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A CRM plus VRM Venn for 2013

January 21st, 2013 Comments off

I spent most of 2012 with head down consulting on a couple of big ‘CRM plus VRM’ propositions – more on those when they show up in the wild. So i’ve been pondering on where to focus on in 2013.

I put together this Venn diagram to help think through which areas of our work might reach tipping point in 2013. It looks at the intersection between our three areas of interest – CRM, VRM and Personal Data Services.

Hot in 2013 Venn

The key points I took out from sketching this out were:

1) As an overall theme, I think 2013 will be the year that large organisations with lots of individuals as customers will wake up to the option to build rich, data-driven propositions around volunteered personal information. Critically, this data will being plumbed directly into existing operational CRM systems from ‘something outside’ (whether that be a personal data service, personal cloud, ‘midata’ or some variant thereof) ; Yes CRM plus VRM for real, and built for large scale. The UK midata project, and equivalents elsewhere, will help in that, although more as the spur that makes organisations think about the issue and build their own propositions with the data they hold than about data actually flowing anything like freely.

2) Whilst many/ all of the items on the venn are on their way to a tipping point, some are more ready than others.

3) Offers management, a perhaps obscure, behind the scenes/ yet to really emerge function, will see significant change this year. The current modus operandi of ‘get a contact point and some input data and push offers to it’ has largely run out of road in many key sectors and demographics. The critical aspect of making this happen is that it requires work from both demand and supply sides, i.e. both buyers and sellers. Without both acting together we just get what we have now. We’ll see a number of ways in which I can pull relevant offers via a channel of my choice emerge in the first half of this year, and if well implemented, get momentum in the second half.

4) VRM/ Personal Data Service Hardware. One can get so far with software, but when one adds genuine customer-side hardware to the mix then we get a lot further. So look out for prototypes and then products in that space.

So that’s my target for the year – a hardware device that pulls in offers that I have personalised. No doubt it will tackle some other VRM issues along the way.

Anyone else want one?

Iain

 

 

Categories: CRM, Project VRM Tags:

Filling in the Empty Space – The Personal Data Store

February 27th, 2010 3 comments

I said here that at present there are very few genuine VRM tools available right for use right now, and that the main reason for that is that the underlying plumbing is not yet in place at any kind of scale.

By ‘plumbing’, I mean that ‘personal data stores’ and all that they imply are not as yet deployed en masse, or with any degree of robust functionality.

Before we get into what it will take to change that, let’s take a look at what I mean by the term ‘personal data store’, because obviously that is open to interpretation, and indeed this has been the subject of much debate in the Project VRM community. To get to the heart of that, I think it is useful to draw a parallel to the deployment of data warehouses within organisations, a process which began some 30 years back, and continues to evolve and extend today. The raison d’etre for a data warehouse within an organisation is normally to pull together the data from multiple operational sources (silo’s), organise that data, enhance it and make it available for use – whether that be for analysis within the warehouse, or via applications that will tap into it. Pulling data in from multiple operational systems is the key, because what is being acknowledged is that no one operational system can pull together a data set that is sufficiently rich, deep and broad to enable all of the functions required to run the organisation. That is to say, we need to distinguish between systems that are there to fulfill a specific task (an operational system such as a CRM application, an ERP instance, or a web site), and those whose main purpose is to generate knowledge, enable understanding and enable sharing information built across multiple business functions.

A further defining characteristic of the data warehouse is that it runs on ‘atomic level’ data, that is to say data that is stored at the lowest level of detail available  from the feeder system (e.g. line item of a receipt). When data is stored in this way, it can be aggregated and summarised where appropriate or necessary for use. This then enables a further defining characteristic of a data warehouse….that one cannot predict in advance all of the uses to which the data might be put which storing at an aggregated level would limit. The same will apply in the personal data store.

So what else is involved in data warehousing that might inform our thinking about personal data stores?

Firstly, i’d suggest there is a (mainly manual) ‘discovery’ phase in both that is about identifying and engaging with valid data sources (i.e. inputs to the store). In practice the data to be sourced is driven by the prioritised functionality sought by the user. For example, if my main purpose for the personal data store is to help me manage my health, clearly i’m going to need my health and my health care supplier data, or links to it, in the store.

