Nice Fella

Quote

“He’s a nice fella,” said Slim. “Guy don’t  need no sense to be a nice fella. Seems to me sometimes it jus’ works the other way around. Take a real smart guy and he ain’t hardly ever a nice fellow.”

– John Steinbeck, Of Mice and Men

Dstillery Platform Tools

(This was originally post on the Dstillery blog.)

In old days of computing, a manager who wanted to figure out the total payroll for his firm by branch office would have to submit a written request to the computer room. There, code would be written and executed by programmers to answer the specific question. The programmers would print out a report and send a stack of papers to the requesting manager through inter-office mail. The manager would review the report at his desk.  If he wanted to make a change, or learn something new, revision requests would need to be re-submitted through the same process. This world seems very foreign to those of us who carry more computing power in our pockets than those computer room operators could imagine.

My father tells a story about how, as a young human resources executive in the early 1980s, it used to take him weeks of back and forth to get the annual salary increases calculated. This was right at the dawn of the PC revolution. The next guy who had his job did this task in hours, using Microsoft Excel at his desk.

Technology Cycles

In any technology cycle, specialists are required until technology evolves to include tools to make it possible for non-specialist users to serve themselves. Computing and data analytics are one example of this; online publishing tools is another.

Technology has a tendency to democratize access, removing barriers and making advanced tools directly accessible to users.

Dstillery’s technology is following this course.

Over the past seven years, we’ve built a world-class decision engine that is the most powerful way to scale digital campaigns. And until recently, it has been impossible to imagine outside users setting up Dstillery campaigns simply because of the complexity of systems and decisions involved in executing a successful campaign.

While our audience identification and targeting have always driven results, accessing these capabilities has required working with a number of Dstillery-employed specialists. Our sales team works with clients to understand campaign objectives and design strategies. Our team of trained Account Managers optimize among the various segments built by our array of algorithms. Our Ad Operations team handles the ensuing complex trafficking requests.

Now, we are on the verge of releasing a suite of Dstillery Platform tools that will allow our customers to access and manage our technology themselves. Agency users will have access to a powerful user interface with an intuitive workflow — a workflow design that is the result of years of our Account Management experience. Customers with more specific needs can utilize a set of APIs to access our technology. All campaigns can be set up using an automatic optimization option, with rules defined by the customer and the heavy lifting executed by the platform.

These developments in no way lessen the value of managed services. While Dstillery’s managed services provide a tailored level of expertise, support and insight, Dstillery Platform Tools will expand access to our technology and provide media buyers greater control and autonomy for their campaigns.

We’re excited to bring the full breadth of capabilities we have built for hundreds of our managed services clients to these new users — and we’re eager to see how they employ Dstillery’s data science to power their campaigns and build their business.

Product Managers and Data Scientists

Over the past couple of decades, product management has emerged as an important function within any organization that is building technology products. Data science has arrived more recently but is also proving its value in helping to solve problems for customers. In data-centric organizations, there is an important symbiotic relationship between the two functions, but it’s not always clear where data science should fit in the product development cycle. Should data scientists be treated like developers? Analysts? Or something entirely new?

While both groups want to solve problems for customers, it is important to understand the differences in their fundamental mindsets. Data science is about exploring data, understanding its predictive possibilities and creating tools that use data to optimize defined performance metrics. Product management is about deciding if the things that are technically possible are actually worth doing from a business standpoint and if so, how those new capabilities should be delivered to customers as products or features.

Collaboration between the two groups is vital, but works best when each team has a clearly defined role and set of duties. Here are some approaches on how to successfully integrate data science into the product development process:

Data Science is the R in R&D – Data scientists need the latitude to explore problems that they think are interesting and come up with innovative approaches. Celebrate this. Not everything they do will address the problems you are currently trying to solve, but it is important to allow them the freedom to explore possibilities. Often, they will surprise you with a solution to another problem that will help shape your business in a positive way.

Data Scientists Build Great Prototypes – Projects work best when the data science team develops working prototypes using whatever tools they want. These prototypes demonstrate the requirements of a new system to software developers and others. Only once there is a working prototype that has been green lit by all the necessary parties should you move forward with building out the new capability in your production systems.

