Why cant i reforge my items

Reforge Requirements. Edit source History Talk 0. Categories Reforge Add category. Cancel Save. Universal Conquest Wiki. Silver Egg x1. Crimson Spirit Orb. Azure Spirit Orb. Emerald Spirit Orb. We have received multiple reports of this issue and are currently investigating it.

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Skip to Main Content. Overwatch League. Log In. My Tickets. Contact Support. Relevant Products:. Common Problems I can't reforge my azerite piece The azerite reforger won't let me redo my traits. Note that each type of tech debt can be sized on the scale of acute to systemic.

To illustrate, with developer efficiency debt They didn't write the tests because the full team committed to doing manual regression of the feature before the experiment launched.

The engineer later takes a few hours to add in the tests as the next round of the referral experiment is being prepared. Because an early-stage engineering team didn't codify practices around testing and alerting, they're now feeling the pain a few years into exponential product growth. Each day they have at least a few production incidents that impact their users, and their NPS score is dropping month over month.

Adding all tests and alerts cannot be done overnight while balancing new development. So, the tech leads sequence it out: start with creating testing and alerting guidelines, apply those guidelines to critical surface areas and features P1 this quarter, followed by P2 areas next quarter, and P3 areas the following quarter. By the end of the year, or months from now , all areas of the product should have uniform testing and alerting in place, based upon the newly established guidelines.

So far, we've gone through assumptions, classifications, and sizing in relation to tech debt. Once you understand those things you can make a strategic decision within the broader context of your product decisions. The key to seeing tech debt strategically revolves around understanding when to zoom in or out of prioritizing tech debt vs.

In order to properly manage tech debt, consider the following vectors:. Confidence - Is there a high probability that this will lead to a significant problem for the team? Impact to User - Does not doing this result in a speed or quality problem that hurts user experience? Sequence - Will this prevent the team from reaching important milestones?

Accumulated debt - How much debt have you chosen to accumulate already? From there, each vector should be classified on a scale of low priority to high priority:. Confidence - Where on the low to high scale is the probability that this piece of tech debt will lead to a significant problem?

Time - When on the not urgent to urgent scale will this piece of tech debt become a problem? Impact to User - Where on the low to high scale does not doing this piece of tech debt result in a speed or quality problem that hurts user experience?

Sequence - Where on the minimal impact to blocking impact scale does this piece of tech debt prevent the team from reaching important milestones? Accumulated debt - Where on the minimal to significant scale does the amount of already accumulated tech debt fall? Based on where the piece of tech debt falls across the comprehensive scale illustrated in the visual below , it's then easier to understand how to leverage the tech debt strategically against other company initiatives:.

Is it necessary to solve for sooner rather than later, because it can harm other product and business initiatives if the tech debt festers?

Is it better to put a temporary remedy into place, to work on even higher potential items for the time-being? In the visual directly above, it's clear it's necessary to solve the systemic tech debt sooner rather than later since there's a high chance of a significant problem, the problem will become urgent soon, the debt will greatly impact reaching set milestones, and there's already a decent amount of accumulated debt.

If some of the vectors - say impact to user and sequence - were smaller and to the left, then it could be fine to remedy the problem through a fast solution or workaround for next few months.

If even more of the vectors - say impact to time, impact to user, and sequence were all smaller and to the left, then it's likely okay to backlog this area of tech debt to a time that makes sense in the future. In each situation solve, remedy, backlog played out above, it's important to consider the piece of tech debt critically, across each vector and the the low to high priority scale.

Evaluating tech debt in this manner allows for the team to make the best strategic decision on how to move forward vs. Another way to approach tech debt strategically is crafting your tech debt portfolio based on tech debt type in relation to your organization's size. Here at Reforge, we classify companies as they go through four stages of growth on the S-curve:. Linear, unscalable efforts to get the growth engine going. Transitioning from unscalable to scalable growth loops.

Optimizing core growth loops. Doubling down on what works. Need to think about PMF expansion and restarting the process all over again or layering on a new growth loop.

Related to the S-curve, each type of tech debt should be balanced appropriately based on the company's stage of growth. Represented visually below are guidelines to consider, for the distribution of tech debt by type of tech debt as an organization progresses over time.

At this point your team should be making engineering decisions for speed over accuracy, stability, process, etc - hence, the large volume of developer efficiency debt. This generally means picking an opinionated full stack framework like Django, Rails, or PHP and developing fast, especially since the vast majority of early products are crude apps and need good integration to web and mobile.

