click through rate for his retargeting ads under control. When he saw it jump from the .7% industry average to 2-3% for qualified traffic, it became pretty clear that qualified traffic was worth the focus.
Bidding higher on a qualified audience is a no-brainer: we see it on ad networks that boast a qualified audience or a qualified system of manual segmentation. It only makes sense that we would apply the same logic to how we retarget our own audience: we want to spend more on the audience that matters, the ones that got away.
Ten years ago Amazon introduced same-day delivery, probably the single most important feature in cementing their dominance of the eCommerce industry. They did this after 10 years of innovating on the online shopper experience – recommended purchases, one-click payments, experiments on how website latency affected conversion rates – and they understood that the biggest source of friction in their buyer experience was waiting for your package to arrive.
We all have an idea in our head about what makes a great customer journey, a great buyer experience. When Francis asked me out of the blue, the first thing that came to mind was my experience buying an engagement ring last year, but I could just as easily point to the experience of creating a new Slack team. They are magical experiences. You never see what’s going on behind the curtain, and you never have any downtime to think about it. Is my package already in Paris? How did Amazon know what I was going to order? Doesn’t matter. It’s already arrived before I can begin to comprehend how they possibly do that at scale.
For SaaS companies today, increasing revenue is often about removing friction. The product team designs and improves features so that customers don’t have time to wonder whether the competition is building a better product. Customer Success is looking at customer health metrics to identify customers before they even think about churning and improve their results.
Marketing & Sales have a plethora of data & engagement tools so that they know everything about who their engaging with from Clearbit-enhanced Drift Bots to segmented Outreach campaigns encouraging prospects to signup for Webinars or jump on a call.
You are the friction.
So why is it that 90% of SaaS companies take more than five minutes to follow up on a request to schedule a demo? Francis suggests going through your own customer journey – ideally by signing up with a friend’s email account, especially if your friend is a great fit for your product – to get the full experience. If it’s not the ~48 hours of follow-up time that’ll make you feel the friction, it’s the ~5 days between the demo request and the phone call that’ll make you rethink your process.
What makes it take so long?
Lead data enhancement
SDR first-response latency
Email back-and-forth to validate interest and find a time to talk.
It’s easy to understand each one of those steps – after all, everything above (accept maybe the emails) feel very logical – the only thing that’s missing in the equation is the customer experience. SaaS companies are eager to over-optimize for the sake of being fair, applying rigorous rules to lead assignment, and this often flies in the face of the customer journey.
One of MadKudu’s most popular features, the Fastlane – an enhancement to signup forms that allows highly-qualified leads to skip the form and go straight to a sales rep’s calendar – is often difficult to implement initially because lead routing takes minutes. The customer eats the friction because of operational friction.
Remove friction. Prioritize customers.
It’s easy to remove friction from the customer journey if you prioritize it. Calendly, for example, offers a great Team Scheduling feature that allows prospects to see an aggregate calendar for every potential representative and then choose a time that works for them, instead of displaying the representative’s calendar after they’ve been round-robined with less available time slots. This puts the customer in the priority seat and sacrifices the possibility that Rep’s who have less immediate availability in their calendar might get routed less leads. In fact, that’s not a bad forcing function for making sure SDRs are prioritizing their time correctly.
Is the AI hype starting to wither? I believe so, yes.
Reality of the operational world is slowly but steadily catching up with the idyllic marketing fantasy. The report Jeffries put together challenging IBM Watson proves alarm bells are ringing. The debacle of the Anderson implementation goes to show how marketing promises can be unrealistic and the downfall will be dreadful.
With that said, not all is lost as we keep learning from our past mistakes. Being part of the Salesforce Einstein focused incubator, we are witnessing first-hand how the CRM giant is looking to succeed where Watson and others are struggling. Hopefully these insights can help others rethink their go-to-market strategy, in an era of unkept commitments.
