Teads – Real-life AWS infrastructure cost optimization strategy

The cloud computing opportunity and its traps

One of the advantages of cloud computing is its ability to fit the infrastructure to your needs, you only pay for what you really use. That is how most hyper growth startups have managed their incredible ascents.

Most companies migrating to the cloud embrace the “lift & shift” strategy, replicating what was once on premises.

You most likely won’t save a penny with this first step.

Main reasons being:

  • Your applications do not support elasticity yet,
  • Your applications rely on complex backend you need to migrate with (RabbitMQ, Cassandra, Galera clusters, etc.),
  • Your code relies on being executed in a known network environment and most likely uses NFS as distributed storage mechanism.

Once in the cloud, you need to “cloudify” your infrastructure.

Then, and only then, will you have access to virtually infinite computing power and storage.

Watch out, this apparent freedom can lead to very serious drifts: over provisioning, under optimizing your code or even forgetting to “turn off the lights” by letting that small PoC run more than necessary using that very nice r3.8xlarge instance.

Essentially, you have just replaced your need for capacity planning by a need for cost monitoring and optimization.

The dark side of cloud computing

At Teads we were “born in the cloud” and we are very happy about it.

One of our biggest pain today with our cloud providers is the complexity of their pricing.

It is designed to look very simple at the first glance (usually based on simple metrics like $/GB/month or $/hour or, more recently, $/second) but as you expand and go into a multi-region infrastructure mixing lots of products, you will have a hard time tracking the ever-growing cost of your cloud infrastructure.

For example, the cost of putting a file on S3 and serving it from thereincludes four different lines of billing:

  • Actual storage cost (80% of your bill)
  • Cost of the HTTP PUT request (2% of your bill)
  • Cost of the many HTTP GET requests (3% of your bill)
  • Cost of the data transfer (15% of your bill)

Our take on Cost Optimization

  • Focus on structural costs – Never block short term costs increase that would speed up the business, or enable a technical migration.
  • Everyone is responsible – Provide tooling to each team to make them autonomous on their cost optimization.

The limit of cost optimization for us is when it drives more complexity in the code and less agility in the future, for a limited ROI.
This way of thinking also helps us to tackle cost optimisation in our day to day developments.

Overall we can extend this famous quote from Kent Beck:

“Make it work, make it right, make it fast” … and then cost efficient.

Billing Hygiene

It is of the utmost importance to keep a strict billing hygiene and know your daily spends.

In some cases, it will help you identify suspicious uptrends, like a service stuck in a loop and writing a huge volume of logs to S3 or a developer that left its test infrastructure up & running during a week-end.

You need to arm yourself with a detailed monitoring of your costs and spend time looking at it every day.

You have several options to do so, starting with AWS’s own tools:

  • Billing Dashboard, giving a high level view of your main costs (Amazon S3, Amazon EC2, etc.) and a rarely accurate forecast, at least for us. Overall, it’s not detailed enough to be of use for serious monitoring.
  • Detailed Billing Report, this feature has to be enabled in your account preferences. It sends you a daily gzipped .csv file containing one line per billable item since the beginning of the month (e.g., instance A sent X Mb of data on the Internet).
    The detailed billing is an interesting source of data once you have added custom tags to your services so that you can group your costs by feature / application / part of your infrastructure.
    Be aware that this file is accurate within a delay of approximately two days as it takes time for AWS to compute the files.
  • Trusted Advisor, available at the business and enterprise support level, also includes a cost section with interesting optimization insights.
Trusted Advisor cost section – Courtesy of AWS
  • Cost Explorer, an interesting tool since its update in august 2017. It can be used to quickly identify trends but it is still limited as you cannot build complete dashboards with it. It is mainly a reporting tool.
Example of a Cost Explorer report — AWS documentation

Then you have several other external options to monitor the costs of your infrastructure:

  • SaaS products like Cloudyn / Cloudhealth. These solutions are really well made and will tell you how to optimize your infrastructure. Their pricing model is based on a percentage of your annual AWS bill, not on the savings that the tools will help you make, which was a show stopper for us.
  • The open source project Ice, initially developed by Netflix for their own use. Recently, the leadership of this project was transferred to the french startup Teevity who is also offering a SaaS version for a fixed fee. This could be a great option as it also handles GCP and Azure.

