This resource contains 64 generative AI prompts to help you use AI tools to their fullest capabilities. With these insights, you can shape product strategy and influence business outcomes.

With this ebook, you’ll learn how to:

• Craft generative AI prompts and probe for valuable information answers you need
• Analyze complex market data to gain insights into your customers, competitors, and market
• Leverage AI’s computing power to develop data-driven product strategies that increase revenue, reduce costs, and improve customer satisfaction
• Gain competitive advantages and reach new markets with innovative product strategies

Download your copy of The Product Manager’s Playbook for Generative AI.

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Don’t let your data projects suffer due to a lack of executive support. Download our checklist today to learn more! And if you’re looking for additional resources, we also offer a webinar and article on gaining executive buy-in.

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For example, what is the product strategy that is driving roadmap priorities? And how is the product strategy linked to the company’s overall strategy?

Since most technology companies’ revenues come primarily from their products or services, you would think that the product strategy would be carefully crafted with the scrutiny of the executive team and that it would be meticulously aligned with an overarching corporate strategy.

But this is often not the case.

Without the engagement of the leadership team most responsible for determining strategy and direction, the risk is suboptimal financial performance at best and complete company failure at worst.

Let’s take a look at some practical approaches to making product management more strategic by engaging executives in key product strategy decisions and encouraging better corporate strategic planning.

The Strategic Product Plan

The essential goal of a product plan should be to ensure that a product delivers some business value to a specific set of customers to meet certain financial goals based on a defined corporate strategy.

A product plan describes the market opportunity, profiles the target customers, specifies pricing, identifies the financial goals, indicates the key priorities for development and enhancement and provides a roadmap for delivery for at least the next four quarters.

A comprehensive MRD (Market Requirements Document) might serve as the product plan for a new product. But each product that continues to be offered to customers should have a product plan updated yearly.

So, how does product management create a good product plan?

Let’s assume that the product management department is already managing several products that are currently serving customers.

After getting feedback from customers, speaking with the sales teams, obtaining a list of the top technical support issues, surveying competitor positions and features, and receiving new ideas from development, the product management team has generated a list of possibly hundreds of potential product enhancements across the product line as well as some new product ideas.

Project prioritization

Since there are limited resources, prioritization is a key step for any product plan.

Unfortunately, many companies apply some arbitrary prioritization scheme based upon the perceived number of times the feature/product has been requested or how much revenue they think the feature can generate.

The product manager (or their development friends) may also make assumptions about value based on how they think the product should be used. The product management team then creates a roadmap and a release schedule based upon these priorities and voila, the product plan is done, right?

No, it certainly is not.

The product plan is incomplete because the company’s strategy has not yet been considered. The executives who are chartered with running the company have not influenced the product plan. The plan merely reacts to random market facts and events. So how exactly does the corporate strategy relate to the roadmap?

Focus on What Increases Revenue

The goal of almost any technology company is to increase revenues.

Without a strategy to indicate HOW the company plans to increase revenue, then just about any product plan could arguably help the company achieve its goal, including the plan we just created.

But with a strategy that specifies how new revenue would be generated, a product plan tailored to support that strategy can then be developed.

For example, your company could plan to grow revenue by selling its flagship product into new geographic regions. Your company could establish a new reseller channel. Your company enhances its existing products to appeal to a wider base of customers. Your company could develop new products that appeal to the existing customer base. Each of these decisions carries with it significant implications on the product plan.

Selling into new geographic regions would require local language support and may have other specific regional requirements. Selling through a reseller channel may require multi-tier administration and branding. Enhancing products to appeal to a wider customer base requires profiling that new customer and understanding his/her unique needs and requirements. And developing new products requires new analysis, requirements, design, and development work.

Each of these strategies would result in a different prioritization of the projects on the product manager’s candidate list and a different allocation of resources. The product plan we created previously is reactionary and haphazard, while the product plan that responds to corporate strategy is directed and intentional.

So why aren’t corporate strategies incorporated into product plans?

There are several possible reasons, but three of the most prevalent ones are:

1. No strategy exists
2. The strategy has not been clearly communicated
3. The strategy appears inconsistent with market and customer data.

Let’s analyze each in the following sections and propose some ways to solve them.

If no strategy exists, then one should be created. At one company, the executive team employed a process where they reviewed and prioritized the top project requests every six weeks. This approach resulted in constantly shifting priorities since the highest priority projects were always related to the biggest sales opportunities at the time. Less critical product features never made the cut, resulting in an increasingly uncompetitive product line. Without a driving strategy behind it, your company risks being pushed by short-term opportunities.

Product management is in a good position to persuade executives to develop a high-level strategy as part of the product planning process.

Here are some key questions that product managers can ask executives to help with product planning that might very well stimulate some strategic discussions.

• What are the top 3 most critical challenges our company will address this year?
• In which geographic regions will we focus on selling our products?
• Will there be any changes to the sales or channel strategy?
• What are the revenue and profitability expectations for each product line?
• Will there be any changes to the focus of marketing and advertising?
• Are new markets or product lines being considered for the future?
• What strategic partnerships are on the horizon?
• What resource changes are expected for the coming year?

Now an astute executive may ask the product management team to answer or help answer many of these questions. And that makes sense since product management sees market opportunities, has heard customer feedback firsthand and aggregates it from others, has tracked competitors’ moves, and has an in-depth view of their products’ financial trends.

But at the same time, you still want to leverage the knowledge and experience of the executive team and make sure they agree with the assumptions and logic being used.

Therefore, a practical approach to strategic planning could involve a meeting (or series of meetings) where product management presents their market and customer information to executives, who then have a chance to discuss what they have heard and how they think it should apply to the future of the company.

You could expand the discussion to include input from other functions like sales, marketing, and finance so that everyone is hearing key information that will lay the groundwork for the strategy.