Lean Data Science is Possible – Once data science has developed a working prototype, the team should figure out the smallest increment that can be put into production to test it in the real world; a Minimally Viable Algorithm. This could be as simple as developing a workable process that provides alerts when certain conditions occur. This approach helps identify errors in assumptions and bugs in implementation before a full release that builds on the ultimate algorithm.

Data Scientists Do Great QA – I’m not referring to regression testing of code. If you are building a data product or an ETL process, a qualified expert needs to validate the data at some point. Data scientists are obviously the best candidates to conduct that oversight, so make their signoff part of the formal criteria for prototype acceptance. And after the development teams have built production versions of the prototype, don’t let the data scientists wander off to get buried in the next great paper-worthy research project. Get them involved in validating that the production features are indeed working as intended.

While a lot has been written about product management, software development and how product development processes should be designed, processes that incorporate the data science function are less well understood. Data science is often essential to the development of products that can compete in a data-intensive market. Developers and product managers are best served by finding ways to integrate data science into their projects from start to finish; from design to QA and through to market feedback. Data science is now an integral part of almost every technology ecosystem, and product managers everywhere should embrace their full involvement.

As published by iMedia Connection on 4/4/2014.

PRODUCT Manager v. PROJECT Manager

I was recently asked to highlight the key differences between Product Management and Project management. While the two functions are clearly seen as different by the practitioners, the lines of responsibility can get blurred for other groups.

In reading what others had to say about the issue, I came across a post by Jeff Lash that almost perfectly captures my view on the issue:

Project managers are responsible for the successful delivery of a project — a one-time endeavor with a goal, scope, deadline, budget, and other constraints. A project manager will work to align resources, manage issues and risks, and basically coordinate all of the various elements necessary to complete the project. As they relate to products, projects can be undertaken to build a product, to add new features to a product, or create new versions or extensions of a product. When the project is complete, the project manager will usually move move to a new project, which may be related to a different product.

Product managers are responsible for the overall and ongoing success of a product. Once the project to build the product is complete and the project manager has moved on, the product manager remains to manage the product through the entire lifecycle. Other projects related to the product may be initiated, with the product manager being the one constant stream throughout, defining the project goals and guiding the team to accomplish the business objectives that have been defined.

I only thing I would change here is to amplify the responsibility of the Product Manager as the voice of the customer in the development process. In owing the product lifecycle, the Product Manager must make sure the projects that are prioritized are the ones that best solve the customer’s biggest problems. This is the most important part of the Product Manager’s job.

Roles and Responsibilities in Pricing

In two jobs prior to Dstillery, among other things, I had responsibility for managing product pricing. In that role, I’ve priced hardware, software, SaaS services, professional services, support services, and labor. At some point, I had jotted down a bunch of notes on how the pricing process should work and I thought I would share them here.

It has been said that pricing is not an event, it is a process. And it is not just a process that should be owned by finance, sales or marketing but one that needs to be truly cross-functional.

This diagram outlines the key activities that go into setting, managing, and measuring prices. The various activities can be, and often are, owned by different functions depending how each organization is structured, but there needs to be someone looking across all of these activities in a holistic manner. The key role of the “pricing owner” is to coordinate across all the interested functions and drive the process to establish the best pricing model for the company.

Pricing_Roles_and_Responsibilities

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Quote: Belief vs. Observation

“Do not believe in anything simply because you have heard it…or because it is spoken and rumored by many…or because it is found written in your religious books. But after observation and analysis, when you find that anything agrees with reason and is conducive to the good and benefit of one and all, then accept it and live up to it.”

– The Buddha

Three Simple Rules for Great Companies

The amount of printed material available about managing and running great companies is pretty staggering.  And, like fad-diets, the philosophies espoused in this literature seem to be either overly simplistic or incredibly weird.  That’s why I found this article in the most recent issue of The Harvard Business Review pretty refreshing.