Building our own monitoring solution

At Teads we decided to go DIY using the detailed billings files.

We built a small Lambda function that ingests the detailed billing file into Redshift every day. This tool helps us slice and dice our data along numerous dimensions to dive deeper into our costs. We also use it to spot suspicious usage uptrends, down to the service level.

This is an example of our daily dashboard built with chart.io, each color corresponds to a service we tagged
When zoomed on a specific service, we can quickly figure out what is expensive

On top of that, we still use a spreadsheet to integrate the reservation upfronts in order to get a complete overview and the full daily costs.

Now that we have the data, how to optimize?

Here are the 5 pillars of our cost optimization strategy.

1 – Reserved Instances (RIs)

First things first, you need to reserve your instances. Technically speaking, RIs will only make sure that you have access to the reserved resources.

At Teads our reservation strategy is based on bi-annual reservation batchesand we are also evaluating higher frequencies (3 to 4 batches per year).

The right frequency should be determined by the best compromise between flexibility (handling growth, having leaner financial streams) and the ability to manage the reservations efficiently.
In the end, managing reservations is a time consuming task.

Reservation is mostly a financial tool, you commit to pay for resources during 1 or 3 years and get a discount over the on-demand price:

  • You have two types of reservations, standard or convertible. Convertible lets you change the instance family but comes with a smaller discount compared to standard (avg. 75% vs 54% for a convertible). They are the best option to leverage future instance families in the long run.
  • Reservations come with three different payment options: Full Upfront, Partial Upfront, and No Upfront. With partial and no upfront, you pay the remaining balance monthly over the term. We prefer partial upfront since the discount rate is really close to the full upfront one (e.g. 56% vs 55% for a convertible 3-year term with partial).
  • Don’t forget that you can reserve a lot of things and not only Amazon EC2 instances: Amazon RDS, Amazon Elasticache, Amazon Redshift, Amazon DynamoDB, etc.

2 – Optimize Amazon S3

The second source of optimization is the object management on S3. Storage is cheap and infinite, but it is not a valid reason to keep all your data there forever. Many companies do not clean their data on S3, even though several trivial mechanisms could be used:

The Object Lifecycle option enables you to set simple rules for objects in a bucket :

  • Infrequent Access Storage (IAS): for application logs, set the object storage class to Infrequent Access Storage after a few days.
    IAS will cut the storage cost by a factor of two but comes with a higher cost for requests.
    The main drawback of IAS is that it uses 128kb blocks to store data so if you want to store a lot of smaller objects it will end up more expensive than standard storage.
  • GlacierAmazon Glacier is a very long term archiving service, also called cold storage.
    Here is a nice article from Cloudability if you want to dig deeper into optimizing storage costs and compare the different options.

Also, don’t forget to set up a delete policy when you think you won’t need those files anymore.

Finally, enabling a VPC Endpoint for your Amazon S3 buckets will suppress the data transfer costs between Amazon S3 and your instances.

3 – Leverage the Spot market

Spot instances enables you to use AWS’s spare computing power at a heavily discounted price. This can be very interesting depending on your workloads.

Spot instances are bought using some sort of auction model, if your bid is above the spot market rate you will get the instance and only pay the market price. However these instances can be reclaimed if the market price exceeds your bid.

At Teads, we usually bid the on-demand price to be sure that we can get the instance. We only pay the “market” rate which gives us a rebate up to 90%.

It is worth noting that:

  • You get a 2 min termination notice before your spot is reclaimed but you need to look for it.
  • Spot Instances are easy to use for non critical batch workloads and interesting for data processing, it’s a very good match with Amazon Elastic Map Reduce.

4 – Data transfer

Back in the physical world, you were used to pay for the network link between your Data Center and the Internet.

Whatever data you sent through that link was free of charge.

In the cloud, data transfer can grow to become really expensive.

You are charged for data transfer from your services to the Internet but also in-between AWS Availability Zones.

