It’s time to look in the mirror, be honest with yourself, and confront the elephant in the room: do you want to be the best you can be, or be another product marketer merely making up the numbers?
We can hear you emphatically telling us you wanna be the heavyweight champ of the PMM division. But to get to the top of the tree, you’ll need a firm understanding of how your efforts are faring in every element of your role - positioning, onboarding, sales enablement, marketing campaigns, the lot.
Luckily for you, analysis can give you the answers you so desperately need.
In this article, we'll be covering:
- The importance of analyzing your product marketing efforts,
- Analysis best practices,
- Types of analysis, and
- Analysis in practice.
The importance of analyzing your product marketing efforts
Analysis is a never-ending job and product marketers should ensure they’re devoted to investing time, effort, and money to get it spot-on. This part of the job is so important because it helps you understand what is working and therefore what to do more of, as well as what isn’t working so you can cut that out.
For example, if you’ve created a sales battle card that’s not helping your sales reps, you need to know about it. If you’ve created a product page your prospects don’t understand, you need to know about it. If lots of customers are leaving after day 30, you guessed it, you need to know about it.
Analysis can give you the answers to the essential questions nipping away at the back of your mind, allowing you to not only beat the competition but also connect with your customers on a much deeper level.
Analysis best practices
From sales collateral and new features, to products, in-app messages, and training materials, product marketers are spoiled for choice when it comes to areas they can analyze, but there are best practices that should be considered.
So, how can you begin analyzing your practice and putting contingencies in place to:
- Maintain your current performance, or
- Improve existing practices to enhance future performance
Envision what constitutes success
At the start of every project, you need to make sure you establish what success will look like and how you plan to measure that success.
Remember, you’re rarely solely responsible for success; internal partners like product, sales, marketing, and customer success will usually weigh in at one point or another too. Without this first step in place, it’s difficult to know what to analyze.
For example, if you’re launching a new feature in an existing product and your objective is to get 55% of your existing customers adopting that feature, you know you need to analyze feature adoption, but as well as that, you need to analyze the parts that contribute to that adoption.
If you scheduled an announcement email and in-app message and produced a feature demo video, they could all impact that adoption, so you’d want to consider factors such as:
- How many people opened the email?
- How many people clicked on the in-app message?
- How many watched the whole video?
If these numbers are low, your feature adoption numbers are more likely to be low, and understanding the performance of these pre-steps can help determine where you focus your optimization tactics.
For instance, if everyone who landed on the video watched the whole thing but only 5% of your base made it to the video, the problem might not necessarily be with the video itself, but with the wording or timing of the message you sent people to point them to it.
This highlights the importance of having a clear and measurable objective, understanding what parts feed into achieving that objective, and remembering these moving parts might not always sit within product marketing.
Avoid vanity metrics
Make sure you’re measuring the right data and don’t get distracted by irrelevant metrics.
For example, let’s say you’ve launched a new landing page and it gets 50,000 hits in the first week. That’s a pretty decent number and it can be easy to get so excited about that success that you forget to focus on the step after that.
But, if your objective was to deliver 1,000 sales-qualified leads to the sales team and only 100 of those 50,000 visitors qualified, that wouldn’t be considered a success and there’s something wrong with either the messaging on the landing page or the audience you’re targeting.
This ties into setting clear objectives from the beginning, but to avoid being derailed by irrelevant data, it might be worth putting a bit of a list together of the data you care about along with your objective.
For example, sticking with the landing page example, the data you would care about is the number of sales-qualified leads, the number of those leads who converted, and the number of people who interacted with your landing page, but didn’t quite make that next step.
Use relevant tools
Equipping yourself with the best tools will also massively help you have the right mechanisms in place to track whatever it is you’re measuring.
Plenty of tools are available nowadays to help with this but more often than not, it relies on scouting out several different sources and amalgamating important data like traffic, conversions, and revenue, which can be time-consuming.
If you can, we’d recommend working with your data or IT teams and building analytics dashboards to help you automatically track these metrics. That way, you and your project stakeholders can take a glance at your project’s most important metrics as often and quickly as you like.
