Achieving Sales Performance Optimization Through Automated Incentive Compensa...
Salesforce Analytics Framework for B2B Marketers to Drive Insights
1. Salesforce Analytics for B2B
Marketers
Isaac Payne
Senior Marketing Analyst – Salesforce Pardot
ipayne@salesforce.com
@Isaac_Pardot
Turn your Data into Insight
3. How to go from data
collection to insight marketing
How to apply a straight-
forward framework to conduct
intelligent marketing
What’s the difference?
How to leverage each to
optimize your marketing
campaigns
Applying the model to a
common scenario
Advanced tips for
customization
Giving your Data a
Meaningful Framework
Strategic and Tactical
Analytics
Putting itAll Together and
Advanced Tips
Agenda
What are we talking about today?
5. The Puzzle
Data without a framework is live trying to solve a puzzle
without any idea what the mixed pieces ought to represent
6. Framework for Understanding Marketers’ Needs
• Foundation of “Demand
Generation” marketing
• Basic Principle: Closed-
Loop Reporting, or Lead-to-
Revenue
• Allows for strategic and
tactical marketing analytics
8. How to Apply the Framework
50% Prospect to MQL
(Need 400 Prospects)
20% MQL to Opp
(Need 200 MQLs)
25% Opp to Closed-Won
(Need 40 Opps)
Revenue Goal: $100K
(10 Deals)
• Strategic analytics benchmarks
relevant KEY PERFORMANCE
INDIACTORS (KPIs)
• Tactical analytics help marketers
deep-dive and optimize the entire
funnel
9. Strategic Reporting
Metric Description Use Case
(1) Inquiry/Prospect/Lead
Marketing generated a new
prospect
Track the top of the funnel to
understand sufficiency for pipe and
revenue goal
(2) MQL
Marketing generated a lead,
qualified it, and assigned it to sales
Track the hand-off from marketing to
sales to understand sufficiency for
pipe and revenue goal
(3) Opportunities
Sales team marks the lead as a
potential new customer
Track the middle of the funnel to
understand sufficiency for revenue
goal
(4) Closed-Won Deals
Sales team closed the opportunity
that marketing assigned
Track revenue to close-the-loop
13. Applying the Framework
50% Prospect to MQL
(Need 400 Prospects)
20% MQL to Opp
(Need 200 MQLs)
25% Opp to Closed-Won
(Need 40 Opps)
Revenue Goal: $100K
(10 Deals)
Prospects/Inquiries
14. Tactical Data
Metric Description Use Case
Visitor to Prospect
Conversion %
The % of web traffic that
marketing converts to
identified prospects
Track this conversion to
understand efficiency of your
forms and landing pages
Prospect to MQL
Conversion %
The % of prospects
generated who become
marketing qualified leads.
Track this conversion to
understand efficiency of your
qualification programs (e.g.
nurture campaigns)
MQL to Opp
Conversion %
Marketing generated a
lead, qualified it, assigned
it, and sales converted it
into an opportunity
Track the hand-off from
marketing to sales to
understand sufficiency for pipe
and revenue goal
Opp to Closed-Won
Conversion %
Sales team marks the lead
as a potential new
customer and closes the
deal
Track the middle of the funnel
to understand sufficiency for
revenue goal
Channel Specific
Metrics
Examples include: Email
click-through-rates; event
attendance/audience
acquisition; SEM
conversions
Track to optimize channel
specific marketing campaigns
Tactical data can get extremely channel
specific
Deciding which metrics to capture will
depend on the user who needs the data
and the audience who will ultimately
consume it
The KEY here is to always track the metrics
that (1) relate to the funnel efficiencies of
the framework and (2) metrics that can
ultimately be tied back to your key strategic
data
17. Putting it All Together
The scenario:
Halfway through the quarter, your
strategic data indicates that you
do not have enough MQLs to hit
your revenue goals
All budget is already allocated, so
simply spending more is not
possible
18. Putting it All Together
Can you optimize your Form
and Landing Page Conversions?