Two researchers looked at the historical performance of over 25k public companies in an attempt to identify a common set of strategies that separate great performers from mediocre ones.  They looked at the impact of innovation levels, risk taking behaviors, M&A activity, hiring practices, and so on.  In the end, none of those factors were determinant.  In fact, the researchers did not find any silver bullets or magic strategies.  What they did find, were three simple rules that are practices by all these great companies:

  1. Better before cheaper—in other words, compete on differentiators other than price.
  2. Revenue before cost—that is, prioritize increasing revenue over reducing costs.
  3. There are no other rules—so change anything you must to follow Rules 1 and 2.

It is obvious if you think about it-if you have a better product or service, more people will want to buy it.  As long as your price is in line with the value it creates, unit sales and revenues will be strong.  This point is often lost by the short sighted equity markets and financial press which seem to love all cost-cutting announcements.  It has said many times, “you can’t cut your way to greatness” and the research here seems to prove this point.

The authors call this discovery “liberating” because it gives managers the flexibility to follow any tactics or strategies that support the first two rules and offer advice:

Here’s how to put the rules into operation: The next time you find yourself having to allocate scarce resources among competing priorities, think about which initiatives will contribute most to enhancing the nonprice elements of your position and which will allow you to charge higher prices or to sell in greater volume. Then give those the nod.

This is, now quantifiably, a recipe for success.

Its Not Rocket Science

Back in January, Duncan Watts of Microsoft Research spoke at m6d’s ADSCON event at NYU.  Duncan has a unique view of the world having begun his career in the hard sciences (math and physics) and migrated to social sciences.  He quoted a passage from his book on the paradox of thinking rocket science requires brilliance but social science less so:

Typically people in these positions [public policy makers, marketers, economists] do not expect to get everything right all the time.  But they also feel that the problems they are contemplating are mostly within their ability to solve – that “it’s not rocket science,” as it were.  Well, I’m no rocket scientist, and I have immense respect for people who can land a machine the size of a small car on another planet.  But the sad fact is that we’re actually better at planning the flight path of an interplanetary rocket than we are at managing the economy, merging two corporations, or even predicting how many copies of a book will sell.  So why is that rocket science seems hard[?]

This is really interesting thought.  Rocket science is, clearly, very hard.  Rockets are complex systems but the physical laws governing the behavior of these systems are predictable and consistent.  People, and social interactions between them, are also made up of very complex systems but human behavior is anything but predictable and consistent.  Given this unpredictable nature, maybe it makes sense that studying human behavior in a scientific way is much harder than it is given credit for.

Product Management and Start Up Maturity Cycle

A few weeks back, I had the opportunity to attend Practical Product Management training from Pragmatic Marketing.  The instructor, John Gatrell, said something about the maturity cycle in early stage organizations as it relates to product management that I had not considered before.

Jon said that in the beginning most startups are engineering led (this part I knew).  Since the founder usually leads the product development team, this is likely the one time when a company is most market-driven; where they are most focused on building the products to solve real market problems.  As the organization grows and functional specialization begins, a professional sales team is hired and the development organization become more removed from the market and loses some of this focus.

With the new sales team in place, the company focuses on top-line growth and the organization tends to become more sales driven.  One down side of a sales driven organization is that the sales team can get overly aggressive and will sell features that the product doesn’t necessarily support yet.  More and more the product roadmap gets determined by the commitments sales people are making to clients in order to close deals.  This approach helps win business but is not a strategic or thoughtful approach on how to evolve a product for long term market success.

Eventually, the sales team asks for help telling the company’s story and the organization will strengthen its marketing capability.  The marketing team will naturally focus on outbound activities like PR, collateral, and lead generation that help build the sales funnel.  At this point the company is talking to the market but probably not listening as effectively as it could be.

When the company reaches this point, it needs to evolve back towards a market-driven, listening organization that is centered around solving real market problems.  The Pragmatic approach emphasizes, and I agree, that the key role of the Product Manager is force this market-centric discipline on the development organization.  The Product Manager needs to synthesize all the information he or she receives to make data-driven decisions about how to evolve the product and not simply respond to the latest “must-have” feature from a sales rep.

As a side note, I’m glad that Pragmatic has certified me after 7+ years of operating without a license to practice product management.  It is quite a relief to be legit.