In a subsequent meeting, product management can replay the conclusions and decisions from the previous discussion(s) and then present a proposed product strategy that responds to them. Hopefully, by then, a consensus on the strategy will be reached, and the product management team (with the assistance of development) can then present an updated roadmap and proposed release schedule for the coming quarters for final review and approval.

The second reason why corporate strategies are not incorporated into product plans is that product managers don’t know about them or don’t understand them.

It is certainly possible that an executive team will define a company strategy and then succinctly describe it in a form that can be handed down to all employees for successful execution. More typically, however, the executive team communicates the strategy to their teams less formally. At one company, the executives felt that the strategy was too sensitive to share broadly and tried to share it on a “need-to-know” basis only. Most of their employees were in the dark about executing the strategy successfully.

The product management team is a key executor of the strategy.

They will translate corporate strategy into product strategy and will create roadmaps that drive the work of many of the company’s employees. So the entire executive team should present the strategy directly to the product management team. This will facilitate the necessary dialogue and allow for a joint understanding of the implications. Product management should then be required to develop a product strategy and proposed roadmap and present it back to the executive team to close the loop and ensure alignment with the corporate strategy.

The third reason a corporate strategy may not be adequately incorporated into product plans is that the strategy itself appears to be inconsistent or contradict market and customer data from product managers.

This is likely if the executive team developed their strategy without being adequately in touch with the market and customers.

If the product management team is being utilized appropriately, then they will be serving as the “headlights” of the company driving the front-end of the product development process, and they will be spending most of their time discovering market opportunities, customer needs, technology trends and competitor positions.

Now executives should always make it a part of their jobs to speak with customers and review market trends. But to ensure they hear the wealth of available market and customer information, it should be considered a prerequisite to developing the corporate strategy to have the product management team present a review of what they have learned.

You may have noticed that in all three of the cases where corporate strategies are not adequately integrated into product plans, the solution was direct communications between product management and the executive team.

Why Product Managers Should Join Senior-Level Discussions

Product management can help educate senior executives with their market and customer knowledge, can help mold the strategy, and hear it first-hand so they can properly execute it. However, there are reasons why this direct communication does not occur.

It is common for the product management team to report to a VP of Marketing or Product Development who represents them at senior-level meetings. These are broad functions with many responsibilities. Marketing executives are often measured and rewarded for driving revenue (with sales) for the company.

Product development executives are expected to deliver quality products on schedule.  So when sitting at the strategy planning table, what types of things are they most concerned with? How well do they understand customer needs and market opportunities? Is profitability one of their primary concerns? Are they concerned with short-term or long-term issues? In other words, will they be good representatives of the market and will they push to defend the bottom-line?

This brings us to a broader issue at many technology companies. Who is actually concerned with profitability and balancing short and long-term goals? Who ensures every key decision is made with key business goals in mind? In short, who is minding the store?

For most functionally-aligned technology companies comprised of sales, marketing, operations/support, development, and finance, the lowest level of management where accountability exists for profitability and long-term strategic issues are the COO, if one exists, or the CEO if  not.

Think about it. The sales organization is primarily concerned with revenue and tends to be short-term focused. Marketing typically supports sales objectives. Operations and support keep the services running well, maintain customer satisfaction and are primarily cost centers. Development focuses on delivering quality products on schedule and is also managed as an expense. Finance tracks revenues and costs but is in a limited position to influence them.

So, who is thinking about profitability and achieving long-term goals? If it is nobody other than the CEO or COO, then there is a real danger that the myriad of decisions made daily by managers across the company will not be made with the right focus.

One solution is to elevate the role of the product management function, given its critical strategic responsibilities, and have it report directly to the COO or CEO (or for larger companies, the relevant business-accountable executive). This makes product management a direct member of the leadership team making strategic decisions about the business.

This product management function will be chartered with providing market intelligence to inform the executive team, managing product profitability, and determining and driving product strategy consistent with corporate strategy.

This function becomes a resource for the CEO/COO/business leader to explore and manage long-term opportunities. The function has P&L responsibility and will drive business decisions deeper into the organization. It is a function that gives business leaders greater control over the plans that drive so many of the company’s resources, roadmaps and product plans.

Since product management is in such an influential position to execute the strategy and needs to work with so many of the other functional organizations during product development and delivery, it may be desirable to include other cross-functional delivery teams in this function as well.

The Strategic Planning Process

So let’s step back and take a look at what an end-to-end product planning cycle might look like when integrated with the company’s strategic planning cycle. Assuming that a company resets its corporate strategy, financial plans, and product plans once per year, the planning process would ideally occur during the 3rd and 4th quarters of the fiscal year in preparation for the upcoming year.

The five basic steps in the planning process (as depicted in figure 1) are:

1. Market review
2. Financial review
3. Corporate strategy
4. Product strategy
5. Product Roadmap and Release schedules

Step 1

product management presents a market review to executive management sharing facts on market trends and opportunities, key customer needs, and competitor moves and positions. Though product management will keep tabs throughout the year on many of these items, this is the opportunity to update the information to ensure it is complete and current. Other functions may be invited to provide their perspectives on the market and customers as well.

Step 2:

The finance team presents results on the company’s financial performance overall, for its sales channels, and for its products. Providing revenue and profitability by product is critical to making good product decisions and developing effective strategies.

Step 3:

This is when the company’s executive team outlines its corporate strategy regarding its vision, financial goals, and plan for achieving those goals. The corporate strategy should be explicitly presented to the product management team to facilitate product strategy development. For some smaller businesses, steps 3 and 4 may be combined into a single step.

Step 4:

Product management develops its product strategy considering market dynamics, customer needs, financial goals, and corporate strategy. It specifies what product changes are needed and indicates the financial plan for each product area. The product strategy should be reviewed by the executive team to ensure alignment with the corporate strategy before proceeding to the next step.

