My product management toolkit (28): testing price sensitivity

April 17, 2018April 18, 2018 marcabrahamLeave a comment

Normally when I talk to other product managers about product pricing, I get slightly frightened looks in return. “Does that mean I need to set the price!?” or “am I now responsible for the commercial side of things too!?” are just some of the questions I’ve had thrown at me in the past.

“No” is the answer. I strongly believe that as product managers we run the risk of being all things to all people — see my previous post about “Product Janitors” — and I therefore believe that product people shouldn’t set prices. However, I do believe it’s critical for product people to think about pricing right from the beginning:

Do people want the product?
Why do they want it?
How much are they willing pay for it?

Answers to these questions will not only affect what product is built and how it’s built, but also how it will be launched and positioned within the market. I’ve made the mistake before of not getting involved in pricing at all or too late. As a result, I felt that I was playing catchup to fully understand the product’s value proposition and customers’ appetite for it.

Fortunately, there are two tools I’ve come across which I’ve found very helpful in terms of my comprehending the value a product is looking to achieve — both from a business and customer perspective: the Van Westendorp Pricing Sensitivity Meter and the Conjoint Analysis respectively.

The Van Westendorp Pricing Sensitivity Meter has helped me to learn about the kinds of pricing-relating customers to ask (target) customers:

At what price would you consider the product to be so expensive that you would not consider buying it? (Too expensive)
At what price would you consider the product to be priced so low that you would feel the quality couldn’t be very good? (Too cheap)
At what price would you consider the product starting to get expensive, so that it is not out of the question, but you would have to give some thought to buying it? (Expensive/High Side)
At what price would you consider the product to be a bargain — a great buy for the money? (Cheap/Good Value)

The aforementioned Van Westendorp questions are a good example of a so-called “direct pricing technique”, where the pricing research is underpinned by the assumption that people have a basic understanding of what a product is worth. In essence, this line of questioning comes down to asking “how much would you pay for this (product or service)?” Whilst this isn’t necessarily the best question to ask in a customer interview, it’s a nice and direct way to learn about how customers feel about pricing.

Example customer responses to the Van Westdorp questions — Taken from: http://www.5circles.com/van-westendorp-pricing-the-price-sensitivity-meter/

The insights from applying these direct questions will help in better understanding price points. The Van Westendorp method identifies four different price definitions:

Point of marginal cheapness (‘PMC’) — At the point of marginal cheapness, more sales volume would be lost than gained due to customers perceiving the product as a bargain and doubting its quality.

Point of marginal expensiveness (‘PME’) — This is a price point above which the product is deemed too expensive for the perceived value customers get from it.

Optimum price point (‘OPP’) — The price point at which the number of potential customers who view the product as either too expensive or too cheap is at a minimum. At this point, the number of persons who would possibly consider purchasing the product is at a maximum.

Indifference price point (‘IPP’) —Point at which the same percentage of customers feel that the product is getting too expensive as those who feel it is at a bargain price. This is the point at which most customers are indifferent to the price of a product.

Range of acceptable pricing (‘RAI’) — This range sits between the aforementioned points of marginal cheapness and marginal expensiveness. In other words, consumers are considered likely to pay a price within this range.

*Van Westendorp price sensitivity meter (example) — Taken from: https://www.qualtrics.com/uk/market-research/pricing-research/*

In addition to the Van Westendorp Price Sensitivity Meter, I’ve also used Conjoint Analysis to understand more about pricing. Unlike the Van Westendorp approach, the conjoint analysis is an indirect pricing technique which means that price is combined with other attributes such as size or brand. Consumers’ price sensitivity is then derived from the results of the analysis.

*Sample conjoint analysis question — Taken from: https://www.questionpro.com/survey-templates/conjoint-analysis-retirement-housing/*

When designing a conjoint analysis study, the first step is take a product and break it down into its individual parts. For example, we could take a car and create combinations of its different parts to learn about combinations that customers prefer. For example:

Which of these cars would you prefer?

