Key performance indicators (KPIs) are like milestones on the road to online retail success. Monitoring them will help ecommerce entrepreneurs identify progress toward sales, marketing, and customer service goals.
KPIs should be chosen and monitored depending on your unique business goals. Certain KPIs support some goals while they’re irrelevant for others. With the idea that KPIs should differ based on the goal being measured, it’s possible to consider a set of common performance indicators for ecommerce.
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Here is the definition of key performance indicators, types of key performance indicators, and 67 examples of ecommerce key performance indicators.
A performance indicator is a quantifiable measurement or data point used to gauge performance relative to some goal. As an example, some online retailers may have a goal to increase site traffic 50% in the next year.
Relative to this goal, a performance indicator might be the number of unique visitors the site receives daily or which traffic sources send visitors (paid advertising, search engine optimization, brand or display advertising, a YouTube video, etc.)
For most goals there could be many performance indicators — often too many — so often people narrow it down to just two or three impactful data points known as key performance indicators. KPIs are those measurements that most accurately and succinctly show whether or not a business in progressing toward its goal.
KPIs are important just like strategy and goal setting are important. Without KPIs, it’s difficult to gauge progress over time. You’d be making decisions based on gut instinct, personal preference or belief, or other unfounded hypotheses. KPIs tell you more information about your business and your customers, so you can make informed and strategic decisions.
But KPIs aren’t important on their own. The real value lies in the actionable insights you take away from analyzing the data. You’ll be able to more accurately devise strategies to drive more online sales, as well as understand where there may problems in your business.
Plus, the data related to KPIs can be distributed to the larger team. This can be used to educate your employees and come together for critical problem-solving.
SLA stands for service level agreement, while a KPI is a key performance indicator. A service level agreement in ecommerce establishes the scope for the working relationship between an online retailer and a vendor. For example, you might have a SLA with your manufacturer or digital marketing agency. A KPI, as we know, is a metric or data point related to some business operation. These are often quantifiable, but KPIs may also be qualitative
There are many types of key performance indicators. They may be qualitative, quantitative, predictive of the future, or revealing of the past. KPIs also touch on various business operations. When it comes to ecommerce, KPIs generally fall into one of the following five categories:
Note: The performance indicators listed below are in no way an exhaustive list. There are an almost infinite number of KPIs to consider for your ecommerce business.
Sales key performance indicators are measures that tell you how your business is doing in terms of conversions and revenue. You can look at sales KPIs related to a specific channel, time period, team, employee, etc. to inform business decisions.
Examples of key performance indicators for sales include:
Key performance indicators for marketing tell you how well you’re doing in relation to your marketing and advertising goals. These also impact your sales KPIs. Marketers use KPIs to understand which products are selling, who’s buying them, how they’re buying them, and why they’re buying them. This can help you market more strategically in the future and inform product development.
Examples of key performance indicators for marketing include:
Customer service KPIs tell you how effective your customer service is and if you’re meeting expectations.You might be wondering: what should the KPIs be in our call center, for our email support team, for our social media support team, etc. Measuring and tracking these KPIs will help you ensure you’re providing a positive customer experience.
Key performance indicators for customer service include:
Key performance indicators for manufacturing are, predictably, related to your supply chain and production processes. These may tell you where efficiencies and inefficiencies are, as well as help you understand productivity and expenses.
Key performance indicators for manufacturing in ecommerce include:
Key performance indicators for project management give you insight into how well your teams are performing and completing specific tasks. Each project or initiative within your ecommerce business has different goals, and must be managed with different processes and workflows. Project management KPIs tell you how well each team is working to achieve their respective goals and how well their processes are working to help them achieve those goals.
Key performance indicators for project management include:
Selecting your KPIs begins with clearly stating your goals and understanding which areas of business impact those goals. Of course, KPIs for ecommerce can and should differ for each of your goals, whether they’re related to boosting sales, streamlining marketing, or improving customer service.
Here are a few key performance indicator templates, with examples of goals and the associated KPIs.
GOAL 1: Boost sales 10% in the next quarter.
GOAL 2: Increase conversion rate 2% in the next year.
GOAL 3: Grow site traffic 20% in the next year.
GOAL 4: Reduce customer service calls by half in the next 6 months.
There are many performance indicators and the value of those indicators is directly tied to the goal measured. Monitoring which page someone visited before initiating a customer service call makes sense as a KPI for GOAL 4 since it could help identify areas of confusion that, when corrected, would reduce customer service calls. But that same performance indicator would be useless for GOAL 3.
Once you have set goals and selected KPIs, monitoring those indicators should become an everyday exercise. Most importantly: Performance should inform business decisions and you should use KPIs to drive actions.
Imagine 25,000 automobile parts sourced in wildly different locations throughout the world, magically converging upon an assembly plant to create a vehicle. Consider the complexity for the CFO — the billions of dollars spent for design, for building plants, and for marketing and advertising.
Consider the attention necessary to address taxation and regulation and the complex economic rationale derived from vehicle line profitability, return on invested capital, cash flow and optimal capital structure.
But if anything, the life of the CFO is about to become even more complex.
For the carmaker and supplier, the financial implications largely end when the vehicle leaves the factory. The car then becomes mostly someone else’s business, except for auto financing and parts supply. The sale is made to the dealer and the revenue recognized.
However, a vast range of new technologies and technological abilities — graphic processing unit chips, LIDAR, mapping software, deep learning and artificial intelligence — are transforming consumer behavior and revolutionizing the way we lead our lives, including how we use our automobiles.
This doesn’t mean the traditional carmaking business is going away anytime soon, but car sales will decline as mobility services reduce the need to own automobiles.
Thus, car companies must change to accommodate a world where revenue comes more from providing services. That is a dilemma for the CFO, a dilemma of business models: the need to serve multiple innovation paces at once.
CFOs must maintain the traditional pace of the business that reflects the sale of cars — consumer interaction once every three to five years — but must also accommodate new business realities for the faster-paced transactions necessary for emerging, service-oriented markets — indeed, consumer interactions as often as many times per day.
Those emerging markets have great potential: mobility services, power provision, fuel services, data aggregation and insurance, for example. This will produce a trillion-dollar market for mobility services alone, thus changing the auto industry.
But adding service businesses requires far-reaching strategic decisions affecting complex revenue models, balance sheets, capital structure, taxation and governance.