This can quickly become an issue when using distributed systems like Kafkaand Cassandra that need to be deployed in different zones to be highly available and constantly exchange over the network.

Some advice:

  • If you have instances communicating with each other, you should try to locate them in the same AZ
  • Use managed services like Amazon DynamoDB or Amazon RDS as their inter-AZ replication costs is built-in their pricing
  • If you serve more than a few hundred Terabytes per months you should discuss with your account manager
  • Use Amazon CloudFront (AWS’s CDN) as much as you can when serving static files. The data transfer out rates are cheaper from CloudFront and free between CloudFront and EC2 or S3.

5 – Unused infrastructure

With a growing infrastructure, you can rapidly forget to turn off unused and idle things:

  • Detached Elastic IPs (EIPs), they are free when attached to an EC2 instance but you have to pay for it if they are not.
  • The block stores (EBS) starting with the EC2 instances are preserved when you stop your instances. As you will rarely re-attach a root EBS volume you can delete them. Also, snapshots tend to pile up over time, you should also look into it.
  • Load Balancer (ELB) with no traffic is easy to detect and obviously useless. Still, it will cost you ~20 $/month.
  • Instances with no network activity over the last week. In a cloud context it doesn’t make a lot of sense.

Trusted Advisor can help you in detecting these unnecessary expenses.


Key takeaways

https://medium.com/teads-engineering/real-life-aws-cost-optimization-strategy-at-teads-135268b0860f

Shopify – 67 Key Performance Indicators (KPIs) for Ecommerce

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.

Table of Contents

Here is the definition of key performance indicators, types of key performance indicators, and 67 examples of ecommerce key performance indicators.

What is a performance indicator?

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.)

What is a key performance indicator?

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.

Why are key performance indicators important?

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.

What is the difference between a SLA and a KPI?

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

Types of key performance indicators

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:

  1. Sales
  2. Marketing
  3. Customer service
  4. Manufacturing
  5. Project management

67 key performance indicator examples for ecommerce

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.

What are key performance indicators for sales?

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:

  • Sales: Ecommerce retailers can monitor total sales by the hour, day, week, month, quarter, or year.
  • Average order size: Sometimes called average market basket, the average order size tells you how much a customer typically spends on a single order.
  • Gross profit: Calculate this KPI by subtracting the total cost of goods sold from total sales.
  • Average margin: Average margin, or average profit margin, is a percentage that represents your profit margin over a period of time.
  • Number of transactions: This is the total number of transactions. Use this KPI in conjunction with average order size or total number of site visitors for deeper insights.
  • Conversion rate: The conversion rate, also a percentage, is the rate at which users on your ecommerce site are converting (or buying). This is calculated by dividing the total number of visitors (to a site, page, category, or selection of pages) by the total number of conversions.
  • Shopping cart abandonment rate: The shopping cart abandonment rate tells you how many users are adding products to their shopping cart but not checking out. The lower this number, the better. If your cart abandonment rate is high, there may be too much friction in the checkout process.
  • New customer orders vs. returning customer orders: This metric shows a comparison between new and repeat customers. Many business owners focus only on customer acquisition, but customer retention can also drive loyalty, word of mouth marketing, and higher order values.
  • Cost of goods sold (COGS): COGS tells you how much you’re spending to sell a product. This includes manufacturing, employee wages, and overhead costs.
  • Total available market relative to a retailer’s share of market: Tracking this KPI will tell you how much your business is growing compared to others within your industry.
  • Product affinity: This KPI tells you which products are purchased together. This can and should inform cross-promotion strategies.
  • Product relationship: This is which products are viewed consecutively. Again, use this KPI to formulate effective cross-selling tactics.
  • Inventory levels: This KPI could tell you how much stock is on hand, how long product is sitting, how quickly product is selling, etc.
  • Competitive pricing: It’s important to gauge your success and growth against yourself and against your competitors. Monitor your competitors’ pricing strategies and compare them to your own.
  • Customer lifetime value (CLV): The CLV tells you how much a customer is worth to your business over the course of their relationship with your brand. You want to increase this number over time through strengthening relationships and focusing on customer loyalty.
  • Revenue per visitor (RPV): RPV gives you an average of how much a person spends during a single visit to your site. If this KPI is low, you can view website analytics to see how you can drive more online sales.
  • Churn rate: For an online retailer, the churn rate tells you how quickly customers are leaving your brand or canceling/failing to renew a subscription with your brand.
  • Customer acquisition cost (CAC): CAC tells you how much your company spends on acquiring a new customer. This is measured by looking at your marketing spend and how it breaks down per individual customer.

