Bhairav Mehta

CEO, Founder

Data-driven Product Marketing: #1

Not every department can attach itself to the positive effects that it has on bottom line business metrics, but if you can, logic suggests you should. This is the first of a series of posts that details how product marketing can become more data-driven to attribute its efforts to revenue growth, churn decrease, and other positive business metrics, all by taking a refreshed look the tools they currently have at their disposal.

Can we do better than content analytics?

May 13, 2022

Bring me straight to the tips at the bottom.

What is Content Analytics?

The relationship between sales and marketing has always been a business critical relationship. In just the last decade, companies have grown into behemoths by optimizing even small parts of that interdepartmental workflow. Sales and marketing need to be in constant communication around the voice of the customer - with sales giving marketing feedback around what’s working (and what isn’t) to craft better marketing experiments, hone persona-specific messaging, and improve product positioning.

Content analytics is one of the more recent attempts to help marketing teams - often product marketing - better close the loop with sales. With near constant-contact with customers and prospects, sales has always been a valuable liaison to help marketing teams keep a pulse on where the market is moving. Collateral passed and presented to customers is critiqued in weekly sync ups with marketing, all powering a slow (but effective) feedback loop.

Content analytics is the instantaneous tracking of various metadata around customized slide decks, collateral, and any other documents used to engage with prospects. How many times was this downloaded? How long did the rep stay on the the competitive slide of the deck? Did they make it to the end of the whitepaper?

Over the last decade, the world of sales engagement has been supercharged with content analytics - collateral view time is now a proxy for how well messaging is received by the market, and sellers can update slide decks with dynamic variables, personalizing positioning to prospects in near-record time.

So, if it’s so good, why might we need to change?

Where Content Analytics Falls Short

Content analytics has a cozy spot in many-a-PMM’s heart, but the issue is, the insights haven’t changed much since they came out. Analytics haven’t improved past time spent viewing collateral or number of times a whitepaper’s been downloaded, despite today’s more buyers being more complex than ever before. With stiff competition in every direction you look, PMMs today need more in-depth feedback on what’s working and what’s not, and content analytics alone might not be enough to face the complex market many companies find themselves in.

What message on Page 12 of the slide deck actually resonated best with the massive deal you just landed, and how can you use that data to support your three sellers on their three deals with similar personas and characteristics? Did you lose that “sure as signed” prospect because of the competitive positioning on the one-pager sent to her, or because the value props didn’t resonate with them? “Time spent on page” just isn’t powerful enough to figure this out.

And, even more unfortunately, content analytics does not give PMMs enough credit. Sure - a PMM can point to a whitepaper leading to more downloads leading to more SQLs leading to that huge winning quarter. And if the company wins, everyone wins. But, whether we like it or not, bottom lines drive budget, and for PMMs especially, attribution can be hard. Separating the impact they drove from the sales, ops, and other functions can be the difference between that desperately-needed extra headcount, and more long nights at the computer.

Right now, one of the only direct means of attribution PMMs can generate quickly is content usage. But, PMMs play a much larger role than that, because Powerpoint doesn’t convince customers, people do.

The PMMs we’ve talked to find the most fruitful parts of their job to be strategic: crafting new messaging for different personas, positioning the company for success in new markets, or stepping in as a subject matter expert in a contested, competitive deal. Their key interventions - direct or indirect - can often turn the tide in the company’s favor, but there’s no good way to attribute these wins to that type of output.

So how do you drive attribution in what really matters?

Atoms of PMM

So, clearly our opinion is that content analytics falls short. But how so? Do the analytics just need to be more in depth?

Well, not so fast. Employees at B2B companies are already slammed with information, notifications, and emails, so as a data offering, we’ve learned - the hard way - that delivered insights need to be differentiated, not just more granular.

As outsiders to the world of PMM, we had a hard time trying to understand what PMMs wanted analytics around. Was it content? Was it usage? Or, was it something else? So we started asking questions like:

How do you attribute PMM output to bottom line?


What data do you find most useful?

and most importantly,

How do you know when something’s not working?

All of these questions had different answers that varied across team size, organization type, market segment, but a single theme emerged: PMMs were often the most well-connected people we spoke to in organizations, playing air traffic control between marketing, product, sales, and success. They were constantly tapped into the voice of customer. A PMM’s day might include talking to sales, running customer interviews, or watching call recordings — all to stay in front of a constantly moving market.

And, when we dug deeper, we found they were asking questions we never expected.

How did this message resonate? How did you handle that prospect’s competitive objection? Did this slide make an impact?

