Me-commerce — from push to pull
“Pull’s so 3008; Push is so 2000 and late.” — Fergie
I’ve been reflecting of late that I’ve now been working in what amounts to direct or data-driven marketing for 31 years. I guess that’s what happens when one’s university course choice is driven by how much free time is involved!
I believe we are about to see the single biggest change in the discipline since its inception some 40 years back — commonly recognized to be in the big mail-order businesses. Yes, a bigger shift than any of these marketing leaps:
- direct marketing
- database marketing
- database marketing
In the near future, the data source used to drive direct marketing will be the individual (buyer) and data-sets under their control.
Individuals will have the tools — and the motivation — to pull marketing content when they need it. That’s a 180-degree shift from the current norm in which direct marketing is almost exclusively pushed from data controlled, partnered for or bought in by the supply side— the seller.
Why do I expect such a dramatic shift? At the top level, this is simple. Marketers will gravitate toward the best data-set available to them — always have done, always will do.
The reason I’m confident is that the tech is now available to deliver the above at Internet scale.
Sure, it will take the next year or two to be widely recognised and ramp up, but my belief is that the 20’s decade will be characterised by the emergence of individual as the best integrator and originator of data about themselves. And sure, it takes robust legal and commercial approaches, and great propositions to really fly, but the tech is the key enabler.
So what do we mean by ‘best data-set’? Typically, that would mean analysis of a number of data quality components:
- Recency (how up to date is it?)
- Completeness (are all data attributes populated for the whole data-set?)
- Accuracy (how predictive is it?)
- Accessibility (how easy is it to use?)
- Compliance (is it legal?)
- Cost (of access and use)
Of these, the 2 ‘C’s’ — compliance and cost— will combine to trigger the change above.
Take this recent ‘web wide data breach’ lodged with European Privacy Regulators by Brave Browser against Adtech in general.
The complaint notifies European regulators of a massive and ongoing data breach that affects virtually every user on the web.
Whether successful or not (and I’d put money on at least partial success), this is a big deal. It would even be a big deal without the massive GDPR fines available to regulators. However, those fines make Adtech-driven data pretty toxic, and potentially very costly. And just look at British Airways’ reaction to their data breach. Promising compensation is probably the right thing to do. But undoubtedly this would not be the case were they not in the firing line for a significant GDPR fine.
So, there is a clear argument that the current model, in which the ‘sell side’ largely does what it wants with data on people in support of their direct marketing, is a busted flush. In fact, some would argue that it always has been. A discipline that regards a 1% response rate (i.e. a 99% fail rate) as a great day at the office is probably believing too much of its own hype. The counter argument has always been, ‘It’s the best option we have’. To a large extent, I buy that, hence my continued existence in the area. In fact, this is validated in some research recently by MediaMath and eConsultancy.
When many people are seeing upwards of 5,000 marketing messages per day, it is not a great idea to push another unsolicited one into that equation. The ‘buying signals’ triggering the vast majority of those messages is just not what it needs to be to justify the interruption. Even when messages cost virtually nothing to send, the net effect remains negative.
‘An overwhelming majority of advertisers agree with the importance of putting the customer first – yet a majority admit they don’t fully do so.’
That’s positive — it would suggest that if a more customer-first model emerged in a way that scaled, then a majority of marketers would engage with it.
Customers taking charge of the relationship has been moot since the earliest days of the commercial web. I certainly spent a lot of time and money investigating that for a consortia of large, trusted brands way back in 1997, but the reality was that the technology around at that time was way too clunky to scale and there was no perceived need on the side of the buyer.
And yet a good example of what can be achieved has existed in the world of B2B Commerce. The Request for Information/ Request for Proposals model (RFI/ RFP) that sees buyer-initiated opportunities is well proven at scale. The buyer does their initial needs analysis and research on their ‘long list’, articulates their needs to that marketplace along with a clear response requirement, mechanism and timetable.
That buying signal emerges from the buyer, then pulls marketing communications, digital advertising and sales activities from the seller. This stands in sharp contrast to sell-side push.