What are key performance indicators for marketing?

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:

  • Site traffic: Site traffic refers to the total number of visits to your ecommerce site. More site traffic means more users are hitting your store.
  • New visitors vs. returning visitors: New site visitors are first-time visitors to your site. Returning visitors, on the other hand, have been to your site before. While looking at this metric alone won’t reveal much, it can help ecommerce retailers gauge success of digital marketing campaigns. If you’re running a retargeted ad, for example, returning visitors should be higher.
  • Time on site: This KPI tells you how much time visitors are spending on your website. Generally, more time spent means they’ve had deeper engagements with your brand. Usually, you’ll want to see more time spent on blog content and landing pages and less time spent through the checkout process.
  • Bounce rate: The bounce rate tells you how many users exit your site after viewing only one page. If this number is high, you’ll want to investigate why visitors are leaving your site instead of exploring.
  • Pageviews per visit: Pageviews per visit refers to the average number of pages a user will view on your site during each visit. Again, more pages usually means more engagement. However, if it’s taking users too many clicks to find the products they’re looking for, you want to revisit your site design.
  • Average session duration: The average amount of time a person spends on your site during a single visit is called the average session duration.
  • Traffic source: The traffic source KPI tells you where visitors are coming from or how they found your site. This will provide information about which channels are driving the most traffic, such as: organic search, paid ads, or social media.
  • Mobile site traffic: Monitor the total number of users who use mobile devices to access your store and make sure your site is optimized for mobile.
  • Day part monitoring: Looking at when site visitors come can tell you which are peak traffic times.
  • Newsletter subscribers: The number of newsletter subscribers refers to how many users have opted into your email marketing list. If you have more subscribers, you can reach more consumers. However, you’ll also want to look at related data, such as the demographics of your newsletter subscribers, to make sure you’re reaching your target audience.
  • Texting subscribers: Newer to digital marketing than email, ecommerce brands can reach consumers through SMS-based marketing. Texting subscribers refers to the number of customers on your text message contact list. To get started with your own text-based marketing, browse these SMS Shopify apps.
  • Subscriber growth rate: This tells you how quickly your subscriber list is growing. Pairing this KPI with the total number of subscribers will give you good insight into this channel.
  • Email open rate: This KPI tells you the percentage of subscribers that open your email. If you have a low email open rate, you could test new subject lines, or try cleaning your list for inactive or irrelevant subscribers.
  • Email click-through rate (CTR): While the open rate tells you the percentage of subscribers who open the email, the click-through rate tells you the percentage of those who actually clicked on a link after opening. This is arguably more important than the open rate because without clicks, you won’t drive any traffic to your site.
  • Unsubscribes: You can look at both the total number and the rate of unsubscriptions for your email list.
  • Chat sessions initiated: If you have live chat functionality on your ecommerce store, the number of chat sessions initiated tells you how many users engaged with the tool to speak to a virtual aide.
  • Social followers and fans: Whether you’re on Facebook, Instagram, Twitter, Pinterest, or Snapchat (or a combination of a few), the number of followers or fans you have is a useful KPI to gauge customer loyalty and brand awareness. Many of those social media networks also have tools that ecommerce businesses can use to learn more about their social followers.
  • Social media engagement: Social media engagement tells you how actively your followers and fans are interacting with your brand on social media.
  • Clicks: The total number of clicks a link gets. You could measure this KPI almost anywhere: on your website, social media, email, display ads, PPC, etc.
  • Average CTR: The average click-through rate tells you the percentage of users on a page (or asset) who click on a link.
  • Average position: The average position KPI tells you about your site’s search engine optimization (SEO) and paid search performance. This demonstrates where you are on search engine results pages. Most online retailers have the goal of being number one for their targeted keywords.
  • Pay-per-click (PPC) traffic volume: If you’re running PPC campaigns, this tells you how much traffic you’re successfully driving to your site.
  • Blog traffic: You can find this KPI by simply creating a filtered view in your analytics tool. It’s also helpful to compare blog traffic to overall site traffic.
  • Number and quality of product reviews: Product reviews are great for a number of reasons: They provide social proof, they can help with SEO, and they give you valuable feedback for your business. The quantity and content of product reviews are important KPIs to track for your ecommerce business.
  • Banner or display advertising CTRs: The CTRs for your banner and display ads will tell you the percentage of viewers who have clicked on the ad. This KPI will give you insight into your copy, imagery, and offer performance.
  • Affiliate performance rates: If you engage in affiliate marketing, this KPI will help you understand which channels are most successful.