Tools like Looker and Tableau are good starting points for this, but you can also check out our PMM Tech Stack and discover more resources that’ve been tried and tested by product marketing experts.
Don’t ignore qualitative data
Not all analyses will be quantitative and it’s important to speak with customers and prospects and generate qualitative data.
It’s important to use both sets of data because numerical data only reveals half the story; this can lead you into a false sense of security which can be dangerous in both the short and long-term.
For example, let’s say your company’s overall revenue goals are being met and you’re generally going in the right direction. It’d be easy to see that as a win, right?
However, what if the only reason people are shopping with you is because there’s no better alternative at that given point in time? There’s a chance they don’t particularly love what you do and have viewed you as the only viable option available to them.
This puts your company in a precarious position because these customers aren’t loyal to your brand; as soon as someone enters the market who does offer what they want, they’ll probably jump ship and it’s then really hard to win them back again.
Having those conversations helps you put measures in place that would keep your company’s current revenue on track, both now and in the future.
Focus on personas and markets
The penultimate point is to remember to split your analysis by persona and market.
Usually, not all personas and markets will perform the same, so segmenting them during your analysis will help you understand how each area is performing and which need more attention, and where that attention needs to be put.
If you take a blanket approach and subsequently make changes, you could end up unintentionally harming your efforts.
For example, let’s say you’re analyzing a landing page and on the whole, the message and call-to-action aren’t performing so you switch it up.
However, persona A resonated with what you had and the new message and CTA don’t appeal to them as much, and as a result, their conversion rate drops. Needless to say, that can be damaging to your numbers.
In reality, you should be targeting different personas and markets with different emails, landing pages, etc., but either way, you must look at their data in isolation.
Types of analysis
There’s no universally accepted definition of which type of analysis product marketers ought to use; when it comes to the crunch, some favor different approaches to others.
While this is the case, there are two commonly used types of analysis used by product marketers: A/B testing and cohort analysis.
A/B testing
A/B testing is the process of comparing two versions of something, whether that be the color of a call-to-action, emojis in a subject line, or copy on a landing page, to see which version performs best.
The primary benefit of A/B testing is it gives you a degree of confidence before rolling out any changes and if it uncovers your variant is less successful, it gives you a chance to either revert back to the original or make further changes before it’s rolled out to everyone and possibly harms your efforts.
For example, when launching our membership plans, we wanted to get the most impact and value from our announcement email. Therefore we tested two subject lines to see which would generate a higher open rate.
We sent a subset of our opt-in database the email with the subject line ‘The wait is finally over' and the other with the subject line ‘PMA membership plans are here’; the former outperformed the latter with a 39% open rate compared to a 13% open rate.
With that insight, we then sent the remaining majority of our email list using the first subject line, knowing it would get more people to open our email.
As well as landing page copy, CTAs, and email components, A/B testing can be used to test things like:
- The thumbnail image of your product demo video,
- The time of day you send release notes,
- How you position your product,
- Which sales script your salespeople use,
- Which content marketing asset you send prospects, and so on.
The opportunities are endless. However, it’s important to test one thing at a time and wait until you collect a meaningful amount of data until you conclude.
Cohort analysis
By definition, a cohort is a group of people with a shared characteristic in a specified timeframe. In many ways, you could say cohort analysis is a bit like segmentation, but the core difference is it focuses more on historical data and has that set timeframe.
In the context of business, there are two types of cohort analysis: acquisition and behavioral. An example of an acquisition cohort could be customers who’ve bought your solution in March, or prospects who signed up for your free trial in the last 30 days.
An example of a behavioral cohort could be a customer who adopted a certain feature in February, or downloaded a report from their dashboard last month or canceled their subscription in the last 60 days.
Unlike when you look at your users as a single unit, cohort analysis enables you to break users into groups and pinpoint patterns that could be leading to moments of success or failure, and this in turn can help you optimize prospect and customer experiences.
Before we take a look at a couple of examples, let’s run through a few essentials.
