Spotted: Sales Marketing Alignment in 2018

Ah, what a time to be in sales and marketing! We’ve written extensively about the many new trends we’ve seen SaaS teams us to scale up by making their data actionable.

But we’ve also seen teams use data to tackle one the decades-old problem of sales marketing alignment.

“You didn’t follow up on our leads!”

“But your leads suck!”

Still familiar?

In this Spotted post, we share the trends, tactics, and techniques we’ve seen scaling SaaS teams use to bring sales and marketing together in 2018.

B-r-e-a-k-i-n-g sales marketing alignment down

Sales marketing alignment is not just one problem. It's multiple problems packaged together. Much has been written about the cultural differences, different incentives (base + commission vs salary + bonuses), and different methods of working.

But besides all the usual debates, there’s a power dynamic that is consistent across every sales and marketing organization.

Teams own tools. Tools own data.

Siloed tools mean siloed data mean siloed teams. Without sharing the same data, sales and marketing cannot share the same context and ‘worldview’ of any lead or customer.

Historically, this has been the role of a sales CRM and marketing automation platform. Salesforce and one of HubSpot, Marketo, or Pardot would stitch these two teams together.

But this partnership is breaking because the way people buy is changing.

The data about your buyers is changing

Since the way people buy is changing, the way we sell needs to change too. There are three trends to highlight in particular.

1. Shared context
2. Buyers are anywhere
3. Buying is now real-time

First, just as buyers are getting the information they need to make a decision — search, social, reviews — so are sellers getting information about their (potential) buyers.

By progressively profiling their customers, companies like Amazon, Booking.com, Uber are able to. They can pre-empt and “fill in the blanks” so we don’t have to tell them again. An Amazon Prime member or Booking.com “Genius” can check out in a few clicks — they already have the data they need — which makes for a far better buying experience.

In B2B, we’re spoilt with even richer data. Stitching together data about a company and each key stakeholder that matters to a deal or has interacted with your brand or product. The abundance of data empowers us to create a better buying experience.

Second, buyers are anywhere. The number of channels, tactics, and mediums to engage with your product, brand, and users have increased dramatically.

As a seller, you need to be able to track leads wherever they interact with your brand and react to conversations “omnichannel” across any of these many platforms beyond your website include social, review and chat. Selling B2B isn’t as a simple as a website, forms, and email anymore.

Third, buying is now real-time. The fastest growing companies enable buyers to buy on their timing, not after a company or rep-enforced process has ticked over.

Think of self-upgrading on a free trial SaaS, booking a meeting via live chat, or just plugging in a company credit card and get started right away.

As a seller, it’s up to you to get the context in place to respond meaningfully in real-time. Chat might be fast, but if your reps lack the immediate context on who they’re talking to, then your selling experience falls flat.

All this means you have to be able to provide the answers — the data — to your sales reps in near real-time. And it’s not just for sales. Real-time data integration matters for marketers too. Messaging needs to react to the buying journey advancing.

Sales marketing alignment in 2018 is a first and foremost a data problem — before it becomes a people problem. As you adapt the way you sell to the way people buy, your tools and data need to keep up.

The problem we’ve seen is many CRM:Marketing automation setups in scaling companies cannot keep up with modern data demands.

It’s hard to put together a complete unified lead and customer profile...

That can track and react to interactions across any channel (not just the channels they were built for)...

And to do this all in real-time — to deliver the buying experience expected in 2018.

What’s more, is the problem extends beyond just sales and marketing. Similar conflicts divide marketing-finance, product-marketing, sales-operations. As the amount of data we have and use increases, the alignment problem becomes even more clear across different teams.

(Watch out for a future Spotted post on total customer data operations…)

With this in all in mind, here are the best practices we've spotted in 2018 for driving sales marketing alignment amongst scaling B2B SaaS startups.

Tactical Takeaway #1: Build a shared ideal customer profile to qualify leads

Yawn…

But we’ve seen few teams have all of this in place already.

Your ideal customer profile is a clear, common, objective definition of who the ideal purchasers and users of your product are.

(Personas are something else. They help define how you talk, not who you talk to. More on data-driven personas in a bit…).