What are key performance indicators for customer service?

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:

  • Customer satisfaction (CSAT) score: The CSAT KPI is typically measured by customer responses to a very common survey question: “How satisfied were you with your experience?” This is usually answered with a numbered scale.
  • Net promoter score (NPS): Your NPS KPI provides insight into your customer relationships and loyalty by telling you how likely customers are to recommend your brand to someone in their network.
  • Hit rate: Calculate your hit rate by taking the total number of sales of a single product and dividing it by the number of customers who have contacted your customer service team about said product.
  • Customer service email count: This is the number of emails your customer support team receives.
  • Customer service phone call count: Rather than email, this is how frequently your customer support team is reached via phone.
  • Customer service chat count: If you have live chat on your ecommerce site, you may have a customer service chat count.
  • First response time: First response time is the average amount of time it takes a customer to receive the first response to their query. Aim low!
  • Average resolution time: This is the amount of time it takes for a customer support issue to be resolved, starting from the point at which the customer reached out about the problem.
  • Active issues: The total number of active issues tells you how many queries are currently in progress.
  • Backlogs: Backlogs are when issues are getting backed up in your system. This could be caused by a number of factors.
  • Concern classification: Beyond the total number of customer support interactions, look at quantitative data around trends to see if you can be proactive and reduce customer support queries. You’ll classify the customer concerns which will help identify trends and your progress in solving issues.
  • Service escalation rate: The service escalation rate KPI tells you how many times a customer has asked a customer service representative to redirect them to a supervisor or other senior employee. You want to keep this number low.

What are key performance indicators for manufacturing?

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:

  • Cycle time: The cycle time manufacturing KPI tells you how long it takes to manufacture a single product from start to finish. Monitoring this KPI will give you insight into production efficiency.
  • Overall equipment effectiveness (OEE): The OEE KPI provides ecommerce businesses with insight into how well manufacturing equipment is performing.
  • Overall labor effectiveness (OLE): Just as you’ll want insight into your equipment, the OLE KPI will tell you how productive the staff operating the machines are.
  • Yield: Yield is a straightforward manufacturing KPI. It is the number of products you have manufactured. Consider analyzing the yield variance KPI in manufacturing, too, as that will tell you how much you deviate from your average.
  • First time yield (FTY) and first time through (FTT): FTY, also referred to as first pass yield, is a quality-based KPI. It tells you how wasteful your production processes are. To calculate FTY, divide the number of successfully manufactured units by the total number of units that started the process.
  • Number of non-compliance events or incidents: In manufacturing, there are several sets of regulations, licenses, and policies businesses must comply with. These are typically related to safety, working conditions, and quality. You’ll want to reduce this number to ensure you’re operating within the mandated guidelines.

What are key performance indicators for project management?

