Elliot Holding, cloud account manager at Cloud Technology Solutions, looks at why collaboration is key for a truly data-driven marketing strategy. The marketing industry is constantly looking for new innovations and ways to drive strategies and influence consumer behaviour. The adoption of technologies like machine learning (ML) and artificial intelligence (AI), which is expected to completely shape the market, holds one of the biggest opportunities for the sector. These technologies are already helping brands uncover new information about their existing and prospective customers, shaping how organisations are targeting consumers. Predictive analytics is a prime example of this.

Social media influencers have become a common tool in a marketeer's toolbox to reach current and potential customers, building awareness, third-party credibility and purchases. But the rapid growth of this channel (in little over 10 years) has brought with it significant debate on its effectiveness and ethics. And those criticisms warrant the introduction of machine learning. Criticisms levelled at the industry and individual influencers have included: a lack of transparency of the effectiveness of influencers, unfilled promises made by those representing influencers, and a lack of knowledge among in-house marketing teams on how to use influencers for their brand effectively. The industry has certainly matured over this time, with steps being taken to address these challenges.

Recommendation systems allow executives and manager to recommend products their customers will like, and in the process take the guesswork out of marketing efforts - but they're still underused, and often under perform. We explain their use cases, how they work, why they might fail, and how to solve these issues. In a world dominated by mail, social media, youtube ads, chat applications and other communication technologies, marketing executives and managers have never had more ways of reaching out to customers. Yet this explosion in communication channels is a mixed blessing: while customers can be reached more easily, marketing teams also need to tailor their message much more than a decade ago: gone are the days of "one message to every customer". Non-tailored messages are often considered like junk or spam, and have become largely ineffective at driving sales.

Thinking about how important the funnel of a customer is for a company, we appreciate when new tools make the process easier. This is why we built Marketeer and today we are presenting version 3.0 with Marketeer Intelligent CRM. I have been working with artificial intelligence for a while – since the end of the 90s to be exact meanwhile I was running an advertising company. In 2012 I decided to invest time and money into building a new product that would help me make communication easier and effective for my clients. And today, we have it!

Campaigning is often based on feeling or experience by marketeers, but the speed of digital developments are forcing businesses to rapidly adapt to the new needs of consumers created by digitization. Of course, marketeers can't leave their data out of the equation nowadays, however this remains a struggle as this often calls for allocating expensive resources such as data scientists. Luckily, technology is catching up on the smart brains of data scientists. So what does this mean for the future of campaigning? And how can big data, machine learning and artificial intelligence be used to improve campaigns?