Can you Optimize your MQL
producing Nurtures?
Can you optimize your keyword allocation?
What Levers Can you Pull?
If your # of MQLs are low, then
you know that you either need
(A) a higher conversion rate
between prospects to MQLs or
(B) more prospects
19. Advanced Tips
Customize to your business
Do you have senior sales reps
who are responsible for closing
marketing-sourced deals, but
ALSO for generating their own
outbound leads and pipeline?
Velocity metrics count
400 prospects today does not
equate to 10 new customers
today
Averages obscure the variations
It’s possible to get to the $100K
with only 1 new customer
*Hey everyone, my name is Isaac and I’m a marketing operations specialist here at Salesforce Pardot.
Intro title
*So a little about me first, I work on our demand generation and campaigns team, and I spend most of my time here building and maintaining our marketing automation and CRM infrastructure (so think data collection and syncing, campaign creation, building forms and landing pages). And additionally, I’m charged with doing this in a systematic way so that we can leverage our technology to make data driven decisions around optimizing our campaigns and, ultimately, so that we can get more ROI out of our efforts.
*So first let’s take a look at the agenda.
So what are we talking about today?
As marketers or salespeople, we’re constantly using our technology stack to execute on our roles.
I mean, think about it. We use technology products to generate leads, to manage those leads in an automated way to reach out to prospects at scale, to and monitor deals.
But at the end of the day, we’re concerned with how to drive more ROI from our marketing and sales efforts. And to do that, ROI being a mathematical formula, we need to govern, collect, and analyze data.
Given this concern and the role that analytics play in it, my main goal for this presentation is to highlight a few ways for you to think about marketing analytics, and to provide some tangible take-aways that you can apply directly to your marketing efforts.
Go over all 3
So with that, let’s dive right in.
So why do I start off a presentation on marketing analytics by referencing this (admittedly) vague idea of a data framework.
Well, the reason that I start with this concept is because just looking at and collecting data without one is akin to trying to solve a puzzle without any idea of what the mixed pieces ought to represent;W/ no insight into what the goal or actual image is that you’re trying to piece together or reconstruct.
Your just guessing at which pieces may or may not fit. All you have is raw information in front of you
Sure, you can solve it; but most likely it will be an inefficient and tedious process, and, once you have it all compiled, what if you just end up with a picture of a silver square, as these pieces might indicate.
The way I like to think about it is that. The picture on the box is a guiding framework what allows you to determine a path to your goal, or a method to which pieces that you choose to match together.
Or rather the “framework" of geometry (the rules and axioms for how to derive insight about the properties of space) , reminds you how to piece together a right angle of a square :-). Eater/or, this is aA way to derive organization, meaning and structure out of the little, squiggly boxes
So in the context of B2B marketing, what’s the goal we’re trying to solve for in our puzzle? What’s the image that we’re trying to reconstruct in a world of email clicks and Facebook "Likes" and white paper downloads more importantly What’s the formula or the path to discovering how too get there?
What;s the framework that can help us get to that goal?
So let’s start off with a framework.
—How many of you are familiar with SiriusDecisions first off?
—For those of you who aren’t, they’re a B2B research and advisory firm, and I’d highly recommend you check out some of their material .
Their Demand Generation waterfall?
Oh well, that’s great.It’s always tough to give a 20 minute talk on marketing analytics given the varus facmiliarity of the group.
Of course we could easily the entirety of discussion on the pros and cons of various analytical models such as this one
(and I stress— This is ONE way to look at and derive meaning from your data
) But we’ve found it to be quite effective here at Pardot.
Because as it turns out, this funnel can be a really great guide and diagnostic tool for improving and making your marketing a repeatable, successful process.
ANd hopefulyl I’ll highlight why that’s the case
I think this is truly a paramount structure that is the heart of “demand generation” marketing
primary principle—> Closed-Loop Reporting
Lead to Revenue; the basic idea here is to allow marketers to follow and track a single lead all the way through the marketing and sales funnel from initial inquiry through marketing qualification all the way to customer acquisition.
