Category Archives: Product Development

AngelCube15 – has your #startup got what it takes?

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Startup Victoria‘s first Lean Startup meeting of the year heralded the launch of AngelCube‘s 2015 accelerator program (#AC15), for which applications are now open. A good opportunity to check in with previous successful applicants, and find out if your startup is made of the right stuff.

Screen Shot 2015-02-25 at 10.03.58 amThe info evening was hosted by inspire9, and supported by PwC, and Nathan from AngelCube kicked off proceedings by giving a run down on the accelerator program, the application process, and the type of startups that are more likely to be accepted.

What does the program offer?

  • A 3-month intensive learning and development experience
  • $20k in funding (in return for 10% of the business)
  • Co-working facilities
  • Working with Lean methodology (focus on Product-Market fit)
  • Access to great mentors and advisers, and early-stage investors
  • Participation in a fundraising roadshow (including time in the US)

There is an application form via AngelList, and the closing date is May 10 (but the sooner you can submit the better). From the hundreds of applications, AngelCube puts together a shortlist of 20, of which no more than 10 will likely be accepted.

What is AngelCube looking for?

  • Globally scalable tech startups (think beyond Australia!)
  • In-house tech skills/resources (it’s not really a matching service)
  • Great teams (more than the ideas themselves)
  • Customer traction (ideally revenue-generating)
  • Consumer-oriented solutions (rather than B2B)

What has the experience been like for successful graduates?

Three alumni of previous AngelCube programs offered some personal insights, and then participated in a Q&A with the audience of 400:

Screen Shot 2015-02-25 at 10.02.34 amFirst up was Peter from Ediply, a service that matches students to the course or university of their choice. Given the growth in education and lifelong learning, and the increasing numbers of students (especially from Asia) looking to study overseas, the business seemed like a natural fit for AngelCube. However, it was still a relatively new or unknown sector in terms of end-user or independent services (rather than in-house marketing and enrollment efforts) – which sort of broke one of AngelCube’s rules for acceptance: no established market. Peter stressed that the main reasons for applying were the need to overcome some development barriers, and to get out of a “Melbourne mindset”.

 

Screen Shot 2015-02-25 at 10.03.01 amAsh from Tablo (“YouTube for books”) probably broke another AngelCube rule, in that he was a sole applicant (not part of a team) and he had limited tech resources. AngelCube made him work harder, think big, and keep going – and helped him to become a disruptive force in publishing, with customers in 130 countries collectively publishing 1 million words a day. He’s also closed a C-round of funding, and has some impressive investors on his share register.

Screen Shot 2015-02-25 at 10.03.28 amLastly, David from etaskr (“a private label elance”) had to quit a full-time job with one week’s notice once he got accepted into AngelCube. He even had to Google how to pitch. Plus he came into the program with a totally different idea, got slammed, failed to get customer traction, and ended up pivoting to an enterprise software solution (and broke another AngelCube rule in the process – no B2B, because of the longer sales cycle). Despite having to live on very little money for 6 months (less than $200 pw) the team persevered, and are now starting to get traction, including overseas markets like Holland. His final words were “risk is not something to fear, but to overcome”.

Q&A with the audience

Most of the questions were about the application process for AngelCube, and how it helped the successful startups, particularly with going global. In large part, this due to some great networks, access to high-profile connections (“we got to meet the first employees at Yammer!”) and links to some influential investors. There was also some discussion about how to secure your first customers (mainly via social marketing techniques), and the challenge of enterprise sales (“it sucks, because you need 100 different minds to all say ‘Yes!'”).

Finally, for more insights, please visit these links to previous posts about AngelCube and some of the successful applicants.)

Next week: Help! I need to get some perspective…

The 3L’s that kill #data projects

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The typical data project starts with the BA or systems architect asking: “fast, cheap or good – which one do you want?” But in my experience, no matter how much time you have, or how much money you are willing to throw at it, or what features you are willing to sacrifice, many initiatives are doomed to fail before you even start because of inherent obstacles – what I like to refer to as the 3L’s of data projects.

