Biz dev done right

Via Andrew Parker of Spark Capital, the key to business development is understanding your audience:

You need to recognize that the person sitting across from you at the table represents their company but will not necessarily act in their company’s best interests… Be brash enough to ask how that person is bonused. Be creative enough to strike a deal that makes that person look like a rockstar to his/her boss instead of designing the perfect deal for the partnering corporation.

The core of this idea is more broad than BD; rarely are organizational interests in perfect alignment with individual incentives. In product, marketing and sales—just like BD—and across companies of all sizes, this concept is the single biggest predictor of GSD.

Analytics and digital decision makers in marketing

HBR sums up the trajectory and redefinition of Analytics, the capital-A version:

Analytics, once a back-of-the-house research function, is becoming entwined in daily strategy development and operations. Executives who were pioneering early digital marketing teams 10 years ago are advancing to the CMO office. Already wired for measurement, they are often amazed at the analytics immaturity of the broader advertising industry. These new CMOs are taking more responsibility for technology budgets and are creating a culture of fact-based decision making within advertising. Technology consultancy Gartner estimates that within five years, most CMOs will have a bigger technology budget than chief technology officers do.

Quantitative marketing is nothing new: today’s growth hacker or data scientist is yesteryear’s database marketer. But what has changed is the ability to answer age-old ROI and attribution questions. Abstracted by buzzwords like Big Data, Multi-Channel Attribution and Single View of the Customer, three important trends are converging:

  • Digital decision makers are coming of age – as young marketers work their way up the corporate ladder, they are bringing new perspectives around digital and measurement
  • Tracking technologies have improved – and will continue to for the foreseeable future, giving marketers access to cross-channel and detailed purchasing data
  • Increased comfort level with metrics – as more data becomes available and more decisions rest of quantitative analysis, marketing teams are creating new metrics to tie their activities to broader corporate goals

It’s an exciting time to be in Analytics.

Startup stages and growth through unit economics

Startups go through phases just like people—you have to crawl before you walk before you run. Fred Wilson divides the startup lifecycle into three stages:

  • While building product your team is singularly focused on executing vision and ensuring your product meets customers’ needs. You hear this called product-market fit or customer development but the process is basically the same.
  • While building usage you start to lay the foundation for growth: more management and more processes. To graduate from this level you need to prove your business model has both scale and viability.
  • While building the business you start treating the company as a product unto itself, focusing on the senior management team, culture and values.

While building usage you must face two big challenges: demonstrating potential scale and proving viability. For consumer apps, the math is depressing. Start with your goal and work backward—the path to a $100MM business is paved with a huge audience, high revenue per customer, frequent transactions or, ideally, some combination of these factors.

For consumer apps, viability can be an arbitrage game. First you need to measure your unit economics, specifically Customer Lifetime Value (CLV) versus Customer Acquisition Cost (CAC). Cash flow and burn constrain your ability to decrease CAC with scale and force you to accept shorter payback periods. Product and design resources constrain your ability to monetize customers and increase CLV. For more on business model viability and the concepts of CLV/CAC, here are some recommended reads:

Quick thoughts on GRPN 4Q12 earnings report

Groupon reported 4Q12 results today and the WSJ has live notes from the earnings call. Five take-aways from the report and call:

  • Email drives less than 50% of transactions. This syncs with Yipit data published in the WSJ earlier this week showing less dependence on daily deals and diversified product mix (chart below).
  • International is continued cause for concern. Revenue was down 16% year-over-year in 4Q12 while North America more than doubled. This despite 22% growth in both segments active customers to a total of 41MM.
  • Marketing spend decreased $94MM year-over-year to $61MM in 4Q12. International accounted for 82% of the total decrease.
  • Groupon Goods is now a $2B run rate business.
  • 40% of transactions happened through mobile in January 2013. Andrew Mason on those customers: “The data is very clear, [mobile users] have a higher lifetime value, retention is higher, so the transition to mobile is a very good thing for Groupon.”