Next, we need to consider the personal equivalent of the ETL processes and tools deployed in data warehouses; ETL is short for Extract, Transform and Load. In recognising the likely need for ETL equivalents, we imply that:

a) the personal data store will have its own target data schema (design), with greater of lesser degrees of flexibility built in dependent on technical choices. I think there will necessarily be open standards around personal data store design. That’s not the case in the data warehousing world (Oracle, SAP, Teradata, IBM are all largely proprietary), but I don’t think that approach is sustainable for the personal variant which needs to run at greater scale and much lower cost.

b) most/ all of the data sources will not hold data in precisely the same format/ design as the target data schema.

Extract, Transform and Load usually consists a set up phase, and then automation; many ETL tools exist in the data warehouse world and it is reasonable to assume that the same will emerge in the individual space (indeed they already are tactically with data exchange formats like OFX) in the banking world for moving transaction data around. Note that ETL may only be a precursor to a direct feed from a source system into the warehouse, whether they be batch, trickle or real time feeds.

Now that we have data in the warehouse/ store the task lies in organising the data and preparing it for use; there are a range of technology candidates in this area from standard RDBMS to NoSQL databases. At this point, it may be worth diverting briefly to a harsh reality, because it is pretty certain that this same reality will apply within personal data stores. This reality is that many data warehouses actually become ‘digital dumping grounds’ into which data is put ‘in case we need it later’ (note the clash with data minimisation principles in privacy law), and/ or it is not organised/ optimised for use. That does not make them a complete waste of time necessarily, it just means that they are not providing maximum value; ……the well worn phrase ‘Garbage In, Garbage Out’ springs to mind. My colleague John McKean tells this story much more eloquently in his first book, The Information Masters, which dates back to 1999 but is as valid today as it was back then. His research amongst the 30 or so ‘Information Master’ organisations sets out what differentiates the tiny percentage of organisations that get mega-returns on their information investments, versus those that just plod along or suffer regular failure to get a return on investment (hint….the master’s don’t regard the issue as something that ‘the IT folks do’).

The further functions of the data warehouse/ personal data store beyond getting data in, and organising it are:

– Data maintenance, i.e. refreshing data as appropriate, and having processes to keep it up to date, whether that be static data, dynamic data, or reference data.

– Data enhancement, either through combining existing data via queries into new attributes or meta data, or by bringing in further external data (e.g. my credit rating or verification via a third party that a data attribute is accurate at that point in time, or otherwise). This verification piece is a key issue, if I can prove for example that I am a gold level flyer on British Airways, or that i’ve not had any speeding tickets in the last 5 years, or that I do have a specific illness to manage then that ultimately takes a vast amount of guesswork and waste out of the current modus operandi.

– Make available for use; i.e. providing a data access layer that enables the data to flow onward to those entitled to it, in the way that they wish  to receive it.

– Archive, there comes a time in the life-cycle of a data attribute, that it is no longer useful. This situation, which will certainly apply in a personal data store, can lead the database manager to either physically move the data elsewhere/ onto back up media (usually after building summary histories that do remain), or just leave it within the warehouse on the basis that storage cost may be less than removal costs.

Two other aspects of data warehousing are probably worth noting

– whilst initially, a data warehouse was most likely to be a single computer (perhaps costing £1m upwards to buy and install), these days the concept of a virtual warehouse is also a perfectly viable option, with data stored physically in different places and brought together as and when required.

– the concept of a data mart has emerged, which means the carving off of a specific set of data to support a subsidiary warehouse tuned to particular task (e.g. a retailer may choose to set up a mart for the team managing the loyalty scheme). Typically the link to the main warehouse remains in place for maintenance and update purposes, but the mart acts more independently in terms of access and use.

So what does all of that mean for the ‘personal data store’ then?

Firstly, I would contend that there is a terminology point to be taken on-board. The data warehouse is a short, fairly well understood term (perhaps because it is 30 years old). But it actually covers a lot of ground, and is much more than just a storage facility. It covers ‘identify relevant data types and sources, enable processes for bringing that into the storage facility, keep it clean and up to date by looking back to the source and other other cross-reference files, aggregate and summarise data where appropriate, enhance and add meta data where useful, and make available for use in a controlled, auditable manner via a range of output mechanisms and formats. That’s a lot functionality to pack into two words….. I think that a personal data store will do pretty much all of those same functions, so the users of the term should ideally aligned with that description, or seek to agree different terms for each of the system components and functions.

Secondly, there should be a recognition that functionality will continue to emerge and evolve over time, rather than all turn up in one big bang deployment.That said, there is clearly a huge upside to deploying with the technology we have available now, than that of 30 years ago. Cost of storage and back up is very low, connectivity is solid, access routes/ devices are many and the range of things that will be enabled by them using the internet/ mobile internet as the main place where this user managed information will be deployed.

