All posts in “eCommerce”

Calculating sales efficiency in a start-up: The magic number that will help you scale

How and why sales efficiency could help tech start-ups unlock growth

Sales efficiency is the best way to understand the economics of a business. To me, it answers the question as to whether a business can ever scale. The harsh truth is, if it can’t scale, investors won’t be interested.

Sales efficiency is more simple to measure than other related concepts like CAC (customer acquisition cost) or LTV (lifetime value). Here’s why:

  • CAC is harder to truly measure, especially new CAC. In a SaaS organization, sometimes it can be hard to allocate those costs to what that new CAC is, as opposed to upsell or cross-sell within the same organization. Salespeople are almost always trying to pursue two goals:
    • Trying to acquire new customers
    • Selling within an existing customer (more seats within an established department, or expanding to a new division)

These activities generate different CAC; trying to strip out only the new CAC can be tricky. Sales efficiency, on the other hand, looks at all net new ARR (annual recurring revenue), which includes new customer ARR as well as expansion ARR.

  • LTV tries to measure the value of a customer over time, assuming both repeat purchases and eventual churn; this gives you a good sense of the ultimate value of that customer to your business over time. The challenge with LTV in SaaS is that the data points that you might use to assume churn and repeat purchase behavior aren’t very robust — there are few SaaS businesses that have enough customers to really make these numbers reliable.

Enterprise businesses should focus on unit economics of sales early. When a business scales, it rarely buys you better economics — usually it just means more losses.

Gracphic for sales efficiency

Image via Ryan Floyd / Storm Ventures

The role of sales efficiency in your ‘go-to-market fit’

At Storm Ventures we use a concept we call finding ‘go-to-market fit’ (GTM fit).

Patreon sells product curation site Kit to GeniusLink

Patreon, the platform for independent content creators to operate membership businesses for their core fans, announced it is selling the assets of Kit.com to localized affiliate link service GeniusLink.

Founded in 2015, Kit is a social-shopping platform where influencers curate bundles of products (“kits”) they recommend. When their fans buy products they featured in a kit, the influencer earns an affiliate fee commission. Kit has 2.3 million monthly web visitors, according to SimilarWeb.

Among the most notable content creators on Kit, YouTuber Casey Neistat curated a kit featuring his favorite camera gear and author Tim Ferriss curated kits featuring his favorite podcasting equipment and the health products recommended by interviewees in his Tools for Titans book.

Screen Shot 2019 09 12 at 8.40.53 AM

Screenshot of Kit.com’s profile site for Tim Ferriss

Patreon acquired Kit in June 2018 in what Patreon’s SVP Product Wyatt Jenkins described to me during my in-depth series on the company as “close to an acqui-hire,” adding that “although Kit is a good revenue source for a lot of creators — so it’s not a shut-down of Kit — we’re maintaining it but not iterating on it.” 

Kit had previously raised $2.5 million in venture capital from backers like Social Capital, #Angels, Precursor Ventures, Expa, and Ellen Pao. While the Kit site remained active, the team behind it was reassigned to lead product development for Patreon’s merch offering

It is unsurprising that Patreon found a new home for the asset. While Kit is a tool for creators to monetize, it doesn’t enhance paid memberships for fans and that’s Patreon’s exclusive focus right now. Even Patreon’s merch product is only for offering merch as a benefit for membership tiers, not for managing an e-commerce store with one-time transactions.

In a blog post today announcing the acquisition, GeniusLink wrote that “The first order of business for Geniuslink is to migrate Kit to the Geniuslink infrastructure and work to improve speed and reliability while our operations team dives into user support. We look forward to adding additional functionality for creators to monetize their kits in the coming months.”

GeniusLink launched in 2009, originally branded as GeoRiot. The bootstrapped company has 13 employees headquartered in Seattle.

Social commerce is a popular trend right now, with other social platforms testing e-commerce integrations for users (particularly influencers) to feature products. YouTube now has a built-in merch section for a creator to sell products under their videos and Instagram lets influencers sell products directly in the app. These have the advantage of providing influencer-curated shopping experiences right where influencers and their fans already are.

