How to Champion the Data and Drive Product Strategy

Welcome and Webinar Overview

Roger Snyder | 00:00:00–00:04:30
Good morning everyone, and welcome to our continuing Product Management Leadership Series of webinars. My name is Roger Snyder. I’ll introduce myself as well as our guest consultant in just a moment.

Today we are talking about how to champion the data and drive product strategy.

We have with us today Ken Feehan, who is a Principal Consultant and Trainer at Productside. He’s been a friend of mine for over five years now. I am the VP of Products and Services at Productside, and I’ve also been with Productside for over five years.

We’re excited to be with you today to talk about this problem of the sea of data and what you do about it.

Before we dive into the main content, let me cover a few administrative items.

In order to continue to grow in your product management career, we recommend that you become part of a larger community—a group of folks that can help each other along the journey of becoming great product professionals.

One opportunity for that is our Product Management Leadership Group on LinkedIn. We’ll pop the link for that into the chat, and I encourage you to click that link and join. You’ll get access to the latest information from Productside as well as a lot of information sharing and question sharing, where professionals can help each other and learn more about product management every day.

Now let’s talk a little bit about webinar logistics.

During the webinar, we want this to be interactive. We want to help you out with questions you have, and we’d love to answer those questions along the way. We do keep some time for Q&A at the end, but if your question is particularly pertinent to the slide that’s up, go ahead and enter it.

Please use the Q&A box for questions. Chat is great, and you can use that to talk amongst yourselves, but when you have a question, put it into the Q&A box. That allows me to see the questions that are coming in, and I can cue Ken to say, “Hey, let’s talk about this particular question now.”

If I don’t answer your question right away, we will reserve time at the end to answer questions, and I’ll even invite you to unmute and ask your question live so we can have more of a conversation.

Let me answer a couple of popular questions up front:

The most popular one is, “Can I watch this webinar later?”
The answer is yes. When you registered for the webinar, you provided us with your email address, so we will send you a follow-up email after the webinar that includes a link to this webinar recording.

Another popular question is, “Can I get a copy of the slides?”
Unfortunately, these contain copyrighted material, so we don’t distribute a copy of the slides. But we do distribute the recording, so we encourage you to go back and use that recording as a resource.

If you’re not familiar with Productside, our whole mission and purpose is to empower product professionals with the knowledge and tools to build products that matter—products that matter to your customers, products that matter to your business success, and products that matter to you as a product professional.

Check out Productside.com for more information about all the things we do to enable you to be more successful in your career.

Unlike many other consulting firms, all we do is focus on the product professional. We provide many different types of services, and they are all focused on helping product managers, product marketing managers, product leaders, and product owners get better at their craft and become more effective and successful.

Again, check out Productside.com for more information.

And now I’m going to turn it over to Ken, who wants to get right into engaging with the audience. Take it away, Ken.

First Poll: Are You Collecting Product Usage Data?

Ken Feehan | 00:04:30–00:09:00
Thank you, Roger.

Good morning everyone, or good afternoon depending on where you are. We have a quick survey for you. We’d like you to answer the following question:

Does your company gather important information from product usage—from the process of your customers using your product?

Do you have aggregate data coming back to you to tell you how that’s going?

The answers here are:

• Yes
• No
• We intend to

Please pick the answer that works most closely for you. It’s a pretty straightforward question, and we hope you’re honest about this because we really want to know where you all are on this journey of understanding and using product usage information.

Fantastic, let’s see what we’ve got.

We’ve got 54% saying “Yes”—fantastic. That means you’re here to get more than the basics.
13% say “No,” and
33% say “We intend to.”

So we’ve got about two-thirds of the group here either doing data today or planning to in the immediate short term. That’s great information. Thank you.

Roger, what kind of information do we gather here at Productside?

Roger Snyder | 00:09:00–00:12:00
Interesting question. We do all kinds of gathering of information.

We monitor our web analytics in great detail, watching, for example, the customer journey. So we use a funnel to track how many people visit the site, how long they stay on a particular page, what the click-through rate is to go deeper, how many people download our syllabi for our various courses, and of course we also track our e-commerce purchase flow.

