How Startups Can Use ML To Enhance Their Digital Marketing Efforts

Author: James Carnell

Digital marketing has been going through major changes recently as the use of the latest technologies including artificial intelligence (AI) and machine learning (ML) gets more and more commonplace in the industry, streamlining marketing processes and making them more effective. While the exact long-term implications of these technologies are hard to predict, it is clear that they are already changing how digital marketing works with some kinds of customer interactions expected to be increasingly managed without human involvement.

Admittedly, these developments do not mean that search engine marketing or social and mobile marketing is going to be fully automated with robots to take over marketing experts’ jobs. Indeed, a survey by QuanticMind has found that the vast majority (97%) of marketing influencers believe the future of digital marketing will be about human marketers cooperating with ML-powered automation. It is becoming necessary for top digital marketers to know how to apply ML technologies in their strategies.

How ML supports Digital Marketing

AI and ML tools are already making digital marketers’ jobs easier in many ways. They are capable of analyzing huge amounts of data and providing marketers with in-depth insights, thus helping them better understand their target consumers and optimize their marketing strategies, said Mark Wai, the co-founder of Orbiter, an ML-based data analytics tool that helps companies monitor customer behavior on a real-time basis. For instance, such tools can enable marketers to track consumer trends related to paid acquisition to better stay on top of traffic or revenue implications.

Besides, ML applications such as ML chatbots – virtual robots that can hold a conversation with humans – have already been implemented across several industries. According to Wai, such chatbots, which can perform searches and answer queries, make it easier for digital marketers to personally engage with targeted audiences. Apart from conversing with users, chatbots collect valuable information on where potential clients live, which products they prefer, etc. The adoption and usage of ML in marketing is continuing to grow.

Choosing the right ML Application

The future of digital marketing will be inextricably linked to ML and every digital marketer who wants to get the most of his marketing strategies will need to learn how to have ML tools automate processes and use available data most efficiently and effectively. Businesses using digital marketing will need to implement ML solutions in their operations, but many of them – especially small and medium-sized companies, as well as start-ups – still do not know how to go about it.

In Wai’s opinion, measuring return on investment (ROI) is a major concern for many companies considering investments in new technologies. To see whether the use of ML directly translates into bigger sales, you first need to decide in which particular channel there is room for improvement and the technology in question could be deployed. Once you have done this and started tracking changes in your performance in that particular channel, it is easier to see if the employed technology brings palpable financial gains.

Winston and Victor Zhang, along with Mark Wai, started Orbiter because they realized that although engineers have ample tools for alerting (like Pagerduty, Sentry, Data Dog), data scientists and product managers still have limited options. They built Orbiter to solve this problem. In times of crisis, the change in the pattern of real-time metrics can easily be analyzed by tools using ML. So, it is not just big companies like Farmstead, Tandem, Rune and DoorDash that benefit from this kind of data software but also early-stage startups which are particularly vulnerable to abnormal changes in terms of customers/purchases, as they do not yet have a stable loyal customer base.

“Machine learning tools can analyze how a metric normally behaves and therefore understand an abnormal spike that may require the marketing team’s immediate attention” stressed the three founders. They further stated that adopting an ML solution only makes sense if it addresses your specific marketing needs. Otherwise, you risk wasting your time and money.

This article does not necessarily reflect the opinions of the editors or management of EconoTimes

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