It is perhaps no surprise therefore that B2B commerce is generally creating more of a win-win environment. The buy side and the sell side know that both parties are likely to get a better outcome with more respectful marketing, communications and data management practices underpinning an inherently relationship-based marketing approach. The opposite is often the case in B2C marketing — in which volume wins over precision.
Because the technological underpinnings for this seismic marketing shift now exist, here’s a first glimpse of how I see that working in the early days. The diagram below shows a buying interest, the equivalent of a request for proposals, being completed by a user of the JLINC beta service (a personal data service built on the JLINC protocol). On the surface this looks pretty much like an existing form being filled in on an ecommerce site or app. But this one has additional capabilities:
- It can only be read and responded to by organisations that have accepted the buyer’s information sharing agreement
- The buyer can decide who gets to see and respond to the request from the panel of available providers
- The buyer can edit the data on an ongoing basis to refine their request
- The buyer can delete the request when finished with it, and remove it from ongoing use by the seller
Note: This current version does not pull in digital marketing (i.e. connect to Adtech); but doing so is a relatively trivial leap. Imagine adverts driven by what you are actually looking to be informed about, evolving through the buying process, and ceasing when the buying window closes…
This approach has privacy and control benefits for the buyer, but more importantly puts the buyer in position to apply decision support tools prior to sharing the buying intention, and filter what gets to them. In the current model both are controlled on the sell side.
It also works well for the seller, albeit they don’t get all their own way anymore – and benefits them accordingly through the improved data accuracy, recency and compliance of the opportunity record. Here is a view of how the individual’s buying intent surfaces in a standard CRM system.
For all intents and purposes this is a ‘Customer Qualified Lead’ (CQL), to borrow from the existing CRM terms ‘Marketing Qualified Leads’ (MQL) and ‘Sales Qualified Leads’ (SQL).
Once in the CRM, these business opportunities act mainly just like any other — individually and in the aggregate. In the screenshot below, these Customer Qualified Leads are all controlled by the potential customer. The potential customer is actively engaged,** pulling** interactions from the various actors on the supply side and ultimately concluding purchases. Note that a customer-driven opportunity is also likely to have better closure; just as in the B2B RFP process, the winners know they have won the business, and the losers get to know they have not, and why not — allowing the losing seller to avoid further wasted marketing expenditure.
So what shall we call this new model? I’m suggesting ‘Me-commerce’ until such time as a better option emerges (so I see Me-commerce as a sub-set of the broader category of personal capabilities evolving over the last decade in Project VRM). With any new piece of jargon should come a definition; so here’s my starter for ten.
Me-commerce is an emerging commercial model in which the individual (formerly known as ‘the consumer) is the driving force behind a requirement to buy something.
Note that ‘buy,’ in a broad sense, refers to a model that equally applies to products and services, including ‘free’ ones that are funded by data access and not financial transactions. The key and defining characteristics of Me-commerce are:
- The individual initiates the expression of buying intent themselves, or has it done on their behalf from a place they control
- The expression of buying intent can be:
- Specific, ‘I am in the market for X, here are the details’
- General, ‘I want to be kept informed about products and services with these characteristics’
- The buying intent data is shared under transparent terms and conditions that both parties sign up to and understand
- An audit trail is maintained to ensure all actors in the eco-system do as they have agreed
All of the above is now up and running in the JLINC beta service; individuals can initiate, manage and control their expressions of buying intent from their JLINC dashboard and see them pull in responses from multiple potential suppliers.
There is no longer a technical barrier to the adoption and growth of the Me-commerce model. This matters because it makes it much easier for the marketer to pilot and deploy this new capability, with minimal change to their existing core technology stack.
There will be an adoption phase; propositions need to be refined, individuals need to step up, organisations need to be prepared to think beyond the current failing model and other Me-commerce service providers should emerge.
But the core plumbing that enables it is now in place and the significant benefits that come with ‘pull,’ rather than ‘push,’ are now in sight.
I’m really hoping I’m right on this one. I don’t think I could cope with spending a lot of time and money on Artificial Intelligence just to have an exciting new tool that could be used in so many useful ways, but just spits out yet more un-opened emails. Wouldn’t it be great if that fancy new AI driven ‘next best action’ predictor popped up a message saying ‘your next best action is to stop bothering this person, they are not in the market right now’.