As Sarah Lacy postulated last year, buying revenue and international growth through acquisitions can be a difficult strategy. This point of view was reinforced by Amazon’s 3Q12 results which exposed a $496MM impairment charge, rumored to be related to international acquisitions gone bad. Just last week, LivingSocial raised $110MM for 7.5% of the company, valuing the company at $1.5B, a far cry from their reported $6B Series E valuation 14 months ago.

No matter how you slice the numbers, this was a big down round for LivingSocial which, like Groupon, must still convince investors of this model’s long-term viability.

Groupon estimated product mix:

Groupon deals by type in North America 2013-02-26

 

Yelp management on competition in the market for local ads

Yelp COO Geoff Donaker and CFO Robert Krolik spoke yesterday at Morgan Stanley’s Technology, Media & Telecom conference. Their comments reinforced data from their recent 10-K (embedded below) about consumer growth and competition for local ad dollars.

On the market opportunity and competition:

Something like 50 million local businesses in the western world, most of whom advertise in one form or another. And today, only a tiny fraction of that spend has moved online in any form… [who] we’re really competing with, are print businesses, radio businesses and local television. We don’t really run in to the other Internet businesses yet… of course, always get asked about Google and Facebook, and of course, they’re going to be competitive in this marketplace over time as well.

Growth in the market for local advertising is predominately online (similar to e-commerce) but digital channels are still a fraction of their offline competitors. Segments like direct mail still capture the majority of the US market, representing a huge opportunity:

BIA Kelsey - US spending on local advertising, by segment

On mobile usage and monetization:

  • 46% of Yelp searches done on mobile app
  • 28% of unique visitors to mobile web
  • 25% of local ads served on mobile devices

Like most major internet brands, it’s unclear when the long-term mobile opportunity will outweigh the short-term downside. Effective mobile ad units are still very much a work-in-progress.

On the home & local category:

  • 22% of revenue
  • 11% of reviewed businesses
  • 4% of reviews

Angie’s List is a major competitor in this segment and Yelp’s data indicates an untapped opportunity. Over the last six months ANGI is up 89.0% compared to YELP at 14.4% and the S&P at 7.3% but there is room for many big companies in this space—including Facebook, Google, Groupon and others, despite Yelp’s comments—as they fight for shifts in local ad spend.

Yelp’s 2012 10-K:

Changes in advertising, attribution and offline retail

Earlier this week I joined Google’s telecom team at their Atlanta offsite to talk about mobile, attribution and related topics. My presentation is embedded at the bottom of this post but we actually spent the entire hour doing q&a. Attribution continues to be a central theme in advertising; more specifically, how do you build models that appreciate the value of channels like mobile and their contribution to offline sales?

Mobile and offline attribution are costly and complex

Today Google started roll-out of AdWords enhanced campaigns designed to simplify the process of multi-device advertising. Scoutmob’s work with thousands of SMBs has reinforced the challenge of selling complex advertising solutions. Most restaurant or boutique owners are more concerned with day-to-day operational challenges than marketing or attribution. Partnerships like Datalogix and Facebook are designed to bridge this attribution gap but complexity—both integration and lack of analytics resources—are still barriers to adoption for local businesses.

Elsewhere, upstream properties like Facebook are fighting an advertising spend battle against last-click channels like search. Just this week Twitter acquired Bluefin Labs to help marketers understand the impact of social media:

At least part of Bluefin’s job will be help close [advertising] sales, and get them to buy again by proving out Twitter’s value to both programmers and advertisers. You can think of it as an analogue to the well-respected research teams that already exist at the big TV networks — except the TV guys have decades of tradition and research backing up their pitches. Twitter’s job is much harder.