We look at:

• How many people look at a product page and say, “Oh, I’d like to sign up for that.”
• When they see the price and more information about the product, how many then click through and actually add it to the cart?
• We also look at cart abandonment rates.

For our courses, we look at survey results, so we understand how effective our course is—both in terms of the course material and our instructors. We’re constantly looking for opportunities to improve.

In particular, I like to look at the verbatims—the qualitative data in the text that people write into the fill-in boxes—so we get more details. We’ll ask, “Is there a trend here? Do we consistently see people wanting more information about certain topics?”

Ken Feehan | 00:12:00–00:13:30
Fantastic. Roger, I’m not meaning to put the whole webinar back on your shoulders, but how has data changed your staff meetings?

Roger Snyder | 00:13:30–00:17:00
Data has changed our staff meetings a lot.

We have a dashboard that we track on a weekly basis, and with that dashboard we’re tracking two broad sets of data.

First, what we were just talking about: our webinar and web data, open rates, click-through rates, and so on.

Second, there are our OKRs or KPIs, where we set goals every quarter:

• How much revenue do we want to generate from e-commerce?
• How many leads do we want to generate?
• How many copies of particular products do we want to sell?

We track those on a weekly basis to see how we’re doing against the goals we’ve set for that quarter.

So three years ago, staff meetings were kind of “analog.” Since then, our meetings have really gone digital. It’s all about the performance of the numbers, and it’s been an enormous amount of work and evolution, but I’m really excited about where we’ve gotten to now.

Ken Feehan | 00:17:00–00:18:00
Fantastic. So when we talk about this desire to be data-focused at Productside, we’re not just telling other people what to do. It’s actually part of our journey too, and Roger is right in the middle of that.

Why Data Matters and Who Uses It

Ken Feehan | 00:18:00–00:22:30
Let’s go ahead and cover this first topic.

First, all kinds of companies use and collect data to drive product strategies.

On a continuum, on the left we have B2C companies, in the middle B2B2C companies, and on the far right B2B companies. The one thing they all share is a huge focus on using data to track user satisfaction and the success of their product.

You’ll see everything from AWS and Salesforce, companies born in the digital age, all the way back to UPS, Disney, and the United States Postal Service. If you double-click on some of these companies—Disney, USPS, UPS—it becomes obvious that they’ve transformed who they are based on using data.

I haven’t been to Disneyland in about ten years, but I’m told by my adult children that you’re walking around with your mobile phone in your hand, finding rides with shorter queues.

I track packages from warehouses to my house and know where the UPS truck is. These companies have embraced data and used it to make their products better. They are inspiration for us.

So if somebody at your company tells you, “We’re not the right kind of company to use data,” point to the example of the United States Postal Service—probably one of the most traditional organizations in the world—using data to improve.

The other capability of data is that it makes your voice as a product manager more powerful and more impactful.

If you’re sitting around a conference room with UI designers, engineers, and marketing people, you’re just another opinion at the table—unless you’re bringing data.

Instead of “I think we should do this,” you can say,

“Eighty percent of our customers are getting stuck on this step,” or

“Our close rate with our target customers is lower than it should be.”

If you’re the product manager acting on data, you are heard more. It’s impossible to ignore someone who is focused on data when making decisions.

A famous quote from Jim Barksdale, CEO of Netscape back in the day:

“If we have data, let’s look at data. If all we have are opinions, we’ll go with mine.”

That’s a truism—and an opportunity for product managers to make their voices heard.

Roger Snyder | 00:22:30–00:24:00
This is one of those areas where, as I’ve led product management teams, my product managers sometimes complained they weren’t being listened to.

The number one response I gave them was:
“Do you have data to back up the position you’re taking?”

If not, let’s go get it. Data really is the lifeblood of being an effective product manager.

OKRs vs KPIs and the Customer Lifecycle

Ken Feehan | 00:24:00–00:29:30
When we talk about data, you usually start hearing the words OKR and KPI. At some companies these get mashed together and used interchangeably. Some companies adopt KPIs but not OKRs.

I want to present a classic view of how OKRs and KPIs are different and complementary.

