The First Large Scale CRM Plus VRM Business?

Nice to see Cheap Energy Club from MoneySavingExpert make it out the door, having worked on that for a big chunk of last year.

At first glance it might look like another comparison site, but there’s lots more under the hood – data built on the individual side, automated checking, mi-data ready, and a few more interesting bells and whistles….

Will this be the first service built somewhere between CRM and VRM to run at large scale? It certainly has the possibility to do so; I saved £135 per year on my switch, and others much more. And MoneySavingExpert have a big subscriber base of people who trust their advice.

No doubt this will be a story to follow up on….

A CRM plus VRM Venn for 2013

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?




Filling in the Empty Space – The Personal Data Store

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…

Sales Process… meet Buying Process; and why context trumps segmentation

I’ve been doing some thinking in advance of getting stuck into the development of open standards for User Driven and Volunteered Personal Information. That work is being done here if you are interested in joining in. I’ve been thinking mainly about how best to explain what happens to buying processes and sales processes when volunteered personal information is added to the mix (underpinned by the personal data store/ My Data as set out here).

Here’s my stab at that explanation. I need firstly to set out a view of how things currently work – that’s in the first diagram below with individuals/ high level buying processes on the left, and organisations/ high level selling processes on the right. In short, at present, buyers and sellers largely do their own thing/ practice non-automated selective disclosure prior to engaging in an actual customer/ supplier relationship. That is structurally the best option for a buyer, certainly in terms of reducing complexity and protecting negotiating positions for more expensive/ complex purchases – but it does lead to a lot of guesswork; the buyer typically evaluates multiple options before deciding on one – that’s part of the guesswork referred to in the diagram below. This ‘one step removed’ approach is not the best option for the seller – which is why they try a wide range to tricks to have the potential customer engage with them. That would appear a sensible practice, but in reality it tends to fill up the ‘sales funnel’ with many potential customers who actually have no right nor reason being there – and why direct marketing conversions from prospect campaigns are often well below 1%. That’s the the other part of the guesswork in the diagram. At the relevant point in the process, the customer chooses one of the supply options and decides to commence the customer-supplier relationship; the other suppliers fall by the way side/ wonder what’s happened. But those who lost out, because they don’t have the information to do otherwise, keep on turning the marketing handle – lot’s of waste comes from that area.

Moving through the process, commencing the supply relationship in the current mode means interacting on a supplier run platform, and signing up to supplier generated terms and conditions (or going elsewhere to another supplier silo/ get the same result). What that then does is put the organisation unilaterally in charge of processes and process improvement around relationship management. As a historical note, in my view this is where CRM ‘went wrong’ in the widest sense – at least in part because many deployments occurred during the economic downturn in the early 2000’s. It moved from a having been brought in as a platform for driving improvements in the customer experience, to being run as a platform for cost cutting and for risk managment; e.g. the drive to automated processes such as web based customer self-service, offshoring contact centres. Sometimes this automation worked for customers (e.g. online banking), in many cases all it did was move the waste/ inefficiency onto to the customer. Of course what then happened was that customers took their business elsewhere, where they had that choice/ a better option, or stayed but with reduced levels of satisfaction – crazy in that customer retention and satisfaction improvements were almost certainly key drivers for the original CRM business case.

go to market space

So, the current process does not work that well; the sales process cannot be optimised much further within the current tool-set . But options for improving upon this are now emerging – and not through pedalling faster within the organisation/ the selling process; it comes from building capability on the buyer side/ enhancing the buying process. (note the clear parallel with how selling professionalised in the B2B world when professional procurement and its processes emerged, and also that in the B2B world deals are often concluded and managed on the customer side systems).

The first thing to note in the updated diagram below is what the individual brings to the party (via their personal data store/ user driven and volunteered personal information. They bring the context for all subsequent components of the buying process (and high grade fuel for the selling process if it can be trained to listen rather than shout). By ‘context’, I mean the combination of a wide range attributes that describe the individual and their specific buying situation. This would typically include their needs, their current understanding of how their needs relate to products/ services, their location, their existing supply relationships, their preferences (brand, colour), their role in the decision-making process, their timescales, how much they wish to/ are able to spend, when they wish to buy. In other words, the individual’s context bundle is what much of the early part of the sales process is actually trying to figure out – but can’t get access to as the individual has no current incentive to release it in full. The best an organisation can do at present is strategic segmentation of their market (differentiating products or services based on aggregated customer requirements), and tactical segmentation of their messaging content, communications channels, sales outlets or pricing. Then it’s over into guesswork mode – can we put our messages out in the right places to attract our potential customers and suck them into our sales process…..