This is when there are signs of product market fit and the product is transitioning into scalable growth loops. Where the team realizes some process is necessary so developer efficiency debt starts to be resolved and is still determining best ways to balance internal process and user experience, hence the increase in technical product debt. This is where security debt, maintenance debt, and decision debt is growing as a result of very rapid growth and not realistically being able to keep up with security updates, clean ups, and 'fixing' past decisions.

Scaling is also where there is the most changes and corrections in test automation, deployment systems, monitoring and alerting, logging and instrumentation, migrations, test and staging, and ETLs. This is where saturation starts to hit. At this point, the business is more mature. Maintenance debt and decision debt continue to increase as a result of the amount of historical code and decisions that took place. Developer efficiency debt is starting to increase again as the team looks for new opportunities for growth outside of the core growth to date and the existing product.

Technical product debt, security debt, and stability debt are balancing out after the previous intense scaling period. As you manage a tech debt portfolio through company growth, there are a few key areas to pay particular attention to: process, tools, sprints, and roadmaps. Below are quick guidance points for each area, for when you're looking to reduce volume of one type of tech debt to balance it out by being able to take on a different type of tech debt as the company evolves over time.

Although it is strategic to accumulate tech debt, there are times where it would have made sense to stop tech debt from even being created in the first place through process implementations. This is especially the case in Inflection and Scale stages of company growth, where developer efficiency debt should decrease as more engineers join the team.

The developer efficiency debt decreasing can then give way to necessary increases in decision debt and maintenance debt. Similar to the processes above, there are also times where investing in foundational tools will prevent some types of debt from being formed. This is particularly important in the Scale stage of org evolution to have more uniform methods in place to avoid security issues security debt , prevent bugs that could impact user experience technical product debt , and allow for code consistency developer efficiency debt.

As an org reaches the scale of having more established on-call responsibilities, the on-call sprint should be spent on fixing fires the purpose of on-call or on reactive work associated with tech debt during downtime from on-call fires. Unlike using on-call time for reactive acute tech debt, putting tech debt into roadmaps should be used for debt that requires significant roadmap alignment and work across teams. Recognize that accumulating tech debt allows your team to make holistic strategic decisions around what initiatives to take on.

Think through common tech debt assumptions your team might be falling into. Stop throwing around the umbrella term of 'tech debt. Size your tech debt to make it less ambiguous.

Is it closer to acute short time to solve or systemic long time to solve? Prioritize tech debt strategically against other company areas. You can prioritize based on vectors like confidence, time, impact to user, and more. Balance a constantly evolving portfolio of tech debt, based on company growth changes and needs. World class operators tend to move from one role to the next. Unless you are one of the lucky few to work directly with them, the knowledge and insights remains trapped in their heads.

We view it as part of the Reforge mission to unlock these earned insights, and pass them to the next generation. EIRs and OIR's lead Reforge Programs, sharing their earned insights and giving mid-career professionals an opportunity to learn from the best.

EIRs get the time and space to choose their next endeavor, synthesize their insights, build their personal brand, develop relationships with peers, and a lot more. Sad, because we've enjoyed spending so much time with them. Excited, because they've found their next big endeavor and can't wait to see it come to life. The impact of their contributions on Reforge members, the functions of product and growth, and the broader ecosystem will be felt for a long time. What this also means is that we are accepting applications for our next set of EIRs.

The EIR program is a 6 or 12 month, 2 to 3 days per week role for experienced Product, Growth and Engineering leaders who are at a transition point in their career. During your time as a EIR, you will lead Reforge programs and will have the opportunity to help define and influence the future of your discipline. EIRs receive a salary, health insurance, and other benefits.

Time To Explore - Have the mental space and hours necessary to explore and fully research the companies, people, and opportunities for your next role.

A Network To Leverage - Gain a network of top-tier leaders and practitioners to leverage and amplify your next endeavor. Frontier Knowledge - Become an expert on multiple high-demand frontiers and important topics in Product Growth. An Inside View - Get an inside view of many different companies, products, and services. We've put together some our favorite insights from this group below. If you are a Reforge Member or just a reader of our blog and have been helped by one of their insights, take a moment with us to thank them on LinkedIn or Twitter.

What's Next: Elena is starting a to-be-announced new company that we are very excited about. She not only helped contribute an incredible wealth of knowledge to the community but helped us define and set an example of what it means to be an amazing EIR.