• OKR stands for Objectives and Key Results.
• KPI stands for Key Performance Indicator.

OKRs are meant to be closely aligned to your strategy. What are you trying to accomplish? Where is “home plate”?

When we are successful, what does success look like?

OKRs define a future state you want to get to: revenue targets, number of users, NPS, and so on. They are aspirational.

KPIs are snapshots in time showing how you are progressing from where you started toward that OKR.

For example, if your OKR is to reach a certain net promoter score by year-end, then each month you might track your current NPS as a KPI.

So:

• OKRs = Where we want to go (strategy, outcomes).
• KPIs = How we’re doing along the way (current performance).

Companies that use data effectively generally take snapshots and define OKRs and KPIs for each of the three stages of the product/customer lifecycle:

Acquisition

• How long does it take to acquire a customer?
• How much does it cost?
• How many do we acquire per period?

Retention

• Are customers actually using the product?
• How often and how deeply?
• Do they renew?

If product usage is very light—minutes per month—you can be pretty sure renewal is at risk.

Monetization

• For satisfied customers, how likely are they to upgrade or buy more?
• How do we increase revenue per customer in a value-based way?

At Productside, for example, at the end of this webinar we’ll have a call to action for other offerings. That’s monetization—but it’s about additional value for satisfied customers, not just extracting money.

KPIs measure business success. They help the business see:

• Are we hitting our targets?
• Where do we need to accelerate or brake?

If we budgeted that customer acquisition cost would be $700, and we’re seeing it come in at $800, that’s a KPI telling us we have a problem to investigate and solve.

Roger Snyder | 00:29:30–00:32:30
In a former industry, my customers were wireless carriers. It was eye-opening when they shared their data with us while we were helping them with customer loyalty.

They pointed out that in the early years of wireless, the reason they had 12- and 24-month contracts was that they were carefully measuring the cost of acquisition.

Back then, the cost of acquisition was nearly $800. With an average monthly bill of about $80, it took 10 months just to break even.

• Months 1–10: you’re paying back the cost of acquisition.
• Months 11–12: you finally get into profit.

So with a 12-month minimum contract, they were only going to get about two months of profit before that subscriber could switch carriers.

That data was crucial to the business. It informed contract lengths, offers, and retention strategies. And that’s the kind of thinking we want product managers to bring to their products as well.

Acting on Data and Partnering with Marketing

Ken Feehan | 00:32:30–00:37:00
Let’s go to our second poll. This one is related to the first, but subtly different:

Does your company act upon the data that’s collected from your product usage?

We already heard some of you collect data or intend to. Now the question is:
If you said “Yes” to collecting data, do you act on it?

Again, possible answers are yes, no, or “we intend to.”

Sometimes this is a crawl–walk–run evolution. If you’re not even collecting data, step one is to start collecting. But you really need to ask, “What decisions do I want to be able to make with this data?”

That brings us back to the three categories of KPIs we talked about: acquisition, retention, and monetization. You don’t want to blindly collect data. You want to think:

• What decisions do we need to make?
• What KPIs do we need to track to support those decisions?
• Therefore, what data should we collect?

Fantastic. We have 42% saying “Yes, we act on it,” 16% saying “No,” and 42% “We intend to.”

So a majority are either acting on data already or planning to, which is healthy.

Roger, this is a good number. Any reflections from your time as our marketing leader on how we adjust to data that comes back?

Roger Snyder | 00:37:00–00:41:00
Sure. One example is email as a marketing channel.

We noticed that while our open rates were high, our click-through rates were dropping. That signaled it was time to do A/B testing.

We send a monthly newsletter to over 40,000 people, and we want it to be useful but we also want follow-up actions. So we:

• Tried a new format in two variants (A and B).
• Measured which format drove more clicks back to our website and resources.
• Ran the test for a couple of cycles.
• Chose the winner and adopted that as our new standard format.

We’re also getting more dynamic. For some campaigns, we’ll:

• Run two different ads for a couple of days.
• See which ad is performing better.
• Quickly pivot to run only the better-performing ad.

So the time frame for taking action varies—some things require weeks or months of data; others you can react to in a few days.