The other adds to this second diagram are the ten numbered boxes, reflecting that the improvements we make to the buying process through user driven and volunteered personal information will impact differently at different points of the buying/ selling process. These ten areas are substantive enough to each require a post of their own, so for now i’ll list them out at the high level below the diagram and come back to them in more detail as the standards work unfolds.

context equals segmentation build

User Driven and Volunteered Personal Information Enabled Improvements

  1. Search/ Target (sometimes referred to as the Personal RFI, i.e. Request for Information) – through the individual bringing much richer context data to the table, suppliers prepared to engage with these new buying support tools will find that their targeting becomes much more precise, better enabling them to find potential customers whose needs closely match the unique selling propositions in the organisations product/ service offering. In turn, individuals will find that the options made available to them have been pre-qualified to fit their context (to whatever level of detail they have shared). Note – at this stage my assumption is that individuals will be engaging anonymously/ pseudonymously as there should be no need to share personal data in this part of the process. It is likely that new inter/ infomediaries will emerge in this space, acting as the individuals buying agent (4th party/ user driven services).
  2. Find (engage)/ Enquiry Management (sometimes referred to as the Personal RFP, i.e. Request for Proposals)  – through having brought richer data to the table in the preceding phase the individual will now be talking to pre-qualified suppliers (and vice versa), with the qualifying data from both parties available for use in the interaction. Typically this interaction will be about having a more refined/ detailed discussion about a need/ requirement/ solution axis – potentially involving either or both parties asking for more detail, including possibly verification of data asserted in the search/ target phase. It is likely that new inter/ infomediaries will also emerge in this space, quite possibly spanning the Search and Find requirement for individuals and done from the perspective of enabling the individual to buy solutions to their needs rather than the components which they subsequently stitch together themselves.
  3. Negotiate – In this stage the individual is talking to one preferred solution option and getting down to the actual proposed ‘deal’ and the terms and conditions around that – provided by either party. Improvements in this area are likely around improved transparency of terms and conditions, initiated by the individual being much clearer about their requirements, and having access to comparison tools earlier in the process. ‘Reputation’ management tools will also come into operation as the individual shares what they find out about suppliers.
  4. Transact – I would expect payment intermediaries/ financial services providers to find creative ways to engage with/ be driven by VPI enabled services; there is certainly much potential for reduction in credit card fraud and card related identity theft from using the much higher levels of identity assurance that will become the norm in a VPI enabled data-set.
  5. Welcome – This ‘relationship set up’ phase is typically about both parties getting to know each other, i.e. getting products/ services bought set up and configured, ensuring any ongoing account management/ billing is up and running smoothly. In the VPI enabled world this phase won’t change too much in the short term as it will still run mainly on supplier systems – but in the mid and long term i’d expect it to shift to a genuine user-centric architecture which will see the individual ‘welcome’ the new supplying organisation to their personal supply network/ federation.
  6. Relationship Servicing – This is what would typically be called customer service, i.e. fixing basic operational/ service delivery problems and dealing with ad hoc issues that come up such as change of address/ change of contact details/ change of payment details. As VPI enabled tools increasingly emerge, i’d expect this whole ‘change of’ to migrate to the ‘my suppliers follow me’ approach rather than the individual have to run around updating silos as per the current model.
  7. Relationship Development – This typically includes the ‘cross-sell/ up-sell’ much beloved in the CRM business case. This stage will change in the VPI enabled world, much for the better. Customer service will be provided within the context of the individuals existing solution set rather than that little snapshot of it that the supplier currently sees/ is interested in. In turn that will mean that cross-sell and up-sell will be not only be much more informed, but it will also be much more welcome from the individuals perspective – because it is now laser sharp, and running within a more equitable customer/ supplier relationship (partnership).
  8. Manage Problems – This stage is only reached if a significant problem emerges in the customer/ supplier relationship; typically this involves escalation beyond tier 1 customer service (and an increasingly frustrated/ angry/ upset customer). I don’t expect the VPI approach to have a high impact in this area, although improvements further up the process might have a knock on effect rendering this stage less painful if/ when it occurs.
  9. Manage Exits – Exits can and will happen, either permanently or for a period of time. They may be caused by significant problems that emerged, or by a change in the customer need, or in their circumstances (their context has changed). Less frequently, a supplier will wish to leave a market or terminate a product/ service line and thus exit those relationships affected. In the VPI world, i’d expect there to be more information around impending exits and reasons for them – some of which will enable creative supplier responses. Along with relationship development, i’d expect improved customer retention to be one of the major wins for the supply side in the VPI world – but the plumbing and mechanics for that have stilled to be worked out.
  10. Re-engagement – This stage might be known as ‘win-back’ in CRM speak, and involves the lost customer being targeted with appropriate offers to return. For the individual this return to the fold might be as a result in a time-driven change of context, or that the ‘grass was not greener on the other side’ – as is often the case in utility service swaps away from an incumbent that has retained quasi-monopoly advantages. In any case, the point being made here is that in the volunteered personal information scenario, the individual would be in position to retain and share the knowledge of the prior relationship – which many current CRM architectures fail to deliver on.

So there we have it. Time to get back to working on that VPI plumbing!!!

Who Said Privacy Was Dead…..?

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….

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

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.


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.