During her time as an EIR, Elena:. Created and led cohorts of the Experimentation and Testing program. Co-created and led cohorts of the Monetization and Pricing program. Developed in-depth cases around the topics of retention, experimentation, pricing, monetization, and so much more. Here are some of our favorite public highlights from Elena.

But the market has shifted and this completely misses the six indirect growth effects of freemium:. More from Elena on freemium here In The Reverse Interview , Elena gives her insight on what she has learned from making good and bad career decisions along the way.

This point hit home on how to value selecting your next company:. I thought it would be fine, the company will figure things out, but they never got figured out. That is insane. Choosing a new company and role is a big choice with bad odds. Your eggs are in one basket at a time, you are at an information disadvantage evaluating the company, and most common advice about how to get information isn't helpful.

Read more from Elena and Crystal on how to change all of this In Monetization vs Growth - It's A False Choice , Elena discusses her experience in how most companies view these two things as a tradeoff vs. This leads to multiple core issues:. The first is that it is seen as a sacred cow. For most products, monetization decisions are set early.

The specifics are typically the result of mostly guess work. Then, if the product is successful, a false positive feedback loop occurs. Because the product grows, we believe that our monetization decisions were accurate. As a result, the organization builds a belief that monetization should not be changed. Another reason is the fear of customer revolt. When someone suggests a change to monetization, the common veto card of "customers will revolt" is often used. But the fear of the customer revolt actually increases your chances of a customer revolt.

The longer you go without changing your monetization, the bigger the hole you dig yourself , then the bigger the eventual change you need to make. What's Next: Bangaly has joined Popshop Live, one of the most exciting new consumer social apps. Contributions: Bangaly was also part of the original cohort of EIRs, setting an example that will be hard to beat.

In his pre-tech career, Bangaly spent a number of years in education. That knowledge has helped inform some of the teaching styles we use today. During his time as an EIR, Bangaly:. Led cohorts of Retention and Engagement and the Growth Series programs. Here are some of our favorite public highlights from Bangaly. This is typically because the current product positioning or experience has too many barriers to adoption for them.

Our insight was that it is critical for growth teams to be continually defining who the adjacent user is, to understand why they are struggling, to build empathy for the adjacent user, and ultimately to solve their problems. Most teams miss Adjacent Users because they are using the wrong tool: Personas. But personas, as they are typically defined, have one or more of the following issues:. They are focused on the current user vs the next user.

They are too static and companies anchor on them for years. It's not enough to know who the adjacent user is; you need to build empathy for them to understand why they are struggling:. Your team are power users of the product. They know the product in and out. To build empathy with the adjacent user and create hypotheses of why they are struggling, I recommend four techniques:. Bangaly breaks down more here Bangaly has helped advise on many career decisions. Compensation and career advancement are correlated, but not the same.

Your compensation increases because you are creating a lot of impact. Compensation is the output; impact is the input. I like to think about Impact as the thing that powers your career progression. It is what you are solving for when you are trying to make career progression decisions. A trap in evaluating impact is that you just need to work on yourself in order to grow your career. For example, "To progress, I just need to get better at [insert skill.

The variables of your environment are just as important as the variables of you. Crystal helped Gojek scale from 20K to 5M orders per day, the business intelligence team from 0 to , and the the growth team from 8 to Led cohorts of Advanced Growth Strategy.

Was a top rated featured guest on topics of Experimentation and Data. Advised Clora as part of the Reforge Advisors program.

Began creating a soon-to-be-announced Data Deep Dive program. One of the most common problems Crystal has helped companies with is making analytics a successful effort. In Why Analytics Fail , Crystal breaks down the root causes of analytics failure:. But commonly these things can be a waste of time and money because you aren't addressing the root cause and real problems. Instead, the root cause typically stems from one or more of the following:.

Understanding the root cause is what separates successful and unsuccessful teams. The real goal, though, is to analyze those metrics. Those two things are very different. The latter is how we make information actionable. Making information actionable isn't about reporting on the number of people that do something, it is about how we separate what successful people do vs.

This nuance is commonly lost, but as you will see fundamentally changes how we approach what we track and how we track it. Mindset of the Business User - Your customer is the business user. Understanding their needs determines the tools to use, the events to track, the names of the events, and the properties on each event. Journeys Instead of Metrics - The right level of event abstraction comes down to tracking journeys, not metrics, through success, intent, and failure events.

Defining Properties - Properties are once again a key to achieving two of our main goals, providing the right level of abstraction and making the data actionable. Pressure Test Understanding - Once you have your set of events and properties defined, you should pressure test understanding and actionability.

