Is AI-Enabled ‘Marketing to Things’ the Future?

By Yaagneshwaran Ganesh On Nov 29, 2017

In a connected world where things are linked to other things and people, there are multiple touchpoints to manage. To a marketer, this means – they have to manage these touchpoints, which are invariably expected to increase in the days to come.

With the way AI is proliferating, it is time for us to rethink who our customer is and explore the concept of Marketing to Things! Is it going to be a human selling to other humans by creating better experiences with hyper-personalization, contextual messaging and more? Or are we going to see a mix of selling to humans as well as to machines? Though the question seems silly now, let’s look at it more closely to fathom the magnitude of Marketing to Things.

Is It Time to Start Marketing to Things?

The fundamental pain-point for marketing professionals today is the overload of messages reaching the targeted prospect, thus becoming increasingly difficult to break through the clutter.

Though the Martech landscape today comprises AI tools such as Fiind Smart Signals, to help you break the clutter by identifying signals indicating prospect’s intent and fit to your products & services, the key question is how much are we leveraging it?

How about marketing directly to machines such as your CRM, ERP than the people championing it? Humans can consume only so much information on a given day!

Empowering machines with AI will make them consume the marketed information, compare the information across its signal library and make an informed decision in minutes. For example, it can compare the price and service offered across the internet landscape and make an unbiased decision on vendor-selection.

Another instance could be the ability to help your supply chain choose the best hub for aggregation and the best combination of logistics to cut costs and more.

Also Read: Datalog.ai Joins the OpenPOWER Foundation and Brings the MyPolly Conversational AI Platform to Enterprises

AI + Connected World: Increase in Marketing Opportunities

Marketing opportunities in itself are likely to increase almost instantly, in such a connected scenario. Once AI begins to balance factors for decision making, the ultimate opportunity for marketers and data science experts would be to understand the algorithms used to calculate & compare and then offer bundles or pitches that suit the machine.

In other words, the most basic qualification of AI vendors enabling marketers would be the ability to crack the intent data.

Now, if that’s not super-relevant and timely marketing, what is? But it all depends on getting the basics intact.

Here are a few key factors to be addressed–

Data accessibility and integration
The quality of output is directly proportional to the quality of input. Make sure that data is integrated across internal & external touch points and is consolidated in a centralized data lake.

Data enrichment
Sanctity of data is paramount, especially with customer data. With data decay being a natural phenomenon, data enrichment needs to be a continuous exercise. Expect the need for data enrichment to increase as the connected world takes off.

Need for closed-loop learning
In a new Marketing to Things environment, with such heterogeneous data, results cannot be 90% and above from Day One. AI algorithms need closed-loop learning, especially to accelerate and be effective at the fascinating intersection of technology and humanity.

It’s Not the Fittest That Survives, but the Most Adaptive

Business problems get increasingly complex and it takes enormous creativity to frame that problem quantitatively, choose variables that approximate the conditions you want to model, stage the data for analysis, and evaluate. Ones that create and adapt their models to changing conditions, yet ensuring sanctity of data, will have the upper hand.

Evaluate AI vendors and their capabilities based on their adaptability. Check if they enable and simplify your life as a Marketer. Ensure that they integrate with your existing ecosystem, thus making it possible to leverage the existing data. Are they agile? Are their explanations, case studies and promises realistic?

Maybe, it’s time to have a Chief AI Officer!