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:

  • Hours worked: The total hours worked tells you how much time a team put into a project. Project managers should also assess the variance in estimated vs. actual hours worked to better predict and resource future projects.
  • Budget: The budget indicates how much money you have allocated for the specific project. Project managers and ecommerce business owners will want to make sure that the budget is realistic; if you’re repeatedly over budget, some adjustments to your project planning need to be made.
  • Return on investment (ROI): The ROI KPI for project management tells you how much your efforts earned your business. The higher this number, the better. The ROI accounts for all of your expenses and earnings related to a project.
  • Cost variance: Just as it’s helpful to compare real vs. predicted timing and hours, you should examine the total cost against the predicted cost. This will help you understand where you need to reel it in and where you may want to invest more.
  • Cost performance index (CPI): The CPI for project management, like ROI, tells you how much your resource investment is worth. The CPI is calculated by dividing the earned value by the actual costs. If you come in under one, there’s room for improvement.

How do I create a KPI?

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.

Key performance indicator templates

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.

KPI examples:

  • Daily sales.
  • Conversion rate.
  • Site traffic.

GOAL 2: Increase conversion rate 2% in the next year.

KPI examples:

  • Conversion rate.
  • Shopping cart abandonment rate.
  • Competitive pricing.

GOAL 3: Grow site traffic 20% in the next year.

KPI examples:

  • Site traffic.
  • Traffic sources.
  • Promotional click-through rates.
  • Social shares.
  • Bounce rates.

GOAL 4: Reduce customer service calls by half in the next 6 months.

KPI examples:

  • Service call classification.
  • Pages visited immediately before call.

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.

Gary Silberg – Are CFOs ready for a changing business model?

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.

New realities

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.

Great potential

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?

Caitlin Stanway – Innovation Is Too Important To Be Left Siloed In The “Innovation” Department

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.

Techcrunch – This tiny agtech company thinks it has figured out something its better-capitalized rivals haven’t

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.

Chris McCann – Blockchain don’t scale (yet), aren’t really decentralized, distribute wealth poorly, lack killer apps, and run on a controlled Internet

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.”
https://twitter.com/naval/status/983016288195829770

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 3DPoWMPoWLLockedIn, 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.

*Even “Peak DApp” isn’t that large: by our rough estimates, CryptoKitties only had ~14,000 unique users and 130,000 transactions daily.

Compared to:

*As another comparison point, even the top 50 apps in the Google Play Store alone get on average 25,000+ downloads per day. (This is just downloads, not even counting “transactions”).

Further trends emerge on closer inspection of the transactions of DApps tracked here:

  • More than half of all DApps have zero transactions in the last week.
  • Of the DApps with any usage, the majority of usage is skewed to a small few (see graph).
  • Only 25% of DApps have more than 100 transactions in a week.

Takeaways

Where we are and what it means for protocols and the ecosystem:

After looking through the data, my personal takeaways are:

  1. We are orders of magnitudes away from consumer adoption of DApps. No killer app (outside of tokens and trading) have been created yet. Any seemingly “large” DApp (ex. IDEX, CryptoKitties, etc) has low usage overall.
  2. All of the top DApps are still very much about speculation of value. Decentralized exchanges, casino games, pyramid schemes, and even the current collectible games (I would argue) are all around speculation.
  3. What applications (aside from value transfer and speculation) really take advantage of the true unique properties of a blockchain (censorship resistance, immutability of data, etc) and unlock real adoption?
  4. For new protocol developers, instead of trying to convince existing DApp developers to build on your new platform — think hard about what DApps actually make sense on your protocol and how to help them have a chance at real adoption.
  5. We as an ecosystem need to build better tools and infrastructure for more widespread adoption of DApps. Metamask is an awesome tool, but it is still a difficult onboarding step for most normal users. ToshiStatus, and Cipherare all steps in the right direction and I’m really looking forward to the creation of other tools to simplify the user onboarding experience and improve general UI/UX for normal users.

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.

VentureBeat – How we plan to bridge the divide between Silicon Valley and the Midwest after the ‘Comeback Cities Tour

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.

Apparel Mag – When Bad Personalization Happens to Good Retailers

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.

Taking the personalization challenge

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.

Retailers that are getting it right

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.

A new way of thinking that’s actually an old way of thinking

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.

Crunchbase News : Q1 2018 Global Investment Report: Late-Stage Deal-Making Pushes Worldwide VC To New Heights

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.

