Analytics for Product Managers
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Analytics for Product Managers
- 1. Analytics for Product Managers What numbers should I pay attention to and how?
- 2. about me Being a Product Manager at Outside.in has meant: • first PM hired • one-person product team • business manager • one of two specialized PMs
- 3. extreme views let numbers make every decision only listen to your gut and creative vision
- 4. you <3 numbers Analytics help: • guide debates • evaluate a new opportunity • track performance
- 5. you <3 numbers Analytics hurt: • different numbers in different tools • not sure which stats to prioritize • numbers you canʼt control
- 6. KPI theories • Fred Wilson: 4-6 metrics that change as the business grows • Dave McClure: Startup Metrics for Pirates (AARRR!)
- 7. reach
- 8. engagement
- 9. conversion
- 10. revenue
- 11. tips & details
- 12. make the most of Google Analytics • campaign trackers: bit.ly/URLbuilder • detailed URL paths + title tags: http://outside.in/park-slope- brooklyn-ny/tags/crime <title>Boston Bars and Clubs | Outside.in local places near Boston, MA | alt4</title> • goals --> funnels
- 13. funnels and drop users come off here from here
- 14. A/B test • fear commitment • donʼt fear change • track all KPIs
- 15. own your external numbers • keep an eye on Compete • get Quantified + use labels <script type="text/ javascript">_qoptions={qacct:"YourAccountNumber", labels: "SegmentName"};</script> <script type="text/javascript" src="http:// edge.quantserve.com/quant.js"></script> <noscript><a href="http://www.quantcast.com/ YourAccountNumber"; target="_blank"><img src="http://pixel.quantserve.com/pixel/ YourAccountNumber.gif?labels=Segment_Name"; style="display: none;" border="0" height="1" width="1" alt="Segment_Name"/></a></noscript>
- 16. donʼt reconcile • sanity check metrics across tools • your numbers wonʼt reconcile perfectly • trust the numbers that pay you
- 17. key points • engagement metrics that track the core value of your product • cram details into your analytics package
Hinweis der Redaktion
- * the role analytics plays in product management * how analytics can guide PMs * data points you may need as a PM * tools and techniques for getting that data
- * my experience with the PM role at Outside.in * OI elevator pitch * anecdote about how widely the role can vary * in all but one of these bullet points I&#x2019;ve had the title product manager * 1st pm: organization, prioritization of competing projects, and process for product planning and development * one-woman product team * operational role: creating traffic and revenue projections and directing strategy for content acquisition * one of two specialized PMs reporting to new VP of Product * focus on content analysis, data integrity, search rankings, and our unreleased API
- * probably goes without saying that there are a variety of viewpoints * no PM is at either extreme * since this talk is so data-focused want to point out that there are a variety of degrees to which data will play a role in different projects * data is a sounding board * in a situation where you feel like you don&#x2019;t have data --> use your creativity to find some * use your instincts and understanding of your product to guide your interpretation of any data
- * most important place for analytics in product management is in prioritization * PMs make tough decisions about how to spend our time and developers&#x2019; time * these debates are always going to be rife with politics and emotions * numbers or even the promise of numbers to the table can help you be a voice of clarity and direction * e.g.: OI built a simple A/B testing platform prior to our redesign and the promise of data-driven decisions helped to diffuse tension over how aggressively to change the site * tracking succes * e.g.: OI tracked change from text link to graphic button-->clicks went up--> clicks went down--> sometimes change makes people click * numbers don't prevent subjectivity, guarantee success, or help you win every argument
- * hellish experience trying to reconcile Google Analytics and Quantcast --> choose a tool to be authoritative * prioritize --> make a decision based on the key value propositions of your product * if you can't control your stats, you may not be optimizing for the right metric for your product or the age of your product * e.g. tracking PVs/visit on OI is frustrating, because it currently has a very simple value proposition: to get users to content they care about. instead we track clicks to third-party content per visit, which is more central to our value proposition
- * FW: (biz metrics, not just analytics) meetup&#x2019;s &#x201C;successful meetups&#x201D; and etsy&#x2019;s customer service tracking * DM: acquisition, activation, retention, referral, revenue * +1 to FW&#x2019;s notion that metrics have to change and be specific to biz/product * DM calls out really specific terms that clarify the mystery behind some of the more general terms * I&#x2019;m going to cite four buckets of analytics (in general terms) and clarify the spirit behind them
- * how many people are interacting with your product * consumer website: unique visitors * desktop/mobile app or subscription service: downloads/signups * publishing tools: like Twitter or Tumblr or Outside.in for Publishers * pub tools: DLs/signups + uniques on your users&#x2019; content
