pmcgrathm

Contrary to the belief that advertising is less data-driven, the complexity and dependency on feature-rich data sets has increased over time. Redundant targeting sometimes happens due to the ad objective of maximizing expected revenue over cost. However, it's more nuanced in practice.

Advertisers aim to optimize expected revenue over cost within a 'payback period', which is typically 1-3 years for large advertisers. This is calculated through customer acquisition cost, retention, and incrementality (the probability of an ad causing conversion).

Only advertisers themselves can effectively calculate incrementality due to access to their specific conversion/retention metrics. This incrementality, along with the optimal mix of channels and ad spend, is the ongoing challenge for sophisticated advertisers. It involves multi-objective optimization across millions of ad assets, campaigns, and targeting criteria.

Privacy regulations since 2015 and subsequent laws like GDPR and CCPA have led to more reliance on probabilistic modeling for targeting. The entire pipeline of targeting, engagement, conversion, and retention forecasts are now based on probabilistic models.

While ad networks offer simplified scaling solutions like 'target roas' and 'campaign budget optimization', they're more useful for average advertisers with limited internal resources - eg, they can't justify hiring ML SWEs, quant traders, technical PMs, etc.

Advertising has become even more data-driven and arbitrage gains for sophistication have increased. Profound gains can be made with investments in marketing and forecasting science, similar to the operations of a quant trading firm.

Source: I've managed $10B+ through automated ad spend systems since 2012.

Product management 101; never choose a an objective metric without setting guardrail constraints.

"For users who opt in, Brave can deliver better quality ads, without the risk of personal data leakage. We do this by using local machine-learning to understand the user better, and making local decisions as to which ads should or should not be shown, and when (the user controls all of this). Furthermore, the user gets 70% of the ad revenue for browser-private ads."

Sorry. This is just wrong. You don't nor never will have the volume of data that the major ad platforms have collected for 15+ years. You don't have the pure analytical power of these companies in compute capacity. You will not serve better ads by disabling third party retargeting platforms.

There are many positive ways to spin an obvious blockchain-craze inspired endeavor, and I applaud the Brave team for blazing a trail, but you are calling out all the wrong value propositions.

Fraud is not as big of a deal as you state it is. It is not a problem for the primary ad networks to which the lions share of the ad revenues goes to in the first place. Companies like WhiteOps and Moat already do a great job of fraud detection when an advertiser is not buying from one of the popular and safer exchanges.

I applaud your conviction, but the problem with advertising is not solved by attempting to de-throne Google and Facebook.

This sounds like a good fit for a friend of mine, but he'd need some kind of documentation to even consider showing up. Do you have anything about the platform that you're willing to share?

Shooter w/ self-inflicted gunshot wound to the chest. Handgun pulled out of purse.

Per a SWAT officer - his team has not been activated, which means the situation is under control. Apparently an attempted murder/suicide. Female shooter. 10-20 shots fired and multiple people being treated for gunshot wounds. No news as to what happened to the shooter.

As someone who is a product manager in the commercialized AI SaaS space, the most important pieces of feedback I would give a new PM here:

1)Don't let your -brilliant- colleagues try to force their -brilliantly complex- solution of a problem - clearly define market problems, and don't let the team try to go the route of trying to force fit a solution to a market problem. Market problems come first.

2)Frame the market problems appropriately for your ML/AI teams, and practice trying to frame the problem from a variety of angles. Framing from different angles promotes the 'Ah-ha' moment in terms of the right way to solve the problem from the ML side.

3)Don't commit serious time to a model before having a naive solution to benchmark against. Always have a naive solution to compare against the AI solution. 'Naive' here may be a simple linear regression, RMSE, or multi armed bandit/Thompson sampling.

They can work in conjunction - In the domain of website optimization, where visitor attributes are often greater predictors of value than website content, a system driven by search space optimization can more easily take into account changes in those variables - eg; time of day, traffic source, device type - and incorporate those inputs to climb multiple 'hills' simultaneously.

The allocation of traffic based on the evolving optimal search space (blue button for visitors from Facebook) can then be driven through an MAB or something similar.

>This is a tragedy of the Internet ad industry. There is a finite amount of eyeballs, and the amount of companies with a substantial money wanting to sell you a face lotion will always eclipse the amount of companies who market unique, relevant, specialty products one may actually be actively looking to buy.

It has little to do with having a large amount of money to spend on ads and more the level of severity of the problem your product solves for the 'eyeball.'

Humans have some needs that are greater than others, and ad dollars spent at alleviating pain or providing for those needs will always get at the most eyeballs. Entire industries - born online - have developed around capitalizing on solving for these needs.

I need money - Predatory loans and credit card offers. I need to be skinnier - Nutraceuticals (Dr. Oz), weight loss, vitamin crazes. I need to be beautiful - Skin care, facial lotions. I need to be less lonely - Online dating, pornography.

The list goes on. The point is - there are more people looking to be skinny, beautiful, rich, and married with children than there are people looking for 'commercial equipment sales.'For now, and until targeting gets smarter and consumers opt to share more data about themselves, those products will win the majority of ad clicks.