To break this down:

• Clear - Your definition must be understood by everyone across your company
• Common - Every team who own tools with customer data must share (and agree on) this definition
• Objective - You must be able to tie your definition to customer data, and use it to easily mark customers (and leads) as best-fit or not

Some teams have one or two elements here, but few have it all.

Your ICP will be used to build your master customer data model (in your unified customer profile), inform what customer data you need, and how you can qualify leads.

This is table stakes for aligning teams.

Remember the power dynamic: teams own tools; tools own data. You have to start by agreeing on the right data, which needs the right definitions.

Voila! Now you have a shared definition of a “best fit” lead.

Tactical Takeaway #2: Automate your lead qualification

With a clear, common, objective ideal customer profile, you can use this to define and build a better qualified lead.

Whilst marketing might qualify best on an initial signal of interest (like submitting a demo request form), this is rarely what a sales rep - who only gets paid when they close - would consider as the only signal that they can close this deal.

But left to their own devices, sales reps can lean on all manner of different biases based on random samples of experience. This sucks.

• It’s slow. Sales reps are choked by decision making and gazing into the proverbial crystal ball…
• It’s inconsistent. Randomized methods are hard to measure, understand, and improve
• It is not scalable. You want to know your sales conversion rates will hold as you increase volume.

Instead, you want to aggregate the data that signals that a lead is a good fit, build a precise segment of just those qualified leads from as many data sources as needed, and then sync only qualified leads to your sales reps.

Besides the initial signal of interest (like submitting a demo request form), there are three types of data we look at.

The first form of data is enrichment data. Use 3rd party services like Clearbit and Datanyze to “fill in the blanks” in your lead data, and identify best fit leads (that match your ideal customer profiles). Using this data, you can create a segment of just your qualified leads to sync through to your sales CRM.

By syncing only qualified leads, sales reps can get on with selling instead of deciding themselves how to qualify a lead thrown over the wall by marketing.

For Lengow, this slowness and indecision resulted in a 50% drop-off in demo request responses. By identifying their best-fit leads upfront, and syncing only qualified leads through to sales, they fixed this drop-off and doubled qualified leads. Reps could focus just on qualified leads, without having to invent rules themselves. Trust was restored.

But data enrichment is not the only part of the data model.

The second form of data is engagement data. How are leads engaging with your product and brand? Are any of these activities signals of buying behavior? Have they reached their “aha!” moment?

• What pages are they visiting on your website? Pricing? Case studies?
• Are they maxing out their free trial of your software?
• What are they talking and asking about your brand on social media?
• Are they visiting your reviews on sites like G2Crowd?

Your data model needs to react to the way buying has changed. For instance, there may be many signals in your free trial which indicate an interest in buying. (To dive into finding and optimizing your trial conversion funnel for sales, read our Complete Guide to Product Qualified Leads).

By aggregating all these buying signals into a profile, you can use that to trigger sales outreach and marketing message to nurture your leads to the next step - more of that layer…

The third form of data is predictive data. Having no data leads to sales reps guessing, but with an overwhelming volume of data — and relationships between data — your centralized segmentation and lead qualification becomes a “guess” too.

This is where machine learning tools like Madkudu can do a far more effective job at analyzing and predicting which leads are worth spending sales rep times in real-time. This “no stone left unturned” approach makes the best use your abundant data to qualify leads automatically.

Voila! Now you can automate your lead qualification and restore trust with sales.

Tactical Takeaway #3: Compute conversation starters for sales

The best marketing teams don’t just sync a segment of qualified leads to sales. They enable sales to do their job better. Conversational sales and marketing should enable team alignment.

The way you sell needs to reflect the way people buy; in conversation, and in context.

Your sales reps should continue the same personalized buying experience that’s been carefully crafted and optimized on your website, onboarding, chat, and so on.

So, marketing should not just be responsible for sending qualifying leads (WHO to talk to) but also what the conversation starter (WHAT to say). These are called lead signals.

We took a deep dive into this in our chapter in the Clearbit Data-Driven Sales book. DigitalOcean sold to over 500,000 developers. How could they engage their sales-averse customer base in a meaningful way?

The answers lay in their data.

