The Dawn of Machine-to-Machine Marketing

Once upon a time …

It probably all started 20 years ago with the “first digital machine wave” characterized by a booming world-wide-web and an ever growing penetration of powerful PCs/laptops/mobile devices. Marketeers not only embraced these trends as new, more direct communication channels. They understood that the increasing access to customer data plus growing capacity to crunch the data created much more granular and sometimes even surprising customer insights. Walmart’s “beer-diaper analysis” probably being the most famous role model story (… or urban myth) for such surprises.

Along with growing analytical capabilities came new tools and interfaces that automatically turned insights into modular propositions, targeted campaigns and better user experience. Customers being part of the configuration process became a standard (e.g. Nike shoes) . The magic words were - still are - big data and mass customization and consultancies around the world can offer broad collections of success cases on how digital technology boosted marketing execution along proposition design, pricing, promotion, sales and delivery.

Sounds great so far? Well, that’s just one side of the story …

Despite the obvious benefits of micro-segmentation and mass customization, there is a challenge with this type of digitally enhanced marketing: It too often treats customers as the subject to inform … more, better, faster. In its extreme, it becomes a kind of Machine-to-Customer Marketing. Robocalls, in that context, are probably the most literal but also worst case examples of how machine-enabled marketing can go wrong. By now, we all have experienced the downside of this development more than once: … being bombarded with irrelevant online ads … wondering why prices keep changing depending on when, where, or under which name and device we logged in … asking ourselves how to stay on top of all these supposingly targeted promotional e-mails, let alone how to separate interesting ones from well designed spam.

So, just more of the same marketing — even more targeted, more customized, and more augmented — surely won’t address customers’ challenges to digest all the newly generated data and information. Even worse, we already see an increasing number of customers disengaging from decision journeys as they feel lost and drowning in information. So far, they have not been able to keep up with accelerating technical abilities of companies, sending more (improved) information and asking for more input and data in return.

… and now what?

The “second digital machine wave” shows clear ambitions to change this — again driven by major advancements in data processing (the word is AI). What will be different this time is the much easier access for end-customers to AI powered applications as we enter the smart machine age. The team around Siri creator Dag Kittlaus even calls artifical intelligence the next “utility”.


What to expect is an increasing number of (ro)bots, artifical skills and virtual assistants that want to help their users, that want to make our live of managing day-to-day topics easier. Scheduling robots like amyclara labs, or genee already offer a first glimpse of what the future virtual support could look like: Having a “bot” interpreting and breaking down a request, asking the right follow up questions, adapting to new information, comparing options, getting missing data points … and all this fully on behalf of the customer. Ultimately, these type of assistants will empower customers to have a very different, much more meaningful dialogue with companies. To be precise: Customers will then have the chance to step back from quite a few of the many less exciting dialogues that they have with companies today. They will simply ask “bots” to cover their side of the dialogue - faster, more in depth, and more granular. Moreover, they will give their “bots” the mandate to share defined pieces of information with the company bots in order to find better deals and ensure better service.

This will be the dawn of “Machine-to-Machine Marketing” and it will not be easy for traditional marketeers to embrace it. It asks for a fundamentally different look at digital technology, robots and artificial intelligence: from “a powerful tool use” to “a subject to study and convince”. Running successful campaigns in this new context, for example, will require to understand digital decision algorithms of virtual customer assistants just as good as the analog drivers of decision making of real customers. Moreover, campaigns will not always target virtual customer assistants. Taking the promises of IoT seriously, campaigns will more and more target devices directly, e.g. the car-(ro)bot that automatically manages maintenance services or refueling on behalf of the customer. Marketeers have make sure promotional messages get the selling points across to both, the (ro)bots representing the customer and those representing customer devices.

This means plenty of new challenges for marketeers in the machine economy - starting from the very basic “how to run a compelling campaign to a virtual assistant” via “how to make sure people trust the suggestions of their robot” to “how to ensure customer experience (see) the actual service … and not only their bot”.

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The original article was published at Medium and LinkedIn