Step 5

This last step involves the development of a product roadmap and more detailed release plans for the coming quarters consistent with the product strategy. This roadmap becomes the official “product plan of record” and should be managed with formal change control procedures. This step is executed after the annual planning cycle and is repeated every 3 or 4 months to allow responses to changing market conditions and deployment schedules and should be re-approved by executive management.

Success is Determined By Product Success

Effective product plans to address market and customer needs AND supports the company’s growth strategy. Creating effective product plans can only be accomplished with strong communications between the product management team and the executive team because:

1. Product management has critical market information that executives need to develop effective strategies;
2. Product management can help develop strategies by asking key questions and discussing product implications; and
3. Product management must clearly understand the company’s objectives and direction to create product strategies and roadmaps.

By leveraging the product management team as a strategic resource, you will ensure that your products have been influenced by the best minds and information your company has available and you will gain greater control in driving your company’s success.

Enroll in Foundations

Foundations is an excellent learning opportunity for product teams who want to improve their communication.

Course Overview:

• Understand the market and the problems it faces
• Use market knowledge to build and sell products people want to buy
• Master the Pragmatic Framework and the activities needed to bring a successful product to market
• Learn to listen to the market, prioritize projects and drive result

Learn More 

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Steve Goodyear, the founder of Realizer Services, is the first person to earn a PMC VIII certification.

Rebecca Kalogeris, VP of marketing for Pragmatic Institute, and Steve discuss the variety of courses offered through Pragmatic Institute, and how each plays a role in elevating the work he does at his company.

They discuss

• How the courses help Steve answer the question: “How do I turn professional services into more of a product.”
• His lessons from the Design course
• How he uses the Pragmatic Alumni Community to build his network and talk through ideas.

Additional Resources

Foundations on Demand

Begin your product training experience with the course Foundations. Even better, this course is both live and on-demand. You’ll also gain your membership to the growing and active Pragmatic Alumni Community.

Course Overview:

• Understand the market and the problems it faces
• Use market knowledge to build and sell products people want to buy
• Master the Pragmatic Framework and the activities needed to bring a successful product to market
• Learn to listen to the market, prioritize projects and drive result

Learn More

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A recent survey indicates that among the companies that have made some investment in AI, fewer than 2 out of 5 report business gains from their investment in the past three years. Per the survey, this number improves to 3 out of 5 when we include companies that have made significant investments. This still implies that 40% of organizations making significant investments in AI do not report business gains.

There are several reasons why companies are not achieving their expected return on their investments:

• Approach: Lack of clear strategy and understanding of what is achievable
• Design: Poor model design, data and governance issues, algorithm bias
• Implementation: Limited internal support for full implementation
• Trust: Lack of trust in the insights
• Action: Inability to translate insights to action

While all these inhibitors are critical, and any one of these reasons could derail the ROI in AI initiatives, I will focus on one of the most common and critical inhibitors: the inability to translate insights to relevant actions.

When designed well, AI systems can deliver valuable insights into your business operations and revolutionize your approach toward providing value to your customers and shareholders. However, if you don’t implement the right actions based on the insights, it can waste resources. You must align all relevant business operations with informed decisions based on the new insights.

The critical question business executives must be asking is: What are the right downstream behavior changes required to act on the insights? How do we manage the changes to ensure we are achieving desired results?

In order to determine the right actions, here is a method to consider:

1. Plan Strategically

Start with defining the current and desired state based on the new insights.

• What are the different operational units that are impacted or need to change behavior to achieve the desired end-state?
• Evaluate all value chain elements, including procurement, product development, marketing, sales, services, maintenance, etc.
• What are the specific behavior changes you expect from each business area?
• What are the desired overall outcomes?
• What are the steps to get there?

Develop an end-to-end plan across all relevant units with a clear change management process to implement.

2. Refine Business Processes

Once the relevant operational units are identified, using the end state as a base, define all relevant processes that are impacted. Evaluate broader cross-unit processes as well as processes within operational units. Consider customer journey and touchpoints as well as upstream and downstream activities.

Upstream activities could include the marketing process that results in the marketing outreaches. Downstream activities include tools and processes applied for the post-sales support processes. Refine and add new processes as required to achieve the end states. Consider change management approaches to ensure process changes are implemented correctly.

3. Build Awareness and Expertise:

Once the relevant operational units are identified and processes are refined, define (within each business operations area) who needs to change behavior. Consider both internal teams and external partners.

Once the right constituents are identified, determine the awareness and training required to implement the new processes and drive new behavior. Consider initial training and ongoing training requirements with more frequent training or refreshes until initial results are achieved. Ensure early learnings are being applied to refine training as required.

4. Refine Management System

An effective management system is critical to ensure that changes are being executed correctly to achieve desired results. Do we have the right operational metrics to get visibility to the operational changes? What are the leading indicators and the final business performance metrics?

Note that the final business metrics may not always be financial and could take the form of NPS scores, customer satisfaction metrics, increased frequency of customer touchpoints, etc. Are the targets realistic and staged appropriately for the near-term vs. long-term? Is the measurement frequency appropriate? Are the rewards and recognition systems aligned to provide compelling incentives?

5. Monitor End-User Feedback

It is essential to ensure a feedback loop with the end-user throughout the changes. Often there are unintended consequences.

A new process aimed at improving responsiveness to customer requirements with operational metrics focused on transaction efficiency could result in hurried customer conversations and impact satisfaction. A new incentive focused on new customer acquisition could drop an average deal size or subsequent sales if the incentives and management systems are not aligned correctly.

Ensuring ongoing end-user feedback will be critical to ensure that unintended consequences are highlighted quickly so you can take corrective actions quickly.