Option: 1

Brand: Volvo

Seats: 5

Price: £65,000

Option: 2

Brand: SsangYyong

Seats: 5

Price: £20,000

Option: 3

Brand: Toyota

Seats: 7

Price: £45,000

This is an overly simplified and totally fictitious example, but hopefully gives you a better idea of how a conjoint analysis takes into account multiple factors and will give you insight into how much consumers are willing to pay for a certain combination of features.

Main learning point: I personally don’t expect product managers to set prices for their products or design price research. However, I do think we as product managers benefits from a better understanding of the pricing model for our products and a better understanding of what constitutes ‘value for money’ for our customers. The Van Westendorp Price Sensitivity Meter and the Conjoint Analysis are just two ways of testing price sensitivity, but are in my view to good places to get started if you wish to get a better handle on pricing.

Related links for further learning:

My product management toolkit (25): understanding the “unit economics” of your product

December 28, 2017 marcabrahamLeave a comment

As a product manager it’s important to understand the unit economics of your product, irrespective of whether you’re managing a physical or a digital product. Unit economics are the direct revenues and costs related to a specific business model expressed on a per unit basis. These revenues and costs are the levers that impact the overall financial success of a product. In my view there are a number of reasons why I feel it’s important for product managers to have a good grasp of the unit economics of your product:

Helps quantify the value of what we do – Ultimately, product success can be measured in hard metrics such as revenue and profit. Even in cases where our products don’t directly attribute to revenue, they will at least have an impact on operational cost.
Customer Value = Business Value – In an ideal world, there’s a perfect equilibrium between customer value and business value. If the customer is happy with your product, buys and uses it, this should result in tangible business value.
P&L accountability for product people (1) – Perhaps it’s to do with the fact that product management still is a relatively young discipline, but I’m nevertheless surprised by the limited number of pr0duct people I know who’ve got full P&L responsibility. I believe that having ownership over the profit & loss account helps product decision making and and accountability, not just for product managers but for the product teams that we’re part of.
P&L accountability for product people (2) – Understandably, this can be a scary prospect and might impact the ways in which we manage products. However, owning the P&L will (1) make product managers fully accountable for product performance (2) provide clarity and accountability for product decisions, (3) help investments in the product and product marketing and (4) steep product management in data, moving to a more data informed approach to product management.
Assessing opportunities based on economics – Let’s move away from assessing new business or product opportunities purely based on “gut feel”. I appreciate that at some point we have to take a leap, especially with new products or problems that haven’t been solved before. At the same time, I do believe it’s critical to use data to help inform your opportunity assessments. Tools like Ash Maurya’s Lean Canvas help to think through and communicate the economics of certain opportunities (see Fig. 1 below). In the “cost structure” part of the lean canvas, for example, you can outline the expected acquisition or distribution cost of a new product.
Speaking the same language – It definitely helps the collaboration with stakeholders, the board and investors if you can speak about the unit economics of your product. I know from experience that being able to talk sensibly about unit economics and gross profit, really helps the conversation.

Now that we’ve established the importance of understanding unit economics, let’s look at some of the key components of unit economics in more detail:

Profit margin per unit = (sales price) – (cost of goods sold + manufacture cost + packaging cost + postage cost + sales cost)

Naturally the exact cost per unit will be dependent on things such as (1) product type (2) point of sale (3) delivery fees and (4) any other ‘cost inputs’.

In a digital context, the user is often the unit. For example, the Lifetime Value (‘LTV’) and Customer Acquisition Cost (‘CAC’) are core metrics for most direct to consumer (B2C) digital products and services. I learned from David Skok and Dave Kellogg about the importance of the ‘CAC to LTV’ ratio.

Granted, Skok and Kellogg apply this ratio to SaaS, but I believe customer acquisition cost (‘CAC’) and customer lifetime value (‘LTV’) are core metrics when you treat the user as a unit; you’ve got a sustainable business model if LTV (significantly) exceeds CAC. In an ideal world, for every £1 it costs to acquire a customer you want to get £3 back in terms of customer lifetime value. Consequently, the LTV:CAC ratio = 3:1.