Third, my working assumption is that there will be both self managed, and hosted options and that people will chose the options that best suit them and their likely uses. It is probable that stand alone personal data stores might not be that common as the market evolves, and indeed the individual buys into a wider set of personal information management capabilities (e.g.  a personal data store, a set of key applications, and a hosting/ back up service).

So, after all that, here’s my working definition of a personal data store:

A personal data store helps me gather, manage, enhance and use information from across multiple aspects of my life, and share that information under my control with other individuals, organisations, or with applications or subsidiary data stores that I wish to enable.

The key, as per above, that this is a multi-life aspect data management platform that is infinitely extensible, and not constrained by the need to operate within a silo-ed context.

Here’s a diagram that seeks to illustrate the personal data store that I think will emerge over time.

One of the big issues around data warehousing is ‘the business case’ for what is typically regarded as a behind the scenes, not very sexy investment. I think the same will apply to the personal data store, but i’ll save that post for another day…

Categories: #Kantara, CRM, Data, Mydex, Project VRM, VPI Tags:

The Customer – Supplier Engagement Framework

January 25th, 2010 Comments off

Over the past few months, The Information Sharing Work Group at The Kantara Initiative has done a bit of a deep dive into an end to end ‘car buying and using’ scenario. We used the diagram below, summarised in this post, to give us context and structure and allow us to break the backwards and forwards interaction between customer and supplier into digestible chunks within an overall framework.

Note: Those studying the model closely will see that i’ve broken out the previous ‘Relationship Servicing’ box into two (Product Delivery/ Service configuration, and Relationship Maintenance); when I was reviewing that area it became clear that there was more detail in there than the one high level heading could support, and that significantly different processes are going on in those areas).


As we have worked with that methodology, it has stood up to the test – so it’s about time it had a name, and a bit of a deeper dive description. So here goes, let me introduce……(drum roll…..), The Customer-Supplier Engagement Framework…..

…So what does that mean then? In essence this work is a built out from many years of work on customer management issues in which colleagues and I used one model in particular (the CMAT model) to describe the big picture of how an organisation manages it’s customers. That model is excellent, has been tried and tested in 600+ organisations worldwide, and has fuelled many a good CRM deployment, but when I think about it with a ‘VRM’ hat on I came to realise that we need not only an equivalent model of what the buyer/ customer on their side of the process, we need a model that shows the inter-relationship between the buying process and the selling process. That’s what The Customer – Supplier Engagement Framework is about; it sets out eleven steps of the combined buying/ selling process and shows the flows within and across the two.

Now clearly we’re not building that framework to show how wonderful the current state is; because it’s not. It’s full of waste and missed opportunities for added value…FROM BOTH PERSPECTIVES.

There are two main reason that this eco-system is full of waste and missed opportunity in my view:

1) One side (the selling side) is fully kitted up with the tools of their trade….data warehouses, web sites, CRM systems, and an army of people paid to do the work. The other (the buying side), has nothing more that some self-assembled, amateur tools….and their brains. They don’t get paid to do it, and they typically don’t have a lot of time set aside for the process (my wife shopping for shoes aside….). That imbalance between the ‘have’s and the have not’s, as in any walk of life, leads the ‘have’s’ to take advantage, and the ‘have not’s’ to rebel against this in whatever way they can, or (more often) they just don’t engage as they might in a more balanced and equitable relationship.

And it’s not as if the individual can just walk away from the imbalanced relationship, in many product/ service categories part of the supplier kit bag is the tactics of ‘lock-in’, and the individual typically does not have the resources or the time to invest in breaking out..

2) Overall supply outweighs overall demand across many categories…so the net position is that there are a lot more suppliers losing out on pitches than there are who are winning the business…., with all that marketing and selling effort being wasted.

So this framework is about enabling us to isolate potential improvement areas without losing the overall context. It’s main impact, I would hope, would be on the buyer side; that’s the area where most time is wasted and where least work has been done to date. But it will also enable much improvement on the seller side, which is where most money is wasted at present.

