Thanks to major self-service advertising platforms like Google Ads and Facebook Ads, and the rise of programmatic ad buying, marketers have been exposed to Marketing AI for some time now. However, much of the machine learning involved in these platforms happens inside a black box.
Similar “magic” takes place with marketing automation and email platforms from vendors like HubSpot and Salesforce. These platforms come with AI options baked in and allow marketers to do lead scoring and qualification without the need to work directly with a data scientist.
More recently, web analytics platforms from Google and Adobe, and even data connection platforms like Datorama by Salesforce have also introduced out-of-the-box artificial intelligence capabilities. These solutions are less about marketing activation and mainly focus on identifying potentially hidden insights in web analytics data.
However, all of those features exist in a black box, which means we can see the output (sometimes), but we don’t understand what happened behind the scenes. As a result, digital marketing managers will often explain away the results of their campaigns with sentences like, “the lookalike audiences found by the platform algorithm performed well.” Great news! Unfortunately, if the senior manager asks for more details or insights, we often end up hearing crickets in the meeting room.
Moreover, black box results from one platform won’t transfer to the rest of your marketing ecosystem in most cases. For example, when looking at global marketing efforts, it becomes difficult to explain how results from AI-enhanced Facebook Ads relate to results from AI-enhanced email campaigns on HubSpot or Marketo. Even with a sophisticated attribution model, how can you gain insightful learnings and useful business intelligence from all the data across all of your fancy AI-enhanced Ad/MarTech and analytics stack?
In fact, the deluge of data is likely to expand over the next few years, as the MarTech landscape continues to grow from approximately 150 companies in 2011 to over 7,000 in 2019 (Source: chiefmartech.com).
Enter marketing’s Big Data problem. When you start combining all the metrics and dimensions available across your entire marketing ecosystem, the challenge of processing the data to feed an omnichannel marketing strategy becomes obvious. If you somehow manage to create a data pipeline sophisticated enough to integrate all your platforms into a single data warehouse or data lake, then what? What will you do with all of this data? By what means can you process and mine it to transform it into meaningful business intelligence and knowledge discovery?
This is the gift and the curse of Big Data for marketers. Proportionately, marketing departments are probably sitting on one of the biggest mountains of data in their organizations. However, due to the sheer size and complexity of the data available, most CMOs and marketing executives are merely harnessing a small fraction of this data. Platforms like Google Analytics and Adobe Analytics are likely finding no more than 20% of the insights available from all the data collected from all the activity recorded on your website. The question is, do you have the right analytics and business intelligence team in place to harness the full range of your data collection capabilities?
In 2018, Emarketer reported on a survey conducted by Blueshift and TechValidate, which dealt with the challenges of making decisions based on customer data. 54% of respondents said their main roadblock is the inability to analyze and make sense of their data. As a result, most marketers end up making decisions based on a very incomplete view of the customer.
Alas, marketers are not even able to turn to most advertising or media agencies for help. The reason is simple; most agencies struggle themselves with data analysis and are drowning in data from multiple client accounts across multiple industries. Based on another poll published by Emarketer in March 2019, acquiring technical skills in data science and analysis will be the number one concern of digital ad agencies worldwide in the next two years. This is not a small statement.
The picture is becoming very clear: everyone is chasing insights. Yet, without access to skilled individuals in analytics and data science, Big Data marketing inevitably becomes a Big Data problem. As stated in an article from the MIT Sloan Management Review titled, The Big Data Problem That Market Research Must Fix, “Big data can support smart market research, but only if researchers embrace the basics of understanding what it is they want to measure — and how.”
The article also goes on to say, “Making data-driven decisions based on poor measures can be infinitely worse than making decisions without data at all.” On the other hand, companies and agencies capable of finding the keys to analyze and interpret large and complex sets of data will, naturally, stand to gain a huge competitive edge in the market.
Let the new marketing arms race begin…
As if marketers needed yet another buzzword, the folks over at datadecision Group came up with one: “Big Data MR,” where MR stands for Market Research. Essentially, the term refers to the merger of three components: Big Data + Market Research/Consumer Insights + Predictive Analytics. They offer the following working definition of the term:
Big Data MR is the art and science of combining consumer data, behavioral data, attitudinal data and advanced analytics to produce better and faster decisions that yield superior business results.
Now, for the record, I don’t believe in silver bullets. The term is simply used as a reference in the subtitle to emphasize what will be the next hot pursuit of marketers who, as we all know, love to chase after silver bullet solutions and bright shiny objects. Except in this case, though Big Data MR might not be a literal silver bullet, it will nonetheless become a necessary tool to remain competitive over the next few years.