Ken Feehan | 00:41:00–00:44:00
That’s fascinating. I want to go slightly off script. You were representing the digital marketing team in that scenario.

What role would you want the product manager to play?

Would you want them to know the cost of acquisition, open rates, click-through rates? Should they be asking you questions about that, or doing the work themselves? How should a product manager interface with an ongoing marketing campaign?

Roger Snyder | 00:44:00–00:47:30
They definitely need to be at the table.

If we’re running an ad for a particular product, we want to give that product manager feedback on which messages land more effectively. That’s data on:

• Which value propositions are more important.
• Which benefits resonate better with customers.

And that directly informs where to invest next in the product. Do we:

• Amplify the benefit that’s resonating?
• Or improve the weaker benefit so it becomes more compelling?

That’s the hard decision in product management: where to invest limited resources.

The other side of this is that we choose what to A/B test based on what the product manager tells us are the key value propositions. For B2C vs B2B audiences, we might emphasize different messages, and that comes from product management too.

So it’s an ongoing conversation between product management, product marketing, and marketing.

And I’ll pause here to invite questions. Please throw your questions into the Q&A box. We’d love to bring your specific situations into the discussion.

Good vs Bad Metrics and a Startup Cautionary Tale

Ken Feehan | 00:47:30–00:52:30
Let’s talk about good metrics vs bad metrics.

Good KPIs and good data should:

• Answer critical questions.
• Measure things that matter to the company.
• Be comparative and understandable.
• Be actionable.

They should be true indicators of value increasing at your company.

Bad metrics, on the other hand, may look impressive but are poor proxies for value.

Let me tell you a story from a startup where I was the Director of Marketing about twenty years ago. We were building something similar to Skype.

Our primary metric was:

“How many people downloaded our application in the last 24 hours?”

For the first two–three months, that was useful. We were seeing about 10,000 users a day downloading and using the app. It gave us a sense of traction.

But we held onto that metric for a full year. We kept going to the board saying,
“Look, we got 11,327 downloads last night!”

The problem was, that metric did not correlate with value. There was a hole in the bucket:

• 96% of people who downloaded the app used it less than twice.

So we were optimizing for downloads, not for ongoing usage or value.

We were celebrating a metric that hid the real problem: no retention, no real engagement.

Good metrics should:

• Correlate with customer value.
• Reveal problems like poor onboarding or poor feature fit.

As product managers, we have to be curious and not accept a vanity metric at face value. A metric can start as a good early signal and later become misleading if we don’t evolve it.

Now let’s ask another poll:

What product KPIs do you use?

Options include:

• Daily / Monthly Active Users
• Session Frequency
• Session Duration
• Feature Usage
• Something else
• We don’t measure our products at this level yet

Please choose the option that best reflects your primary KPI.

Excellent. The results show:

• Feature usage is the top answer.
• Daily / monthly active users are second.
• Session frequency and duration are smaller.
• “Other” and “We don’t measure yet” round out the rest.

That’s encouraging. Feature usage is a great sign that teams are embracing the idea of data-driven product decisions.

If you put out a new feature, make sure you can measure whether people actually use it and how satisfied they are with it.

Roger Snyder | 00:52:30–00:55:30
The next step from feature usage is to track user paths for a task.

If there’s a five-step flow to complete a task, ask:

• At which step do people abandon?
• Why are they dropping there?

For us, for example, the key flow is checkout on our website and the actual purchase. Whatever is getting in the way of that final step is something we want to uncover and address.

For those who aren’t yet measuring product usage, I hope this gives you some clarity on what to measure and why, and how it links back to business KPIs.

What to Measure and DPM’s Six Usage Dimensions

Ken Feehan | 00:55:30–01:00:30
You’re looking to create metrics that move the needle for customers and the business.

You want to know:

• How do they use the product?
• What features do they use most?
• What are they not using?
• What do they find valuable?
• How satisfied are they?

The data you collect should be guided by these questions.

Now I want to drop into some content from our Digital Product Management (DPM) course at Productside.

We talk about collecting information from customer usage in six categories:

Depth

• How deeply do users go into the product?
• Are they using only the top surface features, or advanced, powerful capabilities?