Essential #1
The first is defining your cohort and as well as looking at time-specific measurements like ‘in March’ or the ‘the last 30 days’ this could be by customer demographics, like gender and location, or campaign source, channel source, device, or lifecycle stage.
Essential #2
The second is to understand which metric you’ll apply to your cohort and the options for this are broad. And finally, the third is to define the period you’ll be measuring.
This’ll depend on your circumstances and for some businesses daily might make perfect sense, but for others, it might be more like weekly, monthly, or even quarterly.
So, let’s take a look at cohort analysis in action. To set the scene, let’s position ourselves as a B2C online retailer looking to answer the question “How can I increase each customer’s lifetime value?”
From this data, you can see those who first purchased in April have a higher average lifetime value, so could that be down to a particular campaign, new product, or an event?
If so, how can you replicate that campaign for all future, new§ customers? And how can you put it in front of existing customers to encourage them to buy more?Here’s another example, this time for a B2B SaaS business trying to answer the question “Do I need to invest more into retention?”
So from this table, we can see the number of active desktop users drops quite heavily from weeks one to four in customers who sign-up in March.
However, users who sign up in or after April stay consistently active, and that could be down to a change in onboarding, how the sales reps are positioning and selling your product, a new feature you’ve added, and so on.
On the contrary, the number of active mobile users drops quite dramatically in those who sign up in May and don't recover, so could there be a snag in your mobile app? Is something not working? Or are load times slow?
The key is investigating what’s causing the pattern, whether it’s positive or negative, and using those learning to continually improve what you do moving forward, to ensure future cohorts are set for success.
It doesn’t always have to be fancy...
As well as using techniques like A/B testing and cohort analysis, optimization can be as simple as speaking to your prospects, customers, and internal teams and optimizing assets and processes based on that.
For example, if your sales reps are all asking for a section on ‘the future product roadmap’ to be added to your sales script, that’s a pretty quick and dirty optimization win.
Or, if your customers are telling you they find your onboarding tour too long, condensing that into fewer steps is another.
There are endless options here but the point is if you don’t get out there and speak to people, you’ll never know and you could miss out on incremental optimizations that amount to big differences over time.
It’s important to remember analysis should never stand still; it’s an ongoing process.
Everything, no matter how well it’s performing now, can be refined and improved to work even better for you in the future, so make sure you consistently schedule time in your calendar to focus on analyzing and optimizing both old and new campaigns.
Analysis in practice
Analysis is a mainstay in the day-to-day routine of product marketers, and with good reason; it’s an essential component in the PMM journey.
As is the case with areas such as OKRs, pricing strategies, and customer onboarding, market analysis techniques vary; there’s no definitive approach to take. Some product marketers analyze certain areas more than others, while their chosen methods may differ.
Salil Dhal Hakim, VP of Product Marketing at Highradius provided an outline of how market analysis plays a key role in his company’s daily operations.
“We use market analysis to focus on a few areas at our company.
“When we’re focusing on content marketing, we look at what content assets are performing well in producing leads, MQLs, sales conversations. This mostly occurs bi-weekly, whenever a new content-based campaign goes live.
“We also assess web/online metrics. These mostly take the form of inbound analysis completed by the online teams we work with to decipher and build insights around. For example, they’ll get back to us with conversion metrics they’re seeing on a new set of landing pages, before we work with them to interpret what the data means and how we solve it by updating the messaging experience on the page.
“Last, but not least, we also analyze qualitative listening. This is the most abstract, but also the most important and long-term. Constantly listening to recordings of sales conversations or just being a fly in the wall on sales calls helps us get better at (a) being able to speak the language of the buyer (b) staying up to date about what they are most worried about. I tend to rely on the DIY method for this versus asking a salesperson for feedback because they tend to lose a lot in translation.”
Vijay Sarathi, Senior Product Marketing Manager at Zenoti, also shared how analysis is used at his company.
“There are different areas and different tools to measure the product marketing efforts.
“For instance, I recently worked on a launch campaign for our product. The goal was not only to measure the number of leads but also to monitor the effectiveness of the reach.
“The final results helped us understand the receptiveness and possibly provided some new insights too.”