And what I really enjoy about this framework is our ability to slice and dice our marketing analytics from a strategic viewpoint as well as tactically.
So let’s talk about what this means
o what does this distinction mean?
Strategic analytics are your KPIs.
the benchmarks to lead up to your ultimate goal.
These data answer the “what” of your marketing efforts.
—In other words, what are you trying to achieve?
—And for most marketers, this will be revenue (new business acquisition, cross sell or upset)
—But what do you need to get there?
—>well, you need your sales reps [in the B2B Context] to be generating sales opportunities
—>what do you need to get there?
—>marketing qualified leads
—what do you need to get there? Well, inquiries or prospects
So strategic analysis is all about “the what.” What are our primary goals? What are the major metrics that we need to strive for in order to be successful in our goal of generating revenue?
Tactical: data points answer “the how"
these data answer the “how” do we (1) achieve the benchmarks at each layer and (2) how do we optimize our discrete marketing efforts at each level.
—how many prospects do I need to generate to an adequate level of marketing qualified leads?
—how many customers do I need in order to generate a specific financial goal?
—how well are my nurture campaigns doing at generating MQLs ?
—how effective are my SEM campaigns at each level>
When we tie all of these together, the result is a quantifiable model of demand generation that allows to know our strategic, high level goals, along with our tactical levers that we have at our disposa for optimization
And this is the beauty and simplicity of the demand gen waterfall.
At any point in time, if you know your revenue goal then you can determine(1) what you need at each stage of the funnel (2) you can also determine how your business is trending and (3) where you need to optimize.
—>all with the aim of developing a methodical process of attempting to align your data collection with your overall goal. Just like the picture on the back of the puzzle box;
Let’s dive right into the model, and explore how it can be used to guide your marketing analytics strategy.
In this lead to revenue model, there are really only 4 major, strategic and about 4 corresponding tactical metrics that need to be captured and reported on, and we’ll go through each here.
Here I’ve got a table that you can reference later, but for time’s sake I won;t belabor these here. In general, these major KPIs map to the layers of the sales/marketing funnel:
Propsects
MQLs
Opps
Customer Acquisiton
So let’s assume the puzzle we’re trying to solve is how to drive $100K in new business
So if my goal is $100k in net new business (we’ll leave out cross-sell and up-sell for now),what I need to do is to determine how many customers from a numerical standpoint this equates to.
How being a tactical question by it’s nature, we need a conversion rate that can equate this revenue goal to a number.
SO what I need to look at then is my CRM and determine what my average deal size is in order to determine how many new customers I need to acquire.
So as you can see I have a simple Sales Cloud report here that is tracking both the # of new customers along with the corresponding revenue.
From this, let’s say we derive that each deal is worth about $10k. This means that I need to acquire 10 customers to reach my goal.
Knowing the # of customers that I need to acquire is the key to determining the rest of the funnel. And now that we know how many new customers that we need, it’s time to move to the prior strategic metric in up the funnel.
So as we discussed, to generate new business I need my sales teams to be building their pipeline and working opportunities.
Working back upward what we need to do is to determine how many opportunities do I need to generate in order to get 10 new customers?
Again, “how" being a tactical question in nature, I need some type of conversion data point that tells me how to get from X number of opportunities to Y # of new customers.
So what we’ll do here is we’ll need to determine (1) what % of the opportunities that we create become new customers?
Using Sales Cloud or Pardot, this is an easy number to pull!
Heuristically, let’s say that we determine that 25% of our opportunities become new customers. Well, that means that we need to generate 40 opportunities.
And now that we know what the # of sales opps is, we now need to look back up the funnel.
Since we;eve linked our sales opportunities to marketing’s ability to generate demand among their prospect base, we next need to figure out how many MQLs do I need in order to generate 40 opportunities?