Breadth

• How many different features or modules do they use?
• Do they use just one part of the product, or many parts?

Frequency

• How often do they use it?
• Daily, weekly, monthly?

Paths / Funnels

• What paths do they take to complete tasks?
• Where do they drop out or get stuck?

Sentiment

• How do they feel about their experience?
• Do they have a smile or a frown when they use the product?

Feedback

• What direct feedback do they give you?
• Surveys, in-app prompts, support tickets, NPS, etc.

If somebody says, “We have a usage metric,” be cautious.

“Usage” alone is not very meaningful. Ask:

“Tell me more. Are we measuring depth? Breadth? Frequency? Paths? Sentiment? Feedback?”

That’s where the real insight lies.

Roger Snyder | 01:00:30–01:01:00
Great. We have a question coming in, so let’s bring that in live.

Q&A: Tools and Getting Started with Product Analytics

Roger Snyder | 01:01:00–01:01:30
Hannah, I’ve enabled you to speak. Go ahead and ask your question.

Audience (Hannah) | 01:01:30–01:02:00
Hi. My question is: What tools do you use to get product data, and how do you start the process of gathering product data—beyond traditional methods like surveys or just asking users?

Ken Feehan | 01:02:00–01:05:00
Great question, Hannah. I see two parts to that:

1. What tools?
2. How do you get started?

On tools:

A good starting point for many teams is Google Analytics. Google has free or low-cost tools for developers to put counters inside applications and send that information back.

If your team does this well for 6–12 months, you might outgrow Google Analytics and move to more purpose-built tools.

There are many: if you search for “Google Analytics competitors” or “product analytics tools,” you’ll find a long list.

Roger Snyder | 01:05:00–01:08:00
A few product analytics tools I’ve used in the past include:

• App Annie
• Flurry
• Amplitude

App Annie and Flurry are great for mobile apps. Amplitude is very strong for web and in-app analytics. Many of these now support both web and mobile.

Amplitude, for example, not only tracks behavior but can also inject in-app questions:

• “How are you feeling about this experience?”
• “We noticed you seem stuck—can you tell us why?”

At Productside, we use Google Analytics plus heat maps on our website to track:

• Clicks and scroll behavior
• Where people focus attention
• Where they drop off

How to start the process:

Ken Feehan | 01:08:00–01:11:00
Hannah, since you said you’re new to this, here’s how I’d recommend starting.

On the last slide of this webinar, I suggest you get together with your team and design a data dashboard you’d like to show in the executive suite in about six months.

You probably don’t know all the answers today, but if you sit down with:

• Engineering
• UX
• Data / analytics people

…and ask, “What should be on our executive data dashboard?”, you’ll spark a very productive conversation.

From there, you can:

• Decide what metrics you want to see.
• Work backward to what data you need to collect.
• Then instrument your product accordingly.

Start with the end in mind (the dashboard), then back into the data you need.

Roger Snyder | 01:11:00–01:11:30
And yes, surveys and in-app prompts are still valuable. The answer really is “both”:

• Behavioral analytics and
• Direct user feedback.

Audience (Hannah) | 01:11:30–01:11:45
Thank you so much.

Ken Feehan | 01:11:45–01:12:00
Thank you, Hannah. Good luck on your journey.

Q&A: Legacy On-Prem Products and Telemetry

Roger Snyder | 01:12:00–01:12:20
We have another question. Ahmed, I’ve allowed you to speak—go ahead.

Audience (Ahmed) | 01:12:20–01:13:00
We manage a legacy Windows application that’s on-prem, so we don’t have the luxury of cloud-based analytics. Do you have recommendations for tracking tools that could be embedded in our application and send results back?

Ken Feehan | 01:13:00–01:15:30
Great question.

If you’re still making software updates, you can absolutely embed usage tracking into an on-prem Windows application.

Unless you’re working in national security or very sensitive environments, it’s usually acceptable to add light telemetry that tracks:

• Which features are used
• How often
• In what flows

The intent is to improve the product, not to spy on users.

You can often still start with tools like Google Analytics by sending anonymized event data from the app to a server whenever the app is online. For on-prem, sometimes you do have to “roll your own” tracking, but the principles are the same.