* * *

This is the fifth white paper in Pragmatic Institute’s series by Sciata President Harish Krishnamurthy on ensuring ROI from artificial intelligence, transforming insights into action, and driving a cultural change in how your organization leverages data. Read the previous pieces: 1) “Making the Leap from AI Investments to Business Results,”  2) “Aligning IT and Business Strategy for Project Success,” 3) “Using AI to Maximize Customer Lifetime Value,” and 4) “Designing AI Models to Extract Insights.”

Learn how Pragmatic Institute can train your data team to deliver critical insights that power business strategy.

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The initial conversation went something like this: 

Me: “Who am I writing to? Who is our audience and what are some of the issues that they’re having that I could address in the content that would help them in their journey?” 

Them: “We don’t really know yet, so just write these pieces using these keywords”  

Alternatively, they might hand me a deck with 25-50 slides full of abstract pieces of information and facts. Surface-level insights didn’t help me get any closer to serving the audience. 

After encountering this same situation a dozen or more times I realized this might be a universal issue where companies don’t know their audience in a meaningful way. 

So, I am going to teach you our approach to creating meaningful buyer personas that I use in my business at Bestbuyerpersona.com.   The strategy includes four areas of research and how to segment meaningfully. 

Before We Get Started, Trash the Template 

I encounter this question often, “Do you just have a template we could use?” 

My answer is, “No, because I feel like what a template is going to do is put you in a box and you’re going to fill in the blanks, call it a persona, and never use it.” 

The reason I stress the importance of having a process for learning about your customers over a template is because that’s where you’ll find useful information and true value. 

The Four Areas of Buyer Persona Research

Research Area #1: One-on-One Customer Interviews: For every buyer persona, we conduct 20 customer interviews to uncover the “why” behind the audience’s buying decisions. 

Research Area #2: Social Listening: This element tells us what the target audience is doing and how they refer to themselves online. 

Research Area #3: Digital Intelligence: This area provides us insight into what communities they are a part of and some other helpful behavioral information. 

Research Area #4: Surveys: During buyer persona work, we create many hypotheses and the survey lets us test it with a large group of actual customers. This means, we’re not just creating fictional characters, our insights are based on real answers and actual buyer data. This element makes the information valuable and meaningful to product teams, marketing teams and executives in an organization. 

The Question Everyone Is Asking: “How many buyer personas should you have?” 

At the beginning of every persona project, I am almost always asked, “How many personas will you find or how many personas should we have?”

My answer is always frustrating: “It depends.” 

It has never felt right to me to promise a client that we’d create a specific number of personas. Quota doesn’t correlate with quality. 

I could find as many segments as I want just by coming up with subjective ways to group people, but that’s not beneficial for anyone. 

However, there are norms. It’s probably fair to give a range of three to five to alleviate the tension that comes with uncertainty. But, I’m not going to tell you how many I am going to find before we begin because I haven’t started any of the research. 

Why You Should Use the  “Jobs to be Done” Segmenting for Buyer Personas

After I capture all of the data gathered during the interview, survey, digital intelligence and social listening, I put the information into little buckets based on “jobs to be done.” 

Simply put: it becomes clear over time that there are groups of people trying to achieve particular things with your product. 

These are stronger segments because they keep us focused on serving our customers based on their pain points. 

And usually, there are fewer segments that emerge compared to grouping by demographics, job title, gender or age. 

People are coming to us to help them achieve something, do something, solve something or learn something. When you group those people together, it gives you a powerful way of aligning the work you are doing with the people you are serving. 

How to Keep Buyer Personas Alive in an Organization 

When I start a new persona project, I get buy-in at all levels of the organization. The purpose is to ensure they value the data. 

I don’t jump into the research without any input from leadership and then present it in the end telling them they should appreciate the work and that it was everything they’ve wanted. 

Questions for Key Stakeholder Interviews: 

I want to talk to leadership at the beginning of the process, and I ask them three simple questions: 

1. What is it you know for certain about your buyer? 
2. Are you assuming any characteristics about your buyer? 
3. What do you need to know about your buyers in the next 12 months to make your next initiative successful? 

Case Study: Jobs to Be Done Segmenting in Action 

I have had clients who were buying other companies and that’s a challenging situation. Suddenly, three or four small companies become one large company, and that’s great. But now, they needed to find the personas for the entire enterprise, not just each little company or product on its own. 

This client found that many customers across the enterprise had similar pain points. We were able to compile and condense these personas in a way that just made sense. 

Then, they were able to execute more efficiently with streamlined branding and marketing. The entire organization could have a singular focus with new shared personas. 

Having zero personas is a problem, but so is having too many. Without shared personas, the efforts can be scattered. This solution improves collaboration and communication throughout the entire organization. 

An Argument for Not Naming Your Personas 

An industry standard is to name your persona. 

Example: Mary Marketer 

I don’t. 

Now, the argument for naming is usually that names can create a human connection that builds empathy. 

I am going to place my counterargument. 

When we create “Mary Marketer,”  we manufacture bias. Suddenly, the best buyer is only a white female who is 23 and is new to her position. Or, whatever the circumstance may be. 

There are different types of bias including beauty bias, bias toward race, political bias, age bias or gender bias. 

I can’t think of a single person that doesn’t have some beautiful stock-image person downloaded from a google search. 

Essentially, we create a ripe environment for judgment that may derail effective messaging. We don’t create empathy; we create stereotypes. 

When someone who doesn’t look like our persona, we might subconsciously think, “You don’t look like our best buyer.”  

So, I’ll give one caveat. If you need to write that one email or design that one ad, it’s your job to create humanity.  

Whereas the strategy, goals and feature launches should be done with an acknowledgment that we’ve segmented many people based on one job or one aspect. 