Those features assume an influencer is selling their own products, however, or at least the products of a brand they’ve formally partnered with. For Kit and its affiliate link model, the focus is on influencers as trusted curators for their fans. The influencer can feature a much wider variety of products and do so immediately, without negotiating a deal with each brand. 

That’s also why the model likely doesn’t make sense for many popular influencers — they want more money for their endorsement of a product than a standard affiliate link fee, and recommending lots of products they don’t have formal deals to promote may undercut them in their negotiations with brands.

As GeniusLink adds more monetization features to Kit, perhaps it will make it a more lucrative business activity for small and large influencers alike. 

Why am I seeing this ad? AI, ML & human error in advertising

Ad platforms create equal opportunities for businesses but not equal outcomes.

They’re mostly marketed as self-service and easy to use, however, there are new features added regularly and open-ended ways to set, structure and target. Meaning, countless ways to spend—creating winners and losers in advertising.

This is where machines and digital advertisers are needed, to provide a profitable outcome.

Enter AI, ML and experts as freelancers, via agencies or housed in some of the world’s biggest companies, equipped with ample data, tech and educational resources to match people with companies via ads on search, social, and elsewhere on the web.

But, are the machines still in infancy or too heavily relied upon and do the experts always get it right?

Well, how often are you seeing ads that are irrelevant to what you wanted or where you were or who you are?

An irrelevant ad is an ad paid for by the company advertising but can return zero value as it’s of no use to the person receiving the ad.

As a digital advertiser via my company Adboy.com, I’m always curious as to why I was served an ad and if the company paying makes or loses money from it.

Something I’ve noticed is that in easily avoidable errors, ads can be served to existing customers, people with irrelevant needs and people that can’t be or are far less likely to become customers.

With this article, I’m going to give you the lenses of a fastidious digital advertiser. You’ll spot errors like these for yourself and know how they could occur, what the negative impact could be and how they can be avoided.

Advertising to existing customers

Syte snaps up $21.5M for its smartphone-based visual search engine for e-commerce

Visual search has become a key component for how people discover products when buying online: if a person don’t know the exact name of what he or she wants, or what they want is not available, it can be an indispensable tool for connecting them with things they might want to buy.

Now, one of the companies building technology to do this has raised a round of funding to expand its business further into the US, and not just across digital platforms, but to tap further into the opportunities of bringing visual search into the world of physical commerce, too, by way of smart mirrors and apps for store assistants to better help customers.

Syte, a Tel Aviv startup that works with fashion retailers like Farfetch and River Island as well as those who sell a wider variety of goods like Argos, Sainsbury’s and Kohl’s, has raised $21.5 million in funding. The Series B was led by Viola Ventures, with participation also from Storm Ventures, Commerce Ventures, and Axess Ventures. Syte has now raised $32 million including a previous round in 2017; it’s not disclosing its valuation but is projecting 300 percent revenue growth this year.

The use of visual search — using computer vision to “read” a picture, match it up with its metadata, and then find pictures of products that are similar to it — has become commonplace in e-commerce in recent years.

Among the many other companies that have this kind of tech — including visual search platforms like Pinterest and social media platforms themselves — Syte’s approach is notable in how it engages shoppers in the process of the search. Users can snap pictures of items that they like the look of, which can then be used to on a retailer’s site to find compatible lookalikes. Retailers, meanwhile, can quickly integrate Syte’s technology into their own platforms by way of an API.

Lihi Pinto Fryman, Syte’s CMO who co-founded the company in London with husband Ofer Fryman, Idan Pinto and Dr Helge Voss, said in an interview that the company spent about three years developing its technology — spurred initially by her own surprise, when she was working as an investment banker, at not being able to find a particular dress she spotted in a magazine — and only launched a product about 18 months ago. Since then, she says the company has seen “super hyper” growth because of the gap the company is filling.

The crux of the problem goes something like this: Retailers both online and offline have found that a new generation of shoppers are less interested in visiting their storefronts.

They are instead shopping by browsing social media platforms like Instagram and buying from there, which essentially opens those retailers to whole new set of competitors, and potentially at a great disadvantage, since they are not as well equipped to speak to that audience or anticipate what interests them to trigger sales.

