Making Data-Driven Decisions
Discussions about how different organizations are using data to make informed product decisions
- 14 Topics
- 18 Replies
You're a PM Pro now, but if you took a trip in the Way Back machine, what concepts/ language/ theories/ practices did you have to learn early on to really rock it out today? I'm particularly interested in hearing from folks who stumbled into their roles as PMs, and probably don't have a business studies background (like me - English Lit major). For example, I didn't know a lot about statistics when I started at Heap, so I did some reading/research on basic concepts/applications to better understand the types of data-driven decisions PMs need to make. In what areas did you need to level up or fill in gaps before you REALLY got it?
Creating a data driven culture is a process that doesn’t just happen overnight. I would love to hear what you (or others in your org) did to drive data adoption?Did you hold a monthly team meeting where you shared metrics, did you give your team templates, did you make building a dashboard a part of the official JIRA launch process, did you hold a competition for the person who ran the most queries?Any and all ideas would be appreciated!
Product Team(+ Engineering, Design) Marketing Team(+ Engineering, Design) Sales(+ Product, Marketing) Customer Success(with Product, Marketing) Self-Serve (SS) Startups, SMB and Lower Mid-Market Drives:Acquisition ActivationRetentionMonetization - All Drives:AcquisitionResurrectionSupports:Activation RetentionMonetization - All No touch No touch Bottoms Up Higher end of mid-market, and small Ent. Drives:ActivationRetentionMonetization - Free to paid Supports: AcquisitionMonetization - Renewal,Expansion Drives:AcquisitionSupports:Activation RetentionResurrectionMonetization - All Drives: Monetization - Expansion Drives: RetentionMonetization - Resurrection, Expansion Sales-Assisted Enterprise Drives: Pipeline of hand-raisers (PQL)Supports:ActivationRetention Drives: AcquisitionPipeline of hand-raisersSupports:Activati
Customer Journey and Key Metrics Activation- In PLG motions, Activation and Onboarding means pretty much the same thing. It’s a set of self-serve flows designed to help prospects and new users of paying accounts set up their account, and guide them through experiencing your product’s value for the first time (the “Aha” moment). Many teams include habit creation in the Activation effort. Some refer to it as a complementary metric called Product Adoption Rate that monitors “stickiness”. Either way, you want to ensure that you are optimizing for all these three components (see chart). In sales-assisted or sales-led motions - Activation usually refers to the first milestone in the user-onboarding process. This is the moment focuses on helping a user successfully progress from sign-up to setup to the Aha moment. Establishing habitual use may often require more effort from the Customer Success organization, collaborating with Customer Lifecycle Marketing teams. Why is Activation important?A
Hello! We've done some analysis on our product listing pages and interesting found that conversion rate correlates with sessions with more product impressions - we hypothesize that user with high intent is more likely to take the time to make the right choice and so naturally will have a high number of products and thus have a good conversion rate. However, to truly understand this we need to AB test different size of the same product listing page but we think that the products will also have an impact and of course we can normalise this to an extent but even still it will be to difficult to strip out the impact of the extra skus in the longer grids.Has anyone here come across this problem or done similar testing? If so would love to hear your approach!
We recently renewed our contract and other customers asked how I positioned Heap to our executives. It distilled down to answering four big questions:Is Heap Central to our current and future business intelligence (BI) tech stack?We’ve made it really easy to integrate Heap into our overall BI stack. This is best demonstrated in the examples below. Is Heap used across different teams and what value are they getting? It was clear the Product team gains value from Heap, but they're not the only ones. The Range teams use Heap to understand purchase conversions and whether there’s engagement with products and how that matches up with trading and forecasting. The Data Science team uses Heap as part of carousel automation and also product recommendations. This breadth and depth of usage reassured leadership of their investment. Is Heap incorporated into our existing tools and workflows?Heap is a big tool whose data we try to surface as widely as possible across all areas of the business. Eve
Enjoy this panel on time management and prioritization specifically made for Product Management professionals. It features product leader Rich Mironov, and Heap Sr. PMs Connie Yuan and Josh Roberts. It offers insights for you to develop greater balance and efficiency as a PM. It also includes a segment on transitioning from individual contributor to people manager and suggestions on how to navigate that change. Have additional questions for the panel? Ask in the comments below! Highlights:“Voltaire taught us that ‘perfect is the enemy of the good.’ and a lot of us as product managers are perfectionists. My advice is, before we react to a question by working on something bespoke, ask yourself, do I have something that's pretty close. Rather than spending 18 hours on something that is perfect, can you save yourself that time and provide something that is suitable and decent.”--Rich Mironov“We apply a lot of rigor to forecasting and planning engineering capacity as PMs, but we don't app
There are a lot of metrics out there - which ones do you use to monitor the success of your product’s features? How does the way you measure success figure into the decision of which features you’ll build?I know there are many perspectives out there - it would be great to hear ideas from a variety of roles and industries. Thanks!If you’re looking for measurement ideas, or you want to bring other teams up to speed on success measurement, check out Heap’s whitepaper and worksheet on Measuring Feature Success.
My company has grown very rapidly in the last couple years and as the demands on the product team have grown significantly, the value around adding the Product Analysis function also grew. As more and more new team members joined, we needed to change the structure of our team to be more data-driven. Out of this need, we defined four guiding principles for this new Product Analysis function: Own Instrumentation Suite - Setup and MaintenanceHeap, Optimizely, Google Analytics, A/B testing, and other measurement tools all fall under this new function. Previously, these applications were managed by different teams. These teams were unaware if we launched new pages or changed URLs, and that led to data quickly becoming outdated. It made sense for these to all be centrally owned by someone with deep product knowledge, so that we could ensure the data was current. Provide Product-Level Insights on UsageThis function is all about knowing the customer. Where are they falling out of the funnel?
Something that I am currently working on is looking into the usage of our "Help" options. I have always used funnels - one of my favorite features of Heap, but I have been playing around with the new Effort Analysis option. It is interesting to evaluate the number of interactions between users entering the "Help" option screen, vs them getting to what they wanted to see. As we are in the process of changing that screen, this will be useful in figuring out what needs to be more prioritized.What application interactions are others comparing?
Our company, a B2B SaaS company but with heavy UI usage by low expertise users, recently migrated off our legacy UI. It has been a long and tedious process to get clients to change their habits and move over to our new UI. This month we decided to start leveraging the Funnel as well as Paths functionality to analyze how some specific clients use our platform. This allowed us to identify use cases that we had left out of our initial thinking of the new UI and is making us reconsider some of the assumptions made in the release. We'll definitely continue to use this feature as we further develop the new UI in order to be as efficient as possible for as many use cases as possible! How do you ensure your UI matches up with your users’ expectations?
Although we predominantly use Heap for our product analytics, over the last few months we've seen the benefit in using Heap data to answer business problems outside of the Product team and consequently we’ve worked hard to roll it out to other teams. Below is a summary of the questions asked by our Range, Data Science and Trading teams and how we used Heap to answer those questions. RangeThe Range team manage our collection, they work with the florists to curate the range and explore new product opportunities.Questions they ask: How are our SKUs performing? Which ones should we bring back for next year? How we used Heap to answer the questions: We look at a range of metrics to help us answer this question from SKU mix to NPS scores to resend/refund rates to understand how our SKUs are performing. The position of the SKU within the product listing page has a big impact on SKU conversion and thus the SKU mix. We, therefore, are able to use heap to calculate impression to order CVR pe
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