Additional Resources

Sign up for Adrienne’s weekly newsletter: Persons=People. 

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But, it’s possible not all of that effort pays off.

You’ll know you are working with vanity metrics if the information looks good on paper but doesn’t inform strategy or affect decision-making. 

So, how do you scrap the fluff and focus the meaty measurements? The answer to the question depends on your industry, business strategy and growth stage.

For example, the app Hinge measures “good churn” or the number of customers who stop using their app. This is unique because most companies do everything they can to prevent churn because it means lost revenue. 

So, why would Hinge measure churn in as a success metric? Because Hinge is a dating app “designed to be deleted.” They tell their users that they are so good at helping people find a match with another person that they’ll be able to delete their dating app.

However, despite these variables, there are a few key metrics that great product managers consistently utilize (including churn) to predict revenue growth. 

1. Customer Acquisition Cost (CAC) 

Measuring and tracking customer acquisition costs is important because it helps you (and potential investors) predict the profitability of the business. Your company can use CAC to allocate resources and funds, strategize marketing campaigns and help during the hiring process.

To calculate your Customer Acquisition Cost, simply take your total expenses spent on acquiring customers over a specific time period and divide it by the number of customers you gained in that same time period.

Quick Example: 

TeachIt is a fictitious company that allows individuals to host their online courses on their platform. Teachers pay TeachIt a monthly subscription fee to have access to the platform along with specialized tools and resources. 

The cost of distribution is low because it is a web-based product and customers don’t require much support. Retention is fairly good because once a teacher creates their course content, transferring to a similar platform is both time-consuming and complicated. 

During the last month, the company spent $15,000 on marketing efforts, and 300 new customers signed up to host their teaching account. This suggests the customer acquisition cost is $50. 

How much is too much to spend on CAC? 

The answer depends on your industry and business model, but a good goal is to strive for 25 percent of lifetime value (explained in #2). 

If the average annual subscription is $239.88 or $19.99 a month, and the average customer uses the platform for 3 years, then their lifetime value is $719.64. 

The acquisition cost is about 7 percent of the customer’s lifetime value, which means TeachIt is likely pleased with that metric or maybe it’s their opportunity to invest more in finding new customers.  

2. Monthly Recurring Revenue (MRR)

MRR is at the core of any subscription-based business. It’s a powerful metric that provides insights about how much income you generate each month allowing you to plan ahead with ease and measure growth over time. 

The growth rate of MRR gives you insights into the health of a business. 

MRR calculation is a simple formula: 

Total number of active customers X average billed amount = MRR

Quick Example: 

TeachIt has 7,250 customers paying $19.99 a month, 550 customers paying $29.99 a month and 300 customers paying $39.99. The MRR would be $173,419. 

3. Average Revenue Per User (APRU) 

Simply put, ARPU is the average amount of monthly revenue that you receive per user. ARPU has often been called a vanity metric. Although it may lack depth, at times it can be invaluable because it can identify higher-paying customers, which can increase MRR. It can also help you compare new customers to other segments or measure financial health. 

If your ARPU is too low it could mean that your product is too cheap or your market is too small. Alternatively, you could find that you’re operating in a large market with a high ARPU, which signals growth. 

Calculating ARPU is fairly simple. 

The basic formula is:

 Total MRR / Total Customers = Average Revenue Per User

Quick Example: 

The MRR for TeachIt is  $173,419, and they have 8,100 users, which means the ARPU is $21.40. TeachIt could also calculate the average revenue per user for each subscription tier. 

4. Daily Active User (DAU) to Monthly Active User Ratio (MAU)

DAU/MAU is the ratio of daily active users over your monthly active users shown as a percentage. This is a helpful metric because it is an indicator of engagement or stickiness. 

Combining DAU and MAU is a powerful way to learn how a customer base engages with a product over time. This is especially true in the mobile app space. An app with 2,000 downloads and 1000 active users could be considered more successful than an app with 20,000 downloads and 200 active users. 

Quick Example: 

Of the 8,100 subscribers on TeachIt, 7,000 users are active on the product each month and 1,700 are active each day. That means the DAU:MAU is 20.9%, which is typically seen as good for most companies. 

A company might be inspired to learn more about what makes the DAU different from the MAU and invest more resources toward capturing the types of buyers who use the platform daily. 

This metric could also tell a story about new features or platform updates. If the DAU to MAU ratio drops or increases after a new update, it could inform future business decisions. 

5. Conversion Rate From Free Trial to Customer 

The percentage of users who have upgraded from the free trial period to a paid account is shown with the Trial Conversion Rate. 

Most SaaS models offer a free trial period to let users test out its functions and gauge their overall experience with the product. These free trials are given in the hope that the user will convert to the paid experience. 

To calculate the Trial Conversion Rate you simply take your users who upgrade from free trial and divide them by the trial users or Users / Trial Users = Trial Conversion Rate.

Quick Example:

TeachIt has 450 trial users and 120 of them converted to a full paid account. The Trial Conversion Rate is 26.6%

Using the results a company can identify the type of customer who is more likely to upgrade from free to paid. They can then focus on attracting more of those user types to test their product.

6. Net Promoter Score (NPS)

Net Promoter Score is a metric for identifying the loyalty level of customers. It’s measured through a single-question survey where higher numbers are more desirable. The question usually looks something like this:

“How likely are you to recommend TeachIt to a friend or a colleague?”

The customer responds by indicating how likely they are to recommend on a 0-10 scale, which helps you identify the customer type. The customer types are promoters, passives and detractors. 

The promoter is an enthusiastic customer who is satisfied and highly likely to recommend the product, these customers responded with a score of 9 or 10. 

Passives are satisfied but not likely to go as far as recommending it, their responses fall between the score of 7 or 8. 

