Can marketing be business to business AND machine to machine?

Can M2M marketing work for B2B? Here’s how to prepare for a world where machines have more influence on buying decisions

February 19, 2019

Can marketing be business to business AND machine to machine?

Imagine if forgetting Valentine’s Day didn’t have to mean that you forgot Valentine’s Day.

Instead of waking up last Thursday morning in a panic, desperately trying to figure out how to create a charming homemade card by the time your other half got out of bed, you miraculously received a package with a gift that their favourite store had picked out specifically for them. The doorbell rang exactly 20 minutes before your true love was ready to leave for work, with a spectacular bouquet of the exact same flowers that sat on the table during your first date. When you opened the door, you found a box with your other half’s favourite champagne brand as well.

It turns out you didn’t forget Valentine’s Day at all – because your digital persona remembered all of this for you.

This is the scenario promised by machine-to-machine (M2M) marketing: an ecosystem of algorithms and sensors that predicts what you need before you need it based on increasingly sophisticated modelling, machine-learning and the rapid interplay of the Internet of Things (IoT). And recent developments suggest it’s not quite-so-futuristic a concept as it might appear. So confident are businesses becoming in their predictive capabilities that they are prepared to imagine taking the risk of delivering things to you on the basis that you’ll almost certainly want them.

Amazon has patented ‘anticipatory shipping’ technology that prepares orders for customers (and potentially sends them) before an order is placed. The wording of the patent envisages the possibility of items being sent to a customer who turns out not to want them, confirming that Amazon is willing to take a risk, but it also expects that this will occur rarely enough for those customers to be allowed to keep the items, in order to build goodwill and trust. Presumably, once that goodwill and trust is well and truly built, you start to accept that you really wanted whatever Amazon’s algorithm decided you wanted. You just hadn’t realised it yet.

Can M2M marketing work for B2B?

Imagine, though, an ever-so-slightly different scenario – one that applies M2M to the world of B2B buying.

Let’s suppose that the reason you forgot Valentine’s Day in the first place is because of how hard you’re working. You are one of the tech decision-makers in a traditional bank undergoing digital transformation. You’re holding in-depth conversations with all of the major corporate functions, trying to put in place a digital infrastructure that can support the cloud-based delivery, Artificial Intelligence (AI) systems and blockchain capability that you are going to need to compete with Fintechs and challenger banks. Everything from mobile banking to customer experience to the type of financial products and services that your bank will be able to offer will depend on the choices that you and your colleagues make.

Now imagine that, instead of the exhaustive process of scoping out needs and deciding on the right digital transformation strategy, a digital assistant simply lets you know that it’s decided you need a new cloud solution, so it’s shortlisted the three vendors who are the best fit and booked meetings for you with the top account director at each of them. It’s found a company that does blockchain development and an AI platform that seems best suited to your needs as well.

Do we want our B2B buyer journeys automated?

I imagine your reaction might be quite different to that relief you hypothetically felt on Valentine’s Day morning. It’s not just that an eager-to-please piece of technology is threatening to automate your role. It’s more that you suspect the technology hasn’t done the necessary due diligence that your role requires – and your business depends on. B2B purchases are very different to consumer purchases. They can’t be reduced down to simple predictions about what people want and need in quite the same way. Perhaps most importantly, the things that feel like friction and inconvenience when making consumer purchases (realising that you need to buy something, finding the item that’s right for you, dealing with human beings as you head to a shop or wait for a delivery driver) are actually crucial parts of the consideration process when it comes to B2B.

Settling on what your business actually needs is a case in point. It involves conversations and collaboration with a whole range of corporate functions, not to mention strategic decisions about the outcomes your end-customer wants and the kind of service that can differentiate you from the competition. These are hugely important and not something you want automated away. After all, you can’t sell in your recommendations to your colleagues based solely on the fact that an algorithm told you. Selecting potential suppliers isn’t just a case of finding the right product. For many B2B purchases, you’re selecting a potential partner, whose customer service, support, culture and values you’ll depend on. The purchase journey is an opportunity to try these things before you buy them. You don’t necessarily want it circumvented.

To leverage the potential value of M2M for B2B marketing, marketers need to apply similar technology to that which makes a miraculous Valentine’s Day happen – but they need to apply it in a very different way. They need to use data, personalisation and prediction, but do so while respecting what buyers actually want from their buyer journey.

What M2M marketing means – in B2C and B2B

M2M marketing really means the coming together of other, well-established tech trends that act on a buying decision from two different directions. Automated marketing that is planned and executed by machines targets a digital version of people that exists on machines – and which is equipped to make decisions on behalf of the real, human version. Hence the machine to machine (M2M) name.

That digital persona is created and empowered by Artificial Intelligence (AI). On the marketing side, this activates new types of insights from vast pools of behavioural data: insights like whether a couple celebrate Valentine’s Day and what flowers they like, or whether a bank is in the market for a cloud solution and who’s likely to be involved in choosing one. On the buyer side, the immensely detailed digital personas built up by virtual digital assistants and (in the case of consumers) connected devices around the home, make increasingly confident predictions about what their clients want and when they want them. They predict, for example, that somebody has forgotten to order flowers for their partner on an occasion when everyone else in the country is doing so, using emotional signals from their voice searches to deduce that they are distracted but still in love (i.e. not actually planning to split up with their other half). Equally they might predict that the best cloud solution for a bank will have certain characteristics based on what seems to work for other banks – and what their client’s internet searches and other behaviours suggest they see as a priority. M2M marketing happens when these two things come together. The buyer-side assistant has recognised a need and looked for solutions. The marketing-side automation has recognised the same need and presented a solution that fits the expected characteristics.

