Skip to main content

Top Marketers use Machine Learning & Artificial Intelligence to drive growth


Automation and machine learning technologies are changing the way marketers drive results for their customers and brands. But there’s still a significant gap between those who are just talking about machine learning and those who are taking action. For marketers looking to become leaders in their field, this is an exciting time. It’s a chance to make your mark, get ahead of competitors, and drive real results.



To explore this shifting landscape, we partnered with the Massachusetts Institute of Technology Sloan Management Review (MIT SMR) to conduct a global survey and interview over a dozen executives and academics about their use of automation and machine learning technology and the results they’re seeing.

Do more with your data

Companies with appreciations to use machine learning have more ability to drill-down and see their KPI data in greater detail. This can lead to increased knowledge sharing and transparency, and help teams work together to make smarter, data-driven decisions that achieve corporate-wide goals. In fact, organizations that are incentivized to use machine learning believe they are better than their competitors at making data-driven decisions.

Get a more holistic view of your customers

Business success today relies on your ability to have a current and integrated view of your customers, only then you can develop meaningful relationships with them and deliver long-term brand growth. Our research, found that organizations with appreciations to use machine learning also have KPIs that help them develop an integrated view of their customers. What our research find five years down the road, is that the people who took the early bets in artificial intelligence actually achieve the learning that cannot be copied.

Our findings how there are clear opportunities for marketers to get ahead of the pack. While nearly three-quarters of the survey respondents believe their organizations, current goals would be better achieved with greater investment in machine learning and automation, only half the surveyed organizations have any incentives to make such investments.

Here are a few ways your organization can embrace machine learning and grasp the opportunity.

Make your marketing count

Our research shows that marketing professionals are more than twice as likely as their mainstream counterparts to agree that their organization is already investing in automation and machine learning technologies to drive marketing activities.2 When these marketing activities are linked to business goals, we see a significant upward growth trend.

Stop Reacting and Start Predicting

Organizations with incentives to use machine learning are able to move away from old-fashioned retrospective or reactive KPIs and make smarter decisions with forward-looking and predictive performance goals. That’s because with machine learning technology, KPIs no longer have to be analytic outputs, but rather data inputs that help train algorithms to anticipate opportunities for growth. People can’t tell you what they might want next, so we take cues from past actions to predict future intentions.

Sources:
Google/MIT SMR, Global, Future of Marketing/KPI Survey 2018, n=3200, manager level or higher, 2018.
Google/MIT SMR, Global, Future of Marketing/KPI Survey 2018, n=3200, manager level or higher, KPI index (grouped based on 20/60/20 split) challenged n=645, capable n=1928, leaders n=652, Marketer leaders are defined according to several attributes, including how well aligned their KPIs are to their business outcomes, 2018.
Google/MIT SMR, Global, Future of Marketing/KPI Survey 2018, n=1468, manager level or higher vs n=1468 that do not use machine learning, 2018.



Comments

Popular posts from this blog

Are Your Digital Ads Engaging With Audience

Spends on digital advertising continues to rise, and is forecast to top $203bn in 2018. Yet despite continuing growth, marketers from the most valuable global brand lose confidence in digital advertising’s ability when they are not able to create a seamless, personalized experience, in the decisive moments that matter between people, brands and industry . In today's marketing world most people feel they are being bombarded with increasingly intrusive messages. And while that criticism is aimed at advertising as a whole, the consent is that poorly executed digital advertising is propulsive the trend. How can digital marketers ensure that their campaigns run smoothly across all the channels and platforms of their choice, without alienating people along the way? A new approach to audience targeting This article help business to better understand how people’s online behavior affects their attitude towards digital advertising. The results are instructive: they demonstrate ho

Difference between market research and design research

Market research is an effective tool to assist your business planning. It is about collecting information that provides an insight into your customers thinking, buying patterns, and location. In addition, market research can also assist you to monitor market trends and keep an eye on what your competition is doing. There is a variety of data sources to assist you in researching: Customers Competitors Industry Location There are two types of market research to collect data Primary Research : Referred to as information gathered form original source Survey face -to -face Interviews customer feedback Questionares Secondary Research is information and data that has been already collected and analysed by other sources: industry and trade publications social media and websites marketing and consumer lists newspapers and media IBISWorld Design search is knowledge assembled by a small team dedicated full time to the creation of the product in

How do you detect fraud in financial institutions?

Fraud detection is one of the top priorities for major banks and leading financial institutions, which can be addressed using machine learning. In banking, fraud may include falsify checks or using stolen credit cards. Other forms of fraud may involve emphasize losses or causing an accident with the sole intent for the pay-out. With an unlimited and increasing number of ways someone can commit fraud, detection can be difficult to accomplish . Activities such as downsizing, reorganization, moving to new information systems or confront a cybersecurity breach could weaken an organization's ability to detect fraud. This means facilities such as real-time monitoring for frauds is recommended. Organizations should look for fraud in financial transactions, location, devices used, initiated sessions and authentication systems. How do you detect fraud? The basic approach to  fraud detection with an analytic model is to identify possible predictors of fraud associated with know