I’ve seen companies start with high CAC in order to build scale and then lower the CAC as the business matures and relies more on word of mouth as well as higher LTV. Also, companies like Salesforce are well known for carefully designing additions (“editions”) to increase customer lifetime value (see Fig. 2 below).

Netflix are another good example in this respect, with their long term LTV view of their customers. Netflix take into account the Netflix subscription model and a viable replacement for another subscription model in cable. The average LTV of Netflix customers is 25 months. As a result, Netflix are happy to initially ‘lose’ money on acquiring customers, through a 1-month free trial, as these costs costs will be recouped very soon after acquiring the customer.

Main learning point: You don’t need to be a financial expert to understand the unit economics of your products. Just knowing what the ‘levers’ are that impact your product, will put you in good stead when it comes to making product decisions and collaborating with stakeholders.

Fig. 1 – Lean Canvas template by Ash Maurya – Taken from: https://blog.leanstack.com/

Fig. 2 – Pricing and functionality overview for Salesforce’s New Sales Cloud Lightning Editions:

Related links for further learning:

Book review: “Designing with Data”

November 10, 2017November 11, 2017 marcabrahamLeave a comment

I’d been looking forward to Rochelle King writing her book about using data to inform designs (I wrote about using data to inform product decisions a few years ago, which post followed a great conversation with Rochelle).

Earlier this year, Rochelle published “Designing with Data: Improving the User Experience with A/B Testing”, together with Elizabeth F. Churchill and Caitlin Tan. The main theme of “Designing with Data” the book is the authors’ belief that data capture, management, and analysis is the best way to bridge between design, user experience, and business relevance:

Data aware — In the book, King, Churchill and Tan distinguish between three different ways to think about data: data driven; data informed and data aware (see Fig. 1 below). The third way listed, being ‘data aware’, is introduced by the authors: “In a data-aware mindset, you are aware of the fact that there are many types of data to answer many questions.” If you are aware there are many kinds of problem solving to answer your bigger goals, then you are also aware of all the different kinds of data that might be available to you.
How much data to collect? — The authors make an important distinction between “small sample research” and “large sample research”. Small sample research tends to be good for identifying usability problems, because “you don’t need to quantify exactly how many in the population will share that confusion to know it’s a problem with your design.” It reminded me of Jakob Nielsen’s point about how the best results come from testing with no more than 5 five people. In contrast, collecting data from a large group of participants, i.e. large sample research, can give you more precise quantity and frequency information: how many people people feel a certain way, what percentage of users will take this action, etc. A/B tests are one way of collecting data at scale, with the data being “statistically significant” and not just anecdotal. Statistical significance is the likelihood that the difference in conversion rates between a given variation and the baseline is not due to random chance.
Running A/B tests: online experiments — The book does a great job of explaining what is required to successfully running A/B tests online, providing tips on how to sample users online and key metrics to measure (Fig. 2) .
Minimum Detectable Effect — There’s an important distinction between statistical significance — which measure whether there’s a difference — and “effect”, which quantifies how big that difference is. The book explains about determining “Minimum Detectable Effect” when planning online A/B tests. The Minimum Detectable Effect is the minimum effect we want to observe between our test condition and control condition in order to call the A/B test a success. It can be positive or negative but you want to see a clear difference in order to be able to call the test a success or a failure.
Know what you need to learn — The book covers hypotheses as an important way to figure out what it is that you want to learn through the A/B test, and to identify what success will look like. In addition, you can look at learnings beyond the outcomes of your A/B test (see Fig. 3 below).
Experimentation framework — For me, the most useful section of the book was Chapter 3, in which the authors introduce an experimentation framework that helps planning your A/B test in a more structured fashion (see Fig. 4 below). They describe three main phases — Definition, Execution and Analysis — which feed into the experimentation framework. The ‘Definition’ phase covers the definition of a goal, articulation of a problem / opportunity and the drafting of a testable hypothesis. The ‘Execution’ phase is all about designing and building the A/B test, “designing to learn” in other words. In the final ‘Analysis’ phase you’re getting answers from your experiments. These results can be either “positive” and expected or “negative” and unexpected (see Fig. 5–6 below).