The key is to be transparent with customers about:

• What you’re tracking
• Why you’re tracking it (to improve features they care about)

You might encounter resistance in government or highly regulated environments, but even there you can sometimes make a strong case that usage data helps you build a better product for them.

Roger Snyder | 01:15:30–01:16:30
Probably not an out-of-the-box plug-and-play solution; as Ken said, you may need to build some of the plumbing yourself. But the pattern is the same:

• Instrument events in the app.
• Batch and send them when connected.
• Aggregate and analyze centrally.

Audience (Ahmed) | 01:16:30–01:16:45
Thank you.

Ken Feehan | 01:16:45–01:17:00
Good luck.

Revenue Metrics: ACV, ARR, CAC, LTV, and Churn

Ken Feehan | 01:17:00–01:23:00
Let’s quickly go through some common revenue-related metrics that you’ll hear a lot in digital product management.

ACV — Annual Contract Value

• For subscription businesses, it’s the annualized value of a contract with a customer.
• Helps you understand revenue per account and plan investments.

ARR / MRR — Annual or Monthly Recurring Revenue

• Normalized measure of total subscription revenue.
• Executives will say things like, “Our ARR is trending up,” or “ARR growth has slowed.”

ARPA / ARPU — Average Revenue Per Account / User

• For B2B: average revenue per account.
• For B2C: average revenue per user.
• Helps you see how much each account or user is worth on average, and if that’s growing.

LTV — Lifetime Value

• A finance-informed projection of how much revenue you’ll earn from a customer over their “lifetime” with your product.
• For example, if you’re Netflix, you might estimate that the average subscriber stays for X years and pay Y per month, then calculate LTV.

Now two expense-related metrics that are crucial:

CAC — Customer Acquisition Cost

• The average cost of convincing a customer to make a purchase.
• Includes:

1. Website costs
2. Marketing campaigns
3. Sales team costs
4. Email programs
5. Referral programs

• It’s usually higher than you expect, because it’s fully burdened with all acquisition costs divided by new customers.

Churn

• The percentage of customers who do not renew or cancel their subscription in a period.
• It’s often a key reason Wall Street rewards or punishes subscription companies.

A critical ratio that many businesses track is:

LTV should be at least 3× CAC

If lifetime value is less than acquisition cost, you’re literally throwing money away.

Roger Snyder | 01:23:00–01:25:30
Back to my wireless carrier example:

They were calculating CAC and effectively calculating LTV based on average tenure. Early on, with those 12-month contracts, they were not getting to that 3× ratio.

They needed longer relationships, better retention, and more monetization to get to a healthy LTV:CAC ratio.

So these metrics aren’t just finance trivia—they drive pricing, contract length, product strategy, and investment decisions.

Ken Feehan | 01:25:30–01:27:30
Churn is especially important for product managers:

• It’s always cheaper to retain an existing customer than to acquire a new one.
• High churn is often a product problem: weak onboarding, missing value, poor reliability, or misaligned expectations.

Understanding and reducing churn is one of the clearest ways product managers can move the needle on business performance.

Designing a Data Dashboard and the DPM Course

Ken Feehan | 01:27:30–01:31:30
Let’s come back to Hannah’s question about getting started.

If your company is not yet embracing data, here’s a practical first step:

1. Go to your favorite search engine and look up “data dashboards” or “SaaS metrics dashboard”.
2. Print or save 5–6 examples that look compelling.
3. Call a meeting with:

• Your engineering manager
• UX lead
• A digital / data person

In that meeting, ask:

“If we wanted to show a data dashboard in the executive suite in six months, what should be on it?”

You won’t know all the answers today—that’s the point.

This exercise:

• Gets people excited and creative.
• Often gets engineers and data folks to say, “We could track this, and this, and this.”
• Gives you a target to work toward.

Then you can work backward:

• From dashboard metrics
• To required KPIs
• To the specific events and data that need to be instrumented in the product

This is how you start a six-month journey to a real, useful analytics stack.

Now, many of the concepts we’ve talked about today—metrics, funnels, depth, breadth, ARR, CAC, churn—are covered in depth in our Digital Product Management (DPM) course at Productside.