Finally, the detractors are your unsatisfied or unhappy customers, which are unlikely to buy again and are likely to not recommend the product to others. They responded anywhere from 6 and below.

The calculation for NPS is simply the number of promoters minus the detractors, with the passives being ignored. So an example would be:

Survey results were 40 promoters, 40 passives and 20 detractors.

NPS score is as follows 40 – 20 = 20

If your NPS score is above zero it means customers are more loyal than not. NPS scores above 20 are favorable and above 50 are considered excellent. 

The NPS score can be used to see how you’re stacking up against your competition by comparing your products, stores, web pages, you name it. It can help you identify your target market, as well as how they respond to your services and even social media campaigns. Ultimately the goal is to strengthen your loyal customer base.

7. Customer Retention Rate

Keeping customers is more economical than finding new customers, which is why it’s critical to pay close attention to Customer Retention Rates. 

Having a loyal customer base will help you build a brand that can retain its value through referrals instead of amping up your customer acquisition costs to replace your lost customers. 

To calculate customer retention rate you need to know the existing number of customers at the start of a time period, the number of customers at the end of the time period and the number of new customers added within the time period. 

Here is the simple formula: 

(End of Time Period Customers – New Customers Added During Time Period) / Customers at the beginning of the time period multiplied by 100. 

Quick Example: 

TeachIt began the quarter with 7,700 customers, ended the quarter with 8,100 customers. There were 550 new customers added during the quarter. During the quarter, TeachIt had a 98.7% retention rate. 

A good retention rate is as close to 100 percent as possible. 

You might be thinking, “wait a minute, isn’t this the same as churn rate?” 

The answer is no, but together with churn rate, these two metrics have a big story to tell you about who’s staying and who’s going. Then, all you have to do is figure out why. 

8. Monthly Customer Churn Rate

We started talking about churn rate, so we’ll finish the list here. While retention rate focuses on the percentage of users who continue to use a product or service, churn rate is the percentage of users who cancel their product or service.

For most companies, churn is not ideal, because reducing churn usually results in revenue growth. However, companies like Hinge turned this negative metric into a positive, because they realized that when users leave its often not for a different dating app. Instead, users achieved what they set out to achieve using the app as a tool. And there are other industries and products that could potentially do the same (e.g. people looking to buy or sell a home).

The simplest calculation for churn rate:

Divide the total number of churned customers for a specific time range by the number of customers you had on the first day of the time range.

Quick Example:

TeachIt began the quarter with 7,700 customers and 100 customers left during the quarter, TeachIt had a 1.2% churn rate, which is fantastic. In fact, most companies strive to keep their churn rate between 3-5%

Bonus Metric: Net Revenue Retention Rate 

Net Dollar Retention is a metric that helps you identify if your company is losing revenue from contractions in the existing customer base or if you have engaged customers who are spending more money on the platform over time.

To calculate this metric you need to know MRR, expansions, downgrades and churn. To help you simply calculate this helpful complicated metric we’ve designed an infographic. 

Download The Infographic 

The post 8 Essential Metrics to Help Product Managers Predict Revenue appeared first on Pragmatic Institute - Resources.

The company invested $300 million in a marketing campaign to create the hype surrounding the launch. And who can forget the hoopla?

Acquiring the rights to the Rolling Stones’ song, “Start Me Up,” as the anthem for the iconic “Start” button cost $3 million alone, but it made Microsoft a household name and propelled the brand to the mass market.

Few companies have that kind of marketing budget allocated for their product’s entire lifecycle, let alone an initial launch. The good news is that you don’t have to spend hundreds of millions to attain product launch success. But you do need to pay attention to the planning and preparation because incorporating those elements as an afterthought is a guaranteed path to failure for a product launch.

A successful product launch sets in motion the foundation for a product’s market success, yet many companies execute product launches in a sloppy fashion. Consider research conducted by Michael Eckhardt at Chasm Institute: new product launches were successful only 17% of the time, in a study analyzing 1,150 B2B high-tech companies between 2015-2020.

Many oversimplify why their product launch failed, often citing either:

1. Not starting planning and preparation early enough, or
2. Releasing a product before it’s ready for release.

Hurdling past these two factors is product launch table stakes. However, often ignored, market and competitive intelligence (M&CI) coupled with market research also play a vital role in ensuring a successful product launch.

In the spirit of “launch” as a metaphor, this article explores a three-stage-to-orbit system to describe the ideal product launch process. These stages include determining objectives and corresponding launch type, bypassing common launch mistakes, and continued maintenance for successful orbit.

Stage 1: Determine Objectives and Launch Type

How many times has an organization used inaccurate terms for “announcement,” “launch,” and “release,” only to create customer confusion?

It’s important to define these terms within the organization, along with establishing time intervals between each phase; in days, weeks, and months.

Determine the objectives you want to accomplish and associate it with a launch type. Here are the attributes for three commonly used launch types, which are associated with a respective tier, from most extensive to least extensive:

• Major launch (portfolio-level change): a “full-scale launch,” the goal is to maximize awareness and generate significant leads and sales. This tier targets many vertical segments and is intended to give the product and platform the best chance at success. Many times, this launch puts the company on the map (think Gartner Magic Quadrant). These launches require changes to existing processes and systems because they are the result of a new product, extensive added features or architectural changes. It’s when the term “forklift upgrade” is used due to those significant changes involved.
• Minor launch (product-level change): the product may not be fully ready for prime time and could be deployed to a limited, micro-targeted set of customers in a particular vertical segment. In this tier, it makes sense to get the product out, receive quick market feedback and have the product team iterate quickly. This tier provides a cautious approach as the organization may not have the funds or resources to properly launch the product. There may also be a risk of shipping date slippage or uncertainty of product readiness and acceptance.
• Enhancement launch (update to an existing product): an insignificant, minor revision, the associated release often includes just release notes and an email announcement to existing customers. There’s seldom any press coverage involved.