Main learning point: “Designing with Data” made me realise again how much thinking and designing needs to happen before running a successful online A/B test. “Successful” in this context means achieving clear learning outcomes. The book provides a comprehensive overview of the key considerations to take into account in order to optimise your learning.

Fig. 1 — Three ways to think about data — Taken from: Rochelle King, Elizabeth F. Churchill and Caitlin Tan — Designing with Data. O’Reilly 2017, pp. 3–9

Data driven — With a purely data driven approach, it’s data that determine the fate of a product; based solely on data outcomes businesses can optimise continuously for the biggest impact on their key metric. You can be data driven if you’ve done the work of knowing exactly what your goal is, and you have a very precise and unambiguous question that you want to understand.
Data informed — With a data informed approach, you weigh up data alongside a variety of other variables such as strategic considerations, user experience, intuition, resources, regulation and competition. So adopting a data-informed perspective means that you may not be as targeted and directed in what you’re trying to understand. Instead, what you’re trying to do is inform the way you think about the problem and the problem space.
Data aware — In a data-aware mindset, you are aware of the fact that there are many types of data to answer many questions. If you are aware there are many kinds of problem solving to answer your bigger goals, then you are also aware of all the different kinds of data that might be available to you.

Fig. 2 — Generating a representative sample — Taken from: Rochelle King, Elizabeth F. Churchill and Caitlin Tan — Designing with Data. O’Reilly 2017, pp. 45–53

Cohorts and segments — A cohort is a group of users who have a shared experience. Alternatively, you can also segment your user base into different groups based on more stable characteristics such as demographic factors (e.g. gender, age, country of residence) or you may want them by their behaviour (e.g. new user, power user).
New users versus existing users — Data can help you learn more about both your existing understand prospective future users, and determining whether you want to sample from new or existing users is an important consideration in A/B testing. Existing users are people who have prior experience with your product or service. Because of this, they come into the experience with a preconceived notion of how your product or service works. Thus, it’s important to be careful about whether your test is with new or existing users, as these learned habits and behaviours about how your product used to be in the past can bias in your A/B test.

Fig. 3 — Know what you want to learn — Taken from: Rochelle King, Elizabeth F. Churchill and Caitlin Tan — Designing with Data. O’Reilly 2017, p. 67

If you fail, what did you learn that you will apply to future designs?
If you succeed, what did you learn that you will apply to future designs?
How much work are you willing to put into your testing in order to get this learning?

Fig. 4 — Experimentation framework — Taken from: Rochelle King, Elizabeth F. Churchill and Caitlin Tan — Designing with Data. O’Reilly 2017, pp. 83–85

Goal — First you define the goal that you want to achieve; usually this is something that is directly tied to the success of your business. Note that you might also articulate this goal as an ideal user experience that you want to provide. This is often the case that you believe that delivering that ideal experience will ultimately lead to business success.
Problem/opportunity area — You’ll then identify an area of focus for achieving that goal, either by addressing a problem that you want to solve for your users or by finding an opportunity area to offer your users something that didn’t exist before or is a new way of satisfying their needs.
Hypothesis — After that, you’ll create a hypothesis statement which is a structured way of describing the belief about your users and product that you want to test. You may pursue one hypothesis or many concurrently.
Test — Next, you’ll create your test by designing the actual experience that represents your idea. You’ll run your test by launching the experience to a subset of your users.
Results — Finally, you’ll end by getting the reaction to your test from your users and doing analysis on the results that you get. You’ll take these results and make decisions about what to do next.

Fig. 5 — Expected (“positive”) results — Taken from: Rochelle King, Elizabeth F. Churchill and Caitlin Tan — Designing with Data. O’Reilly 2017, pp. 227–228

How large of an effect will your changes have on users? Will this new experience require any new training or support? Will the new experience slow down the workflow for anyone who has become accustomed to how your current experience is?
How much work will it take to maintain?
Did you take any “shortcuts” in the process of running the test that you need to go back and address before your roll it out to a larger audience (e.g. edge cases or fine-tuning details)?
Are you planning on doing additional testing and if so, what is the time frame you’ve established for that? If you have other large changes that are planned for the future, then you may not want to roll your first positive test out to users right away.