For DigitalOcean, seeing someone creating a "Test scaling droplet" or something similar was an indicator they might be exploring higher value products, particularly if paired with high existing CPU usage, and they were from a company with a large development team. The outreach here could then be specific to how DigitalOcean could scale their infrastructure.

Data gave the context to enable conversational sales.

The same enrichment, engagement, and predictive data that you can use for defining your ideal customer profile and qualifying leads can also be used for conversation starters.

• Do they look like they’re exploring a subject, topic, or something they want to learn more about? (e.g. page views, features viewed but not used, campaigns from UTM parameters)
• What are they getting stuck on? (e.g. Abandoned search, abandoned flows)
• Limitations or next steps should they take (e.g. Product trial/plan limits)
• What are other users or companies like them doing that might be of interest?

But there are also individual signals that might indicate how you talk to someone. We’ve spotted some teams triage sets of data like job seniority from Clearbit with social data (like topical interest) from Mention and psychographics (like predicted DISC profiles) from Crystal build data-driven personas. Sales reps have cues in their CRM and templating in their messaging for different persona types.

Critically, the sales rep shouldn’t have to go searching for all this context. The data should come to the tool the rep is using. Just as with lead qualification, you don’t want to leave the reps guessing what to say first (or say next). Don't have reps hunt for the conversation starters. Sync the clues and cues to where they work proactively.

Voila! Now you can enable your sales reps to speak to potential buyers in their context.

Tactical Takeaway #4: Trigger sales activity in real-time

With the right leads (WHO to talk to), and conversation starters (WHAT to say), how should sales reps prioritize who to talk to?

The focus is on NOW.

Remember, the way people buy has changed.

• Free trials and low-priced plans mean they’re already using your product
• Website means they can already search for all things
• Social and third party review sites are where the conversations about your brand and product are already happening.

All these signals of engagement with your product and brand are very timely. Depending on the context, the perfect moments to “react” and reach out might be right away (not tomorrow, or next week). The operational challenge is bringing that rich, valuable context (data!) to your sales reps in real-time so they can use it in conversation and deliver that better buying experience.

Appcues, like many SaaS startups, operates a free trial sales model. Outside of the handful of mid-to-large companies, the vast majority of those leads don’t justify a sales rep to dedicate time to them. But the “you miss every shot you don’t take” problem happens by not having a sales reps reachig out.

This is where low-touch sales automation can come in.

Appcues use Hull to aggregate all the streams of activity occurring within their product and website, compute matching segments and attributes, and using those to trigger real-time:

1. Internal notifications - email or Slack notifications to assigned sales reps with all the clues, cues, and context on how to take action
2. Sales cadences - workflows of drip email, chat, and other channels that send (semi-)automatic templated, personalized messages

Now teams trying out Appcues could get contextual, helpful messages reinforcing the value proposition as they setup flows in the product, hit their trial limits, viewed pricing pages, and more but without the manual overhead of a sales rep being involved.

Computing and acting on sales triggers is not just about creating a better buying experience.

According to a study by MadKudu, radically increasing sales velocity (think of the “dollars per month” output of your sales team) is not easily achieved by radically increasing lead volume, close won rates, or speed of enterprise deals.

Rather, they’re best achieved by closing more small-to-mid “deers” faster. (Here's a summary of the elephants-deers-rabbits framework for classifying deal sizes from Christoph Janz).

The study finds “deer-hunting” SaaS companies often stall sales velocity within their (long, often 30-day) free trial periods.

By using sales triggers to engage leads whilst they’re in the moment, you can create more touchpoints (and more contextual, meaningful touchpoints) with leads, accelerate the sales process, and increase your sales velocity.

Voila! Now you can reach out and react to your buyer’s activity in real-time. (And sales behaves more like marketing.)

Tactical Takeaway #5: Build hyper-segmented mid-funnel nurturing

Appcues’ automated low-touch outreach shows how sales can increase both pipeline and sales velocity. This enables sales to build a wider funnel (by engaging more leads) and go up the funnel (by engaging before the lead might reach sales) without increasing headcount or selling in an obsolete, faceless way.

Where does this leave marketing?