Track Decisions Made Without Data - No matter how thorough you are with the above process, there will always be changes you need to make. The business, goals, and product are constantly changing, creating new needs. Crystal goes into more detail here. She says:. What kind of analysis should the data team be doing? Should I be using [Insert Analytics Tool]? This creates three issues:. Crystal lays out the approach to looking at data as a strategic lever for growth:.

Viewing data from this perspective leads to different answers on the questions we started with around team and tools. What does it look like when data is treated as a strategic lever for growth?

I recommend walking through four areas:. Strategy - What are your points of leverage? How does data improve those points of leverage? Stage - What stage of maturity is our product in? What stage of maturity is our Data in? Team - What people do we need to achieve the data strategy? Are they set up for success internally? Tools - What tools do we need to adopt to facilitate the team's impact? Crystal breaks this down into her framework of Data Informed to Data Led:.

In this framework there are two parts:. Most companies can fit themselves into one of three stages:. Stage 1: Data Informed. These companies are focused on building the business and getting to product-market-fit stable user retention rates.

The key business need is for data to provide operational visibility. Stage 2: Data Driven. These companies have reached product-market-fit and are actively optimizing for specific users, behaviors, and experiences in the product at the feature-level.

Stage 3: Data Led. These companies are operationally run by data products, infrastructure, and services. The successful advancement from one stage to the next requires two things:.

Capabilities : The dependencies and foundations required for the next stage have been built and unlock new leverage and capabilities". More here from Crystal Dor is a library full of marketplace insights from his experience at Lyft, Uber, and Groupon. Co-Led the Product Strategy program. Co-Led the Product Leadership program. Contributed cases from his Lyft experience around monetization strategies and developing a product team. One of the keys:.

Instead, "managing up" is about driving and maintaining alignment among your goals, your manager's goals, and the organization's goals. Support for you and your projects is what follows. Dor and team talked about the three most common failures around managing up:. Here is Dor on his personal experience around right idea, wrong time:. What's the difference between doing it now versus doing it after our other priority?

On right idea, wrong delivery Dor notes that you have to go the extra mile to understand your audience. An example:. Pregaming the content with someone close to the key decision maker helped me predict any blind spots I might have by helping me look at it from the other person's perspective. This helped me learn what would be a surprise or what wouldn't fit their mental model in advance. I would do this with almost every big presentation.

More from Dor and team on Managing Up here. The first strategy, comprehensiveness, is about building an extensive and diverse supply base, giving customers many different options for any single transaction.

For many winning marketplaces, comprehensiveness is the core value proposition that allows them to own demand and provide a sticky product. Dor talks about some of the challenges of this strategy from his time at Groupon:. We then created pages for these merchants so we could tell them: 'You had this volume of people interacting with your page on Groupon, you should do a deal with us.

For example, de-duping merchant pages and consolidating the right information from different data sources was an incredibly complex task. This and other challenges eventually leads to what they call the comprehensive asymptote - As supply coverage increases, each additional unit of supply requires more effort to add. More from Dor, Casey, Anne and others on marketplace supply strategy here What's Next: Anne will be joining Grey Point to consult on monetization strategy for a marketplace product.

Contributions: During her time as an OIR. Assisted in leading the Scaling Product Delivery program. In The Power User Trap , Anne, Bangaly Kaba, and Fareed Mosavat cover one of the hardest things to get right in product strategy - when to build for your power users, and when to ignore them:.

Falling off one side of the tightrope results in Failure Mode 1: over-catering to your Power Users at the expense of your product ecosystem as a whole. Alternately, you can fall off the other side of the tightrope into Failure Mode 2: neglecting your Power Users and alienating the people who generate significant value for your product.

This is The Power User Trap. While the failure modes look different, falling off either side of the tightrope takes you to the same place: a dying product with a declining user base. One of the keys that Anne and team talks about is needing a new definition for what a power user is:. A Power User is also an outlier whether they intend to be or not. The conventional L28 Power User still fits this definition they're an outlier in terms of frequency of engagement , but the important thing to remember is that a power user can be an outlier on many types of behavior and influence - monetization, creation, feature engagement, audience growth, or costs.

Knowing when to build and when to ignore requires you to understand what behavior they are an outlier on and how it impacts your overall growth model. Read more about the different types of Power Users from Anne here Some of the key insights Anne and the team surfaced include:.