And again, we’ll reference Pardot or Sales Cloud to determine how many MQLs that we generate become opportunities
Well looking at my data, I see that my MQLs convert at 20% so this means that I need 200 MQLs to get to 40 opps.
And again, we work back upwards. Since we’ve linked our ability to get MQLs from a layer above of prospects, we need to figure out How many prospects do I need to get this many MQLs? Referencing our Pardot data, we see that about 50% of my prospects become MQLs this means that I need 400 prospects.
And now we have a complete funnel view here, along with all of our data.
By breaking out our data this way, we can begin to intelligently frame it so that we’re no longer just collecting clicks and leads, but rather we’re bringing our essential #s together in way that allows us to know (A) what our goals are, and (B) how we will get there.
How do we get to our revenue goal? By generating 400 prospects and by converting those prospects into MQLs at a 50% rate of conversion, and so on and so forth.
So as you’re probably gathering, at each stage of analysis we’re collecting a strategic data point or goal. And we’re also capturing that tactical one as well.
These strategic data points are great because
Leading Indicators [are we going to hit our overall goal this week, month, Quarter or Year]
Benchmarks or KPIs for Campaign Success
—e.g. if you’re running a campaign to generate top of the funnel leads, then you should be able to speak to MoM or Q/Q growth based on this metric
—if you’re running a nurture campaign to qualify your prospects, then you should be able to tie those programs to increases in your MQL volume
But at this point, you’re probably wondering why I’ve left out a discussion on email clicks and SEM conversions, right?
Well let’s jump to that side of the coin more in depth.
s I did with the strategic data, I’ve got a table here that you can reference when we send out the slides, but we won;t spend too much time here.
But firs, I want to make some clarifications. Tactical data can get super channel specific, and it’s best to keep your audience in mind when considering which metrics you’ll want to track (so is this really data that my CMO needs to see?)
AND you need to keep the needs of the end user who will consume the data in mind.
As we saw, from a demand gen perspective we’ll need to keep tabs on our conversion rates throughout the funnel. These are key tactical data points (% of prospects to MQLs, etc.), and these are our levers that we can pull when it comes to optimization
BUT, For example, as an email marketer, or a social marketer, or an events marketer, or a an SEM marketer, they may want to look at different trends and key tactical metrics that aid CHANNEL specific efforts.
The real capstone here is to ALWAYs track the metrics as they (1) relate to funnel efficiencies of the framework and (2) as they relate to the overall strategic goals that you have set.
So there are a ton of examples here and they only proliferate as you begin to think about all of the tactics that your team employs, but I’ll go through two briefly.
Again, remember all of these metrics will be channel specific, but the goal here is to tie these to our framework. And, of course, the goal here is think about and collect metrics that aid your ability to answer “how” do we optimize these channels?
*Whoever is in charge of your landing page/form generation should be tracking overall conversion % of your forms and landing pages. That is to say, of the total # of anonymous vistiors who are viewing your website or your online ads, what % of those convert into identified prospects?
Moreover, this person should ALWAYS be keeping track of (A) with which pages are the BEST at converting traffic, and (B) what are the key variables that can be manipulated and tested to improve these conversions.
—The use case here is that your capability at converting web traffic is a HUGE source of your prospects, and to be effective at this type of marketing you must know which CTAs, which combination of graphics and copy are the offer the BEST value prop for prospect acquisition.
—At Pardot, we’re huge fans of multivariate testing, which is a helpful mode of analysis that allows marketers to see which changes can influence higher conversions. The idea here is to test 2 different scenarios out for the same offer that you have in marketing, manipilating a single variable (the CTA, the copy, the graphics, etc) and see which performs better at prospect acqusisiton.
*If email marketing is a large tactic for your team, then your email marketer should be tracking a variety of metrics
—click through rates; delivery rates; open rates; click to open rates, A/B testing results
These are the data points that will allow your email marketer to optimize her programs; to get better engagement from email sends; to get prospects to submit more information and to engage them with content that they find appealing and interesting.
But how do you tie these back into this framework?
