Startup Lessons Learned

NOVEMBER 29, 2008

The technical interview is at the heart of these challenges when building a product development team, and so I thought it deserved an entire post on its own. Still, a startup product development team is a service organization. As they optimize their solution, they eventually wind up deriving Bresenhams circle algorithm.

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San Francisco Lean Agile Java 146

Startup Lessons Learned

JANUARY 30, 2009

Build a world-class technology platform, with patent-pending algorithms and the ability to scale to millions of simultaneous users. Build the product in stealth mode to build buzz for the eventual launch. Without conscious process design, product development teams turn lines of code written into momentum in a certain direction.

San Francisco 150

San Francisco Lean Agile Continuous Deployment 150

Startup Lessons Learned

SEPTEMBER 15, 2008

Focus on the output metrics of that part of the product, and you make the problem a lot more clear. I had the opportunity to pioneer this approach to funnel analysis at IMVU, where it became a core part of our customer development process. Whatever its purpose, try measuring it only at the level that you care about.

San Francisco 166

San Francisco Lean Technical Review Continuous Deployment 166

ConversionXL

JANUARY 24, 2017

Everyone has railed against these practices ( including us , many times), but it still seems that much of the industry is focused on short term metrics and surface level changes. With the overlapping areas of marketing or product with optimization, it helps to be skillful at one of the former areas. More Human Intelligence Needed, Too.

Conversion 48

Conversion Technical Review Artificial Inteligence Analytics 48

YoungUpstarts

FEBRUARY 11, 2019

Easy does it: The right CRM can work magic, serving as a single platform that can span across the entire breadth of an organization — sharing information and providing value for customer service, sales, product development, management, operations and more. Today’s best SMB-focused CRMs capture metrics more effectively than ever before.

Artificial Inteligence 127

Artificial Inteligence Sales Cycle Colorado Retention 127

Startup Lessons Learned

NOVEMBER 13, 2008

Because five whys kept turning up a few key metrics that were hard to set static thresholds for, we even had a dynamic prediction algorithm that would make forecasts based on past data, and fire alerts if the metric ever went out of its normal bounds. You can even read a cool paper one of our engineers wrote on this approach).

Software Review 167

Software Review Technical Review San Francisco Continuous Deployment 167

Startup Lessons Learned

JULY 29, 2009

Startups especially can benefit by using technical debt to experiment, invest in process, and increase their product development leverage. The biggest source of waste in new product development is building something that nobody wants. Leverage product development with open source and third parties.

Technical Review 168

Technical Review Software Review San Francisco Lean 168

Startup Lessons Learned

JANUARY 4, 2009

More common is to use a one-way hashing algorithm to map the data to be accessed to one of the shards that store it. Theres no complex algorithm to go wrong, just a simple lookup table. Of course, you could use URL-based sharding to "wrap" a CH algorithm (or any hashing scheme you wanted).

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San Francisco Architecture Startup Database 151

ConversionXL

MAY 27, 2022

Stage 1: Target the right metrics for an effective long game. In today’s market ( 8,000 martech products alone ), it’s easier to attract the right customer with material they value than it is to chase down and convert a prospect who isn’t ready to buy. Metrics for your demand generation funnel. Demand generation is no exception.

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Demand B2B Distribution Channel 124

Startup Lessons Learned

OCTOBER 11, 2008

Youll need bundles, promotions, category and segment managers, and algorithms for upsell, cross-sell, and conversion optimization. For example, IMVU is a user-generated, a la carte, merchandising product. In other words, a marketing department. You can use these three questions to analyze existing businesses.

San Francisco 68

San Francisco Lean Continuous Deployment DC 68

Startup Lessons Learned

JANUARY 12, 2009

These days, successful programming is as much about processing data as creating algorithms. Startup Visa update ► February (5) Kiwi lean startup + Australia next Why diversity matters (the meritocracy business) Beware of Vanity Metrics (for Harvard Business Rev. Incoherent (but huge) standard library.

PHP 166

PHP Software Review Ruby San Francisco 166

ConversionXL

DECEMBER 11, 2018

Ensuring that design is present during product development and has a seat at the table when business or technology teams establish limiting parameters. Product development is iterative—rapid prototyping, fast changes, continual improvement. Put the consumer experience and brand above stale marketing metrics.

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Campaign Marketing Design Differentiation 134

Occam's Razor

APRIL 23, 2012

While there is a great deal of appreciation for the power of metrics/data, I've come to realize that Sr. Leaders don't quite appreciate the deep, and often corrosive, consequences of choosing metric x over metric y as a key performance indicator (KPI). It is a standard metric. Don't believe me?

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Metrics Analytics Search Channel 152

Occam's Razor

MAY 27, 2015

All that technology and real-time bidding and trading desks and brilliant algorithms and ad exchanges, everything comes down to… right message. Bonus: Magnificent Mobile Website And App Analytics: Reports, Metrics, How-to! < But, answering them with immense thought and care is absolutely critical. Does Yahoo! Magnificent.

Analytics 60

Analytics Marketing Mobile Audience 60

ConversionXL

OCTOBER 19, 2017

The centralized model is based on one team of employees, typically data scientists, and has the benefit of localizing expertise so that employees can develop a long-term optimization strategy, including developing better statistical algorithms and experimentation tools. Your main-focus metrics. Your optimization budget.

Chad 90

Chad Analytics Technical Review Framework 90

ConversionXL

APRIL 3, 2019

What to do with AI: Predict churn ; Identify which offers to send to an individual; Accelerate innovation; Personalize content; Account-based marketing ; Algorithmic attribution; Forecast future lift; Predict blame. Setting up your experiments for success: Define success metrics. Try out different tactics, tools, offers, and designs.

Artificial Inteligence 114

Artificial Inteligence Technical Review Metrics SEO 114

ConversionXL

JANUARY 17, 2017

They have a ratings system and build distribution algorithms to best distribute lawn care providers to customers. In short, they need to approach data and experimentation from multiple fronts, so it gets a bit complicated in how they actually do this, as well as how they balance experimentation with their typical product development process.

Algorithm 48

Algorithm CTO Hire Distribution Engineer 48