After determining your objectives and launch type, schedule a timeline complete with milestones, assign roles and responsibilities, and allocate a budget.

Your budget should range from 5% to 20% of anticipated revenue. It takes an average range of three to nine months for a successful launch, depending on the launch type, maturity of the market, and type of offering.

Stage 2: Bypass Common Launch Mistakes to Achieve Liftoff

Mistake 1: Not Using Metrics in Launch and Post-Launch Assessment

In a product’s discovery phase, you must do research and analysis to understand the existing market and the impact of your product’s launch. A not so common applied metric is return on product launch (ROPL) —and it’s an important yardstick to share with financial planning and analysis (FP&A).

McKinsey highlights that more than 25% of total revenue and profits comes from the launch of new products. Sales Benchmark Index (SBI) has developed a calculator to help with this return on product launch assessment.

Competitive intelligence metrics can only be assessed post-launch, and many of these effects are not seen immediately. However, they’re vital in justifying the value that competitive intelligence brings to an organization.

Those metrics are:

• Supported deals, and improved win/loss ratio
• Defended sales revenue (existing), and influenced sales pipeline (future)
• Increased average deal size, and shortened sales cycle
• Derived features listed on the product roadmap
• Produced field enablement tools and sales training material

Mistake 2: Not Knowing the Phase in the Tech Market Model (TMM) You are Competing In

How do your customers view your and your competitors’ tech product or service offering? Are they ready to adopt the innovation? Are customers delaying adoption? Or, worse, are they going to permanently reject the offer?

Chasm Institute’s Tech Market Model (TMM) can be used to assess your company and product’s market stage during a launch. This model maps, analyzes and illustrates the likely adoption or acceptance of a new product or innovation.

For example, venture capitalists love to ask about the total addressable market (TAM) as one of their key investment criteria. But it’s useless during the early market. Ideas are still being generated during this phase. At the time of their Series A funding, many successful venture investments would have had relatively insignificant or even undefined TAMs—Amazon, eBay, Google, LinkedIn and Uber are some examples.

Another mistake made in an early adopter market is biting off more than you can swallow by going after too many markets. Before a launch, market segments need to be clearly defined. Ideal customer profiles and effective buyer personas should be developed through interviews and industry research.

One caution: Don’t confuse the product lifecycle with the Tech Market Model. The product life cycle is important, but it’s company-based and internally focused. The Tech Market Model takes an outside-in approach, looking through the customers’ lens.

Mistake 3: Your Launch Creates Confusion with New or Existing Customers

We started out by talking about the wildly successful Windows 95. But there was another product that Microsoft released that still causes people to cringe: Windows 8.

A key reason for this was the use of a new, tile-based UI, full-screen “Start” menu and a confusing interface for keyboard and mouse users that was Microsoft’s attempt to mimic the iPad—but the company forgot the institutional memory of its own PC user base.

Microsoft should have conducted a needs assessment, based on research and testing, to ensure that they could compel buyers to both make the shift to the new operating system and, once purchased, quickly grasp how to use the operating system.

Windows 8 was a muddled offering with an identity crisis. Microsoft treated the product like a disruptive innovation for early adopters, while it should have treated and launched as a more continuous innovation for their mainstream installed base.

Chasm Institute highlights three requirements for a product to be a truly disruptive innovation that appeals to early adopters:

• A change in skill
• A change in behavior or mindset
• A change in workflow or process

In looking at the evolution of Microsoft operating systems, Windows 3.0 was the product poster child for disruptive innovation. Previously, users were accustomed to using DOS and its command-line interface for computers. Windows 3.0 revolutionized how PC users expected to view and interact with their information.

When thinking about your product or service’s launch, make sure to employ various methods to validate the hypotheses around your offering. Validation methods include prototype design, surveys, and A/B testing. These methods also help when refuting executives’ and product professionals’ “best thing since sliced bread” soapbox.

Mistake 4: Mimicking the Competitors’ Pricing

Pricing is an instrumental part of your product launch, but many companies make the mistake of setting their price based on competitors’ pricing.

Also referred to as “the going rate,” this creates a customer perception that your product or service is a “me-too” offering that requires them to choose between two companies’ indistinguishable products. Mimicking competitor pricing is also a great way to initiate a price war that leads to pricing erosions and subsequent lower margins.

By not factoring in your customer segments’ willingness to pay, overall company strategy, and variable cost structure, mimicking the competitors’ pricing just leads to additional competitor vulnerability. When setting prices, it’s the one time when it’s OK to say, “We’re not focusing on the competition.”

The better alternative to use is value-based pricing. Value-based pricing requires a deeper understanding of customers’ needs and wants, and it reinforces sustainable differentiation.

Setting value-based pricing enforces the company to ignore fixed costs, since they’re irrelevant in this pricing model. It’s easier for software companies to grasp this concept because there are no economies of scale involved; computer hardware and manufacturing-intensive companies, in industries like consumer-packaged goods, have a harder time accepting this.

Mistake 5: Not Anticipating the Reaction to Your Launch

For every action, there is an equal and opposite reaction. Enter war games, which predict competitive responses that allow you to adjust your launch to counter reactions.

While war games can be used in several areas of product management and product marketing, wargaming pioneer Ben Gilad estimates that 90% of the application of his war games are focused on product launch, whether it’s one’s own company or a competitor’s.

For example, a consumer product company known for a low-cost, low-priced product planned to upgrade that product and then launch it at a higher price point. A war game predicted a fierce response from the market leader who had a close relationship with a dominant retail channel.

The retailer would be at-risk of losing its loyal customer base that depended on the low-price product. Ultimately, the plan was scratched. Later, the company launched a different, higher-end product with its own brand. The company also targeted a different retail route for its market entry point.