Fig. 6 — Unexpected and undesirable (“negative”) results — Taken from: Rochelle King, Elizabeth F. Churchill and Caitlin Tan — Designing with Data. O’Reilly 2017, pp. 228–231

Are they using the feature the way you think they do?
Do they care about different things than you think they do?
Are you focusing on something that only appeals to a small segment of the base but not the majority?

Related links for further learning:

Design with Data.jpg

My product management toolkit (17): Assess market viability

January 21, 2017 marcabraham2 Comments

Whether you’re a product manager or are in a commercial or strategic role, I’m sure you’ll have to assess market viability at some point in your career. For that reason, I wrote previously about assessing markets, suggesting tools that you can use to decide on whether to enter a market or not.

A few weeks ago, I listened to a podcast interview in which Christophe Gillet, VP of Product Management at Vimeo, gave some great pointers on how to best assess market viability. Christophe shared his thoughts on things to explore when considering market viability. I’ve added my sample questions related to some of the points that Christophe made:

Is there a market? – This should be the first validation in my opinion; is there a demand for my product or service? Which market void will our product help to fill and why? What are the characteristics of my target market?
Is there viability within that market? – Once you’ve established that there’s a potential market for your product, this doesn’t automatically mean that the market is viable. For example, regulatory constraints can make it hard to launch or properly establish your product in a market.
Total addressable market – The total addressable market – or total available market – is all about revenue opportunity available for a particular product or service (see Fig. 1 below). A way to work out the total addressable market is to first define total market space and then look at percentage of the market which has already been served.
Problem to solve – Similar to some of the questions to ask as part of point 1. above, it’s important to validate early and often whether there’s an actual problem that your product or service is solving.
Understand prior failures (by competitors) – I’ve found that looking at previous competitor attempts can be an easy thing to overlook. However, understanding who already tried to conquer your market of choice and whether they’ve been successful can help you avoid some pitfalls that others encountered before you.
Talk to individual users – I feel this is almost a given if you’re looking to validate whether there’s a market and a problem to solve (see points 1. and 4. above). Make sure that you sense check your market and problem assumptions with your target customers.
Strong mission statement and objectives of what you’re looking to achieve – In my experience, having a clear mission statement helps to articulate and communicate what it is that you’re looking to achieve and why. These mission statements are typically quite aspirational but should offer a good insight into your aspirations for a particular market (see the example of outdoor clothing company Patagonia in Fig. 2 below).
Business goals – Having clear, measurable objectives in place to achieve in relation to a new market that you’re considering is absolutely critical. In my view, there’s nothing worse than looking at new markets without a clear definition of what market success looks like and why.
How to get people to use your product – I really liked how Christophe spoke about the need to think about a promotion and an adoption strategy. Too often, I encounter a ‘build it and they will come’ kind of mentality which I believe can be deadly if you’re looking to enter new markets. Having a clear go-to-market strategy is almost just as important as developing a great product or service. What’s the point of an awesome product that no one knows about or doesn’t know where to get!?

Main learning point: Listening to the interview with Christophe Gillet reinforced for me the importance of being able to assess market viability. Being able to ask and explore some critical questions when considering new markets will help avoid failed launches or at least gain a shared understanding of what market success will look like.

Fig. 1 – Total available market – Taken from: https://en.wikipedia.org/wiki/Total_addressable_market

1000px-tam-sam-market

Fig. 2 – Patagonia’s mission statement – Taken from: http://www.patagonia.com/company-info.html

screen-shot-2017-01-20-at-07-21-29

Related links for further learning:

Lending revisited: Bond Street

October 11, 2016 marcabrahamLeave a comment

Bond Street lends to small businesses that might typically struggle to get a loan from traditional banks. In a recent talk on a MIT Fintech course that I was doing, David Haber – Bond Street’s CEO/Founder – mentioned how Bond Street saw a clear niche in the market for small business loans and acted on it. Haber encountered a problem that seemed pretty common for early stage, online small businesses: banks or other financial services offering small loans for short durations at high rates. To resolve this problem, Bond Street offers loans range between $50k-$500k, for as long as 1-3 years and with rates starting at 6% (see Fig. 1 below).