This sea change in the industry requires new key performance indicators for the CFO to set a new drumbeat for measuring growth, profitability and sustainability. Some metrics that will be important: revenue passenger miles, recurring revenue and number of active customers.
New indicators for profitability are: passenger revenue per available seat mile, revenue per available seat mile, cost per available seat mile, customer acquisition costs and recurring margins.
Entering the mobility service or any service business market is a profound change.
For the office of the CFO it will mean a radical difference in how they operate. Both the metrics for strategic drivers and key finance considerations require rethinking, restaffing and reinvesting in an infrastructure to accommodate an entirely different kind of business model.
While it can seem overwhelming, it’s important for CFOs to be thoughtful and lead by looking outside the industry to find innovative solutions and business models to meet these challenges.
The question for CFOs is not if the business model will change but rather are they ready for the drastic changes coming to the automotive industry?
Linda has spent 17 years with W. L. Gore & Associates serving multiple roles. In the Medical Products Division, Linda led new product development teams from ideation to commercial launch; drove technical resourcing for manufacturing engineering; and owns two patents. Linda dedicated the past two years to the creation and launch of the Gore Innovation Center in Silicon Valley, where she took it from its original concept to the facility’s execution, completion, and launch. Now, she’s working to jointly develop products, technologies, and business models where Gore materials can uniquely add value.
How do you encourage a culture of innovation?
Gore’s unique culture encourages Associates to pursue questions, ideas and innovations as part of their daily commitments. Associates are encouraged to use their “dabble time” to explore areas that they believe may be of value to Gore, even if they are outside their current division. Gore’s history of innovation has resulted in important problem solving and business creation as a result of genuinely curious Associates who came up with an idea and had the passion to pursue it.
For example, the Elixir Guitar Strings were born out of the W. L. Gore concept of “dabble time.” During a testing session of slender cables for Disney puppets, a group of Gore engineers noticed the cables were too difficult to control and the prototype failed. Instead of giving up on the project, Gore encouraged them to use some “dabble time” to think of alternative solutions. The group decided they needed a smoother, lower friction cable and realized they could use guitar strings as a substitute for the prototype. Once the strings became integrated into that process, the engineers realized they could create a stronger and longer lasting guitar string by combining the existing strings with Gore polymers.
While this is one proof point, for fostering an innovative mindset, we also believe in the power of creating an internal accelerator, a small team that is available to pursue ideas that germinate from employees. Most employee-generated ideas go nowhere because there are insufficient resources with the right perspective. A broad set of business-building skills are required to take a good idea and an adequate resource pool to a minimum viable product and then build it into a successful business.
Do you have any tips on how companies can have a more innovative mindset?
Innovation is too important to be left siloed in the “innovation” department. Innovation can come from anywhere inside or outside the company. For any company, it is key to be open to the ideas from any source and then take the time to flesh out and prioritize the ideas. Once priorities have been established, the company needs to devote the time and resources necessary to make the ideas successful, then announce the success across the company. A more innovative mindset across the entire company can build from one employee-generated success. Employees are highly motivated by such a success, which improves morale and promotes a supportive culture of innovation across the company.
How do you encourage a culture of innovation in a small company versus a large company?
In all companies, regardless of size, employees need to understand their contribution to company innovation goals. So, leadership is the key. Leaders must communicate their expectations of employees and put infrastructure in place to enable employees to pursue their ideas and curiosities. Companies can give employees a percentage of their time to pursue ideas or have employee idea contests. The most success is when companies have taken the time to educate employees on design thinking, lean innovation, business model innovation, open innovation and more. Leadership, setting expectations and providing infrastructure that supports those expectations, works the same across both large and small companies. Gore started as 2 people in a garage 60 years ago. It is now a $3billion company with over 9,500 Associates. Gore grew up with a culture of innovation and took the necessary steps to ensure that as the company grew, it stayed true to the roots of its culture, made changes when necessary, and allowed Associates to be free to innovate. Leadership was and continues to be critical to this journey.
How important is workplace diversity to innovation?
Innovation comes not just from breadth of experience and deep technical knowledge, but through the involvement of diverse teams. Pioneering ideas result when all those involved — everyone from engineers to customers — tap into their individual talents and experiences. Gore is a stronger enterprise because we foster an environment that is inclusive of all, regardless of race, sex, gender identity, sexual orientation or other personal identifiers.
Are companies having to innovate faster than they have had to before? If so, what tools are helping them do this?
Yes, over time the focus of innovation has shifted from internal only for some companies to bringing in external ideas and working with external partners. This shifts the pace of innovation as startups are on a faster timeline than corporations. To ensure we are working with the best startups and getting them what they need, we need to move faster. One area that facilitates faster innovation is to ‘deconstruct’ corporate practices in legal, procurement, supply chain, and other functions that aren’t built for speed but for the purpose of reducing risk in core businesses. The innovation team embraces risk when it explores new opportunities and speed is critical to these explorations. Internal processes that worked in the past sometimes only hinder innovation today. Applying a ‘deconstruction’ mindset puts the innovation leader in the position to rewrite policies that accelerate innovation, just like a startup CEO writes policies that support the startup’s mission.
How do you prepare for disruption?
We work closely with startups, universities, and customers to understand emerging technologies and business models. Taking a cross-sector approach allows us to capitalize on best practices from a variety of fields. Disruption at its best capitalizes on the agile nature of start-ups, the expertise and infrastructure of established corporations, and the exploratory mindset of academic institutions, all while focusing on the problem that needs to be solved. Innovation for innovation’s sake means nothing unless it truly makes a difference – addressing a challenge, improving a life, increasing efficiency etc. Preparing for disruption means factoring in all these inputs to improve the status quo.
How is the nature of innovation and organizations’ approaches to it set to evolve over the next five years?
One challenge facing companies is finding the right balance across all three business creation phases, Ideation-Incubation-Scaleup. Many companies invest in one of these areas while under-investing in the others, resulting in a large number of projects failing to move the needle for the company. In the next few years, companies will gain more insights from data about where their innovation programs fail to support promising projects, and companies will fix the gaps by balancing investments across all three business creation phases.
In addition, new tools like machine learning and artificial intelligence will continue to shape the way we develop businesses and processes. The potential for increasing efficiency on many fronts and across industries is huge. Organizations that build business models around these disruptive tools will realize success in a way that inflexible institutions are unable to.