Product launch failure typically is a result of a disconnect between plan designers (e.g., senior executives, VPs, strategic planners) and those who need to implement the launch plan (e.g., marketers, salespeople, sometimes R&D and manufacturing). A war game that combines these two groups brings the market reality back to the plan at a fraction of the cost of failure.

Stage 3: Maintaining Orbit

At this stage, your product has launched smoothly, and your product or service has been well received by the market. Your deliverables can be broken into two phases of the product life cycle: idea-to-launch and launch-to-withdrawal. While there’s no clear demarcation between the two phases, idea-to-launch is about achieving a successful liftoff while launch-to-withdrawal is about maintaining a sustained orbit. Your launch activities start with idea-to-launch and continue through launch-to-withdrawal. Here’s a comprehensive, sequential launch activity list:

1. Needs/problem analysis
2. Market opportunity assessment
3. Ideal customer profile and persona creation
4. Product ideation and design
5. Pricing and profitability analysis
6. Messaging and positioning
7. Product and sales training delivery
8. Sales tools implementation
9. Collateral and website creation
10. Marketing programs execution

Reactive companies introduce market and competitive intelligence later, often right before the product is slated for release; it’s generally one-dimensional, in the form of internal field enablement tools. While better than no intelligence, this type of posthumous intelligence often is inefficient, less effective and leads to duplication of efforts. And, in the end, it creates more work.

Proactive companies introduce market and competitive intelligence from inception. This ensures that, from day one, product development is only building the features that drive business outcomes. It also addresses both the idea-to-launch and launch-to-withdrawal phases, with product managers traditionally involved in the former phase and product marketing managers in the latter phase.

Congratulations on your successful launch but remember to treat the product launch as a journey, not a destination.

The post Avoiding Product Launch Disaster appeared first on Pragmatic Institute - Resources.

This technical article was written for The Data Incubator by Dan Taylor, a Fellow of our 2017 Spring cohort in Washington, DC. 

This article was originally published on October 25, 2017, on The Data Incubator.

For many of us with roots in academic research, MATLAB was our first introduction to data analysis. However, due to its high cost, MATLAB is not very common beyond the academy. It is simply too expensive for most companies to be able to afford a license. Luckily, for experienced MATLAB users, the transition to free and open source tools, such as Python’s NumPy, is fairly straight-forward.

MATLAB has several benefits when it comes to data analysis. Perhaps most important is its low barrier of entry for users with little programming experience. MathWorks has put a great deal of effort into making MATLAB’s user interface both expansive and intuitive. This means new users can quickly get up and running with their data without knowing how to code. It is possible to import, model, and visualize structured data without typing a single line of code. Because of this, MATLAB is a great entrance point for scientists into programmatic analysis. Of course, the true power of MATLAB can only be unleashed through more deliberate and verbose programming, but users can gradually move into this more complicated space as they become more comfortable with programming. MATLAB’s other strengths include its deep library of functions and extensive documentation, a virtual “instruction manual” full of detailed explanations and examples.

MATLAB’s main drawbacks, when it comes to analysis, stem from its proprietary nature. The source code is hidden from the user and any programs written with MATLAB can solely be used by MATLAB license holders. With this in mind, it is important for academics transitioning into professional data science to broaden their skill set to include free and open source tool kits.

Python is often a data scientist’s first choice for data analysis. It is an open source, general programming language with countless libraries that aid in data analysis and manipulation. Because Python does not include a user interface, data scientists need to utilize a third-party user interface. Such interfaces allow nearly all of MATLAB’s functionality to be reproduced in Python.

All MATLAB users should become well-acquainted with NumPy, an essential Python library. NumPy provides the basic “array” data structure, which forms the backbone of multidimensional matrices and high-level data science packages, including pandas and scikit-learn.

Proficient MATLAB users should find NumPy to be quite intuitive, as the NumPy array functions very similarly to MATLAB’s cell array data structure. The biggest challenge could very well be learning the syntactic differences between the languages. Here are some examples of equivalent code in both languages:

Python example code:

In [1]: import NumPy as np

In [2]: a = np.array([1,2,3,4]); b = np.array([5,6,7,8])

In [3]: a[0]

Out[3]: 1

In [4]: a[1:3]

Out[4]: array([2, 3])

In [5]: a * b

Out[5]: array([ 5, 12, 21, 32])

In [6]: a / b

Out[6]: array([0, 0, 0, 0])

In [7]: a * 1.0 / b

Out[7]: array([ 0.2       ,  0.33333333,  0.42857143,  0.5       ])

MATLAB example code:

>> a = [1 2 3 4]; b = [5 6 7 8];

>> a(1)

ans = 1

>> a(2:3)

ans = 2     3

>> a .* b

ans =     5    12    21    32

>> a ./ b

ans =    0.2000    0.3333    0.4286    0.5000

Both languages support vectorization and easy element-by-element operations — care needs to be taken with MATLAB as the default operations are often matrix operations.

• Defining an array in Python requires passing the NumPy function a list, whereas in MATLAB, defining a vector is very flexible and does not require commas.
• In Python, indexing starts at 0 and is performed with brackets, whereas in MATLAB indexing begins at 1 and is performed with parentheses.
• In Python, slicing is left inclusive and right exclusive, whereas in MATLAB slicing is inclusive at both ends.
• In Python, the element type of an array is decided when the array is defined. In MATLAB, the default element data type is a double float, which is important when performing element-by-element division.

Ultimately, every aspiring data scientist should be familiar with the variety of tools available to them. Those who are transitioning from academic research will find Python’s NumPy library to be a natural transition point because of its similarity to the MATLAB programming language. Proficiency in NumPy brings the data scientist one step closer to unlocking Python’s full potential for comprehensive data analytics.

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