It’s available as:

• Self-study at your own pace, and
• Instructor-led, where people like me work with you and your team, lecturing some and then doing workshops together to apply these techniques to your real products.

At the end, there is a certification exam to become a Certified Digital Product Manager.

Roger Snyder | 01:31:30–01:35:00
Our DPM course is focused on digital product management, starting with:

• What is a digital product manager?
• How is it different from “traditional” product management?

Then it moves into:

• Being outcome-focused, not just output-focused. It’s not about how many releases you ship; it’s about how much value you deliver.
• How that value drives monetization, lowers churn, and improves business performance.

We’re also launching a Product Management Day sale. Product management got its start back in the 1930s at Procter & Gamble, and Product Management Day commemorates that origin.

To celebrate, we’re offering 15% off our courses—including Digital Product Management—until May 23rd.

We know some of you need approvals from your company, so:

• Go to the website
• Get the information you need to justify the course
• Come back and purchase while the discount is available

Final Poll, Advanced Q&A, and Closing

Roger Snyder | 01:35:00–01:36:00
We’ve launched our last poll asking how helpful you found today’s webinar. While that’s running, let’s tackle a couple more questions.

Audience (Jung / Young) | 01:36:00–01:36:45
How can I start to unify customer data—profile data, transactional data—if I have multiple applications, each independently collecting data? Are there off-the-shelf tools that help with this?

Roger Snyder | 01:36:45–01:40:00
Great question.

There’s a whole category of tools designed to combine data from multiple sources and give you a unified view of the customer journey.

Examples include:

• Tableau
• Looker
• Microsoft Power BI
• Analytics add-ons from Salesforce, Oracle, and others

These tools can:

• Pull in web usage data
• Pull in in-app usage
• Pull in transactional / billing data
• Let you build dashboards and reports that combine all of it

If you look up “Tableau competitors” or “Looker competitors,” you’ll find a long list.

The pattern is:

1. Identify where your data lives today: web, app, CRM, billing, support.
2. Use a BI / analytics tool to connect to each source.
3. Build views that show cross-system journeys and cohort behaviors.

That’s how you get to that unified view.

Audience (Brooke) | 01:40:00–01:40:45
Do you have recommendations for challenging status quo metrics that leadership is accustomed to? We have metrics leadership loves, but I’m not sure they’re actionable, and I want to get them thinking more about user engagement metrics like breadth and depth.

Ken Feehan | 01:40:45–01:43:30
Fantastic question.

It’s common for companies to get comfortable with a set of metrics that sound good but aren’t truly actionable or value-aligned.

My recommendation is to attach new, better metrics to a specific upcoming release:

1. For your next feature release, define new metrics you want to track—maybe depth, breadth, or a task-completion funnel.
2. Work with engineering to instrument those metrics for that release.
3. After launch, report on those new metrics alongside the traditional ones.

If you can show:

• “Here’s our old metric. It’s flat,” and
• “Here’s our new metric. It clearly shows that this feature is highly valued by 75% of users who try it, but only 8% discover it”

…then leadership starts asking,

“Why aren’t we measuring this for our other products too?”

You don’t usually win this battle by arguing the theory. You win it by showing a concrete, better signal tied to a real release.

Roger Snyder | 01:43:30–01:45:00
Another angle is to start from a corporate objective—an OKR leadership already cares about.

If the existing metrics don’t help you see progress toward that OKR, you can say:

“Given this new strategic goal, we need different metrics to see if we’re on track.”

Then propose engagement metrics—breadth, depth, completion rates, etc.—as the metrics that will actually tell you if you’re making progress.

Linking new metrics to existing strategic priorities is a powerful way to get buy-in.

Ken Feehan | 01:45:00–01:46:00
Great questions today. Thank you all for engaging so thoughtfully.

Roger Snyder | 01:46:00–01:47:30
Ken, thank you so much for your expertise today.

And to everyone who joined, we really appreciate you taking time out of your busy schedules to be with us. We hope you found this useful.

Check out Productside.com for more resources to help you on your product professional journey.

Thanks again, and have a great day.