In my hypothetical example, we imagined how a B2B buyer’s digital persona could confidently book meetings on his or her behalf, based on the marketing content it’s been exposed to from different tech providers. That’s not all that far-fetched. In May 2018, the Google Duplex extension to Google’s digital assistant caused a huge stir when it demonstrated how it could call restaurants and hair salons to make bookings, without those on the other end of the line apparently realising that they were talking to a machine. One of the big questions in M2M marketing is where the line should be drawn between human agency and automation. Amazon’s anticipatory shipping seems to envisage removing that line altogether. However, B2B marketers can’t afford to ignore it.

Making effective use of M2M in B2B marketing isn’t going to involve short-circuiting all buyer journeys and automating B2B purchases. But it is going to involve responding to new types of signals about what buyers need – and responding to them in a way that gets past the digital gatekeepers helping to prioritise what they see and consider. We don’t have to wait for some new ‘Age of M2M’ to start marketing in this way. M2M is effectively an extension of technologies that we’re already dealing with – and so it makes sense to start adopting elements of it into B2B marketing strategies now.

Here’s where I see as the sensible places to get started:

Think about where your business sits in terms of value and consideration

M2M marketing has different potential implications depending on the type of B2B solution that you are actually marketing. In the tech space, buying end-user hardware like laptops or printers has always involved a very different journey to buying enterprise software. Deciding which courier company to use involves a different consideration journey to deciding which recruitment agency will deliver the talent and skills your business needs.

Whether you’re marketing to a decision-making machine or a B2B buyer, the essential starting point is deciding exactly what value looks like to whatever’s making the decisions. Is it a product that meets their needs as cost-efficiently as possible? Is it an ongoing partnership with success depending on the supplier’s expertise and attentiveness? In the first case, any machine you’re marketing to is searching the internet for clear product and pricing messaging. In the second case, it’s looking for signals of expertise, testimonials and evidence of responsiveness and customer service. You need to prioritise the right signals in the content that you create – and how you distribute it.

Don’t be passive – M2M isn’t necessarily inbound

One of the big unresolved questions in visions of an M2M future is where the impetus and initiative comes from. Is this an inbound marketing world where the equivalent of Microsoft Cortana or Google Assistant decides that its owner needs something and then goes out and finds the best-looking solution? Or is it a more outbound marketing world where businesses detect signals of buying intent and start targeting their audience members’ digital personas with messages that their digital assistants can intercept and respond to? Amazon’s patent seems to imply the latter: it’s the company trying to sell you things that decides what you might need and takes the initiative in offering a solution. B2B marketers should follow their lead. If you want to demonstrate your value to the algorithm drawing up a shortlist then focus on delivering relevant, timely content to the digital persona that algorithm works for. You don’t have to approach M2M in a passive manner.

Lead with content and ideas

I’ve argued in this post that most B2B buyers, most of the time, won’t want their purchases automated by M2M technology. Instead, their digital gatekeepers will be anticipating a need for information and expertise – and searching for that. As a B2B marketer, you’ll need a content strategy that can send the right signals to those buy-side algorithms. Build on what you’re already doing in the thought-leadership space and focus on being more relevant, and more timely, at more stages of the buying journey. An M2M world isn’t just a buy-first-think-later world. It’s a world where signals of quality and credibility will be analysed by machines more intensely than ever. Think super-sized SEO.

Focus on the whole business, not the individual

One of the reasons why M2M is easier to imagine for B2C than B2B is the number of decision-makers involved. As a consumer, most of your purchases don’t involve buying committees or collaboration with a range of people with different needs and agendas. If marketing algorithms can understand your digital persona then they can predict fairly confidently what you’ll need and what you’ll do. It’s a different game entirely when businesses are making buying decisions. Even if you’re the decision-maker, it’s not a case of a machine picking up on your signals and predicting what you want. It needs to anticipate the needs that other functions and departments will have of you – and how best you can respond to them.

It's vital for B2B marketers to focus on the whole business, not an individual decision-maker. Right now, the most effective sales and marketing organisations identify all of the members of a buying committee, target different members with content specifically relevant to their needs, and track engagement with each of them separately. This is the approach that M2M marketing strategies will need to take as well. Marketing automation can’t afford to oversimplify how decisions are made.

Think about where your targets’ relevant digital personas sit

If you’re developing M2M marketing for different digital personas that correspond to different members of the buying committee, then you’ll need to plan around where those digital personas actually live. Which algorithms do you need to convince of the relevance and value of your business? An eCommerce environment like Amazon can use its own data to decide who wants to buy what and when, but in the B2B space things are more complicated. Let’s imagine that Microsoft Cortana or Google Assistant is helping you with you a B2B purchase. It won’t be using just its own data to inform its recommendations. It will use search engines to find what seem the most relevant companies, and that will include analysing signals of expertise and engagement on social media and other content platforms. Just like the human-to-human kind, M2M marketing will involve taking informed decisions about the right environment in which to target your audiences – and send your digital signals.

Build on the M2M marketing you’re already doing

If a lot of these ideas sound familiar, that’s because smart B2B marketers are already adopting many of the ideas that would make sense in an M2M marketing world. They have SEO strategies that focus not just on keywords, but on identifying the intent behind those keywords – and delivering types of information and content to match it. They have content strategies that pay attention to the types of value buyers are looking for – and the formats they expect to find it in. They use platforms like LinkedIn to identify the most likely members of a buying committee, target relevant content at each, and track engagement across all the people involved in a decision. They introduce sales into their B2B marketing, personifying expertise and leaving a digital signal of their responsiveness and their values.

Whether we find ourselves marketing to human beings or machines a few years from now, we’ll still be counting on the same techniques for being relevant and signalling our relevance on digital platforms. The best B2B marketing already has a lot of the characteristics of a realistic approach to M2M.