In November, we told you about Farmers Business Network, a social network for farmers that invites them to share their data, pool their know-how and bargain more effectively for better pricing from manufacturing companies. At the time, FBN, as it’s known, had just closed on $110 million in new funding in a round that brought its funding to roughly $200 million altogether.
That kind of financial backing might dissuade newcomers to the space, but a months-old startup called AgVend has just raised $1.75 million in seed funding on the premise that, well, FBN is doing it wrong. Specifically, AgVend’s pitch is that manufacturers aren’t so crazy about FBN getting between their offerings and their end users — in large part because FBN is able to secure group discounts on those users’ behalf.
AgVend is instead planning to work directly with manufacturers and retailers, selling their goods through its own site as well as helping them develop their own web shops. The idea is to “protect their channel pricing power,” explains CEO Alexander Reichert, who previously spent more than four years with Euclid Analytics, a company that helps brands monitor and understand their foot traffic. AgVend is their white knight, coming to save them from getting disrupted out of business. “Why cut them out of the equation?” he asks.
Whether farmers will go along is the question. Those who’ve joined FBN can ostensibly save money on seeds, fertilizers, pesticides and more by being invited to comparison shop through FBN’s own online store. It’s not the easiest sell, though. FBN charges farmers $600 per year to access its platform, which is presumably a hurdle for some.
AgVend meanwhile is embracing good-old-fashioned opacity. While it invites farmers to search for products at its own site based on the farmers’ needs and location, it’s only after someone has purchased something that the retailer who sold the items is revealed. The reason: retailers don’t necessarily want to put all of their pricing online and be bound to those numbers, explains Reichert.
Naturally, AgVend insists that it’s not just better for retailers and the manufacturers standing behind them. For one thing, says Reichert, AgVend’s farming customers are sometimes offered rebates. Customers are also better informed about the products they’re buying because the information is coming from the retailers and not a third party, he insists. “When a third party like FBN comes in and tries going around the retailers, the manufacturers can’t guarantee that FBN is giving the right guidance about their products.”
In the end, its customers will decide. But the market looks big enough to support a number of players if they figure out how to play it. According to USDA data from last year, U.S. farms spent an estimated $346.9 billion in 2016 on farm production expenditures.
That’s a lot of feed and fertilizer. It’s no wonder that founders, and the VCs who are writing them checks, see fertile ground. This particular deal was led by 8VC and included the participation of Precursor Ventures, Green Bay Ventures, FJ Labs and House Fund, among others.
Naval Ravikant recently shared this thought:
“The dirty secrets of blockchains: they don’t scale (yet), aren’t really decentralized, distribute wealth poorly, lack killer apps, and run on a controlled Internet.”
In this post, I want to dive into his fourth observation that blockchains “lack killer apps” and understand just how far away we are to real applications (not tokens, not store of value, etc.) being built on top of blockchains.
Thanks to Dappradar, I was able to analyze the top decentralized applications (DApps) built on top of Ethereum, the largest decentralized application platform. My research is focused on live public DApp’s which are deployed and usable today. This does not include any future or potential applications not deployed yet.
If you look at a broad overview of the 312 DApps created, the main broad categories are:
I. Decentralized Exchanges
II. Games (Largely collectible type games, excluding casino/games of chance)
III. Casino Applications
IV. Other (we’ll revisit this category later)
On closer examination, it becomes clear only a few individual DApps make up the majority of transactions within their respective category:
Diving into the “Other” category, the largest individual DApps in this category are primarily pyramid schemes: PoWH 3D, PoWM, PoWL, LockedIn, etc. (*Please exercise caution, all of these projects are actual pyramid schemes.)
These top DApps are all still very small relative to traditional consumer web and mobile applications.
Further trends emerge on closer inspection of the transactions of DApps tracked here:
Where we are and what it means for protocols and the ecosystem:
After looking through the data, my personal takeaways are:
What kind of DApps do you think we as a community should be building? Would love to hear your takeaways and thoughts about the state of DApps, feel free to comment below or tweet @mccannatron.
Also, if there are any DApps or UI/UX tools I should be paying attention to, let me know — I would love to check them out.
For those who have not recently driven down the Ohio Turnpike, images of abandoned steel mills and shuttered factories may come to mind. But the view from the car window today looks far different — one can stop by the Youngstown Business Incubator to see how additive manufacturing startups are utilizing 3D printers, or check out the construction underway at the newly launched Bounce Innovation Hub in Akron.
The Ohio Turnpike is just a small slice of Interstate 80, which connects our nation’s coastal communities from New Jersey to California, cutting straight through Ohio. One of the original routes of the Interstate Highway System, I-80, catalyzed economic growth 60 years ago by bringing together Americans from all corners of the country, creating a center of gravity for a number of industries.
In February, we took to the highway, bound for several stops across the Midwest. Our goal was not to promote investment in the Midwest’s highways, but investment in its burgeoning entrepreneurial ecosystem. We were joined by more than a dozen venture capital investors from Silicon Valley and New York to take part in a “Comeback Cities Tour” through Youngstown and Akron, Ohio; Detroit and Flint, Michigan; and South Bend, Indiana.
Why the Midwest and why us? Rep. Ryan represents Northeast Ohio and led this tour to show VCs that his community, and communities that look like his, are open for business. For experienced VCs like Nitin and Patrick, this trip was an opportunity they could not miss. The Midwest startup ecosystem has been experiencing a bit of a renaissance, attracting increased investment and showing significant results as the area experienced 37 company exits valued at over $5.1 billion in 2017, up from $1.6 billion in 2016. Together, the members on the trip represent the growing percentage of the VC community interested in learning on the ground and bringing resources to areas of the US other than just Silicon Valley and New York. Most importantly, we know that investing is not transactional — it’s a relationship, which requires showing up in person.
Meeting dozens of entrepreneurs on the tour, we recognized similarities among them: passion for building impactful companies and a desire to see their cities once again thrive as business epicenters. The Midwest has long been a source of talent, and it makes sense that people located around research universities and iconic industries will create innovative companies.
Standing in the way of further progress is the lack of a more developed network with reliable sources of early stage capital, connections to broader networks, and companies spanning different stages of development. Though some local startup capital is available, the risk appetite and access to venture resources and growth capital are limited. Through Nitin’s work with Unshackled Ventures, Patrick’s experience building companies, and Rep. Ryan’s political leadership in the Midwest, we know firsthand how important each component is for success.