In B2B SaaS, most teams we observe appear to divide into three functional areas as they scale up:

• Brand
• Product
• Demand gen (who are on the line for delivering leads)

Whilst brand may build out the top of the funnel “up and out”, we consistently see demand gen marketers focus the (unsexy) mid-funnel.

The top of the funnel has the big vanity metrics (traffic! followers! signups!), but it lacks the depth of data on each visitor and lead.

The bottom of the funnel has all the money and plenty of data about each lead, but 1:1 interaction isn’t scalable.

Only the mid-funnel has a depth of data (as you progressively profile leads) and the ability to scale (since marketing can send one-to-many messages. This is the most fertile ground to personalize and automate messaging at scale.

There’s a mid-funnel content marketing method we’ve spotted that dramatically improves conversion to qualified leads, and enables sales reps to close deals faster.

As teams scale, lead generation is typically not the biggest problem (of course, more is always nice). The challenge is converting those leads through to sales.

Instead of taking a top-down — “how do I convert more leads for sales?” — view to maximizing the number of leads who engage, teams take a bottom up — “what does it take to convert this lead?” — view to maximizing the engagement within specific accounts.

For instance, creating an educational webinar, industry-specific case study, or targeted ad campaign with competitor’s testimonials. These are all MOFU tactics you know, but they’re created for and aimed at a specific lead - not a generic audience.

Think of this as Account-Based Content Marketing (ABCM).

But with the highly-targeted content and campaign at hand, marketing can execute it at scale by sending not just to one lead or target account but other "lookalike leads” with very similar problems. They still get leverage.

By creating niche webinars for targeted segments of leads, Oz Content was able to increase their marketing qualified leads by 20X within two months. That’s 2000%.

Moreover, they did this by sending significantly less email, with a 3x higher open rate, and (when the topics resonated significantly) leads would forward their webinar emails to their entire team.

If the topics were resonated significantly, the Oz Content team noticed teams would forward their webinar invite emails to the entire team.

With the success of the mid-funnel, Oz Content ditched their cold outbound sales and focused their sales team on closing the qualified leads coming in from marketing instead. They adapted the way they marketed AND sold for how people want to buy.

This becomes a highly defensible content strategy too. Compared to their competitors, Oz Content was the only brand that was able to produce and deliver this hyper-targeted, hyper-focused content to their leads at the time - everyone else (optimizing for volume) have far less relevant messages at the key point in their buying cycle.

For sales reps, leads from these focused mid-funnel content become far easier to talk to (see Tactical Takeaway #3 on conversation starters) since they’ve already (and recently) raised their hand to a specific pain point and engaged with a specific solution.

Voila! Now you can convert more leads to sales, whilst empowering reps to close them too. (And marketing behaves more like sales)

Tactical Takeaway #6: Focus both sales and demand gen teams with SLAs

Your home, job, marriage… everything that really matters gets put in a contract.

Contracts enable people to be explicitly clear in their mutual demands and expectations.

The best sales and marketing teams we spot share a service level agreement (SLA) outlining what they expect from each other. The process of putting together a sales-marketing SLA also brings up the right kind of questions, discussions, and debates.

At Drift, this looks like:

• Growth/Marketing to Sales: Number of qualified leads each month. (where those leads will convert at an expected rate)
• Sales to Growth/Marketing: Time to touch those leads (since the value of a qualified lead decreases dramatically over time — see Tactical Takeaway #4)

(There’s a three-minute segment within this video from 0:59 where Drift talk about how growth-marketing shares a leads OKR, how they align growth-market-sales-ops each week, and the SLA with sales).

Without an SLA, it can be difficult to coordinate your separate teams (and sub-teams) to deliver the buying experience people expect. A lead goal doesn’t capture the complexity of the buying journey, or of responding in real-time.

With abundant data, there’s no excuse for marketing to be sending through large volumes of poor-fit leads — the biggest gripe.

With sales triggers of leads moving through the buyer’s journey, there’s no excuse for sales to be dropping the ball on good-fit leads (particularly with the way people want to buy today).

But even with world-class customer data management, you need to lay down expectations across all the teams involved. Sales-Marketing SLAs make this happen.

Voila! You now have a common agreement between sales and marketing for delivering a modern buying experience

Tactical Takeaway #7: Build and leverage a data operations team

Earlier, we broke out the problem of sales marketing alignment into tools, teams, and data.