  • Bullish Key Finding: In terms of total venture dollar and deal volume, Q1 2018 sets fresh post-Dot Com Bubble records for investment worldwide
  • Bearish Key Finding: Late-stage funding, in terms of both deal and dollar volume, appears to be growing faster than seed and early-stage deals, prompting questions about future deal pipeline issues.

Without further ado, let’s figure out what happened in the world of VC during the first quarter of the new year.

Global Funding Activity: A View From Cruising Altitude

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.

Pace of Dealmaking

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.

Projected VC Dollar Volume

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.

Most Active Lead Investors

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:

  • Lots of established venture funds like Sequoia Capital and New Enterprise Associates (NEA).
  • Some corporate venture investors like GV (formerly Google Ventures, which Crunchbase News profiled in mid-January), Tencent Holdings, Alibaba Group, and SoftBank.

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.

Stage-By-Stage Analysis of Q1 2018 VC Funding Trends

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.

Angel And Seed-Stage Deals

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:

  • Y Combinator’s seasonal batch size grew to 141 companies (135 of which can be found in this Crunchbase List), it’s easy to see how the program is head-and-shoulders above others in terms of deals struck.
  • Hiventures is a state-owned venture capital firm based in Hungary. It has €182.3 million under managementand offers a full raft of funding options, ranging from an accelerator program to growth equity, to Hungarian entrepreneurs.
  • Crunchbase News mentioned TEDCO in its coverage of the American South’s most active investors. TEDCO is a fund sponsored by the state of Maryland and is focused to funding new ventures that commercialize intellectual property developed in the state’s research universities.

Early-Stage Deals

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.

We find that primarily U.S.-focused venture firms are in the minority among the most active early-stage investors.

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:

  1. Matrix Partners China
  2. IDG Capital Partners
  3. Tencent Holdings
  4. Shunwei Capital
  5. GGV Capital

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.

Late-Stage Deals

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.)

Technology Growth Deals

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 Q1Q2, 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.

Conclusions From Q1 2018

On a global scale, the venture capital market in Q1 is a microcosm of a number of salient trends.

  • Big Rounds Reign – Rounds are getting larger at basically every stage. And, importantly, the largest rounds are growing larger, faster, across the board. Crunchbase News, independent analysts like Ian Hathaway, and VCs like Seth Levine from Foundry Group have covered this phenomenon in the past quarter. And barring something unexpected, there’s no external force in sight to slow this trend down.
  • Concentration Of Growth At The Top – In terms of both raw numbers and on a percentage basis, some of the fastest growth has been at the latter stages of the startup funding cycle. At a global scale, this hasn’t come with material declines in seed or early-stage deals, like it has in North America. But nonetheless, there’s a very real possibility that high-dollar deals with comparatively less-risky late-stage companies will draw more attention from investors over upstream opportunities.
  • US Funds Are Getting Larger – American venture capital firms have raises some very, very large funds in the past quarter. This may be a response to foreign competition from the likes of SoftBank and large corporate VCs in China, or it may just be an effort to anticipate capital requirements to maintain positions in their portfolio companies. If companies are staying private longer, pro rata investment opportunities follow on, and on, and on. Regardless, this results in more dry powder investors will have to marshal carefully, lest valuations go up in smoke due to unrealistic expectations.
  • There’s A Truly Staggering Amount Of Money Sloshing About – As a follow-up point to the above, the first quarter of 2018 highlighted a trend from the prior year: the private market grows in importance as a venue for fundraising. A recent  Wall Street Journal analysis suggests that, at least in the US, companies raised more through private capital investment than by raising in public markets. Between rising round sizes, more capital flowing into bigger funds, extended timelines for going public, 2018 will likely be no different if Q1’s momentum keeps up.

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.

Methodology

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.

Glossary of Funding Terms

  • Seed/Angel include financings that are classified as a seed or angel, including accelerator fundings and equity crowdfunding below $5 million.
  • Early stage venture include financings that are classified as a Series A or B, venture rounds without a designated series that are below $15M, and equity crowdfunding above $5 million.
  • Late stage venture include financings that are classified as a Series C+ and venture rounds greater than $15M.
  • Technology Growth include private equity investments in companies that have previously raised venture capital rounds.
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