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My product management toolkit (17): Assess market viability

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:

  1. 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?
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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).
  8. 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.
  9. 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:

  1. http://www.thisisproductmanagement.com/episodes/assessing-market-viability
  2. http://www.mindtheproduct.com/2013/05/poem-framework/
  3. http://smallbusiness.chron.com/determine-market-viability-product-service-40757.html
  4. https://en.wikipedia.org/wiki/Total_addressable_market
  5. https://blog.hubspot.com/marketing/inspiring-company-mission-statements
 

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Lending revisited: Bond Street

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:

  1. https://www.thebalance.com/what-does-apr-mean-315004
  2. https://bondstreet.com/blog/category/resources/
  3. http://www.forbes.com/sites/laurashin/2015/06/18/6616/
  4. http://www.peeriq.com/p2p-explosion-business-models-may-change-risks-still-need-managed/
  5. https://bondstreet.com/blog/an-introduction-to-small-business-financing/
  6. https://bondstreet.com/blog/a-beginners-guide-to-cloud-based-accounting-software-ii/
  7. https://www.fundera.com/blog/2016/06/01/application-process-works-bond-street
  8. https://angel.co/bond-street
  9. https://www.nav.com/blog/376-decoding-a-loan-offer-from-bondstreet-4788/
  10. https://www.fundera.com/blog/2016/06/01/application-process-works-bond-street

 

 

 

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Seamless payments – Learning more about Dwolla and their API platform

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:

  1. http://11fs.co.uk/podcasts/ep111-interviewed-innovators-really-changing-banking/
  2. https://www.bbva.com/en/news/disciplines/shamir-karkal-building-financial-future-bbvas-platform/
  3. http://finovate.com/open-api-shamir-karkal-to-head-bbvas-new-developer-platform/
  4. http://apievangelist.com/2015/09/03/dwolla-just-released-a-white-label-version-of-their-api-are-you-ready-for-the-wholesale-api-economy/
  5. http://www.ibm.com/support/knowledgecenter/SS9H2Y_7.5.0/com.ibm.dp.doc/oauth_threeleggedflow.html
  6. https://www.dwolla.com/updates/breaking-down-real-time-secure-authentication/
  7. http://help.dwolla.com/customer/portal/articles/1940212-bbva-compass-dwolla-faq?b_id=5440
  8. https://www.bbvacompass.com/compass/dwolla/
 
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Posted by on September 27, 2016 in Data, FinTech, Open Data, Technology

 

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Elliptic – Investigating Bitcoin transactions

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:

  1. http://dupress.com/articles/trends-blockchain-bitcoin-security-transparency/
  2. http://insidebitcoins.com/news/sabr-io-identifies-illegal-activity-on-blockchains-willing-to-work-with-law-enforcement/34307
  3. http://fsroundtable.org/cto-corner-what-is-a-blockchain-and-why-is-it-important/
  4. http://www.coindesk.com/network-analysts-view-block-chain/
  5. http://www.financemagnates.com/cryptocurrency/innovation/elliptic-launches-bitcoin-big-bang-anti-money-laundering-tool/
  6. https://bitcoinmagazine.com/articles/elliptic-launches-anti-money-laundering-visualization-tool-1435089559
  7. http://anonymity-in-bitcoin.blogspot.co.uk/2011/07/bitcoin-is-not-anonymous.html
  8. http://arxiv.org/abs/1107.4524
  9. https://www.elliptic.co/financial-institutions/
  10. http://www.dfs.ny.gov/legal/regulations/revised_vc_regulation.pdf

 

 

 

 
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Posted by on March 15, 2016 in Data, FinTech, Open Data

 

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My product management toolkit (3) – Goal setting

As part of my product management toolkit, I’ve thus far covered the creation of a product vision and the definition of a product strategy. The next thing to look at is goal setting: what are the business goals that a product strategy and or roadmap need to align with? I’ve learned the importance of goals to help define or assess a product strategy. I would even go as far as saying that if your product strategy, roadmap, backlog or – low and behold – your actual product don’t align with your business goals, you’re setting yourself up for failure.

Tool 3 – Goal Setting

What are goals? – This is what Wikipedia has to say about “goals”: “A goal is a desired result that a person or a system envisions, plans and commits to achieve; a personal or organizational desired end-point in some sort of assumed development. Many people endeavour to reach goals within a finite time by setting deadlines.” In other words, what is it the that we are looking to achieve, why and by when?