Historically in B2B, we’ve seen sales own and operate a CRM. This would integrate with marketing tools - the centerpiece being the marketing automation platform.

Though Salesforce and other CRMs are widely used, there are three, consistent problems with CRMs we see in today’s sales environment:

1. Sales reps prefer to be elsewhere. Observe at the rise of sales enablement tools (Salesloft, Outreach, Close.io) and conversational sales tools (Intercom, Drift). The reps get more done with their day (and make more money) outside old school CRMs.
2. CRM data integration is limited. Whilst ecosystems like the AppExchange are extensive, they can’t ingest, transform, and sync the volume and complexity of modern data needs. They’re also incredibly expensive as “databases” from a $/MB perspective - you’re strongly incentivized to store (and therefore silo) your data elsewhere. (At Hull, we accidentally saved Mention $15,000 on their Salesforce subscription).
3. Reporting is limited (by data). Though the reporting tooling is often very advanced, with the limitations of data integration, it only casts a partial view of how modern sales happens. Most teams Salesforce instance can’t report on every person’s interactions with your brand and product ever. Data analysis tends to happen on top of data warehouses.

Marketing automation is just as isolated. As the way people buy changes, the number of channels and tactics explode, the “all-in-one” platforms cannot keep up and stay best in class. The “marketing stack” splinters.

The reality is the whole axis of tools-teams-data is shifting. Modern sales marketing alignment recognizes this. Instead of being siloed by functional departments (and the old CRM-Marketing Automation axis), we’re noticing teams pivot towards the customer.

• Customer engagement: the messaging layer (sales outreach, chat, email, content)
• Customer intelligence: the data layer

The data layer (particularly the customer data layer) works across many separate teams. Growth, marketing, sales, product, operations, finance…

This is why it’s important to breakdown the problem of sales marketing alignment - the data layer is an alignment problem between all and every team. We’ve seen no sales teams manage this alone, nor marketing teams (despite marketing historically having wrangle multiple teams across an organization).

We consistently see customer data being owned at an operations level in scaling SaaS startups — not sales or marketing. At Drift, the owner of customer data management tools like Hull and Salesforce is not their VP Sales, VP Growth or Director of Marketing. It’s their VP Business Operations.

Operations manages dedicated data engineers to integrate data across all teams.

The number of data engineers has 10X’d over the past five years (broadly in line with the growth of that crazy martech 6000 supergraphic).

It’s the job of data engineers to truly integrate all the tools, teams and data across your organization — including sales and marketing.

In the fastest growing companies we’ve seen, sales and marketing no longer own this problem, but they’re amongst the biggest beneficiaries. They (and everyone else) works off the same data set as standard. This means they can:

1. Pick, choose, and change the tools they want to work with (without worrying about data and compatibility with older CRM and marketing automation platforms)
2. Ignore aligning sales and marketing tools. Data integrated through a customer data platform like Hull is tool agnostic.
3. Focus on generating and closing leads to a common standard off of shared data

Without the overhead of data management, we see sales, marketing, and growth teams able to move much faster.

Voila! You now have the tools and teams to deliver a modern buying experience

Best-fit for sales marketing alignment

These tactics are written with scaling B2B SaaS companies in mind. We’ve observed the best-results amongst teams who fit these criteria:

1. You are post-Product-Market fit

You have a clear ideal customer profile that you as a company have found to work.

2. You have a sales and marketing operation setup

The basics are in place. Marketing is generating leads. Sales have ramped reps that can close them and meet the quota. There might be tension, but there’s momentum.

3. You have resource for optimizing (sales and marketing) operations

It takes dedicated headspace to focus on these kinds of problems. As in Tactical Takeaway #7, the best practice is to have a dedicated, centralized data operations team with data engineers.

Results we’ve seen

The biggest result we’ve seen is consistent sales and marketing alignment. Working off a common set of data means there’s a single, shared ‘worldview’. This creates a culture of trust.

(As we mentioned at the beginning, there are other dynamics - particularly around teams - that need addressing to for total team alignment).

We also notice that shared enables increases in qualified leads to sales (2-20X) and sales opportunities created (4x).