The build below seeks to illustrate the current situation in terms of the capabilities that sit behind each of the parties. Those on the buyer side have been most actively built out over the last 20 years, beginning with the data warehousing boom of the  late 1980’s, and then through the CRM and e-commerce fuelled growth spurts. The spend on this side is enormous, CRM software applications alone are thought to amount to a $10bn a year market, and that’s only a tiny proportion of the spend overall represented on the diagram. On the seller side, there’s clearly a lot goes on, but in terms of capabilities able to engage on anything like a ‘peer to peer’ relationship with the seller side, then it’s pretty much empty space right now. But the good news is that there are lots of people working away on that. It’s tough, no doubt about that, because just as on the seller side, the base level data management capabilities that span in-house silo’s are key; we’ll call them ‘personal data stores’, although this a generic term for a fairly wide range of personal data gathering, managing, analysing and (optionally) sharing processes and tools. Just as in the data warehousing area on the organisation side, there is no real business case for the personal data store  as a stand alone entity; but when it is tied to the applications that tap into it, then the potential value/ return on personal investment is enormous.

I guess that’s enough complexity for one post, so my plan from here is to take each key area in turn (either capability or improvement opportunity), targeting one per week – and building out that story. I’ll kick that off with a deep dive into the personal data store, the base level plumbing for the buyer side of the framework.

Categories: #Kantara, Project VRM, Uncategorized, VPI Tags:

More on the Privacy Fight-back

July 22nd, 2009 Comments off

Now this is nice….self-destructing digital data controlled by the data subject…..

Categories: Data, Privacy, Project VRM Tags:

Who Said Privacy Was Dead…..?

July 17th, 2009 1 comment

BT decides against deploying Phorm behavioural tracking.

The mobile phone directory Connectivity/ 118800 shut down by pressure from individuals who did not want their details scraped and published.

Facebook found to be in breach of Canadian Privacy law.

So, what have Phorm, Connectivity and Facebook got in common? Referring back to the Personal Data Eco-system framework – you’ll see that all three have reached out and surreptitiously tried to grab data from one of the other categories and move it into ‘Your Data’ (that owned by the organisation in question) in order to make money from it:

– Phorm tries to grab the web site use data from where it currently resides (un-structured, difficult to access ‘My Data’) and move it into their own domain (Your Data – both Phorm and BT variants in this case)

– Connectivity scrapes data from a range of ‘Their Data’ direct marketing files and turns that into another ‘Their Data’ data-set/ domain

– Facebook fails to put adequate processes around ‘Our Data’ (keeps it for an unlimited period) and thus attracts the attention of a regulator.

Exposing these various data grabs is now much more common – because there are now enough people watching and willing to act on it.

Privacy is on the way back…..albeit almost from the grave….

Categories: CRM, Data, Privacy, Project VRM, VPI Tags:

That’s Good, Now We Can Get Started With CRM….Meet VRM

July 13th, 2009 Comments off

This post by Paul Greenberg is the first i’ve read on ‘Social CRM’, and it looks like I came across it at the right time – Paul has drawn a line on what has clearly been a long debate, and set out a detailed definition and description of Social CRM that he will run with. Other than being an excellent summary of what Social CRM is/ is not; the timing works for me, because I think it’s time we in the VRM dialogue start to be more engaged on the mechanics of VRM and how it engages with CRM, rather than the theory of what a VRM world will look like. I say that because it now seems to me, that in UK at least, VRM is a mainstream business/ political discussion – and regarded as a ‘when’ rather than an ‘if’.

First, to Paul’s definition – so that we are clear what VRM is engaging with. His definition is below.

“CRM is a philosophy & a business strategy, supported by a technology platform, business rules, workflow, processes & social characteristics, designed to engage the customer in a collaborative conversation in order to provide mutually beneficial value in a trusted & transparent business environment. It’s the company’s response to the customer’s ownership of the conversation.”

I’m fine with that definition, but will also set out my own context of where ‘CRM’ (social or otherwise) sits in the wider business eco-system – because that will shape my views as to how VRM will engage. The model below was first developed in 1997 by QCi (since acquired by Ogilvy) and was designed to help clients engage with Customer Management/ Customer Relationship Management – which was then an emerging hot topic. This model has now been used over 900 times in organisations worldwide to assess their customer management capabilities. This model is underpinned by 240 best practices – which have been updated 5 times over the period between ’97 and now – to reflect that best practice is necessarily an evolving beast. So, the model is a good start point – not perfect, and there are others out there, but this is the one i’m working with.

Critically – in this model – Customer Relationship Management is central to Customer Management, but the latter is a wider set of capabilities. Practically speaking, one can ‘buy/ deploy/ build’ CRM’, but that then has to be seen in the context of the wider business system that is customer management. CRM is optional, Customer Management is not (unless you don’t have any customers….). A perfectly valid assertion from a Customer Management standpoint, in some sectors (e.g. FMCG) could be ‘we don’t want a relationship with our customers, and they don’t want one with us’ – we make stuff, they buy it.