Ad spend shifting toward quantifiable formats

Secular trends away from media channels with difficult-to-quantify or weak returns create an even more difficult challenge for sales teams. It’s two years old but Hal Varian’s share of advertising chart succinctly conveys the magnitude of these shifts:

This trend is even more pronounced when you drill down to SMB spend. Between 2010 and 2015, BIA/Kelsey projects that 98% of growth will come from online, commerce and solutions with traditional media flat:

BIA Kelsey - Advertising by US SMBs

Retail sales key to ad spend changes

In three posts last year, Andreessen Horowitz partner Jeff Jordan chronicles the changing face of retail as online business models flex their muscles:

  • The case for e-commerce acceleration: “Online retail is relentlessly taking share in many specialty retail categories… Physical retailers are highly leveraged and often have narrow profit margins. Material declines in their top lines [due to online competition] make them unprofitable and quickly bankrupt.”
  • When Black Friday comes: “Physical retailers are not inept; they’re cemented to a business model that is uncompetitive.” Online wins on price and selection and offline’s convenience advantage is being undermined by Amazon Prime, ShopRunner, etc.
  • Why malls are getting mauled: “… I believe we’re seeing clear signs that the e-commerce revolution is seriously impacting commercial real estate.  Online retailers are relentlessly gaining share in many retail categories, and offline players are fighting for progressively smaller pieces of the retail pie.”

It’s easy to rain on the offline parade; it’s much harder to rain online. But although verticals like supermarkets skew the overall data, offline is still massive. Despite 726% growth over the last 13 years, the most recent seasonally adjusted data shows offline accounting for almost 95% of total retail sales:

US Census Quarterly Retail Sales (2013-02-07)

More interesting to me is the increasingly blurred line between online and offline, a trend that owes much to smartphone and app innovation. Scoutmob drives millions of dollars in offline sales to local businesses through our mobile apps—what we call the “cost per door swing” model. Promotions like our partnership with Crif Dogs have used a sense of urgency to drive real volume in very short time frames (e.g. 1K+ in < 1 week).

Bigger brands are aggressively pushing online-to-offline as well, taking advantage of the best parts of line and offline respectively. Results from Walmart illustrate this point well:

In April [2012], Walmart began allowing shoppers to order merchandise online and pay for it with cash… Even without the cash option, in the six years since Walmart has allowed online items to be picked up in stores, customer demand has been high. More than half of the sales from Walmart.com are now picked up at Walmart stores…

If trends continue, online will continue to eat into offline’s share of market in CPG, electronics and other verticals. As attribution tools struggle to keep pace with changes in ad channel mix, marketers will continue to struggle with uncertainty in ROI calculations and naturally gravitate toward direct and last-click channels which are easily quantifiable.

Scoutmob presentation on mobile advertising

Choose your flavor of analytics

Most startups can bucket their analytics into three groups:

  1. Experimental analytics is about testing assumptions and hypothesis. If you can remember your high school science classes—where problems take the form if this, then that—then you understand experimental analytics. Product, marketing and sales can all benefit from this approach. Throw out the subjective and let data speak for itself. Experimental analytics, specifically A/B tests, are central to the product development and marketing processes of companies like Google, Amazon and others.1
  2. Predictive analytics makes use of historical data (and the occasional assumption) to predict future behavior. It is the sexy side of big data; it’s also nothing new. Predictive analytics is foundational to many business models—banks loan using credit scores, insurance companies predict fraudulent claims, email service providers identify and filter spam—and you find models in diverse industries like telecom churn management (uplift modeling) and sports management (think Moneyball).2
  3. Archaeological analytics is retrospective and it can be one of the most time-consuming branches of analytics. Even at startups with a small user base, it’s easy to drown in data. Analyzing cause/effect in the rear view mirror requires breadth of data to control for variables and depth of understanding to distinguish a business model’s random variation from real changes. To support this process, I find it helpful to keep an event log—a shared spreadsheet to track product launches, changes in data storage, new press and any change that potentially affects your key metrics.

The common thread between these three flavors is effective storytelling. There’s a time and a place for each in startup analytics.

