Data Analytics, AI & Innovation: What I Hope Marketers, Agencies & Publishers Will Be Doing In 2019

In this guest post, Dave Sanderson (pictured below), founder and CEO at Nugit, reveals his wish list for digital marketers for 2019…

Using Automation to improve people’s experiences with data

As we transitioned from human to machine reporting, the quality of insight and storytelling in marketing reports has regressed. Telling stories has been replaced with slick dashboards. Previously, marketing analysts would share campaign performance results in well structured PowerPoint and Excel presentations that included annotations, key insights and recommendations.

While this manual process placed a massive burden on analytics teams and dozens of hours required to process data, visualise and build these stories for each activity, the outputs were usually pretty good.

Enter automated dashboards a few years ago which offered a tempting alternative to alleviate this time suck, however it created new problems. While dashboards have undoubtedly driven efficiencies, the data is left to the client or reader to interpret for themselves and the story around the results has all but vanished.

The dashboard is considerably more appealing to an analytics teams (the report producers) than to the end business user (the consumers). Therefore, a more balanced consideration between driving productivity through automation and meeting and exceeding the needs of the end user is required for automation to succeed. After all, the report is built for readers, so make sure it’s what they need. This shift in thinking might seem nuanced, but it means a lot in terms of impact. I believe with maturity of automation technology will lead to a shift in focus from analytics technologies being all about the analyst and productivity enablement, to be focused on delivering a more complete story for the people that rely on this information to make informed decisions.

Tech will change marketing roles and AI will begin to bite

There has been a lot of hyperbole about the introduction of artificial intelligence (AI) and machine learning driving job losses. However, it’s actually really difficult to find examples of where this has lead to actual job losses thus far. In the coming year, this will start to change and marketing is likely to be one sector where it could be felt most. Particularly in agencies with reduced fee structures, productivity economics is front and centre in company planning. Teams are expected to manage more output per head, and one of the only places left to find margin is is reducing man-hours.

Automation and new technology will also lead to massive changes in the nature of existing jobs. For agencies and marketing organisations this means questioning the skills and experience needed to fulfil these evolving roles. For example, in 2019 you can be an amazing analyst without knowing Structured Query Language (SQL) or programming language Python, but to be successful you must be a great communicator and storyteller. From what I can see, communication and presentation skills was at best a nice to have when filling data analytics roles in 2018.

These trends might be disruptive, but they shouldn’t be feared, they’re driving a lot of amazing opportunities. If you’ve been considering a new career path for a while, this could be the right time. There will also be more jobs created that haven’t existed before. Head of AI, Automation, Innovation, Data Storytelling, or some other buzzword are popping up everywhere. Because there isn’t a ready talent pool of people who’ve got five years plus experience, to find candidates employers will need to be much more open to transferable skills. What aptitudes and skill sets overlap from other fields? what are the critical requirements in the role to be successful vs. are just boilerplate requirements that have existed since the job description was first crafted? Most importantly, why do you need to have worked in an industry or vertical before? While the overhead of training is a bit higher than having someone hit the ground running, imagine the new ideas and thinking that can be introduced into the business with people from more diverse backgrounds.

Test and learn with automation

Businesses needs to decide if they are going to embrace automation, or just react to it. Embracing it means not just talking about it and putting it on slides, but setting aside real budget to develop proof of concepts and experiments within the company. Even if it’s a modest investment, it will be vital in generating organisational learnings and build skills within the company. There are a lot of early adopters who are building this experience into their marketing functions now and it will be these organisations that will ultimately gain the biggest advantage as the technology matures. Sure there will be failures along the way. The key here is to plan for these failures, make sure lessons are learnt, and incremental knowledge is gained.

For example, we are running Jasmine by Saleswhale (saleswhale.com), an automated sales agent that often follows up with visitors to our website, helps schedule product demonstrations and shares content. She’s a key part of our sales team, doesn’t require a desk and does amazingly well covering all time zones. While Jasmine doesn’t always get it right, through the setup and management of this technology we were able to collect and view data that refined our customer lifecycle, together with optimising our messaging and content served to each user segment. She’s also improving every month and has the data to back up her performance.

Automation to drive increased transparency

One of the most dominant issues this year has been transparency. Marketers are rightly demanding increased accountability and evidence of return on campaign investment more than ever before. Automation, with machines sharing data, and making decisions, helps drive transparency and objective insights. Telling a preferential story is much harder to achieve with an automated system of data management than it is when humans are involved. As more marketers move to automated data analytics this will have a significant impact on investment decisions and overall marketing strategy.

Enterprise needs to be startup friendly

Many of the advances in AI and intelligent software are being driven by startups and for enterprises this poses an interesting question. Are they prepared to be startup friendly? Being open to work with startups is more than just doing a hackathon or starting an innovation centre, it requires a coordinated effort across business units, legal and procurement to create fast lanes for startups to engage with enterprises in limited capacities, before they have to go through the full rigor: This reduces project risks and helps projects move faster. It’s also less of a burden on the startups with limited resources so they can spend more time doing what they do best. .

We’re seeing this with some enterprises that now have short and friendly contracts for SaaS software, in place of the rigid and one-sided Master Services Agreements that we’re common place only a few years ago. This makes total sense and enables these organisations to quickly onboard cloud software, and gain efficiencies in legal and procurement as well as the business teams.

Another way enterprises are becoming startup friendly is through Innovation centres. it’s great to see these traditional organisations embracing open spaces and design thinking in sourcing new solutions to drive their business forward, however they still have fundamental problems to work through. In quite a lot of experiences with these centres, success is measured on the number of meetings held, number of startups they meet, or the number of introductions they make, but crucially, and frustratingly for business who participate in this very tempting form of business development, it’s difficult to get further than that because the innovation centers seldom hold budget and the traditional business teams are not working at the same pace.

We get invited to ‘pitch’ to the innovation teams at companies from telcos, finance and FMCG. In the vast majority of cases there’s a lot of introductions and excitement, but little decision making ability or budget to back it. Ultimately its unproductive for all concerned. If enterprise are truly investing in innovation they should be also backing their sourcing teams with enough investment to let them do the job, fund POCs directly, and not rely on core business teams that worry more about the day to day to fund activities. Only then will real innovation begin. A business getting this right is Singapore’s DBS Bank. Recognising the increasing headwinds of technology disruption and the threat of being disintermediated, it started a digital transformation process, with data at its heart in 2013. But rather than platitudes, DBS has embarked on a company wide process of data and digital transformation that is being reflected across the culture of the organisation. As technology continues to advance at speed, many organisations will need to take a leaf out of DBS’ playbook if they want to maintain and grow their market share.

As we close out 2018, we have a lot to look forward too in terms of how technology can make our working lives better. We are at a critical tipping point where truly intelligent technology has evolved to augment the knowledge workers abilities not just to drive productivity, or make things faster, better and cheaper, but scalable and more impactful. While it’s natural to fear the changes happening in the business world, I hope you can embrace automation and a world where man plus machine work together in new and exciting ways. Knowledge workers still have so many advantages over these systems, and adding automation to your skill set is surely going to position you well for career growth and success.