Silicon Valley already has a robust network of investors at each stage of a company’s growth. Our goal is to build access points from Silicon Valley inward to states including Ohio, Michigan, and Indiana. This starts with building relationships and trust between investors, business leaders, corporations, incubators, and accelerators. Next, we need help from the local business ecosystem to understand and tout local strengths that set this region apart.
Business communities in Rep. Ryan’s district, like Akron and Youngstown, offer investors and employers an attractive business environment that includes a low cost of living, proximity to outstanding universities, solid infrastructure, and clusters of local enterprise customers. Packaged together, it’s an enticing offer for the outside investor community. Similarly, large family foundations and funds can be access points, fostering the flow of investments and knowledge in both directions. Partnering with local investors and business leaders will be very helpful to identify, evaluate, and leverage these strengths — and that’s what we started to do in February.
The Comeback Cities Tour is already paying dividends for entrepreneurs in the Midwest. Those on the tour immediately recognized critical masses of customers in these cities, and conversations have already started between Silicon Valley startups, VC funds, and the regional customer base. This progress is on top of $75,000 pledged by Patrick in partnerships spanning Ohio. Over the coming months, we will deepen the relationships developed during the tour, to drive opportunities and flow of capital in both directions. And we will hold ourselves accountable on follow-up action.
Our Comeback Cities Tour started the dialog, but more needs to be done to grow partnerships of trust. So where do we go from here? There is no better model than Interstate 80. The interstate of the future will connect talent, ideas, and capital as much as the road system of the past connected physical commerce. With the right coordination and relationships, Midwest innovation combined with coastal capital and business experience will drive economic growth from the center of our country outward.
Because entrepreneurship is not a zero-sum game, we all stand to win by working together.
Retailers are regularly mocked about being terrible at personalization. Late last year, Bloomberg stroked brands with one hand while giving a slap with the other when it published a column titled “Personalization Helps Retailers; Too Bad They’re Terrible at It.” This was a blanket accusation. Others get more specific.
Every so often an article will come out featuring a story like this: Person looks at a pair of pants on, say, the J.Crew website. Person buys those pants in a J.Crew store. Person is then retargeted online with an ad for the same pair of pants. The article’s conclusion? J.Crew (or other retailer du jour) is terrible at knowing its customer.
Despite the glaring implications of these articles, retailers aren’t stupid; personalization is just really hard.
Knowing that a specific customer looked at something online and then bought it in a physical store is difficult enough to pull off on its own. But to then feed that information to an ad network so it can stop serving up retargeting ads featuring the item the customer just purchased? That’s no easy feat.
But it doesn’t mean retailers shouldn’t try.
Currently, there are companies that attempt to solve this problem by working with retailers to track individual customers across every single touchpoint and channel with which they interact. It’s a noble task, but not one that’s easily — or even usually, successfully–pulled off.
The first, most basic step these companies might recommend is identifying each customer and prospective customer through data from customer relationship marketing (CRM) systems, data management platforms, the devices they use, the social media they participate in and a variety of other sources.
That’s a tall order, but we’re just getting started. The next steps involve knowing what customers buy, view and consume, why they make their decisions and who and where they are. Next, it’s time to make personalized recommendations based on their actions, preferences and interests and deliver these messages in the context of where they are, the recent events around them ― oh, and the time of day and year.
Rather than mocking those who are doing it wrong, an easier task might be to look at who’s doing it right. And what ‘it’ even is.
When people talk about personalization, they’re usually referring to technologies that enable A/B testing or purchase recommendation engines. However, these activities are outcomes that offer tactical ways for brands to deliver distinct messages to individuals. They aren’t personalization. True personalization strategies come from a position of deep knowledge, and a brand’s deepest, most easily grasped knowledge is what it knows about its products.
Take the clothing shopping service Stitch Fix, which assigns each of its garments 100 or so different attributes (things such as material, color, season, garment type and so on) to get a deep understanding of the variables to which different people respond. Stitch Fix then combines this knowledge with feedback that customers give to their stylists about what items they like and don’t like. Data science then kicks in to understand patterns between things the customer likes across items and pinpoint the exact attributes to which they’re consistently drawn. The result is a dynamic recommendation capability that allows the company to present apparel more likely to please any given shopper.
That’s a very different strategy than, say, throwing products that are supposed to appeal to young professional males in a monthly package and hoping for the best.
Another highly effective personalization strategy comes from Netflix. Todd Yellin, vice president of product at Netflix, likes to say his company has a “three-legged stool” approach to helping people find shows and movies they’re likely to enjoy. According to Yellin, “The three legs of this stool would be: Netflix members, taggers who understand everything about the content, and our machine-learning algorithms that take all of the data and put things together.”
Netflix is in a unique position because its data, its communications, its product and the customer’s experience with all of these things reside in the same place. Retailers, on the other hand, don’t usually see their customers daily so they have to prioritize the personalization of outbound interactions, such as email or online ads, that bring customers back to engage and buy.
Email’s potential for personalization is particularly high for a couple of reasons: 1) shoppers have deliberately opted in to receive communications, and 2) it allows for a cohesive series of messages that retailers can use to create an ongoing narrative with customers over time.
Personalization becomes a lot more interesting and effective when brands start thinking of it in these terms rather than as a blunt instrument for re-selling the customer on an item s/he’s engaged with — or worse, an item the retailer simply wants to offload.
What seems like a new approach is actually in line with how marketing teams were structured before the digital revolution. For people who worked in the pre-Internet era, marketing channels are just that ― channels. They weren’t strategies.
Take marketers who wanted to sell, say, Cocoa Puffs cereal (and you thought those chocolatey poofs sold themselves!). They wouldn’t talk about a television strategy or a magazine strategy. They would start off by asking, “Who buys Cocoa Puffs?” and answering, “Moms who have busy days.” Based on that, they’d advertise in women’s magazines or on daytime television during soap operas, all while talking about getting kids to eat a good breakfast.
Then they’d ask “Who influences the purchase?” The answer would be kids, so they’d talk about how delicious Cocoa Puffs are and they’d go out with a memorable commercial that has a crazy bird who gets coocoo for Cocoa Puffs. They’d run that spot during cartoons and have ads in kids’ magazines or around schools and rec centers.