Salesforce, a quick refresher
A few weeks ago, I was being interviewed for an internal Salesforce video. The question was “how has the Salesforce eco-system helped your startup?”. To contextualize my thoughts it’s important to know that while Salesforce is one of our main integrations, we consider it as an execution platform among others (Segment, Marketo, Intercom, Eloqua…). I’ve always admired Salesforce for its “platform” business model. Being part of the Salesforce ecosystem facilitated our GTM. It gave MadKudu access to a large pool of educated prospects.
However I believe the major value add for startups is the focus induced by working with Salesforce customers. Since Salesforce is a great execution platform there are a plethora of applications available addressing specific needs. This means, as a startup, you can focus on a clearly defined and well delimited value proposition. You can rely on other solutions to solve for peripheral needs. As David Cohen reminded us during our first week at Techstars, “startups don’t starve, they drown”. Salesforce has helped us stay afloat and navigate its large customer base.
What is Salesforce Einstein?
I’m personally very excited about Salesforce Einstein. For the past 5 years, I’ve seen Machine Learning be further commoditized by products such as Microsoft Azure, Prediction.io… We’ve had many investors ask us what our moat was given this rapid democratization of ML capabilities and our answer has been the same all along. In B2B Sales/Marketing software, pure Machine Learning should not be considered a competitive advantage mainly because there are too few data sets available that require non-generic algorithms. The true moat doesn’t reside in the algorithms but rather in all the aspects surrounding them: feature generation, technical operationalization, prediction serving, latency optimization, business operationalization… The last one being the hardest yet the most valuable (hence the one we are tackling at MadKudu…).
Salesforce Einstein is the incarnation that innovation will be in those areas since anyone can now run ML models with their CRM.
We’ve been here before
Just a reminder, this is not a new thing. We’ve been through this not so long ago.
Remember the days when “Big Data” was still making most of the headlines on Techcrunch? Oh how those were simpler times…
Big Data vs Artificial Intellligence search trends over the past 5 years
There were some major misconceptions as to what truly defined Big Data especially within the context of the Enterprise. The media primarily focused on our favorite behemoths: google, facebook, twitter and their scalling troubles. Big data became synonymous for Petabytes and unfathomly large volumes of data more generally. However scholars defined a classification that qualified data as “big” for 3 reasons:
– volume: massive amounts of data that required distributed systems from storage to processing
– velocity: quickly changing data sets such as product browsing. This meant offline/batch processing needed an alternative
– variety: data originating from disparate sources meant complex ERDs had to be maintained
In the Enterprise, volume was rarely the primary struggle. Velocity posed a few issues to large retailers and companies like RichRelevance nailed the execution of their solution. But the main and most challenging data issue faced was with the variety of data.
What will make Salesforce Einstein succeed
Einstein will enable startups to provide value to the Enterprise by focusing on the challenges of:
– feeding the right data to the platform
– defining a business playbook of ways to generate $$ out of model predictions
We’ll keep the second point for a later blog post but to illustrate the first point with DATA, I put together an experiment. I took one of our customers’ dataset of leads and opportunities.
The goal was to evaluate different ways of building a lead scoring model. The objective was to identify patterns within the leads that indicated a high likelihood of it converting to an opportunity. This is a B2B SaaS company selling to other B2B companies with a $30k ACV.
I ran an out-of-the-box logistic regression on top of the usual suspects: company size, industry, geography and alexa rank. For good measure we had a fancy tech count feature which looked at the amount of technologies that could be found on the lead’s website. With about 500 opportunities to work on, there was a clear worry about overfitting with more features. This is especially true since we had to dummy the categorical variables.
Here’s how the regression performed on the training data (70% of the dataset) vs the test dataset (30% that were not used for training and ensuring if a company is part of the training it is not part of testing – see we did not fool around with this test)
model performance on test dataset using available data points
Not bad right?! There is a clear overfitting issue but the performance is not dreadful apart for a blob in the center
Now we ran the same logistic regression against 2 feature: predicted number of tracked users (which we know to be highly tied to the value of the product) and predicted revenue. These features are the result of predictive models that we run against a much larger data set and take into account firmographics (Alexa rank, business model, company size, market segment, industry…) along with technographics (types of technologies used, number of enterprise technologies…) and custom data points. Here’s how the regression performed:
model performance on test dataset using 2 MadKudu features
Quite impressive to see how much better the model performs with fewer features. At the same time, we are less running the risk of overfitting as you can see.