Fig. 1 – Loan size, rate and terms comparison between Bond Street and other small business lenders – Taken from: https://bondstreet.com/

screen-shot-2016-10-11-at-07-42-33

Fig. 2 – Overview of Bond Street positioning – Taken from: https://bondstreet.com/blog/an-introduction-to-small-business-financing/

bond-street-v2

In the MIT talk, Haber mentioned that OnDeck – a direct competitor of Bond Street – offers small business loans for an average amount of $35k, 10 months’ duration and charges of 40% Annual Percentage Rate (‘APR’). Bond Street competes on rate and speed, but as Haber explained, the business is very focused on “offering more value beyond the economics of a loan, since capital is essentially a commodity.”

Haber then explained that technology allows Bond Street to not just innovate on the loan transaction itself, but to provide a great customer experience on either side of the transaction. For example, by offering a borrower data about similar size businesses, the borrower can then make a better informed decision about taking up a loan.

Fig. 3 – Screenshot of Bond Street online loan application form – Taken from: https://www.nav.com/blog/376-decoding-a-loan-offer-from-bondstreet-4788/

screen-shot-2016-10-11-at-07-56-36

Haber mentioned one other thing which really resonated with me: “building an ecosystem around your business.” By, for example, leveraging data on an entrepreneur across a network of (similar) entrepreneurs, Bond Street and others can really help people grow their businesses. This doesn’t mean committing data violations, but using data to build an ongoing relationship with one’s customers, and being able to warn them about potential risks or suggest new market opportunities.

A great example is how easy Bond Street makes it for its customers to link to their accounting packages (see Fig. 4 below). I see this is a simple but good example of creating an ecosystem where data is combined in such a way that people and business can derive tangible benefits from it. Through linking to your accounting package as part of the loan application process, businesses save a lot of precious time and effort, since they no longer have to manually input all kinds of financial data.

Fig. 4 – Screenshot of Bond Street’s functionality which links to one’s accounting software – Taken from: https://www.nav.com/blog/376-decoding-a-loan-offer-from-bondstreet-4788/

bondstreet-accounting-link

Main learning point: Even though lending isn’t a new proposition, I really like what Bond Street are doing when it comes to offering loans to small businesses. It has carved out a specific market niche – small, early stage businesses – that it targets with a compelling proposition and an intuitive customer experience to match.

Related links for further learning:

Seamless payments – Learning more about Dwolla and their API platform

September 27, 2016 marcabrahamLeave a comment

I recently heard Shamir Karkal, Head of Open APIs at BBVA, talking about open platforms and I was intrigued. In the podcast episode Shamir talked about the power of APIs, but at the same time stressed the importance of having a strong platform that these API end points can hook into.

Shamir talked about building a product with a platform attached. Instead of just building a set of APIs, we should treat APIs as a way in for customers, developers and third parties to hook into the capabilities of our business. For example, hooking into all the things that banks typically tend to do well: compliance, risk management and customer support.

My ears really perked up as soon as Shamir started talking about Dwolla. Dwolla is US based peer-to-peer payments company, whose mission it is to facilitate “Simple payments. No transaction fees.” Dwolla is powered by APIs, making it easy for US users to link their Dwolla account to a US bank account or credit union account to move money. Setting up a Dwolla account is free, and there’s no per transaction fee. Users can collect payment on an invoice, send a one-time or recurring payment, or payout a large number of people at once. Dwolla also offers this a white label solution (see Fig. 1 below).

dwolla-white-label-api

Fig. 1 – Dwolla’s white label version of their API – Taken from: http://apievangelist.com/2015/09/03/dwolla-just-released-a-white-label-version-of-their-api-are-you-ready-for-the-wholesale-api-economy/

