Research and studies in the growth of machine learning market show promising growth rates that averages at 20% annually to reach nearly $16+ billion by 2019. This does not necessary mean that businesses must leverage the technology. But it does mean they need to find ways to be current in how they provide marketing services, mainly for agencies trying to compete.
Applications of Machine Learning
To understand how machine learning or machine computing works is beyond the average CMO. But what’s important is to see the potential and seek the opportunity to create an innovative product that can bring the technology home to the business community and at the same time not be too experimental to require more investment to materialize.
To put this into prospective, lets consider machine learning for Public Relations (PR) and for customer services.
New ways for PR
One way machine learning can work for Public relations; especially that social media is just the digital extension to PR; is through the social monitoring tools that are currently available. What we have thus far are tools that can search for noise online. Noise can be on social networks, blogs, and since most newspapers are digitized, on news portals. The tools currently take it further and provide analysis of the tone of the noise, also known as “Sentiment”. The sentiment can be Positive, Negative, or Neutral. The machine learning behind the software uses multiple algorithms to segment and classify the words used in the social context or in the newspaper article to provide a general feel to the content and give it a Sentiment. Brands across depend on this to lift the satisfaction level of customers with products and companies in the B2B sector are no different. The main difference is the investment and focus given to such software is usually high when the business is more customer centric a.k.a B2C.
Using Sentiment analysis in social media is not new. The use of machine learning and the enhancements in the technology, the availability of more structured data and the level of connectivity all add up to contribute to better monitoring. What I think can take this further; if systems learn as they listen to let businesses know when to respond with what might resonate better. In the case of the B2B sector that can be quite handy as it might save the cost of hiring a full time social media agency in favour of a system that can support the existing marketing team manage the brand image.
Better Customer Services
In the B2B sector the customer does exist, but it is another company vs the average consumer. Distributors are customers to large organizations that manufacture products but have no means or wish to sell directly to the end-consumers. With this example in mind, customer services are as important to the B2B sector as it is for the B2C one, since without distributors such organizations won’t be able to operate. The exercise currently deployed by most companies to measure customer satisfaction, beyond the social monitoring tools mentioned above, is the use of surveys and questionnaires. Machine learning can be of great value if the questionnaires and surveys get automated and run based on current data and transactions carried out with the customer. Most transactions are run by a web enabled system, it being an ERP or a CRM. With that, a back-end machine learning engine can be running and, with some analysis based on set criteria, can automate developing questionnaires that truly speak to the nature of the relation. I am thinking of taking the one-size fits all type of surveys to a level that can actually get responses across. The fact that the machine gets insights from the data processed for the customer, makes the survey ever evolving and the human intervention is kept to a minimal. I must add that there need to be some sort of intervention to enable marketeers the ability to inject elements based on their strategy for the brand. Elements such as type of questions to ask, frequency, and timely delivery need to be based on the research into the persona of the target audience.
Final thought is Innovate!
Some might be reading this and would think, easier said than done! However, that is true to anything that is new and unfamiliar. I was reading an article about getting marketing ready for 2018 and one message that was repeated over and over is to try and innovate! Marketing for any sector has evolved one way or another especially that the mediums or channels are digital. This changed the norm and for many simplified the integration, but that is all in the past and for us new marketeers digital was always the base, so where to go from here!
AI, VR, Machine Learning, or Robotics ! Yes to all; just Innovate & Integrate!