Image taken from "Computers at Work" © 1969 The Hamlyn Publishing Group

Image taken from “Computers at Work” © 1969 The Hamlyn Publishing Group

Reflecting on work I have been doing with various clients over the past few years, it seems to me that despite their commitment to invest in system upgrades, migrate their content to new delivery platforms and automate their data processing, they often come unstuck due to fundamental flaws in their existing operations:

Legacy

This is the most common challenge – overhauling legacy IT systems or outmoded data sets. Often, the incumbent system is still working fine (provided someone remembers how it was built, configured or programmed), and the data in and of itself is perfectly good (as long as it can be kept up-to-date). But the old applications won’t talk to the new ones (or even each other), or the data format is not suited to new business needs or customer requirements.

Legacy systems require the most time and money to replace or upgrade. A colleague who works in financial services was recently bemoaning the costs being quoted to rewrite part of a legacy application – it seemed an astronomical amount of money to write a single line of code…

As painful as it seems, there may be little alternative but to salvage what data you can, decommission the software and throw it out along with the old mainframe it was running on!

Latency

Many data projects (especially in financial services) focus on reducing systems latency to enhance high-frequency and algorithmic securities trading, data streaming, real-time content delivery, complex search and retrieval, and multiple simultaneous user logins. From a machine-to-machine data handover and transaction perspective, such projects can deliver spectacular results – with the goal being end-to-end straight through processing in real-time.

However, what often gets overlooked is the level of human intervention – from collecting, normalizing and entering the data, to the double- and triple-handling to transform, convert and manipulate individual records before the content goes into production. For example, when you contact a telco, utility or other service provider to update your account details, have you ever wondered why they tell you it will take several working days for these changes to take effect? Invariably, the system that captures your information in “real-time” needs to wait for someone to run an overnight batch upload or someone else to convert the data to the appropriate format or yet another person to run a verification check BEFORE the new information can be entered into the central database or repository.

Latency caused by inefficient data processing not only costs time, it can also introduce data errors caused by multiple handling. Better to reduce the number of hand-off stages, and focus on improving data quality via batch sampling, error rate reduction and “capture once, use many” workflows.

Which leads me the third element of the troika – data governance (or the lack thereof).

Laissez-faire

In an ideal world, organisations would have an overarching data governance model, which embraces formal management and operational functions including: data acquisition, capture, processing, maintenance and stewardship.

However, we often see that the lack of a common data governance model (or worse, a laissez-faire attitude that allows individual departments to do their own thing) means there is little co-operation between functions, additional costs arising from multiple handling and higher error rates, plus inefficiencies in getting the data to where it needs to be within the shortest time possible and within acceptable transaction costs.

Some examples of where even a simple data capture model would help include:

  • standardising data entry rules for basic information like names and addresses, telephone numbers and postal codes
  • consistent formatting for dates, prices, measurements and product codes
  • clear data structures for parent/child/sibling relationships and related parties
  • coherent tagging and taxonomies for field types, values and other attributes
  • streamlining processes for new record verification and de-duplication

From experience, autonomous business units often work against the idea of a common data model because of the way departmental IT budgets are handled (including the P&L treatment of and ROI assumptions used for managing data costs), or because every team thinks they have unique and special data needs which only they can address, or because of a misplaced sense of “ownership” over enterprise data (notwithstanding compliance firewalls and other regulatory requirements necessitating some data separation).

Conclusion

One way to think about major data projects (systems upgrades, database migration, data automation) is to approach it rather like a house renovation or extension: if the existing foundations are inadequate, or if the old infrastructure (pipes, wiring, drains, etc.) is antiquated, what would your architect or builder recommend (and how much would they quote) if you said you simply wanted to incorporate what was already there into the new project? Would your budget accommodate a major retrofit or complex re-build? And would you expect to live in the property while the work is being carried out?

Next week: AngelCube15 – has your #startup got what it takes?