But that’s not the way advertising works today. Today, rather than following a top-down strategy where all channels are working toward a common and unified thought, retailers seem to take a bottom-up approach where each channel has its own rules and those rules don’t necessarily influence or get affected by other channels.
The rules of the road
An email team, then, is limited by some arbitrary rules around how often someone should receive emails — rules that someone truly believes are the right rules for all prospects. They might mean well, but that’s not good enough.
Steve Madden is a good example of what can happen when a brand rethinks its personalized customer contact strategy and unchains itself from arbitrary email rules. Before we began working with the brand a couple years ago, Steve Madden’s strongest personalized efforts were triggered cart abandonment reminder emails. But even those had limitations: the system only allowed these messages to be sent once a day, and then only to site visitors who were logged in at the time they abandoned their cart.
Since then, Steve Madden has worked to reconfigure its cart abandonment emails to send a designated time after the activity ― not just once a day. But that was still just the beginning: the marketing team tested things like which product categories customers had the highest affinity for and algorithms that could predict the likelihood of conversions and unsubscribes.
The impact of these efforts became clear when the Steve Madden team decided to run a test on the effectiveness of these models. The team sent the same email featuring its line of Freebird shoes to two different groups: an audience of past purchasers and an audience who had a high-predicted affinity for the line of shoes despite not having purchased them in the past.
To the surprise of everyone, the group of customers with a predicted affinity for the shoes spent twice as much as the group of past purchasers. The Steve Madden initiative demonstrated that personalization can go beyond triggers to reflect consumer interactions with a specific product. By pairing product attributes with customer affinity insights, the brand was able to deliver the right messages to an audience that needed and wanted Freebird products ― an audience a traditional marketing team would have overlooked.
Personalization is powerful, but that power can be used for good or evil. Done well, it will boost engagement, responses and sales. Done poorly, or without the right data, it can give a brand a bad rap for not knowing their customers and challenge its hard-won reputation as a reliable source of information on what consumers will like. Fortunately, marketers have exactly what they need to do it well right at their fingertips: knowledge of their products’ attributes, an understanding of their customers and the ability to determine where those two intersect.
The first quarter of 2018 came in roaring for the tech industry but ended up a little rough around the edges.
As the U.S. president does battle with Amazon, social networks’ privacy policies come under greater public scrutiny, dreams of fully-autonomous electric cars collide with technical limitations, and a cold trade war that grew hotter by the tweet, it’d be easy to think that Q1 2018 was, at best, so-so. And for many big tech companies, particularly those trading on public markets, that’d be a fair assessment.
But the global venture capital market seemed to pay no heed to the choppy waters downstream. According to projected data from Crunchbase, global venture capital deal and dollar volume in Q1 2018 eclipsed previous highs from Q3 2017, setting fresh quarterly records for post-Dot Com startup investment.
Like in previous quarters, we at Crunchbase News venture into a cavern of data from the first quarter. Here, we’ll focus primarily on investment into startups. But, fear not, we’ll follow up shortly with our analysis of startup liquidity in Q1.
Before diving in, here are two key takeaways to keep in mind.
Without further ado, let’s figure out what happened in the world of VC during the first quarter of the new year.
Around the globe, venture capitalists kicked off 2018 where 2017 left off: by setting new records.
In this section, we’re taking a look at the global venture capital market from a relatively high vantage point. We’re going to evaluate some key metrics for the market overall – including the overall size and quantity of venture deals – before digging into the stage-by-stage numbers in the next major section.
By taking a look into the recent past, we’re able to see how last quarter stacks up compared to the past year. And, apart from the Q4 hiccup from last year, the trend is generally upward.
The chart below plots projected data from Crunchbase for venture dollar volume in Q1 2018 in addition to the previous four quarters. (For more information about Crunchbase’s projections and methodology, see the Methodology section at the end of this report.)
On both a sequential quarterly and year-over-year basis, global venture deal volume is up. With an overall quarter-on-quarter expansion of over twelve percent, the market made up for ground lost in Q4.
As we’ll see in our stage-by-stage analysis shortly, most of those gains in deal volume were driven by growth at two different ends of the funding spectrum. Some of the most impressive gains, from a percentage perspective, came from late-stage deals which pushed total dollar volume higher. However, since angel and seed-stage deals make up such a large proportion of overall deal volume, a rising tide there raises numbers for the whole market.
Overall venture capital dollar volume follows a similar pattern, except instead of angel and seed-stage deals pushing the new, record highs, it’s a jump in late-stage funding that pushed the overall metric to a local maximum. In other words, since late-stage deals account for the lion’s share of global dollar volume, growth (or contraction) there drives the numbers for the market overall.
The chart below shows Crunchbase’s projections for venture dollar volume, subdivided by funding stage.
On both a quarterly and year-over-year basis, venture dollar volume is up at stages but “technology growth” since last quarter. Q1 2018 delivered one of the largest percentage-based jumps in dollar volume in recent memory. And with a projected total of nearly $77 billion worth of venture deals last quarter, dollar volume was over twice that of the same quarter last year.
And just for some added perspective of just how big $77 billion in quarterly investment is, at least in relative terms, Crunchbase’s projections show that there was about $150 billion invested around the world in all of 2015.
Now that we’ve explored the contours of the global startup funding market for last quarter, let’s take a look at who’s leading the charge. In venture, leadership is an important skill for many reasons, not least of which is the ability to source deals and organize funding rounds.
In some, but not all rounds with investors attached, Crunchbase designates which investor led the round. And based on an analysis of reported data for 4,951 venture funding rounds from the last quarter, we identified around 1,940 distinct investors – both individual and institutional – that led at least one round in Q1. The chart below shows some of the most prolific round-leading investors in the market last quarter.
The ballooning size of YC’s seasonal batches aside, the makeup of this list is more or less in line with two broad groups you’d expect to see:
But there are a few investors which stand out from the rest in this ranking, both with interesting angles into the venture space:
It should go without saying that there is a very long tail on this chart (again, nearly 2,000 investors total) and is subject to change as more deals from Q1 are added to Crunchbase over time. Regardless, what makes the top here—and just below the threshold for making it to the chart—are mostly just the usual suspects.
Now let’s see what’s going on within each stage.
Earlier we promised a section where we’ll go over some of the global VC market’s internals in greater depth. Well, congrats, we made it here together.
There’s a lot of data to cover in this section, so we’ll try to move fairly quickly.