I won’t dwell further on this model, other than to say i’ll keep referring back to it in discussions around how VRM applications, tools, processes engage with organisations.

CM & CRM

So – we now understand what CRM is, where it fits in an organisation, and that in its most recent evolution ‘the customer owns the conversation’. Given that, what will VRM do over and above that? My contention is that via VRM tools/ applications/ services:

– the customer will own (and share) some, not all the data; but that provided by the customer will transform huge chunks of CRM and Customer Management over time.

– big chunks of traditional data mining will go away, to be replaced with more value adding data services emerging.

– much improved data privacy will be a spin-off benefit

– the customer will be the initiator of the majority of CRM processes, and that organisations will becomes much better listeners than they are broadcasters.

– the net effect will be the elimination of much guesswork in the pre-transaction component of the customer journey, and much waste from the post-transaction component. This guess work and waste elimination will lead to overall cost reduction, some of which will be shared with the customer who has ‘co-eliminated*’ it.

In practical terms, VRM will bring new individual-driven data to the market, and will propose new processes that organisations who wish to become VRM-enabled should engage with. Contractual terms for accessing these data/ processes will also be part of the mix. I’ll build on what I mean by that over the next few weeks.

* I’m not sure there is such a word, but it seems a nice counter-point to co-creation.

Categories: CRM, Data, Privacy, Project VRM, VPI Tags:

The Personal Data Eco-System

June 20th, 2009 2 comments

This post is a short(ish) summary of a working session led by Drummond Reed and me at the recent West Coast VRM Workshop, and also an introduction to the Kantara workgroup in which we are going to move this debate forward. It is also part of the thinking that will short emerge in a Mydex white paper.

At the VRM workshop, we discussed the need for the concept of the Personal Data Store, what it would do in practice, and what that will ultimately enable.

Why we need such things – because individuals have a complex need to manage personal information over a lifetime, and the tools they have at their disposal today to do so are inadequate. Existing tools include the brain (which is good but does not have enough RAM, onboard storage, or an ethernet socket……thankfully), stand alone data stores (paper, spreadsheets, phones, which are good but not connected in secure ways that enable user-driven data aggregation and sharing), and supplier based data stores (which can be tactically good but are run under the supplier provided terms and conditions). NB Our current perception of ‘personal data stores’ is shaped by the good ones that are out their (e.g. my online bank, my online health vault); what we need is all of that functionality, and more – but working FOR ME.

What they will do/ enable – the term Personal Data Store is not an ideal term to describe a complex set of functions, but it is what it is until we get a better one (the analogy I’d use in more ways than one is the term ‘data warehouse’ – again a simplistic term that masks a lot of complex activity). A Personal Data Store can take two basic forms:

Operational Data Stores – that get things done, and only need store sufficient breadth and depth of data to fulfill the operation they are built for (e.g. pay a credit card bill, book a doctor’s appointment, order my groceries).

Analytical Data Stores – that underpin and enable decision making, and which typically need a more tightly defined, but much deeper data-set that includes data from a range of aspects of life rather than just that from one specific operation (e.g. plan a home move, buy a car, organise an overseas trip).

A sub-set of the individual’s overall data requirement will lie in both of the above, this being the data that then integrates decision-making and doing.

In both cases, the functionality required is to source, gather, manage, enhance and selectively disclose data (to presentation layers, interfaces or applications).

We also discussed ‘who has what data on you’ and introduced the following diagrams to explain current state and target state (post deployment of Volunteered Personal Information (VPI) tech and standards).

The key terms that require explanation are:

My Data – is the data that is undeniably within, and only within, the  domain of an individual. It’s defining characteristic is that it has demonstrably not been made available to any other party under a signed, binding agreement. This space has been increasingly encroached upon by technology and organisations in recent history (e.g. behavioural tracking tools like Phorm) and this encroachment will continue. Indeed a general comment can be made that ‘my data’ equates to privacy in the context of personal data; so the rise of the surveillance society and state is a direct assault on ‘My Data’. Management of ‘My Data’ can be run by the individual themselves, or outsourced to a ‘fourth party service’.

Your Data – is the data that is undeniably within the domain of an organisation; either private, public or third sector. Proxy views of this data may exist elsewhere but are only that. This data would include, for example, the organisations own master records of their product/ service range, their pricing, their costs, their sales outlets and channels. Customer-facing views of much of Your Data is made available for reproduction in the ‘Our Data’ intersect.