  1. More A/B test examples on Quora. [↩]
  2. Check out the Predictive Analytics World conference series. It’s run by Eric Siegel and brings together some of the best thinkers and doers in this branch of statistics. [↩]

Mixpanel and the market for startup analytics

Mixpanel launched Revenue Analytics today. For a long time, Mixpanel provided a very generic event-tracking system, allowing customers to centralize tracking across systems and channels. But over the past year, their product development has accelerated:

  • Retention 2.0: basic cohort analysis capabilities and visualizations (Jan-12)
  • Flow: a free tool for path analysis (Apr-12)
  • People Analytics: drill down to individual user actions and basic outbound messaging (Jul-12)
  • Engage: more robust notification functionality and marketing tools (Oct-12)
  • Activity Feed: timeline visualization for individual users (Nov-12)
  • Revenue Analytics: more cohort analysis tools like lifetime value, giving more insights into marketing mix (Jan-13)

Some of these launches were new and others were existing features repackaged but, taken as a whole, they bring Mixpanel into direct competition with several other leaders in the market for startup analytics. Most of these competitors are converging toward similar features that target product/marketing practitioners and not necessarily analytics or engineering teams. They are also increasingly going after the same customers:

  • KISSmetrics: founded in 2008 by Neil Patel and Hiten Shah; they take a “people not pageviews” approach.
  • Mixpanel: founded in 2009 by Suhail Doshi and Tim Trenfen; original focus on tracking and reporting now shifting toward actionable analytics. 
  • Chartbeat: founded in 2009 and now led by Tony Haile; extremely focused on real-time analytics and strong penetration of the publishing industry with their Chartbeat Publishing product.
  • RJMetrics: founded in 2009 by Robert J. Moore and Jake Stein based on their work together at Insight Venture Partners; focused on ecommerce marketing analytics.
  • Custora: founded in 2011 by Jon Pospischil and Corey Pearson to product insights from transactional data; heavy focus on predictive customer LTV calculations and customer segmentation.

While this is only a small slice of the analytics marketplace—think Google Analytics Premium, BigQuery, GoodData and other established players in the enterprise BI space—it’s where much of the product innovation is happening. For more reading, see Des Traynor’s write-up on the future of analytics which compares more platforms than are covered here.

 

A structured approach to traction

Over the last couple months, Gabriel Weinberg has been churning out post after thoughtful post related to startup marketing. Earlier this week he presented the Bullseye Framework for startups to structure their thinking around traction:

There are about 20 different traction verticals that we see startups use to get traction. It is hard to predict beforehand exactly which traction vertical will work best for your startup at a given time. You can make educated guesses (and you should!), but it is hard to tell until you start running tests whether a given vertical is the best one for you right now.

Their traction process is broken up into five steps: (1) brainstorming, (2) ranking, (3) prioritizing, (4) testing and (5) focusing. When people say “growth hacking” they generally mean steps #2-4 in this framework, an iterative process which leads to investments in acquisition channels (focus) by way of experiments.

If you’re interested in this topic, Gabriel has several other good posts on related subjects:

  • Traction mistakes covers several recurring mistakes, such as ignoring marketing while focusing on product development (because it’s a more comfortable topics).
  • Orders of magnitude provides some mental rules for thinking about scale/growth in factors of 10.
  • Traction verticals lists top channels that successful companies have leveraged to generate user growth.

One metric that matters

I just pre-ordered Lean Analytics from Alistair Croll and Ben Yoskovitz. It’s due out in March but you can find a sneak peek on their blog. In their words, the goal is to release “the definitive guide to using analytics to build a better startup faster.” I like better and I like faster. I also like their idea of one metric that matters:

[One Metric That Matters] doesn’t mean there’s only one metric you care about from the day you wake up with an idea to the day you sell your company. It does, however, mean that at any given time, there’s one metric you should care about above all else. Communicating this focus to your employees, investors, and even the media will really help you concentrate your efforts.

Once you identify your metric—they walk through the process, with questions to ask yourself and examples for context—it comes down to focus. This all begs the question, what is the OMTM is for their book?