Many people think they are managing up effectively but are not, falling into two common failure modes: the politician or the problem-finder. The most successful upward managers cultivate empathy toward their leaders. Specifically, this looks like: 1 aligning personal and organizational goals; 2 taking an empathetic approach to working together; and 3 packaging problems in a way that makes helping easy.

Contributions: During his time as an OIR, Britt helped kickstart some new community efforts within Reforge bringing knowledge from his days at Slack. Some of the key insights:. Bad experiments only advance metrics. Good experiments drive impact by solving real user problems. They have strong, well-reasoned hypotheses grounded in data analysis, customer insights, and market research. Good experiments are about understanding true customer behavior around the things that matter.

Bad experiments move metrics by confusing or tricking your users. They make things harder for your users, rather than solving underlying problems. Good experiments are conceived as bets. You know they have a chance to fail, but based on the info you have available, it is a good investment to make.

They help you learn about the things that matter, enabling you to take bigger bets over time. Bad experiments are endless optimizations. They adjust things around the edges in an attempt to improve performance in a marginal way. They steal time, energy, and resources from validating more meaningful bets. What's Next: Zainab is spending some time advising early stage startups. Instead, companies should think about the relationship between mission, strategy, roadmap, and goals as a stack of distinct concepts.

Product Strategy is the connective tissue between company level assets and how your product team executes:. We cannot have product goals without knowing our product strategy.

Given this relationship between the layers, Product Strategy serves a critical role—it is the connective tissue between the objectives of the company and the product delivery work of the product team. Read more about how to get your product strategy right along with examples here The EIR program is a 6 or 12 month, 2 to 3 days per week role for experienced product, growth and engineering leaders who are at a transition point in their career.

During your time as a EIR, you will lead Reforge programs and will have the opportunity to help us define and spread the future of your discipline. EIR's receive a salary, health insurance, and other benefits.

A Network To Leverage - Gain a network of top tier leaders and practitioners to leverage and amplify your next endeavor. Deepen Your Knowledge - Become an expert on multiple high-demand frontiers and important topics in Product Growth. If you are interested in more details on the program or would like to apply, check it out here. You can also email questions to EIR -at- Reforge -dot- com.

Keya Patel is an Operator in Residence at Reforge. She focuses on freemium, free trial, and subscription models. Objectives and key results OKRs are usually thought of as the holy grail operating mechanism. Just recall how:. You spent hours and hours refining this quarter's OKRs, since it was the top priority for your manager and the company. Or, how the annual OKRs map to revenue growth goals to then go after the next funding round. In almost all operating conversations, OKRs come to the forefront to solve for the company level operating cadence.

But, OKRs don't solve for the team level operating cadence. In practice, most leaders look to syncs and standing meetings to solve for team level operating. When it comes down to it, they think of the value of team operating mechanisms incorrectly. The biggest mistake is holding team operating processes around syncing meetings to solely gain alignment. Rather than alignment meetings, it's necessary for team operating systems to solve for an underlying growth need like accelerating velocity of feature rollouts or evolving strategy based on new audience expansion.

This is where rituals come in, as the building block for growth-oriented team operating. Instead, teams need to operate in a way that fortifies company growth by accelerating velocity, emphasizing learnings, connecting actions to outcomes, evolving strategy, or focusing on personal growth. This post features operating tools at the team level, instead of the customary way of operating at the company level through OKRs. We'll specifically cover:. What team operating cadences should NOT look like and issues that syncs create.

The 5 areas a team operating cadence should solve for, to fuel org growth. Why rituals are not all created equal and over-indexing in the right way. Every team needs an operating cadence separate, but complementary, to the company level operating cycle that revolves around OKRs. When leaders introduce operating processes to their team, they often default to meetings to sync across cross-functional teams.

Defaulting to these sync meetings is incorrect and creates the following 3 issues:. Syncs create a false sense of people being on the same page, even when they are not. This is due to the faulty conditions of a standing sync such as a having only a limited and scheduled amount of time to talk, b trying to please others, c processing thoughts in the moment instead of before a sync, or d falling into group think when seeing others' emotional response.

Each condition indirectly encourages alignment for the sake of alignment, instead of critically considering business decisions. Syncs may start with the best of intentions, with a core group of employees trying to accelerate decision making. But, as an organization scales, the number of agenda items and additional meeting attendees becomes an unjustified, expensive waste of time.

This is because the sync slowly transformed from a valuable decision making space to a general bulletin board update. Oftentimes syncs feel purposeful because of positive interactions with peers and a sense of 'fake alignment.