As we’ve done in previous quarters, we’ll start fairly “close to the metal” by analyzing angel and seed-stage deals, and move on to later stages from there.
The first check of outside funding is among the most difficult a startup will raise. Q1 2018 appears to be another banner quarter for angel and seed-stage deals. In Crunchbase, this is mostly comprised of angel and seed rounds, smaller convertible notes, and equity crowdfunding rounds.
The chart below shows projected deal and dollar volume for angel and seed-stage deals in Q1 2018 and a prior year’s worth of quarterly data.
Projected angel and seed-stage investments make up 58 percent of the total deal volume in Q1 2018 but just four percent of the total dollar volume of venture investment. On both a sequential quarterly and annual basis, both metrics are up, with dollar volume leading the way.
That’s due in no small part to a rise in funding round size over the past year leading up to Q1. Below you’ll find a chart revealing an uptick in reported average and median round size of angel and seed-stage deals over time.
Here too, both metrics are either flat or positive both quarter-on-quarter and year-over-year. As we’ll see throughout the remaining funding stages, this is something of a common thread.
And who were some of the most active investors in Q1? From reported rounds data for the quarter, we identified 1,856 unique individual and institutional investors connected to angel and seed-stage deals, worldwide. The top-ranked startup backers are displayed in the chart below.
It should come as no surprises that the most active investors in angel and seed-stage deals are, for the most part, accelerator programs and dedicated seed funds.
A few groups stand out:
It’s at the early stage of the funding cycle (primarily Series A, Series B, and certain large convertible notes and equity crowdfunding rounds) when we start talking about real money. With 33 percent of global deal volume and 32 percent of the total dollar volume, ebbs and flows in early-stage deal-making can make a serious impact on the market overall.
And considering that many of the companies raising early-stage deals today could go on to raise late-stage deals in the future, a close look at this stage gives a peek at future deal flow.
To see how early-stage funding in Q1 stacks up against the last year, see the chart plotting projected deal and dollar volume below.
Relative to both Q4 2017 and Q1 2017, early-stage deal and dollar volume are up markedly. Nearly twice as much capital was invested in early-stage deals in Q1 2018, relative to the same period last year. And while the number of deals is also up year-on-year, dollar volume grew faster and thus continues to push the average size of early-stage rounds higher.
Below, you’ll find a chart of average and median early-stage rounds – based on reported data in Crunchbase – in Q1 2018 and the four prior quarters.
Early-stage rounds around the world were larger in Q1 2018 than the prior quarter and last year. Because the median figure is on the rise, it’s likely we’re seeing a population-wide trend here; in other words, it’s not just a few very large rounds skewing the average upward.
Despite rising average check size, plenty of investors continue to pump lots of capital into early-stage deals. In the chart below, we plot the most active among them.
There’s nothing much interesting to report on in the above ranking, as the funds included are about what you’d expect to see. That said, there is one tidbit to keep in mind. Five of the eleven firms listed in this chart have a direct connection to China:
Once we account for the Business Growth Fund, an active investor in U.K. startups, we find that primarily U.S.-focused venture firms are in the minority of this particular ranking.
All the companies that didn’t fail, sell out, or just stop raising capital after Series B graduate to late-stage ventures. In Q1 2018, late-stage deals (mostly Series C, Series D, and beyond) accounted for just eight percent of total deal volume but a whopping sixty percent of the dollar volume, giving this stage of deals a lot of sway over aggregate dollar figures for the quarter.
In the chart below, we’ve plotted Crunchbase projections for total late-stage deal action for Q1 and the prior year.
Late-stage deal and dollar volume are definitely on the rise, with fairly consistent quarterly growth over the last year or so, with the exception of a single quarterly decline in deal volume between Q3 2017 and Q4. Growth of late-stage dollar volume – both raw figures and on a percentage basis – and deal volume (just on a percentage basis) outpaced all earlier stages quarterly and year-on-year.
To get an idea of what might be driving dollar volume growth, let’s see how the size of late-stage rounds have changed, globally, over the past five quarters.
Despite some modest growth in median round size over time, the average is growing much faster. So while Q1 2018’s late-stage deals, as a population, may be slightly larger than the same time a year ago, it’s likely that a few very large rounds per quarter are skewing averages higher, faster.
Q1 has plenty of examples of really, really big late-stage rounds. Here are just a few:
And here are the firms which invested in the most late-stage deals in the last quarter.
It’s not #BreakingNews that established, generally well-regarded venture firms with lots of capital under management tend to invest in a lot of late-stage deals, either as de novopositions or by exercising follow-on rights.
What is worth noting, though, is that many of the firms listed above are participants in the Q1 trend of announcing or launching really, really, big new funds. Here’s just a sample from the chart above:
And it’s possible that other firms on this list will be raising new funds later this year. (Andreessen Horowitz, for example, has historically raised a new $900 million-$1 billion fund every two years since 2012. The firm’s last publicly-disclosed fund – its fifth, just a hair under $1 billion – was closed in June 2016.)
As a category of funding rounds, “technology growth” is a bit of a strange one. The idea here is to capture super-late-stage funding deals, typically struck with companies headed toward going public.
Longtime readers of Crunchbase News’s quarterly reports may remember that this category presented some vexing challenges over time, particularly concerning definitions.
For our Q1, Q2, and Q3 reports for 2017, technology growth rounds were defined as “any ‘private equity’ round in which a ‘venture’ investor also participated.” This didn’t work for a few reasons, chief among them being that many of these rounds have only one investor, a private equity fund.
Starting in Q4 2017, and here for Q1 2018, technology growth deals are defined, in plain English, as “any ‘private equity’ round raised by a company that has previously raised ‘venture’ financing in a prior round, such as a seed round or Series C.” By focusing on the company’s funding history, rather than how its investors are labeled, the News team believes it’s capturing a more accurate picture of growth equity investments by PE firms in technology companies.
Just like in prior quarters, deal and dollar volume for tech growth rounds are kind of all over the place, as the chart below shows.
For reasons we’ll discuss shortly, we believe it’s best to focus on deal volume inside of this category. For technology growth deals, there’s been positive growth since last quarter and the same time last year. This signals continued investor interest in very late-stage private companies, which is matched by companies interest in raising from private markets.
This being said, there hasn’t been much change in the size of tech growth rounds overall, apart from some outliers that push the average up. The chart below shows average and median round size of tech growth deals.