Our Data – is the data that is jointly accessible to both buyer and seller/ service provider, and also potentially to any other parties to an interaction, transaction or relationship. It is the data that is generated through engaging in interactions and transactions in and around a customer/ supplier relationship. Despite being ‘our’ data, it is probably technically owned, or at least provided under terms of service designed by the seller/ service provider; in practical terms this also means that the seller/ service provider dictates the formats in which this data exists/ is made available.

Their Data – is the data built/ owned/ sold by third party data aggregators, e.g. credit bureaux, marketing data providers in all their forms. It’s defining characteristic is that it is only available/ accessible by buying/ licensing it from the owner.

Everybody’s Data – is the public domain data, typically developed/ run by large, public sector(ish) entities including local government (electoral roll), Post Offices (postal address files), mapping bureau (GIS). Typically this data is accessible under contract, but the barriers to accessing these contracts are set low – although often not low enough that an individual can engage with them easily.

The Basic Identifier Set/ Bit in the Middle – this is the core personal identity data which, like it or not, exists largely in the public domain – most typically (but not exclusively) as a result of electoral rolls being made available publicly, and specifically to service providers who wish to build things from them. This characteristic is that which enables the whole personal eco-system and its impact on data privacy to exist, with the individual as the un-knowing ‘point of integration’ for data about them.

Propeller Current State

The ovals in the venn diagram represent the static state, i.e. where does data live at a point in time. The flow arrows show where data flows to and from in this eco-system; I use red to signify data flowing under terms and conditions NOT controlled by the individual data subject.

Flow 1 (My Data to Your Data, and My Data to Our Data) – Individuals provide data to organisations under terms and conditions set by the organisation, the individual being offered a ‘take it or leave it’ set of options. Some granularity is often offered around choices for onward data sharing and use, i.e. the ‘tick boxes’ we all know and which are one of the main bitsof legacy CRM that VRM will fix.

Flow 2 (Your Data to Your Data, including Our Data) – Organisations share data with other organisations, usually through a back-channel, i.e. the details of the sharing relationship are typically not known to the data subject.

Flow 3 (Your Data, including Our Data to Their Data) – Organisations share data with a specific type of other organisation, data aggregators, under terms and conditions that enable onward sale. Typically the sharer is paid for this data/ has a stake in the re-sale value.

Flow 4 (Everybody’s Data to Their Data) – Data Aggregators use public domain data sources to initiate and extend their commercial data assets.

The target state is shown below, a different scenario altogether – and one which I believe will unfold incrementally over the next ten years or so…..data attribute by data attribute, customer/ supplier management process by customer/ supplier management process, industry sector by industry sector. In this scenario, the individual and ‘My Data’ becomes the dominant source of many valuable data types (e.g. buying intentions, verified changes of circumstance), and in doing so eliminates vast amounts of guesswork and waste from existing customer/ citizen managment processes.

The key new capabilities required to enable this to happen are those being worked on in the User Driven and Volunteered Personal Information work groups at Kantara (one tech group, one policy/ commerce one), and elsewhere within and around Project VRM. The new capabilities will consist of:

– personal data store(s), both operational and analytical

– data and technical standards around the sharing of volunteered personal information

– volunteered personal information sharing agreements (i.e. contracts driven by the individual perspective, creative commons-like icons for VPI sharing scenarios)

– audit and compliance mechanics

Around those capabilities, we will need to build a compelling story that clearly articulates, in a shared lexicon (thanks to Craig Burton for reminding us of the importance of this – watch this space), the benefits of the approach – for both individuals and organisations.

The target state that will emerge once these capabilities begin to impact will include the 4 additional individual-driven information flows over and above the current ones. The defining characteristic of these new flows is that the can only be initiated by the data subject themselves, and most will only occur when the receiving entity has ‘signed’ the terms and conditions asserted by the individual/ data subject. The new flows are:

Flow 5 (My Data to Your Data (inc Our Data) – Individuals will share more high value, volunteered information with their existing and potential suppliers, eliminating guesswork and waste from many customer management processes. In turn, organisations will share their own expertise/ data with individuals, adding value to the relationship.

Flow 6 (Everybody’s Data to My Data) – With their new, more sophisticated personal information management tools, individuals will be able to take direct feeds from public domain sources for use on their own mashups and applications (e.g. crime maps covering where I live/ travel)

Flow 7 (My Data to (someone else’s) My Data) – An enhanced version of ‘peer to peer’ information sharing.

