By: Tom Swanson, Senior Engagement Manager at Heinz Marketing
Throughout our work, we get plenty of questions about metrics. This is a challenging spot for many B2B leaders and contributors. The way you lay out how you measure performance has a big impact on how you make decisions (like who to target) and deploy finite resources. Everyone wants to get this part right.
So to help you, here are some of the most common and how we typically answer them.
How can you prove marketing’s impact?
Your metrics need to have a through-line to impact (pipeline and revenue). Historically, the way to do this has been to track marketing influence back to revenue. Using an original lead source is often the best option for this type of tracking.
However, this is dated and creates unhealthy infighting over “credit” for deals.
The better way is to map out your data models across your GTM motions. There isn’t a reliable way to decouple revenue performance from sales efforts and just see what marketing did. However, with a solid data model showing volume and conversion metrics across the buyer and customer journeys, you can tie marketing efforts to shifts and changes in those numbers.
The ideal state, however, is to work alongside sales and align the metrics of each team towards impact. This yields some very interesting methods that can be used to show marketing’s impact like:
- Qualitative feedback from sales about marketing – how well is marketing supporting sales’ ability to close?
- Benchmarked conversion and close rates before and after testing
How do we compare marketing efforts against one another?
This is a relatively simple one that gets messed up often. Here is the simple answer: if you are comparing efforts in the same GTM motion (let’s say mid-market lead-gen), then use volume metrics. If we are looking at meetings booked from email cadence one vs. email cadence 2, no problem.
If you are looking across GTM motions with questions like “Is ABM outperforming MM lead-gen?” then you have to get more specific. In these cases outperformance usually means pipeline or revenue per unit spend.
Efficiency metrics should be the go-to when comparing efforts across GTM motions. Conversion rates are the obvious ones (win rate, lead-opp, opp-qual, etc…), but spend efficiency is probably the more useful (pipeline/spend, revenue/spend). These give you more of a feel of how far your dollar goes.
What are the right goals/KPIs/OKRs for our team?
This is a subjective question that we get all the time. The goals you set should be based on the strategic direction of the company. That being said, here are some guidelines:
- Some volume goals are unavoidable, but try to focus on efficiency
- Efficiency promises scalability
- Spend efficiency is the most important
- One of your goals should always be around improving time-to-market
- This forces workflow conversations and improvement, which builds crucial infrastructure for scaling
- Be careful of burnout or “workaround” culture
- Set goals with sales and CS, not independent of them
- Growth is a whole company objective
- This is not to say marketing should have accountability, but growth objectives should be shared
Why don’t my executives understand our metrics?
It is easy to have unrealistic expectations of marketing. When I hear that there is disagreement or misunderstanding of marketing’s goals and metrics, this is usually the source. It isn’t that they don’t understand them, it is that they don’t see the through-line to revenue.
Brand is an obvious example here. How do we know the brand investment is paying off? Well, you can’t really. In any marketing effort, at some point, you don’t know why the buyer makes the decision, even if they tell you. To some extent, marketing requires a bit of faith.
Good reporting’s job is to minimize how far you have to stretch that faith. So if you are struggling with this, it is likely because you are asking too much of the faith your executives have in marketing. This will vary depending on the individual, but examine how you are telling the story. Tweak your approach, you are a marketer so understanding your audience should be your top priority.
What should be our single source of truth?
Your CRM. Easy. Anything that doesn’t integrate with your CRM should be in your MAP.
How do we tell what is working?
This is one of those questions where it seems really simple. You did a thing, you see if you got the ROI. Easy.
If you are intentional about what you are doing, this shouldn’t be a burning question for you. Each tactic should have inputs and desired outputs that fit with the goals you set for it. Then it is just a matter of comparing the resources in vs. the outcomes.
However, things are rarely that simple, what this usually means is “what, among all the things I am doing, is working?”. If you have this problem, then the first step should be going back to your strategy and revisiting why you are doing the things you are.
You need to have a solid understanding of the buyer’s journey to know how your top-funnel tactics feed into your mid-funnel, and then into the bottom to really see “what is working”. Remember, not all tactics need to (or even should) have a clear, direct line to revenue. But if you can’t see the theoretical path, then you might be doing something just to do it.
Evaluate each tactic on its own impacts. Even though it is called a “vanity metric”, sometimes engagement is the goal. Brand awareness is a long-term play without a 100% trackable pathway to revenue, but that doesn’t mean you can’t see if it is working.
In the right context, all metrics can work.
What is the best way to assign credit across teams?
This is one of those “the only way to win is to not play” kind of situations. Assigning credit will always be a problem for as long as you do it. No amount, less than 100%, is fully acceptable, and compromise leaves everyone feeling underrepresented.
The better way to handle this is to set your goals with the other revenue eteams and work together to hit a goal. Then look at the revenue functions together.
If the company is hitting growth targets, the revenue teams are doing great.
Should I use my MAP or my CRM to track my data?
Yes.
In seriousness, if you can, the CRM is probably the way to go (see above). This isn’t always the right fit, but it should be the default.
What does a good data model look like?
There are plenty of answers to this question. My favorite is that a good data model does 3 things:
- It shows a clear, straightforward pathway through the buyer’s journey
- Filters the available metrics to just the ones that tell the story to revenue
- It allows for different GTM motions to be compared against one another
On the first point, the model should have a clear pathway to revenue. Not every metric is a direct line to it, but the model as a whole has to go from awareness to sale, and from sale to expansion.
On the second point, it is tempting when accomplishing the first point to put every available metric onto a spreadsheet. You must resist this urge. The key of a great data model is identifying which metrics tell the story best.
This doesn’t mean to ignore all the channel-specific and half-step metrics, those are still crucial to understanding your tactics and resource deployments. However at the high-level, the “is marketing working” level, you need to filter it out and tell a clean, understandable story.
If you do the first two right, then this one should be easy. The model should be able to be replicated for each GTM motion. Each one has the same template, but the specific metrics can vary. This allows for things to be compared along conversion and efficiency lines. Looking at spend/unit or conversion rates lets you compare easily across your motions.
Why can’t I trust my data?
This usually comes up when one system is saying one thing and another system says a different thing. Very common problem.
There are 3 big reasons for this:
- Historically lackadaisical data practices leading to trash data
- Human error and inconsistency
- Too many tools/integrations doing too many things
There are some others, but these are the big 3. For 1 and 2, the treatment is a database health effort that includes sales training and accountability.
For the 3rd, it is a tech audit and possible a few conversations with your tool CS reps.
How can we use both qualitative and quantitative data?
This is my favorite question, and admittedly is not as frequent as I would like it to be.
Here is the deal: marketing (and B2B marketing in particular) drastically undervalues qualitative data. There is just nothing better than getting a true understanding of the context available in qual data. You can get this in so many ways from customer interviews to surveys to “how did you hear about us” on forms.
However, what you do with it is very important. Qualitative data on its own is time consuming and ahs the same problem as quantitative data: it only tells half the story.
If you want the full picture, you need to use mixed methods analysis, which is a particular type of data review that combines qual and quant data to look at a full picture. It rocks and I love it.
There are plenty of resources to review on this, but here is something to think about: qual data has never been so easily analyzed as it is today thanks to large language models like ChatGPT. It is already impacting other areas of marketing.
If you are daunted by your qual data, then see what your company policy is for using LLMs to analyze qual data at scale. You will find some very interesting stuff.
So that’s the FAQ. If you want to chat with us about any of it, reach out to book a strategy call.
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