First off, the size of technology growth deals is quite variable. As examples:
With much more variability in round size just within the past quarter, it’s difficult to make any definitive claims about the state of tech growth funding in the last quarter. It might be back to the drawing board here.
And with that, we’ve covered the world of startup capital inflows in the first quarter of the year, at least in broad strokes.
On a global scale, the venture capital market in Q1 is a microcosm of a number of salient trends.
Some may take solace in the fact that much of this is just an acceleration of historic trends. But at the same time, there are very few mechanisms to point to which can slow this train down, and investors don’t seem keen on pumping the brakes. After all, things are just now picking up from a sluggish Q4. So much for taking an extended breather.
The data contained in this report comes directly from Crunchbase, and in two varieties: projected data and reported data.
Crunchbase uses projections for global and U.S. trend analysis. Projections are based on historical patterns in late reporting, which are most pronounced at the earliest stages of venture activity. Using projected data helps prevent undercounting or reporting skewed trends that only correct over time. All projected values are noted accordingly.
Certain metrics, like mean and median reported round sizes, were generated using only reported data. Unlike with projected data, Crunchbase calculates these kinds of metrics based only on the data it currently has. Just like with projected data, reported data will be properly indicated.
Please note that all funding values are given in U.S. dollars unless otherwise noted. Crunchbase converts foreign currencies to US dollars at the prevailing spot rate from the date funding rounds, acquisitions, IPOs, and other financial events as reported. Even if those events were added to Crunchbase long after the event was announced, foreign currency transactions are converted at the historic spot price.
The second most common reason why VCs pass on an investment is some version of “it’s not big enough.” For a VC to generate a great fund-level return, they typically need to invest in at least one company that has billions of dollars of enterprise value. To do that, most VCs decide that each one of their investments needs to have the potential to exit at or above that amount, even if it’s very unlikely to be the reality for every single investment.
The problem is, most really exciting companies seem “not big enough” to a lot of investors, especially really early on. These startups are often going after markets that don’t currently exist or seem like a niche opportunity (but in reality, are much bigger).
So if you’re a founder choosing to take the VC path, how can you counter investors’ objections about market size?
Below are some different approaches. Keep in mind that some of these are left-brain sort of approaches and others are more right-brain. Both are important and could be effective for different sorts of investors (and different sorts of founders). And if you gravitate towards one, keep in mind that investors that make team decisions will come at this question from multiple angles.
Most market sizes are top-down. “The market for marketing software is $XB dollars so it’s big enough to support some really big companies.” It’s the simplest way to think about market size, so most investors will gravitate that way, especially if you are building a company that is going after an EXISTING market.
One way to augment this is to essentially take the same approach but show brick-by-brick how your market opportunity may be bigger than it seems. This means showing:
You will still need to be going after a pretty large core business for this to resonate in any way. But doing a build up like this can be effective when a prospective investor does believe that the market is somewhat big but would love to see more upside to get fully comfortable.
The previous approach completely fails when you’re talking about markets that don’t quite exist yet or when an investor is not at least on the fence about market size.
Another approach is to do a bottoms-up analysis to demonstrate the scale of market demand for a service like yours. Start with the total number of potential end-users, and use reasonable estimates around customer demand, pricing, market share, etc. The key things that you’ll be pushed on with this sort of an analysis is a) how you are defining the reasonable scope and segmentation of the potential customers, b) how realistic your market share assumptions are, and c) the fact that this is really all conjecture.
One way to address c) is to include solid data points that lend credibility to your assumptions (like a reasonable estimate of how much customers already spend to solve a similar problem or some ROI analysis on your product/service that can be used to estimate reasonable pricing and the “no-brainer-ness” of what you are proposing). Also keep in mind the “vitamin vs. pain-killer” analogy. Bottoms-up approaches tend to work better for “pain-killers” than “vitamins,” even if the ROI of the vitamin seems to hold together.
Being in lock-step with a broad mega-trend is another way that investors get over a seemingly small market. This means that the investor (consciously or not) believes that the mega-trend will either a) drive massive market growth or b) drive the new company to have unusually high market share.
A simple example of this was the shift of enterprise software to the cloud. Once investors believed this was happening, it became more reasonable to think that a new software product in a specific vertical might enjoy extremely rapid adoption and enough market share to build to $100M+ in revenue and $1B+ in enterprise value reasonably quickly. Without this mega-trend, it’s harder to believe this because the pace of adoption may be too slow and it would be too difficult to dislodge existing players with a similar approach without being 10X better, faster, or cheaper.
Another example is IoT. Historically, investors have hated the idea of investing in consumer electronic products. Any early investor or operator at Ring will tell you that early on, almost nobody believed that a smart doorbell company could be “big enough.” But as a mega-trend emerged in this category, we saw more suspension of disbelief in this area for a period of time, for better or worse.
Using analogies can be tricky because they may not land. But if they do, I find that a lot of investors often get fixated on an analogy and that can sufficiently build conviction. When doing this though, it’s important to not just list out similar companies or big exits in the space, but internalize what those analogies communicate.
For example, if there have been some large exits in a seemingly small market, this can be a blessing or a curse. Yes, those analogies exist, but how well do investors know the comp that you are citing? Was it actually a really teeny business bought for pure strategic reasons? Are there actually only one or two buyers who would pay that kind of premium? How many investors would take that bet?
Productivity software is in this category. One could point to companies like Evernote, Sunrise, Acompli, etc. as examples of companies with really nice exits or private market valuations. But looking at this another way, one could say, “Wow, outside of Microsoft, who will pay a premium? The best companies only exited for at most a couple hundred million? Wow, doesn’t Evernote show that it’s really tough to be a truly venture-scale, independent company in this category?”
I find that the best analogies are ones that tend to connect to one of two things. Either, it ties to a mega-trend. For example, “Just like the shift to the cloud allowed for the rise of great companies in different categories, the shift to mobile computing in the enterprise will do the same. So this application that does X is the beginning of a mobile-first HR product that will be like Workday but for mobile.”
The second analogy is to connect yourself to a company with a similar ethos or founders with the same super-power. This is a lot harder to do, and probably happens by inception more than through direct argument. You would probably not say “We are just like the Airbnb founders, so you should believe we can make this work.” But I have heard investors who have gotten to know founders over some time say something like “Wow, these founders are unbelievably obsessed with design and user experience in a way I haven’t seen since (person X). Maybe they really can pull it off!”