PMMs we talked with often watched calls or talked to customers to see if the individual parts of their company’s message resonated with that prospect, segment, or user persona. They watched bits of conversation and even prospects’ facial expressions right after messaging or value props were delivered by reps, tracked it all in a spreadsheet, and then dug right into Salesforce. They took Google Docs filled with qualitative data from calls and manually joined it with CRM reports. They were able to track things like messaging update effectiveness, competitive win rates from newly-crafted objections, and even back data-driven updates to enablement.

Messy? Sure. But, effective. We’ve heard massive wins from these efforts - better playbooks for sales, smashing of competitive win rates, and multiplier improvements of marketing outreach.

PMM output is central to the go-to-market and positioning of a company. So, naturally, we knew they would desire quantitative feedback. But, we were constantly surprised at the level of feedback they asked about: it was at a much lower granularity than we had previously thought. They asked about individual components of content, not entire slide decks. And, they asked about how it resonated, not just if it was used. More than analytics, they cared about effectiveness.

Tracking effectiveness - especially when it comes to content or a content’s building blocks - works wonders, but any PMM knows how much effort it really takes. And, what’s worse is that despite the clear ROI from tracking it, not enough resources are spent on doing so. Executives are trained at scaling go-to-market organizations, but often, product marketing - the specialists trained with quantifying effectiveness - don’t scale with it. Ratios of 1:20 or 1:30 between PMM and go to market teams are not uncommon. Without the strategic outlook product marketing can bring, a company’s go-to-market teams might end up slamming their heads straight into a wall, when they really could just be walking around it.

What can we do about it?

Tips and Tricks for (Content) Effectiveness

In talking to hundreds of PMM leaders, we’ve compiled a few tips and best practices that we’ve seen to get bang for your buck when it comes to a better version of content analytics. Especially if you don’t have a content effectiveness program in place today, these tips might help you get up and running, or help you expand the scope of your analyses while spending the same amount of time. While some of these tips are tailored to PMMs with access to call recording libraries, we’ve seen PMMs get crafty - running the same analyses from Salesforce notes, survey responses, and even community forums. Need help getting started? Feel free to reach out at bhairav[at] for some tips!

  1. Break your content into atoms This one’s easy 🥳 , because this is what you and your team do each and every day! While enablement and collateral makes sense to group together thematically when passing to sales reps, for tracking content effectiveness, the opposite is actually key. By breaking each bit of content into singular value props or objection handlers, you’ll be able to track effectiveness of each individually, while also laying the groundwork for more exciting analyses later.
  2. Get your data right Start by pulling the data sources you’ll run your analyses on - have a sales call library? Ask your RevOps team to help you dump your transcripts into a file, and maybe even join the data with account and opportunity data from your CRM. Ask for an engineer to help you build a simple search program that looks through the transcripts and finds search words! With these two tools, you’ll be able to find how often different atoms are being brought up in wins and losses, or how often they are being brought up in particular funnel stages. For even more powerful analyses, think of using a no-code tool like Retool or Airtable, both of which can import spreadsheets.
  3. Ask for help Ask your sales reps or CSMs to take specific notes about how particular atoms of messaging are landing, or what slides elicit the most responses, questions, or confusion. With targeted questions to help them know what to look for, your reps will have a much easier time gathering actionable feedback for you to use to update atoms. If you have already have a call recording tool, ask to be connected with your provider’s CSM to pick their brain on how to drive your initiatives. They might even connect you with their solutions engineers to better understand how you can build trackers that can help you run reporting in the tool itself. Unfortunately, this might bring you into the world of professional services, which can get expensive. And, using professional services won't allow you to test hypotheses iteratively, but it's a start!
  4. Get specialized software At the end of the day, call recording software is not built for tracking effectiveness. It’s built for tracking sales process - metrics like did reps talk too much in calls, or did they wait enough time after prospects stopped speaking to respond? These are things sales leaders care about most, so that part makes sense. But for tracking what atoms are resonating most and driving deals forward? Not really in their wheelhouse. Being able to use the same dataset - for example, call recordings - for iterative research is critical, because you'll never be able to know what you don't know. Being able to quickly iterate and build upon hypotheses is going to help you scale your output. This is why Point (2) - having the right data infrastructure - may take time to set up, but will be worthwhile to make fast progress as you learn more from findings about your PMM atoms. Luckily, the world of machine learning and artificial intelligence has progressed leaps in bounds in the last decade, meaning that with the exact same call recording data, you can spin it to help you connect your product marketing outputs directly to what drove sales outcomes. You can - and perhaps should - start tracking the effectiveness of your content, and start getting the budget you deserve!

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