Flow 8 (My Data to Their Data) – The (currently) unlikely concept of the individual making their volunteered information available to/ through the data aggregators. Indeed we are already starting to see the plumbing for this new flow being put in place with the launch of the Acxiom Identity Card.

Propeller Target State

The implications of the above are enormous, my projection being that over time some 80% of customer management processes will be driven from ‘My Data’. I’m pretty confident about that, a) because we are already see-ing the beginning of the change in the current rush for ‘user generated content’ (VPI without the contract), and b) because the economics will stack up. Organisation need data to run their operations – they don’t really mind where it comes from. So, if a new source emerges that is richer, deeper, more accurate, less toxic – and all at lower cost than existing sources; then organisations will use this source.

It won’t happen overnight obviously; as mentioned above specific tools, processes and commercial approaches need to emerge before this information begins to flow – and even then the shift will be slow but steady, probably beginning with Buying Intention data as it is the most obvious entry point with enough impact to trigger the change. That said, the Mydex social enterprise already has a working proof of concept up and running showing much of the above working. A technical write up of the proof of concept build can be found here. And the market implications of this are explored in more detail in new research on the market value of VPI shortly to be published by Alan Mitchell at Ctrl-Shift.

The two hour session at the VRM workshop was barely enough to scratch the surface of the above issues, so the plan is to continue the dialogue and begin specifying the capabilities required in detail in the User Driven and Volunteered Personal Information (technology) workgroup at The Kantara Initiative. The workgroup charter can be found here. A parallel workgroup focused on business and policy aspects will also be launched in the next few weeks. Anyone wishing to get involved in the workgroup can sign up to the mailing list here and we’ll get started with the work in the next couple of weeks.

 

Categories: #Kantara, Data, Mydex, Privacy, Project VRM, VPI Tags:

“The Personalisation of Today is Like Lipstick on a Pig….”

June 11th, 2009 2 comments

I just love that quote from James Gardner of LloydsTSB, who goes on to say…

‘No, the only way to get to markets of one is if customers make the products themselves. This is where the “mash up” I spoke of my in my last post comes in. Customers, who are able throw together bits of offers in unique ways, and then share them with other like minded customers, are the way things will eventually pan out. These are crowds at the centre of the financial services value chain, which will be highly distributed, highly chaotic, but not subject to the system risks of a centralised banking system.’

Spot on i’d say, and that’s where we’re looking to get to with Mydex – allowing the individual to genuinely be the point of integration for the personal data, and the processes/ applications/ mashups that engage with it. I don’t think banking will be the first to engage, but it will probably be a fast follower.

Categories: Mydex, Project VRM, VPI Tags:

Scotweb 2

June 11th, 2009 Comments off

I’ll be speaking at this event next week in Edinburgh about VRM and the Mydex initiaitive.

Also, moves are afoot to get a Scotland based ‘chapter’ up and running to do some local pushing forward on VRM initaitives.

Categories: Mydex, Project VRM, VPI Tags: ,

Personal RFP’s….what are they, and how do we make them happen?

May 28th, 2009 4 comments

At the VRM West Coast workshop, Don Marti led a session on Personal RFP’s, which then led to the issue being debated further on the mail list and built out in this post by Alan Mitchell. Here’s my contribution, looking as much from the CRM/ recipient perspective as the VRM one – ultimately I think that until we look at both simultaneously then we won’t get much up and running at any kind of scale deployment.

Firstly, I think we need to get our terminology in order; as Craig Burton pointed out…we do not yet have a clear VRM lexicon accepted and understood by all project participants.

Here are a couple of references from Wikipedia, that relate to/ illustrate the background to the terms Request for Information (RFI) and Request for Proposals (RFP). I think we need to look at both in tandem because typically they interact with each other.

Request for InformationA request for information (RFI) is a standard business process whose purpose is to collect written information about the capabilities of various suppliers. Normally it follows a format that can be used for comparative purposes. An RFI is primarily used to gather information to help make a decision on what steps to take next. RFIs are therefore seldom the final stage and are instead often used in combination with the following: request for proposal (RFP), request for tender (RFT), and request for quotation (RFQ). In addition to gathering basic information, an RFI is often used as a solicitation sent to a broad base of potential suppliers for the purpose of conditioning supplier’s minds, developing strategy, building a database, and preparing for an RFP, RFT, or RFQ.