“Young people are on Instagram for hours each day,” Fryman said. Indeed, Instagram is one of the only big social networks that’s seeing usage rise at the moment. “Retailers need to find a way to compete with that and remain in the market, and they can’t just continue what they’ve always done.”

On the other hand, while there are a number of visual search tools out in the market, not all of them are useful enough. “If you are searching for a ruffled floral yellow dress but you get a blouse, it just doesn’t cut it,” she noted. “And if it takes seven seconds to get an answer, that’s also not good, because people will give up after 2 seconds. Millennials and Gen Z shoppers have a very short attention span, so you need to be accurate and fast.”

The idea is that a product like Syte’s addresses both of these issues, and then some. In addition to its camera-based search service, it provides a recommendation engine to retailers, plus tagging services for its back catalog to complete the service.

“Rarely do we find companies that have managed to solve a technological problem that tech giants have been working for years to solve without success,” says Ronen Nir, General Partner at Viola Ventures, in a statement. “The feedback from the market is clear and swift and the rate of adoption of Syte’s solution is unparalleled. We are excited to lead a significant funding round that would be able to take the company to the next level.”

Syte’s more recent foray into physical commerce is an interesting turn as well. Smart mirrors have been more of a wishlist item than something that has seen critical mass adoption so far in changing rooms.

If the idea does catch on, I wonder what kind of a digital divide it might create among retailers, since the cost of refurbishing changing rooms to include these, along with all the backend changes that would need to be made, will likely be only the kind of service that bigger or high-end boutiques will be able to shoulder. More interesting, perhaps, is the idea of app-based tools for assistants, many of whom already carry a smartphone and would likely be grateful for recommendations to help sell better to customers.

“We have a vision to transform product discovery, and thus the eCommerce experience, for both retailers and consumers.” said Ofer Fryman in a statement. “That vision is what has led us since we founded Syte, and it is what continues to lead us as we enter this stage.”

How early-stage startups can use data effectively

“If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” – Jim Barksdale

It is a commonly held belief that startups can measure their way to success. And while there are always exceptions, early-stage companies often can’t leverage data easily, at least not in the way that later stage companies can. It’s imperative that startups recognize this early on — it makes all the difference.

In this piece, I draw on my experiences using data to take Framer from seed round to Series B. More concretely, I’ll describe what to (not) focus on, and then, how to get real results.

There are good and bad ways for startups to use data. In my opinion, the bad way unfortunately is often preached on saas blogs, a/b test tool marketing pages, and especially growth hacker conferences: that by simply measuring and looking at data you’ll find simple things to do that will drive explosive growth. Silver bullets, if you will.

The good way is comparable to first principles thinking. Below the surface of your day to day results, your startup can be described by a set of numbers. It takes some work to discover these numbers, but once you have them you can use them to make predictions and spot underlying trends. If everyone in your company knows these numbers by heart, they will inevitably make better decisions.

But most importantly, using data the right way will help answer the single most important – but complex – question at any moment for a startup: how are we really doing?

Let’s start with looking at what not to do as a startup.

Table of Contents


Common pitfalls

Don’t measure too much

Technically, it’s easy to measure everything, so most startups start out that way. But when you measure everything, you learn nothing. Just the sheer noise makes it hard to discover anything useful and it can be demotivating to look at piles of numbers in general.

My advice is to carefully plan what you want to measure upfront, then implement and conclude. You should only expand your set of measurements once you’ve made the most important ones actionable. Later in this article, I provide a clear set of ways to plan what you measure.

A/B tests are anti-startup

To make decisions based on data you need volume. Without volume, the data itself is not statistically significant and is basically just noise. To detect a 3% difference with 95% confidence you would need a sample size of 12,000 visitors, signups, or sales. That sample size is generally too high for most early-stage startups and forces your product development into long cycles.

While on the subject of shipping fast and iterating later, let’s talk about A/B testing. To get reliable measurements, you should only be changing one variable at a time. During the early stages of Framer, we changed our homepage in the middle of a checkout A/B test, which skewed our results. But as a startup, it was the right decision to adjust the way we marketed our product. What you’ll find is that those two factors are often incompatible. In general, constant improvements should trump tests that block quick reactionary changes.

Understand your calculations