I typically look at goals from either of the following two angles: metrics or ‘objectives-key-results’ (‘OKR’s). From a metric perspective; what is the single metric that we’re looking to move the needle on, why and and by when? What does this impact look like and how can we measure it? For example, a key business goal can be to increase Customer Lifetime Value with 1% by June 2016. To be clear, a metric in itself isn’t a goal, the change that you want to see in metric is a goal.

From an OKR perspective, the idea is to outline a number of tangible results against a set, high level, objective. For example:

Objective: To enable sellers on our marketplace platform to make business and product decisions based on their sales and performance data generated from their activities on our platform.

Result 1: Our sellers making key business and product decisions before and throughout Christmas 2015

Result 2: Our sellers can look at their historic sales data so that they’ve got more sales context for their decision-making

Typically, there will be a set of overarching business goals that have been established and our responsibility as product managers is to link our product goals to these objectives, so that our product strategy is fully aligned with the business strategy.

okr-deck

Taken from: http://www.businessinsider.com/googles-ranking-system-okr-2014-1?IR=T

What goals aren’t – Goals aren’t a strategy or specific features. This might sound obvious, but often see cases where people do confuse things; setting goals without a strategy to achieve them or having a roadmap that doesn’t align with business goals.

In contrast, the point of a strategy or a roadmap is to highlight the ‘how’, the steps that need to be taken to achieve specific goals.

When to create goals? – It’s simple: if you join an organisation and hear “we don’t have business goals”, you know what to do! My point here is that a product strategy or roadmap that isn’t aligned with broader business goals, is just a loose collection of features or random solutions. The one thing to add is that some early stage startups tend to get really hung up on a whole range of specific goals or metrics. I’d always recommend to keep it simple and focus on a single goal or metric, understand what your (target) users’ needs are and how are they actually using your product.

Characteristics of good goals – I can imagine that a lot of you will have a heard of SMART goals:

  • S = specific, significant, stretching
  • M = measurable, meaningful, motivational
  • A = agreed upon, attainable, achievable, acceptable, action-oriented
  • R = realistic, relevant, reasonable, rewarding, results-oriented
  • T = time-based, time-bound, timely, tangible, trackable

 

SMART 2Example taken from: http://business.lovetoknow.com/wiki/Examples_of_SMART_Goals_and_Objectives

Main learning point: In my view, setting and understanding goals is just important as creating a strategy to achieve them. Before I delve into creating a product strategy or roadmap, I’ll always try to make sure I fully understand the business objectives and translate those into specific, measurable product goals.

 

Related links for further learning:

  1. https://medium.com/@joshelman/the-only-metric-that-matters-ab24a585b5ea#.z74gt29wa
  2. https://rework.withgoogle.com/guides/set-goals-with-okrs/steps/introduction/
  3. http://www.theokrguide.com/
  4. http://www.businessinsider.com/googles-ranking-system-okr-2014-1?IR=T
  5. http://www.producttalk.org/2014/01/how-to-set-goals-that-drive-product-success/
  6. http://www.kaushik.net/avinash/web-analytics-101-definitions-goals-metrics-kpis-dimensions-targets/
  7. https://marcabraham.wordpress.com/2013/05/03/book-review-lean-analytics/
  8. http://www.slideshare.net/abrahammarc1/product-roadmaps-tips-on-how-to-create-and-manage-roadmaps
  9. https://www.projectsmart.co.uk/smart-goals.php
 
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Posted by on January 28, 2016 in Data, Measuring, Product Management

 

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App review: Amazon Seller App

How do the likes of eBay, Amazon Handcraft, Notonthehighstreet, Rakuten and Etsy go about supporting the small businesses who sell products through their platforms? What are some of the typical data and customer insights that these sellers benefit from and why? Amazon recently launched its Seller App aiming to “help grow and manage your selling business on Amazon.” I had a quick look at the Amazon Seller App and these are my initial thoughts:

  1. How did the app come to my attention? – Since I’ve started working on online marketplaces I tend to keep an eye out for new technology and tools available to the sellers on these marketplaces.
  2. My quick summary of the app (before using it) – I expect a mobile app, which helps sellers to keep a close eye on their sales figures and manage their orders.
  3. How does the app explain itself in the first minute? – The first screen of the app asks me to select my marketplace (see Fig. 1 below). It doesn’t provide any further context but I presume that if you’re an active seller on Amazon you might not need any further info.
  4. Getting started, what’s the process like? – I’m not a seller on Amazon, but looking at some of the screenshots and the data provided, I can imagine that sellers will find it relatively easy to use the app (see Fig. 2 and 3 below). What I’m curious about though is the data syncing between devices, making sure your sales data is as ‘real-time’ as possible. I also couldn’t get a sense of whether (and how well) the Seller App integrates with Amazon’s Mobile Credit Card Reader.
  5. How does the app compare to similar apps?  The Amazon Seller App feels very similar to the Sell on Etsy app and SellerMobile. For example, the Etsy app enables sellers to manage their open orders and revisit completed ones on the go (see Fig. 4 below). The Etsy app also offers sellers the opportunity to check their Etsy shop and product views, but I’m not sure whether this analytics feature is included in Amazon’s Seller App.
  6. Did the app deliver on my expectations?  Yes, based on what I could tell from the screenshots and app description. The app looks the provide the key stats and insights that marketplace sellers tend to be interested in. What I could not tell from the screenshots is how the app facilitates sellers who sell on multiple marketplaces, for example in the UK and the US. I know this is a reality for lots of small businesses and it would be good to find out how the user interface of the Amazon Seller App accommodates for this use case.

Main learning point: The Amazon Seller App looks fit for purpose, providing sellers with key sales information that’s visual and easy to manage on the go. Analytics and multiple marketplaces are two areas where I’m not sure how (well) they are covered by this app. However, if you sell products through Amazon and want to keep a close eye on your orders and sales, then this app should give you the key information to help you manage your activities on Amazon’s marketplace.

Fig. 1 – Screenshot of opening screen on Amazon Seller App (iOS)

IMG_2737

 

Fig. 2 – Screenshots of Amazon Seller App (iOS) – Taken from: http://www.allmediatalks.com/amazon-in-launches-its-seller-app-in-india-amazon-online-selling/

Amazon Marketplace

Fig. 3 – Screenshot order detail view on Amazon Seller App (iOS) – Taken from: https://itunes.apple.com/us/app/amazon-seller/id794141485

Amazon product detail

 

Fig. 4 – Screenshot of the ‘Sell on Etsy’ App – Taken from: https://blog.etsy.com/news/2014/introducing-new-mobile-app-just-for-sellers/

Screen Shot 2015-07-05 at 15.22.33

 

Related links for further learning:

  1. http://tamebay.com/2015/06/amazon-marketplaces-eu-release-seller-app.html
  2. http://techcrunch.com/2014/03/06/amazon-debuts-an-official-mobile-app-for-amazon-sellers/
  3. http://www.retailwire.com/discussion/18003/its-good-to-be-an-amazon-marketplace-seller
  4. http://www.fiercewireless.com/europe/story/mobile-app-helps-amazon-sellers-shift-2b-items-2014/2015-01-05
  5. http://www.allmediatalks.com/amazon-in-launches-its-seller-app-in-india-amazon-online-selling/
  6. http://www.wired.com/2014/08/amazon-mobile-credit-card-reader/
  7. http://techcrunch.com/2014/10/23/etsy-moves-further-into-the-offline-world-with-launch-of-card-reader-for-in-person-payments/
  8. https://blog.etsy.com/news/2014/introducing-new-mobile-app-just-for-sellers/
 

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Gathering meaningful data during the user journey

Since I started looking into omni-channel metrics last year, I’ve been learning how to best gather meaningful data at each step of the user journey. I recently came across a great piece by Gary Angel titled “A Data Model for the User Journey”. In his article, Gary aims to address the multi-source nature of our data touchpoints, and the issues brought about by the differences in the level and type of detail data. He rightly points out that these differences in data make any kind of meaningful analysis of the user journey virtually impossible. Gary provides a number of useful steps to tackle this problem:

  1. Create a level of abstraction – Gary first suggestion is to get to a level of abstraction where each data touchpoint can be represented equally. One way of doing this is to apply Gary’s “2-tiered segmentation” model. In a 2-tiered segmentation model, the first tier is the visitor type. This is the traditional visitor segmentation based on persona or relationship. The second tier is a visit or unit-of-work based segmentation that is behavioural and is designed to capture the visit intent. It changes with each new touch. Gary summarises this two-tiered approach as follows: “Describing who somebody is (tier 1) and what they are trying to accomplish (tier 2).”
  2. Capture visit intent – One of the key things that I learned from Gary’s article is the significance of ‘visit intent’ with respect to creating a user-journey model. Visit intent offers an aggregated view of what a visit was about and how successful it was. Both the goal and the success of a visit are important items when analysing a user journey.
  3. 2-tiered segmentation and omni-channel – Gary points out how well his 2-tiered segmentation model lends itself to an omni-channel setup. The idea of 2-tiered segments can be used across any touchpoint, whether it’s online or offline. The intent-based segmentation can be applied relatively easily to calls, branch or store visits and social media posts. The model can also be applied – albeit less easily – to display advertising and email (see Fig. 1 below).
  4. Good starting point for journey analysis – When you look at the sample data structure as outlined in Fig. 1 below, with one data row per user touchpoint visit or unit of work, you can start doing interesting pieces of further analysis. For example, with this abstract data structure you can analyse multi-channel paths or enhance user journey personalisation.
  5. Combine visitor level data with user journey data – It sounds quite complex, but I like Gary’s suggestion to model in the abstract the key customer journeys. This can then be used to create a visitor level data structure in which the individual touchpoints are rolled up. Gary’s example below helps clarify how you can best map different data touchpoints to related stages in the user journey (see Fig. 2 below) .

Main learning point: The main thing that I’m taking away from Gary Angel’s great piece is the two segments to focus on when measuring the user journey: the visitor and their goals. The data structure suggested by Gary lends itself really well to an omni-channel user experience as it combines visitor and user journey data really well.

Fig. 1 – Sample data structure when applying the the 2-tiered segmentation to a user journey data model – Taken from: http://semphonic.blogs.com/semangel/2015/03/a-data-model-for-the-user-journey.html

  • TouchDateTime Start
  • TouchType (Channel)
  • TouchVisitorID
  • TouchVisitorSegmentCodes (Tier 1)
  • TouchVisitSegmentCode (Tier 2)
  • TouchVisitSuccessCode
  • TouchVisitSuccessValue
  • TouchTimeDuration
  • TouchPerson (Agent, Rep, Sales Associate, etc.)
  • TouchSource (Campaign)
  • TouchDetails

Fig. 2 – Example of modelling the acquisition journey for a big screen TV – Taken from: http://semphonic.blogs.com/semangel/2015/03/a-data-model-for-the-user-journey.html

  • Initial research to Category Definition (LED vs. LCD vs. Plasma – Basic Size Parameters)
  • Feature Narrowing (3D, Curved, etc.)
  • Brand Definition (Choosing Brands to Consider)
  • Comparison Shopping (Reviews and Product Detail Comparison)
  • Price Tracking (Searching for Deals)
  • Buying

With an abstract model like this in hand, you can map your touchpoint types to these stages in user journey and capture a user-journey at the visitor level in a data structure that looks something like this:

  • VisitorID
  • Journey Sub-structure
    • Journey Type (Acquisition)
    • Current Stage (Feature Narrowing)
    • Started Journey On (Initial Date)
    • Time in Current Stage (Elapsed)
    • Last Touch Channel in this Stage (Channel Type – e.g. Web)
    • Last Touch Success
    • Last Touch Value
    • Stage History Sub-Structure
      • Stage (e.g. Initial Research) Start
      • Stage Elapsed
      • Stage Success
      • Stage Started In Channel
      • Stage Completed in Channel
      • Channel Usage Sub-Structure
        • Web Channel Used for this Journey Recency
        • Web Channel Used for this Journey Frequency
        • Call Channel Used for this Journey Recency
        • Call Channel Used for this journey Frequency
        • Etc.
    • Stage Value
    • Etc.

This stage mapping structure is a really intuitive representation of a visitor’s journey. It’s powerful for personalisation, targeting and for statistical analysis of journey optimisation. With a structure like this, think how easy it would be to answer these sorts of questions:

  • Which channel does this visitor like to do [Initial Product Research] in?
  • How often do visitors do comparison shopping before brand narrowing?
  • When people have done brand narrowing, can they be re-interested in a brand later?
  • How long does [visitor type x] typically spend price shopping?

Related links for further learning:

  1. http://semphonic.blogs.com/semangel/2015/03/a-data-model-for-the-user-journey.html
  2. http://semphonic.blogs.com/semangel/2015/03/in-memory-data-structures-for-real-time-personalization.html
  3. http://semphonic.blogs.com/semangel/2011/04/semphonics-two-tiered-segmentation-segmentation-for-digital-analytics-done-right.html
  4. http://semphonic.blogs.com/semangel/2015/02/the-visit-is-dead-long-live-the-visit.html
  5. http://semphonic.blogs.com/semangel/2015/02/statistical-etl-and-big-data.html
 

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