We'll get there. (eye roll) Maybe decentralization is the way? :). I'd love to give the Google/FB duopoly a kick in their 'walled garden.'

As someone who has been responsible for a bit north of $1B in profitable, attributable digital ad spend in my career, I can say with conviction that the problem comes down to analytics and misalignment of incentives, NOT the performance of digital media channels.

1)Agencies charge on a % of total media spend, and are thus incentivized to spend more.

2)Advertisers net benefit from expected lifetime value and revenue generation, but are often reticent to share this type of information to an agency.

Agencies are commonly unable to get access to business-level health metrics such as churn, RPU, LTV, and thus optimize to top of the funnel metrics that often do not correlate with attributable lift but do correlate with showing the value of increased levels of spend. Such metrics include click-through rate, viewability, brand awareness and safety, fraud mitigation, etc.

This, combined with the improved ease of use for major digital platforms (I know quite a few startup CEOs who manage all of their PPC/Facebook ad spend), is why the agency model has started to fail. And that is a good thing.

The less intermediaries that touch an advertising campaign, the less likely it is that we as consumers will see an irrelevant ad.

To be fair (and I am not trying to defend these 'Slick guys'), sometimes there is a lot more happening behind closed doors that a PM might let on. Fights for resources, maintaining a current team, hiring, ownership, etc.

Oftentimes those people trying to portray a 'Slick' exterior are doing so due to the need to portray a sense of success/confidence for their team, to make sure they retain their existing budget and that the team doesn't get moved to other projects or terminated entirely.

Netflix and my current organization religiously practice 360 degree feedback. For my own projects, I practice 360 degree feedback as well. I don't think a team or organization can succeed if employees cannot speak to each other candidly about performance.

My typical feedback from engineering:

1) I state resolutions of a problem without clearly defining the problem.

This was/is my biggest failure as a product manager and is something I work on daily. I enjoy the 'fun' of solving problems but respect that my job is not to solve the problem. My job is to understand the market, define customer and their needs, and create requirements that need to be met to resolve those customer needs.

2) I over-engineer. I like to solve problems with complex, scalable, 'sexy' solutions. At Netflix, my team built a real time marketing analytics platform that used kafka/spark/elasticsearch and an enormous cluster to aggregate marketing data from 5+ marketing platforms. The client was built in angular/d3 and returned aggregations on 1B+ rows of data in < 100ms.

We were so invested in scale and performance that minor changes to the underlying schema (which happened often, as marketing priorities shifted) required a lot of work. This was a huge over engineering mistake on my behalf.

3) I can come off as patronizing. In an effort to describe a problem space or market, my tone has been perceived as patronizing.

4) I do not practice enough active listening. I end up driving conversations and do not make people feel heard.

Being humble and asking for feedback is the best way to learn to be a better PM. Of the PMs I've seen rise(and fall) through the ranks of management, I have generally found that humility, integrity/accountability, and communication skills are the most correlated with success.

PM @ a well funded AI startup. Previously PM at Netflix. Management consultant prior.

Undergraduate degree in Psychology. Background in marketing/business and customer acquisition. I learned enough programming to automate my marketing activities, and found that I liked driving a roadmap more than I liked acquiring customers.

-Communication and conflict resolution skills are key. You are in a role where you must drive influence without having any direct reports. This means effective, articulate communication skills are required. Know how your voice needs to change between communication to engineering versus communication to an executive or board member.

-At Netflix, I was often told my job was to add clarity. Add clarity to a technical specifications document. Add clarity to the marketing teams understanding of a product feature. The best PMs are able to consolidate their understanding of a 35 page technical document into two sentences.

-Market sizing and back of the envelope calculations. Know how large the market is for your product. How much more can you charge for your product if you add X feature? How long is X feature going to take in engineering cycles? Is this the best way to spend your engineering resources? In my daily routine, I probably make 10 calculations like this and have a response ready for either our product director, CEO, board member, or customer.

-Financial modeling. I've found that modeling skills are absolutely key - know how to model out customer lifetime value, churn rates, and cash flow. You should be prepared to be a 'mini CFO,' because at the end of the day, you are asking for more resources from your executive suite, and are best off making those requests in CFO format.

-Know your technology. Know what is possible and know how to articulate requirements that speak to your technology. This is why there is often a technical barrier for PMs - you have to know how things work, and what is physically possible versus cost prohibitively impossible. This doesn't mean you need to know how to code - but that is helpful. Know source control and developer operations processes. Know how to plan for scale. Know how to recognize elegant solutions for difficult problems, and reward your engineering team for failing spectacularly.

-Finally - be humble and be accountable. It is always your fault, because you are accountable for the success of your product. Don't throw your engineering team into the middle of a sh*tstorm of management politics - be their umbrella. Don't blame customers, politics, or resources. It's always your fault. Find a way to fix it.

It isn't the ad itself that is causing those streams of requests, but rather the ad technology vendors buying or selling the data from the ad server from which the ad was delivered.

Don't blame the publisher or advertiser for not noticing that their 'anti-fraud' vendor is sending itself events every millisecond.