What the *%@#? Dave McClure vents his spleen…

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The final Lean Start Melbourne event of 2014 was a Q&A with Dave McClure, tech entrepreneur, early-stage investor and founder of 500 Startups. It was certainly an ear-opening experience, as Dave laced his comments with enough expletives to fund a small start-up (if only the organisers had thought to provide a swear jar…).

But while he was vociferous in his refusal to answer questions like “what’s hot?”, or “where’s the next big thing?”, he did provide some refreshing insights on how founders and investors need to adjust their expectations on funding and returns.

The event was hosted by inspire9, with sponsorship from BlueChilli, General Assembly, and Loud & Clear. Adrian Stone from Investors’ Organisation was acknowledged for helping to bring Dave to Australia, and Amanda Gome was the MC for the evening.

Dave’s investing model is basically a numbers game – identify a large enough pool of startup opportunities, place smaller “bets” on each one, in the expectation that only 10% will succeed, and of those, only 10% will be really successful, and very, very few will reach an IPO – but the spread of successful bets should each return between 5x and 20x. Whereas, some investors still try to “bet on unicorns”, in the expectation of a 20x-25x exit every time. Such opportunities will be increasingly unlikely, as the technology costs of production continue to decrease, therefore startups don’t require the same level or type of funding.

Based on current trends, Dave sees huge potential in video commerce, mobile video, and anything that monetizes search – e.g., influencing followers via social media, and converting this traction to sales driven by personalised recommendations. He’s also big on Spanish- and Arabic-speaking markets, and “anything that arbitrages sexism and racism” – hence his interest in women and minority entrepreneurs.

Dave’s advice is pretty simple: get the product, market and revenue model right, and then build scale into the business as quickly as possible. As such, he hates people asking him his opinion on their startup ideas (“what do I know?”); instead, he emphasises the need to get paying (and profitable) end users plus building scale through marketing as the true proof of concept.

Throughout the evening, Dave talked a lot about unit economics – not just production costs, but the real cost of customer acquisition, and time to convert leads to sales. It was also interesting that unlike some speakers at previous Lean Startup events, he was not particularly negative towards startups developing enterprise solutions – rather, he prefers to segment clients based upon their decision-making and purchasing limits. So, he looks at revenues based on the respective number of end users, SME customers and enterprise clients, because of their different price points and procurement methods, as well as the different customer acquisition costs.

Finally, he encouraged potential startups to think of the “most boring and mindless” business activities or processes, and figure out ways to make them more interesting via apps that use gamification and social media tools.

 

Online Pillar 2: #Finance

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Along with the launch of the iPhone 6, Apple also announced a new mobile payments system. OK, so it’s not the first smart phone app that will help you manage (read: SPEND) your money, but it’s likely to be a market leader very quickly. After all, financial services mean big money in the interconnected online economy.

This week’s blog is #2 in my mini-series on the Three Pillars. Away from NFC solutions, digital wallets and virtual currencies, what else is helping to drive online innovation in financial services?

First, as with last week’s look at Health, it’s important to consider that despite being both a defined business vertical, and a highly regulated industry, the financial services sector is also vulnerable to market disintermediation, horizontal challengers and disruptive technologies.

Although most of us tend to stick with a single financial institution for the bulk of our banking products and services, we will likely use different providers across our credit cards, insurance policies, personal investments, retirement plans and foreign currency. The major banks don’t always do a good job of being a single provider of choice because they tend to manage their customers from a product perspective, and not always from the vantage point of a life-cycle of different needs.

Most retail banks have launched customer apps – mainly for account management and transaction purposes – and likewise, other platforms such as PayPal offer smart phone solutions. As with our other two pillars (Health and Education), Finance apps proliferate – e.g., calculators, account aggregators, budgeting tools and branded customer products from major financial institutions. But unlike Health apps, at least the Australian retail banks have to comply with consumer information requirements – although I suspect this is more a requirement of APRA than Apple. (Question: should apps offering stock market data, or enabling customers to plan investment strategies have to include product disclosure statements, or ensure customers have first completed a mandatory risk profile?)