This was also true of remarketing ads when they first emerged about a decade ago. The early adopters of this technology gained an advantage over competitors who didn’t use programmatic remarketing. As the technology became democratized, the competitive edge went to those with the ability to do smarter remarketing campaigns. For example, more companies started using technologies like a DMP. More recently, there has been a major trend toward CDPs and Creative Management Platforms (CMPs).
In the coming years, Big Data MR can help marketers see further and faster with their marketing data to potentially achieve better business results or capture new opportunities for growth. An article called Reducing time to insight with AI posted by Think with Google stresses the importance of investing in your AI-enablement strategy, right now. Ultimately, Marketing AI and data science is about building models capable of analysing the whole customer experience across marketing channels. More importantly, these models need to draw insights from multiple data sources. Based on these research findings, AI can then effectively contribute to a truly competitive omnichannel marketing activation strategy, which leads to further insights… From there, the cycle goes on, ad infinitum.
Based on the coming challenges of Big Data marketing over the next few years, we’ve seen businesses speed up the pace toward developing data science and data engineering capabilities. What is gradually happening is a shift of attention from pre-baked/in-platform AI towards custom and business-specific AI models with an omnichannel approach.
As you continue to run the black box algorithms in your ad or email campaigns, the idea is to develop and train your own algorithms to reverse engineer insights, then bring it all together. The main business motivation is to excogitate AI-powered consumer insights from your data, from which you can improve your marketing strategy, optimize and automate global campaign efforts (not just Facebook Ads or HubSpot in silos) and feed business intelligence teams with new questions. When your team is able to operationalize your data at this level, you can declare your company to be well on its way in the journey toward fully augmented analytics.
Way back in 2017, McKinsey published a study showing how 60% of enterprises are in the process of adopting AI. According to Gartner (via Contentstack), by 2022, 50% of new digital business revenue streams will be discovered using machine-generated dynamic metadata. Where does your marketing department stand today with respect to those industry trends? Are you keeping up the pace? How many AI or data science projects are currently on the table in your company, division or team?
It should come as no surprise to find out, again according to Gartner, that Marketing AI was identified as one of the four most important emerging trends in marketing for 2020. From a marketing activation standpoint, AI is already gaining traction with automated content tagging and real-time personalization. However, as mentioned earlier, an even greater opportunity and concern is in leveraging data science and AI for deeper knowledge discovery and consumer insights analysis. The emerging field of Big Data MR will create new intersections of collaboration between the CX Expert, the Analytics Developer/Specialist and the Data Scientist/Engineer.
The combination of all three skill sets can provide the omnichannel marketer with the right balance between quantitative and qualitative analysis across channels, both online and offline. Naturally, achieving this capability implies an important learning curve for companies and organizations. Finding talent capable of materializing the above vision is a significant problem in the industry (as shown by the two Emarketer studies quoted earlier). Attracting CX Experts, Data Scientists/Engineers and Analytics Developers/Specialists to your noble business cause is hard enough. Getting all those good folks to work together and speak the same language is a whole other ball game.
Yet, for those who can design the right strategy and operational model to overcome those obstacles, omnichannel AI surely holds great promise for the near future. In order to take advantage of this new frontier of opportunities, the journey starts with a first step. There are countless machine learning or deep learning algorithms and use cases for marketing to choose from. How do you determine which models are relevant for your business needs? How can you test the waters of AI with small projects to reduce the steepness of the learning curve, while avoiding costly mistakes on large-scale projects (which could end up wasting hundreds of thousands or even millions of dollars in failed experimentation!)? Taking on large-scale AI projects without prior small projects under your belt is very risky. It could very likely lead you directly into the dreaded valley of dissolution in Gartner’s Hype Cycle.
A better and safer way to get started is simply by first organizing Marketing AI workshops with your teams. Invite people from different departments and pair them up with expert AI consultants in the field, then brainstorm about what data science could do for your business goals. Start identifying short- to long-term practical use cases with significant potential for impact. Set a preliminary roadmap with some milestones for this year as you gradually flesh out the way forward. Hire a firm already doing Marketing AI to run small projects with your existing data. Test, learn, refine and scale. With each step, you build increasingly more momentum until your reach the point of takeoff. The key thing is to focus more on what artificial intelligence will bring to your marketing strategy than on the AI strategy itself. Without a good grasp on the former, the latter is prone to failure.
If you need help setting up Marketing AI workshops or building a roadmap, Adviso has a team of data scientists who are also industry experts in marketing. We would be glad to work through the process with you, without breaking the bank in the process.