- * how much people are interacting with your product * promise behind it is like that of the engagement ring: how much people love your product and how loyal they&#x2019;ll be * conversion should measure what you hope you&#x2019;re giving users * choose an engagement metric that exposes the main value proposition that your product makes for users * e.g. pageviews/visit versus clicks per visit for OI * typical: PVs/visit, time on site, bounce rate, return visitors, logged-in sessions * for pub tools: on your users content
- * conversion should measure what you hope your users are giving you * generally: how frequently people do a specific thing that you want * account registration * upgrade * providing info * OI e.g.: clicks on ads as conversion
- * revenue is a sad ghost sad ghost bcs many early-stage companies defer tracking revenue * other analytics should drive revenue * some companies have a mandate from investors to focus on building a product they love, but it's important to keep the spectre of revenue and how you'll track it in mind while you build your product * ads can be paid on CPM (flat rate per thousand impressions), cost per click, or cost per action * subscription or license feeds * one-time purchases * BizDev deals like foursquare&#x2019;s deals with bravo and the history channel
- * a few practical tips to help you tracking and understanding these types of numbers * most of these can be boiled down to: think like a hacker about where you can cram variables into your tracking tool and add as many as possible
- * Google Analytics is limiting and often inaccuarate but startups can&#x2019;t afford to invest in Omniture * suck it up and hack GA ==campaign trackers== * campaign trackers page is written for people tracking the success of ad campaigns * just a bunch of query strings that you can define any value for (utm_content, utm_campaign, utm_medium * in twitter&#x2019;s emails * track clicks in the &#x201C;campaigns&#x201D; view under &#x201C;traffic sources&#x201D; ==detailed paths and title tags== * analytics &#x201C;content&#x201D; view lets you filter types of pages by info in the URL paths or title tags * make sure they have as much info as possible, but optimize your identifying strings for search queries because paths and titles are important to SEO ==goals== * set up goals for engagement or conversion tracking * multi-step conversions create funnel visualizations * if you want to use funnels, make sure each step of your conversion process has a unique URL (variables ok, just use head match or regular expression) * see the results in the funnels section under goals * shows where users are coming from to enter your conversion process, how many proceed, and where they drop off * identify steps in conversion that need usability testing or distracting links along the way
- * see the results under &#x201C;goals :: funnel visualizations&#x201D; * diagram that shows what pages users are coming from to reach the first step in your conversion process * for each step, how many proceed to the next step and how many drop off * shows how many drop offs go where * identify pain points for usability testing * identify distracting links
- * a/b testing lets you fear commitment to new features without stifling progress and iteration * Google&#x2019;s Website Optimizer can set up simple a/b tests for static landing pages * OI built a simple a/b testing tool that sets a number between 0-1 in the session and compares it to the % of visitors we want to show each template * make sure you can get every KPI for your product in both templates * a change you make for engagement could negatively affect monetization * have a token representing the template you&#x2019;re testing in your title tag for analytics, in your ad tags for revenue monetization, in your campaign URL trackers for conversion or engagement
- * pay attention to public reach-trackers because other people are* if the numbers you say in public don't match the numbers on Compete and Quantcast, you'll be laughed at or distrusted * sign up for a Quantcast account --> greater accuracy + privacy settings * labels let you add a string you define 3x in your tracking code * labelled traffic shows up in &#x201C;audience segments&#x201D; in your Quantcast profile (can be made private) * use labels to segment your traffic however makes sense * OI uses these to measure which users of our publisher dashboard users are generating the most reach and traffic
- * have more than one window into your numbers so you can make sure they are generally aligned * don&#x2019;t waste your time trying to reconcile them perfectly, but choose a package to be authoritative. this should be the numbers that pay you. * be on the lookout for dramatic differences as a heads-up that something is awry * OI found that Analytics was reporting 200% fewer visitors and pageviews than Quantcast for the widgets distributed by users of our self-service publishing product * ad server, Google Ad Manager (which doesn&#x2019;t track reach) roughly agreed with Quantcast on the pageviews * no discrepancy for our main site * hack for tracking JS widgets on other domains --> wrote in iframe that called a source page on our domain w/tracking code for GA + Quantcast and ad tags for Ad Manager * Google Analytics could not track a large portion of visits made in IE via this hack but Quantcast and AdManager code didn't have that discrepancy * AdManager numbers were luckily the highest of the bunch
- * engagement metrics should play to the key value proposition you want to give users * conversion metrics should play to the key value you want to GET from users * learn how your analytics package works and cram as many details into it as possible
- * email * twitter * neglected blog