This is some version of “Today we are doing X, but that just puts us in a great position to do Y, which is obviously huge.” There are a couple flavors of this.
The first is the bank-shot. This is where X is actually not the foundation of a great sustainable business but could be a gateway to more. A lot of VCs have a hard time with bank-shots, unless you are already demonstrating some really remarkable traction. Usually, the right approach here is to focus on growth and scale as quickly and efficiently as possible when accomplishing X, and make most of your money doing Y down the road when you have a network effect, customer lock-in, or can provide a valuable service that no one else could provide without your scale.
The second version of this is when X is actually pretty decent. Maybe it won’t be “the next Facebook,” but it could certainly get you to a pretty attractive place. Usually, this works well when the underlying business could be profitable and decently large without being too capital-intensive, which gives you more freedom to pursue the bigger opportunity as a next step. This allows an investor to say to themselves, “I could reasonably get a 5–10X on the core business, and there is some small probability that this could actually be a 20X or more.” Usually, this means that the company is in a market that has decent prospects for future funding or M&A, such that if the business hits a double but not a home run, it still could be a good outcome.
One additional approach that I’ve seen founders use quite successfully is what I’ll call “the future bet.” The approach here is to deflect discussions about current market size and focus the discussion on a single, simple bet about the future.
For example, this can be used in almost any rental or sharing economy company (clothing, transportation, equipment, etc). Even though most rental markets aren’t very large, the bet goes something like “do you really believe that people are going to continue spending thousands of money on products that not utilized 90+% of the time? Our bet is that consumers are rapidly moving away from ownership towards sharing and renting, and those multiple billions of dollars are going to shift towards the companies that get this right.
First, don’t forget about what margins mean for market potential. High-margin businesses like software or marketplaces (when revenue is correctly accounted for) can support 10X+ revenue multiples. So the bar for a large scale opportunity is the potential to generate hundreds of millions of dollars in revenue to be worth billions of dollars down the road. For low-margin businesses, the revenue bar for a larger scale opportunity is higher. So when you are talking about how your business can build using a bottoms-up analysis or comparatives, make sure you keep this in mind.
Second, the landscape of potential acquirers plays into this discussion as well. Generally, I don’t recommend founders spend too much time talking about buyers and M&A opportunities, and we don’t obsess over it much here at NextView. But when you are a company that may very well find that the market opportunity is not as big as one thought or hoped, it’s comforting to be in a category with a strong set of folks who would buy you for a reasonable amount. Most investors don’t really focus on downside protection. But psychologically, this could make a difference when one is on the fence because of market size or the risk associated with a bank shot / scope-expansion strategy.
Here at Bowery Capital, we continue to outline where CxOs are in their current enterprise technology upgrade cycle, a trend that lies at the core of our investment thesis. As a result of this shift, we expect “next-generation” technology spend to hit $468B over the next ten years as legacy technologies are swapped out for new. In addition, we see some of this $468B coming from net new areas of spend where humans are displaced by new process, new system, or new automation software. Taking a look at exit data since our last update on this, we continue to believe that we’re early in this cycle. Cumulative revenues of exited next-generation companies is roughly $60-70B of replacement which in our models represent only about 15-20% of the estimated market opportunity. Over the last few years, a number of key themes have emerged corroborating our view that most of the enterprise opportunity lies ahead of us and that enterprise technology spend shifts are upcoming.
1. The Displacement Of On-Premise Solutions Is Early. There’s a cloud option out there for almost every IT workload, but a survey from the Uptime Institute indicates that about two-thirds of enterprise computing is still done in company-owned data centers. While projections vary and you are still seeing product end markets in SaaS infrastructure and SaaS applications grow by 40%+ y/y we are still moving in the right direction with a lot of opportunity. The story is not fully written and enterprise technology spend shifts will continue to occur here.
2. Traditional Vendors Are Increasingly Investing In Next-Gen Offerings. Startups aren’t the only ones taking advantage of this massive shift in spending. The “tech titans” are fully offering as-a-service versions of their own products. Microsoft, Oracle, SAP, and other company’s cloud revenue continues to grow at a huge clip. IDC has predicted correctly every year for the past three years that about 20%+ of all new business software purchases will be SaaS, benefitting long-standing tech leaders and startups alike.
3. Next-Gen Solutions Are Growing The Overall Market. By replacing spend previously allocated to services or personnel, next-gen solutions are bringing new dollars into the tech market. Yesterday’s personnel or consulting expenditures become tomorrow’s SaaS or IoT revenues. This is the core message behind Marc Andreessen’s now-famous “Software Is Eating The World” piece. The key takeaway here is that even Fortune 500 companies are starting to understand that every non-core function has the potential to be replaced by newer, easier-to-adopt alternatives; and their spending behavior is beginning to reflect that mindset as companies work with increasingly young and innovative vendors.
4. Greenfield Opportunities In Vertical Markets Remain Untapped. Several cloud software companies have already made waves serving vertical markets: Veeva Systems, Fleetmatics, Guidewire, and MindBodyare just a few public-company examples. Per Bain research, however, fewer then 15% of companies in transportation, energy, manufacturing, and several other sectors view themselves as being active cloud software adopters. As we detail in another recent report of ours (“Opportunities In Vertical Software”), the Bowery team expects many more vertical SaaS success stories as the specialization of enterprise tech continues and we will continue to make investments here beyond our portfolio beyond companies like Transfix (trucking), CredSimple (healthcare), and Fero Labs (manufacturing).
5. As Consumers Migrate To Mobile, Companies Need Next-Gen Tools To Follow. It’s already well known that consumers are rapidly adopting mobile to manage nearly every aspect of their lives, including how they buy products and services. Certain categories of commerce moved online seemingly overnight (e.g. flowers, office supplies), for example, and the same is happening in mobile. In order to market to, track, engage with and support customers on new platforms like mobile, enterprises must employ next-gen solutions that can transact in new forms of data. Connected industrial sensors, in-store beacons, mobile marketing attribution and mobile CDN are just a few examples.
The enterprise spend shift to cloud software is underway, and by our measure, we’re still early in this cycle. Over the next few years, growth in enterprise tech upgrades will likely out-shadow anything we’ve seen to date. And that means unprecedented opportunity for smart investors, corporate innovators, and startup founders alike that want to get in on these enterprise technology spend shifts.