The TL;DR is that no amount of algorithmic brute force applied to these B2B data sets will ever make up for appropriate data preparation.
In essence, Salesforce is outsourcing the data science part of building AI driven sales models to startups who will specialize in verticals and/or use-cases. MadKudu is a perfect illustration of this trend. The expert knowledge we’ve accumulated by working with hundreds of B2B SaaS companies is what has enabled us to define these smart features that make lead scoring implementations successful.
So there you have it, MadKudu needs Salesforce to focus on its core value and Salesforce needs MadKudu to make its customers and therefore Einstein successful. That’s the beauty of a platform business model.
I also strongly believe that in the near future there will be a strong need for a “Training dataset” marketplace. As more of the platforms make ML/AI functionalities available, being able to train them out-of-the-box will become an important problem to solve. These “training datasets” will contain a lot of expert knowledge and be the results of heavy data lifting.
MadKudu demo Jam 3
PS: To be perfectly clear, we are not dissing on IBM’s technology which is state of the art. We are arguing that out-of-the-box AI have been overhyped in the Enterprise and that project implementation costs have been underestimated due to a lack of transparence on the complexity of configuring such platforms.
A couple weeks ago, I ended up taking Steli’s click bait and read his thoughts on sales automation and AI. There isn’t much novelty in the comments nor objections presented. However I felt compelled to write a answer. Part of the reason why, is that MadKudu is currently being incubated by Salesforce as part of the Einstein batch. Needless to say the word AI is uttered every day to a point of exhaustion.
The mythical AI (aka what AI is not today)
The main concern I have around AI is that people are being confused by all the PR and marketing thrown around major projects like Salesforce’s Einstein, IBM’s Watson and others – think Infosys Nia, Tata Ignio, Maana.io the list goes on.
Two months ago, at the start of the incubator, we were given a truly inspiring demo of Salesforce’s new platform. The use-case presented was to help a solar panel vendor identify the right B2C leads to reach out to. A fairly vanilla lead scoring exercise. We watched in awe how the CRM was fed google street view images of houses based on the leads’ addresses before being processed through a “sophisticated” neural network to determine if the roof was slanted or not. Knowing if the roof was slanted was a key predictor of the amount of energy the panels could deliver. #DeepLearning
This reminded me of a use-case we discussed with Segment’s Guillaume Cabane. The growth-hack was to send addresses of VIP customers through Amazon’s mechanical turk to determine which houses had a pool in order to send a targeted catalogue about pool furniture. Brilliant! And now this can all be orchestrated within the comfort of our CRM. Holy Moly! as my cofounder Sam would say.
To infinity and beyond, right?
Well not really, the cold truth is this could have also been implemented in excel. Jonathan Serfaty, a former colleague of mine, for example wrote a play-by-play NFL prediction algorithm entirely in VBA. The hard part is not running a supervised model, it’s the numerous iterations to explore the unknowns of the problem to determine which data set to present the model.
The pragmatic AI (aka how to get value from AI)
Aside from the complexity of knowing how to configure your supervised model, there is a more fundamental question to always answer when considering AI. This foundational question is the purpose of the endeavor. What are you trying to accomplish with AI and/or automation? Amongst all of the imperfections in your business processes which one is the best candidate to address?
Looking through history to find patterns, it appears that the obvious candidates for automation/AI are high cost, low leverage tasks. This is a point Steli and I are in agreement on: “AI should not be used to increase efficiency”. Much ink has been spilled over the search for efficiency. Henry Ward’s eShares 101 is an overall amazing read and highly relevant. One of the topics that strongly resonated with me was the illustrated difference between optimizing for efficiency vs leverage.