Disruption in the banking and finance sector is coming from a variety of directions:

  • traditional retailers extending their existing credit card and insurance services into deposit accounts and investment products;
  • technology startups creating online payment systems;
  • trading platform Alibaba offering microfinance, trade finance services, deposit accounts and investment funds; and
  • online retailers and market places collecting a lot of useful behavioural data on customer creditworthiness and implied financial risk – for example, platforms like eBay and PayPal are using transactional data to assign customers a quasi-credit rating score or ranking.

Elsewhere, the financial services sector drives the use of data and technology to streamline stock trading and settlement – across algorithmic trading strategies, low-latency trading, straight-through securities processing, transaction and security data matching, market identifiers and real-time data analytics. The use of social media sentiment and stock #hashtags is also creating new trading strategies among savvier investors – one major Australian bank I spoke to recently boasted of having a Media Control Centre, where they can monitor client engagement, customer activity and brand profiles across the social web.

Crowdsourcing services, along with other platforms for raising capital and early-stage funding (plus new online listing and share trading platforms) threaten to disintermediate established stock exchanges, investment banks and stock brokers. Yet I see a huge opportunity for traditional bank and non-bank lenders to use these techniques for themselves. For example, banks love asset-backed and secured lending, as opposed to overdraft or cashflow lending. However, most startups don’t have physical assets such as plant or machinery, and young entrepreneurs are less likely to own property that can be put up as collateral.

So, what if banks see startup clients as a new channel to market? By investing part of their marketing costs or R&D budgets to underwrite new business ventures, they could help fund early stage ideas, and gather valuable information on customers and suppliers. Some banks are sort of moving in this startup direction – NAB and RBS, for example – but they have yet to demonstrate new business models or innovative product solutions that align with the lean startup and new entrepreneurial generation. I have observed many founders bemoan the lack of support from banks when it comes to offering merchant services that align with the needs of startups.

On another level, banks could do more to connect ideas with capital, customers with vendors, and buyers with suppliers – as the increasingly online and highly networked economy introduces new supply chains and innovative business models. (Hint to my bank manager: referrals and recommendations are often the most cost-effective way to acquire new customers – so, maybe we can help each other?)

Of course, where financial institutions really need to lift their game is in coming to grips with the shared economy. If consumers no longer see the need to buy or own assets outright (thereby reducing the reliance on mortgages, personal loans, hire purchase agreements and even credit cards….) what are the implications for financial services? Maybe banks need to take more interest in these “shared” asset eco-systems. For example, if I have taken out an investment loan to buy an apartment, which I plan to list on Airbnb, wouldn’t it be in the bank’s best interest to make sure I am getting as many bookings as possible – by helping to market my property to their other customers, or by making it really easy for people to book and pay for the accommodation via their smart phone banking app, or by enabling me to run online credit checks on prospective customers?

It’s nearly ten years since the term “distributed economy” was coined to encapsulate the new approaches to innovation, collaboration and sustainable resource allocation. Apart from microfinance and some developments in CSR and ethical investing, I’m not sure that financial institutions really grasped the opportunities presented by the distributed economy – sure, they were quick to outsource and offshore back office operations, but this was largely a cost-cutting exercise. Innovation in financial products mainly resulted in complex (and risky) derivative instruments – and ultimately, led to the GFC.

In the current low/slow/no growth economic climate, banks have to look at new ways of generating a return on their capital. They can’t just keep paying out higher shareholder dividends (not when banking regulations require them to increase their risk-weighted capital allocation); so they must engage with the new business models and the people behind them, and they must be willing to do so with a new mindset, not one built on staid financing models. Sure, they need to maintain prudent lending standards, and undertake relevant due diligence, but not at the risk of stifling innovation in the markets where their customers increasingly operate.

(For a related article on this topic, see here. Since I drafted this blog, PayPal has launched an SME loan platform, and it has just been announced that the ex-CEO of bond fund PIMCO has taken a key equity stake in an online Peer-to-Peer lending platform.)

Next week: Online Pillar 3: #Education