Request for ProposalA request for proposal (referred to as RFP) is an invitation for suppliers, often through a bidding process, to submit a proposal on a specific commodity or service. A bidding process is one of the best methods for leveraging a company’s negotiating ability and purchasing power with suppliers. The RFP process brings structure to the procurement decision and allows the risks and benefits to be identified clearly upfront. The RFP purchase process is lengthier than others, so it is used only where its many advantages outweigh any disadvantages and delays caused. The added benefit of input from a broad spectrum of functional experts ensures that the solution chosen will suit the company’s requirements. Effective RFPs typically reflect the strategy and short/long-term business objectives, providing detailed insight upon which suppliers will be able to offer a matching perspective.

I think the background to these terms is key to how we must think of them in VRM world if we are to understand how best to deploy them. What does that mean in practice?

  1. The RFI and RFP processes originate from professional procurement functions, that have the time, funds and incentive to make the process work
  2. There is an implicit logic in the process for both parties, architected around eliminating guesswork and waste; i.e. we’ll tell you what we want to know about (RFI) and, based on that information, what we want to buy (RFP) to save you having to market and sell to us; and by being more organised we’ll be able to do a more efficient deal for both and generate a win-win
  3. They are business processes, not just technologies or data flows
  4. The communications channels through which the interactions and transactions are exchanged should be standard, mass market, not niche
  5. They need two parties, issuers and respondents, both of whom understand how the process works, and both of whom have to do a lot of work to make the exercise work
  6. They typically relate to fairly complex requirements, because the cost of the process is high enough to eliminate the value in applying it to simple/ low cost purchases
  7. The buyer requirement/ seller response is rarely just about lowest price, items suited to that are dealt with in commodity markets

In addition to these characteristics, it is also worth noting that over time intermediaries have emerged (e.g. TEC) who, amongst other support services, make whole series of standard RFI and RFP templates available at no or low cost in order to stick themselves into the value chain.

My view of the above is that a) the originators of the terms RFI and RFP now have finely honed processes for dealing with them, they do enable win-wins for buyer and seller, and intermediaries have emerged to deal with some of the hard stuff – like finding common terminology, and b) they are typically not automated processes and thus not not at all like what will actually be required to do the things we have commonly described as Personal RFPs in VRM discussions, (e.g. i’m here, and I need a stroller for twins).

SO: Before we progress, we may wish to change our terminology around the RFI/ RFP issue – to more accurately reflect what the individual needs; otherwise we risk being confused with the prior deployments of the terms which actually have very little in common with what the individual might deploy right now.

Here’s my view of what those needs are:

  • To be able to articulate a requirement for information about a product or service in ways that can be discovered by potential suppliers or other third or fourth party service providers (assume by a machine but not exclusively so). This area is where Alan suggests there is the biggest gap at present; and that’s quite right – if that gap was not there we’d have had personal RFP type things going on years ago.
  • To share that requirement for information without compromising ones data privacy beyond that required to receive the information sought.
  • To match ‘information requests/ buying intentions with their equivalent information provisions and proposals (that’s the really smart bit!!!!).
  • To receive responses to the information request through one or more communication channels.
  • To be able to interact with responses, including follow up to complete a sale, or to extend an information request.

If we look hard enough we’ll find that there are already architectures out there, that do 2, 3 and 4 – and bits of 1 are around that can be picked up and added in, either directly or (more likely) via fourth party services. For example, the architecture below has been doing its stuff on the web since way back in 2000; a proposition called 2busy2surf that was way ahead of its time. Unfortunately that business has now gone, but the architecture and buyer-seller matching engine has been white-labelled into 20 or so propositions since then. It is still churning out stacks of permissioned requests for information and requests for proposals, and returning matched information packages or offers. These come direct from the selling organisation, or more typically through the affiliate markets (third party services).

RFI & P Architecture 1

So, to get what we used to call personal RFP’s up and running, what we need to do, in my view, is:

  1. Sort out our terminology/ lexicon
  2. Build out the Requirements Articulation piece, adding search maps, comparison engines and other added value buying services into the spec)
  3. Tell the story of the architecture
  4. Get it running in a few business in a more overtly VRM way
  5. Publish the architecture as an open standard

That’s going to take a bit of time and effort. It’s on the agenda for the User Driven and Volunteered Personal Information working group at Kantara; this group has now been approved and will be up and running shortly. I’ll post the details on how to join that as soon as I have them.

Thoughts anyone?

Iain