With that in mind, here are some examples of tasks that are perfect fits for AI in Sales:
Researching and qualifying
Email response classification (interested, not interested, not now…)
Email sentiment classification
Email follow up (to an email that had some valuable content in the first place)
Demo customization to the prospect
Sales call reviews
So Steli is right: No, a bot will not close a deal for you but it can tell you who to reach out to, how, why and when. This way you can use your time on tasks where you have the highest leverage: interacting with valuable prospects and helping them throughout the purchase cycle. While the recent advent of sales automation has led to an outcry against the weak/gimmicky personalization I strongly believe we are witnessing the early signs of AI being used to bring back the human aspect of selling.
AI, Big Data, Data Science, Machine Learning… have become ubiquitous in B2B. It is therefore our duty as professionals to educate ourself as to what is really going on. These domains are nascent and highly technical but we need to maintain an uncompromising focus on the business value any implementation could yield.
Want to learn more or discuss how AI can actually help your business? Feel free to contact us
99% of the B2B SaaS companies I talk to don’t segment their free trial users.
This is a shame because we all know our trial users can be very different from one another.
For example, have you heard of accidental users? Those users signed up thinking your products was doing something else and leave soon after realizing their mistake (much more common than what you might think!).
Or what about tire-kickers? Yes, a surprisingly large number of people like to try products with no intention of buying ever (more about it in this great post from Matt Pope).
There are also self-service users. They are actively evaluating your product but don’t want to talk to a human being, especially a sales person.
The enterprise buyer is an interesting profile. She will likely buy an expensive plan and will appreciate to get help from an account executive.
“Sure thing… why should I care now?”
Fair question. Here is what happens when little is done to identify the different types of trials.
1. The overall conversion funnel has little meaning
A SaaS company we work with was worried because their trial-to-paid conversion rate had decreased 30%. Is this because of the new product feature they just released? Or maybe there is an issue with the email drip campaign? The explanation was simpler: A large number of tire-kickers coming from ProductHunt suddenly signed up. Their very low conversion rate crashed the overall conversion rate.
Looking at the trial-to-paid funnels by customer segment is the best way to understand how your product and sales activities affect conversions, regardless of variations in customer signups.
2. You are selling and building the wrong product features
Understanding how your product is used is essential to effectively sell and improve your product.
But looking at overall product usage metrics is misleading. The accidental users and tire-kickers usually make up a large chunk of your customers. Looking at overall usage metrics means that you may well be designing your sales and product strategy to fit your worst customer segments!
When looking at product usage, make sure to focus on your core user segment. The features they care about are the features to sell and improve.
3. You are spending your time and money on the wrong trial users
There are lots of ways in which a lack of segmentation hurts your sales and customer success efforts:
Tire-kickers take away precious time from sales and customer success. This time could be spent on selling and helping core users.
Customers with high potential value don’t get extra love. Many sales teams spend huge amounts of time on tiny customers while underserving larger customers.
Trying to get buyers to use your product and trying to get users to buy is a waste of everybody’s time. In B2B, the buyer is often not a heavy user. For example, a CTO will pull the credit card and pay for an app monitoring software, but he or she will use the software only occasionally. Educating the CTO on the nuances of the alert analysis feature doesn’t help anyone!
Sales trying to engage self-service users hurts conversions. Some users appreciate having an account representative help them evaluate a product while others want to do their evaluation on their own. Knowing who’s who is critical for both customers and sales teams.
How to get started?
One way, of course, is to use MadKudu (passionate, self-interested plug). Otherwise the key is to start simple. Talk to your best customers to get a qualitative feel of who they are, and look at your customer data to find out what similar characteristics are shared by your best customers. Then put together a simple heuristic to segment your customers and implement this logic in your CRM and analytics solution.
This effort will go a long way to increase your trial-to-paid conversion rates.
Now back to you. Do you have different segments for your trial users? If no, why not? If yes, what are those segments? Who is using them? Continue the conversation on twitter (@madkudu) or email us email@example.com!
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:
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?
A couple weeks ago I attended a Point Nine and Algoliahappy 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…
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.