So what do we mean by 'Empowered Customers'?
... and what's in that customer stack?
In recent years there has been a lot written on what it will be like in a world of empowered customers. But it is difficult to really know what will be different, and what will be somewhat similar to what we have now until we can see that alternative running at scale and over a long enough period for a broad range of impacts to become clear.
We hope to make a dent in that by describing what we see emerging in the JLINC My Data Simulator. For those un-familiar with that work, it is a demonstrator/ learning environment with 250 individual ‘people records’, 1,000 organisation records; and the plumbing to show deep interaction and data exchange between the two. It was built on a rich synthetic data-set based on open banking data. That data-set and what it implies looks and feels real, but because it is based on data that does not relate to any real person it is safe to work with in situations that would otherwise be highly governed.
The customer (aka citizen, user, patient, employee) side of the Simulator is the ‘new’ capability that we can now study and understand for their individual and collective impacts. So, what’s in that ‘stack’, and why?
(Software) agents: Step one for the individual in the Simulator is to effectively understand what they are signing up for, and then appoint a (software) agent that will act on their behalf.
Relationship Management: Whilst the software agent and related algorithms and AI underpin many roles and tasks in the ecosystem; their over-arching role can be seen as managing the many to many digital relationships for the individual.
Interface: Individuals need a dashboard/ control panel to understand and control the various connections and processes in their supply relationships.
Self-sovereign identifiers: The customer must have the capability to 'bring your own identifier’ (BYOI). Every data exchange on The Internet goes from one digital identifier to another digital identifier. Whoever controls the identifiers, controls at least half of the data exchange (each being a two-party exchange, even if there are then many similar instances). In day-to-day terms this can be thought of as ‘the customer brings their own customer number to the party’. This becomes a much better model than numbers being allocated by the organisation (which in doing so controls both sides of the data exchange).
Personal Data management: As an individual, if one does not control the data entered into an exchange then that data is already under the terms set by another data controller. It’s use may be theoretically governed by regulatory constraints, but actual control is gone.
Credentials: There are many scenarios in the Simulator that require ‘things to be digitally proven’ as part of personal or business processes.
Structure: The data under management requires multiple degrees of organisation and structure – expressed by schema’s, ontologies and taxonomies.
Audit Log: The Simulator ecosystem requires a full audit log of data transactions to underpin and reenforce the governance model.
We will dig into each of those components in detail over coming weeks and months. But we can already see that the cumulative effect of all of the above capabilities working for the individual is a massive step up in what they are able to do independently, and then a much greater swathe of opportunities as others join their ecosystem over time. So there will be significant discussions and learnings around ‘adoption’, from both individual and organisation perspectives.
There will also be some very significant questions that emerge, that don’t as yet have answers. For example, can an empowered customer act as their own data controller (in the legal as well as physical sense). And if so, what knock-on effect does that have? For example, do organisations who outsource the management of personal data to the person become data processors and not controllers?
There have been three prior waves of innovation at this level of massive change in relation to customer management.
1. In the mid 1980’s, Teradata introduced their ‘Terabyte data warehouse (https://en.wikipedia.org/wiki/DBC_1012#). That volume seems like nothing now, but back then that was the first analytical machine that allowed the largest managers of customers at the time (telco’s, banks and mail order led) to ingest and analyse all of their customer, product and transactional data, learn from it, and take actions based on it. Organisations that bought into that innovation early were running data driven businesses long before The Internet emerged as a commercial ecosystem.
2. In the middle 1990’s, Siebel launched and largely single-handedly created the CRM sector. I recall the late Professor Merlin Stone telling me ‘you need to see this new Siebel thing; you can run Marketing, Sales and Customer Service from the one system and offer a much better customer experience based on the data and tools you now have. And sure enough, he was right; until it all went wrong after the 2000 dot com crash when the accountants figured out that the same tools could be used to ‘reduce customer cost to serve’.
3. Also in the mid 1990’s Tesco and their data/ analytics agency created the Tesco Clubcard; and in doing so proved and massively benefitted from the fact that customers will trade their data for rewards in the form of data driven loyalty programmes. That innovation still evolves today and is critically important to many large retailers in building their ‘first party data relationships’ and the retail media networks that run on the data. On the other hand, Big Tech, with Apple and Google in particular, are attempting to nibble into the loyalty space with their smartphone wallets.
From what we can see already in the Simulator, Customer Empowerment, whether it might have that name, or VRM, or Mydata or something else, has the potential have the same level of impact as those key innovations.
Up next…., the user stories we can explore in the Simulator.