In essence, what Dwolla does is enabling real-time payments between Dwolla accounts and another bank account that users want to send money to. Dwolla are integrated with banks such as BBVA, having Dwolla APIs ‘talk’ to the bank’s APIs. Dwolla has created some form of a protocol in the form of FiSync which aims to make it more secure for users to transmit information between accounts. FiSync enables the use of secure authentication and tokenisation in the comms between Dwolla and accounts like those of BBVA Compass. This way, BBVA Compass account holders don’t have to share their account info with Dwolla (see Fig, 2 below).

screen-shot-2016-09-27-at-20-35-26

Fig. 2 – Workflow of a connecting a FiSync-enabled bank account to Dwolla – Taken from: https://www.dwolla.com/updates/breaking-down-real-time-secure-authentication/

Main learning point: I love how Dwolla’s proposition is almost entirely API based, making it easy for its users to transfer money to bank accounts and credit union accounts. Dwolla definitely feels more seamless, secure and cost-efficient compared to the way in which users traditionally transfer money from one account to another.

Related links for further learning:

Elliptic – Investigating Bitcoin transactions

March 15, 2016March 20, 2016 marcabrahamLeave a comment

The other day I wrote about blockchains, looking into this new technology. I then came across a company called Elliptic that specialises in “identifying illicit activity on the Bitcoin blockchain.” It made me realise how blockchains can be used for all kinds of illegal activity. Also, I can now see a clear link between digital identity management and blockchains.

Transparency is a key aspect of blockchains and, going back to the original purpose of blockchains, it helps Bitcoins to complete financial transactions through the chain. Naturally, there are lots of users who don’t like the transparency aspect and use anonymizing services to cover tracks when doing transactions through the blockchain.

I read an interesting article about how anonymous users and their transactions can still be identified, tracking users’ activity both in real-time and historically. There are a number of centralised services within the blockchain e.g. wallets and exchanges which have access to user and transaction info. Also, by doing an activity or user network analysis, one can find out more about the type of transaction and the identity of the users involved (see example in Fig. 1 below).

Fig. 1 – An example of a sub-network between the thief, the victim and three other vertices – Taken from: http://anonymity-in-bitcoin.blogspot.co.uk/2011/07/bitcoin-is-not-anonymous.html

Network analysis

The majority of Elliptic’s clients seem to be either law enforcement agencies or financial institutions. For example, one of the uses cases that Elliptic caters for is making sure that the bitcoins a client acquires aren’t derived from the proceeds of criminal activity. Elliptic says that in the past year it has been able to map the entire 35 GB transaction history of the bitcoin blockchain.

Interestingly, Elliptic has created a visualisation technology to provide a number of anti-money laundering (‘AML’) services. If you look at the sample visualisation below (see Fig. 2), you can can see how Elliptic can visualise ‘known’ entities e.g. exchanges whilst naming illicit marketplaces and money laundering services.

Through an API, Elliptic’s clients will thus get real-time alerts about any bitcoin payments linked to known thefts, illicit marketplaces and other criminal activity, which are all identified by name. As a result, financial institutions can effectively do real time compliance, adhering to compliance regulation as transactions take place through the blockchain.

Fig. 2 – “The Bitcoin Big Bang” visualisation by Elliptic – Taken from: https://bitcoinmagazine.com/articles/elliptic-launches-anti-money-laundering-visualization-tool-1435089559

elliptic-launches-anti-money-laundering-visualization-tool

Main learning point: As I mentioned in my previous blog post, the world of blockchains is a new one to me. Learning about how people can abuse this new technology is therefore just as new to me. Learning about how Elliptic helps financial institutions and law enforcement agencies to identify illicit blockchain activity has given me a first understanding of how one can work through to blockchain networks to figure out its users and transactions.

Related links for further learning:

As I learn …

This site is about everything digital, giving an update on new things as I learn

Category: Data

My product management toolkit (28): testing price sensitivity

My product management toolkit (25): understanding the “unit economics” of your product

Book review: “Designing with Data”

My product management toolkit (17): Assess market viability

Lending revisited: Bond Street

Seamless payments – Learning more about Dwolla and their API platform

Elliptic – Investigating Bitcoin transactions