Well let’s jump into a real world example to see how these data can all work together.
Now that we’ve talked about the lead-to-revenue model, and how we can think about and track marketing analytics from a strategic and tactical viewpoint, let’s focus on how to put all of this together with an example.
So I want to give you a scenario and, given that marketers are almost always asked to do more w/ less I’m hoping that this resonates with at least a few of you.
So what we have here is a snap shot from Pardot, which displays our entire funnel. And what we’re seeing is that, while our MQLs are up we simply don’t have enough given our conversion rates.
So, keeping our operational model in mind here, what levers can we pull?
Well, if you’re # of MQLs are low then in this model this means that we really have 2 options. We can either (A) optimize our tactical conversion rate between prospects and MQLs. or (B)increase the total # of prospects that we generate (i.e. tweak the strategic data)
So for (A) we’re looking to see with the same or less amount of prospects we can get a HIGHER conversion rate, though we haven’t changed the absolute # of prospects that we have in selection pool we have increased our ability to convert that audeicen more effectively into MQLs.
SO one way that we can do that is to take a detailed analysis of our form and LP conversion rates.
According to Unbounce (http://unbounce.com/conversion-rate-optimization/how-to-optimize-contact-forms/)
3 form fields have an optimum conversion rate of 25%
4-5 have 20%
and 6+ have 15%
So reducing your forms by 3 fields can lead to a 50% increase in conversion rate.
—Are certain forms or LPs performing worse in this time period?
—Can you implement a better use of progressive profiling or potentially remove the # of form fields to increase conversions?
From another angle, we can look at other programs that we have in market that work to generate our tactical conversion rate data.
—>how effective are you at driving demand among your prospects?
—>which email campaigns are prospects engaging with the most, and which are helping to generate the most MQLs? which are generating leads with the best grade/score combination?
Can you make tweaks here on this basis? Again, not spending another $ we have a vast toolbox at our disposal. We can use our tactical data about email programs, point them at our model, and see where we can get results in terms of our tactical data.
Secondly, from a strategic # of prospects perspective, we can begin to look at our campaigns that are aimed toward generating top of the funnel prospects.
(3) Let’s take a paid example into account. If you’re investing a lot in SEM, you should re-evaluate your keyword allocation and your paid search campaigns.
Which ones are driving the MOST prospects? Are there underperforming ones or underperforming keywords that you’re bidding on that you could move budget away from?
Again, without adding another dollar in spend, we can use our demand generation model to know what we have at our disposal.
Everyone’s marketing playbook is different, but
At the end of the day, what we have here is a tangible operating system for how we can consistently monitor and report on our marketing/sales funnel.
We tie our metrics back to our framework, we evaluate empirically what is working, and we constantly integrate to make our ROI and marketing success repetable.
That’s the power of marketing analytics when conducted in this manner.
One thing that I want to note here is that this model is very flexible and that you'll need to adapt it for your business, to your sales process and to your marketing process.
**Outbound Sales Reps
If you remember from the model, in many organizations sales reps are responsible for generating their own leads along with creating opportunities from them. Of course these will also trickle down into revenue and a complete demand gen model needs to take this opportunity source into account to get a complete view of revenue and to get a complete method for optimization.
**Velocity
Moreover, certain industries and businesses have various sales cycle lengths and velocities, meaning that a prospect generated this month might take 1, 2 or 3 months to move down the entire funnel to a new customer.
These are data points that you’ll need to explore especially if you’re looking at a monthly revenue goal. So simply generating 800 prospects is NOT necessarily gratin those 100 new customers WITHIN THE MONTH that they are generated.
**Customer Acquisition
When we looked at the bottom of the funnel and that our goal was $100K, we found that, on the average, this equaled to 10 deals.
But i’d argue that if you pursue the average, then you’ll achieve an average result.
Do you deal sizes greatly vary by industry? By market segment? By geographical location?
If so, it may make sense to have (1) an overall Demand Waterfall, and (2) others based on these differentiators.
