Last year a former product team peer referred Datorama to connect with me. Usually I don’t review vertical industry analytics solutions but the marketing angle was appealing to me. Secret be told…I fell in love with data back in the early 90s during my marketing major courses…marketing research, statistics and statistical process control. I am an American Marketing Association member and have always truly relished marketing throughout my technical career.

After a briefing with Datorama, I was shocked to learn how many data sources the average marketer today has to aggregate in reporting. According to Datorama’s research of over 2000 active analytics installations, the average marketer uses up to 70 data sources to assess marketing performance.

Average marketer uses up to 70 data sources to assess marketing performance

I was even more shocked to see the latest May 2017 marketing technology landscape that has grown to include over 5000+ solutions… WOW!

Source: Chiefmartec.com

When Datorama showed me what they built and explained why, I thought their combined marketing industry model + automated “plug and play” data source integration engine was amazing. In fact, I have told numerous groups that Datorama has the best automated integration engine that I have ever seen. I am only aware of one other partially automated integration engine in the general BI and analytics space.

datorama

In my opinion, Datorama is a role model for what automated analytics integration experiences could be like in the future. During our meeting, they were excited to show me the gorgeous dashboards. I’m not sure they even recognized the beauty in the integration engine…so I convinced them to let me write about it.

Datorama

Rather than try to reiterate what I have learned from Jay, in this rare guest post I’m having him share his knowledge directly with you. Typically marketing is ahead of other business areas when it comes to analytics and embracing innovative technology. Artificial intelligence is mainstream in his world. Let’s see how it is making an impact.

Guest post by Jay Wilder who leads Product Marketing at Datorama where he works closely with global clients, analyst partners and internal teams to define, position, and bring to market solutions that leverage unified marketing data and analytics.

Marketing Intelligence and AI

Marketing is quickly becoming one of the most interesting and challenging spaces developing within business data and analytics. This year, Gartner predicts that the CMO will outspend the CIO on the technology.  At the same time, they report that Marketing is getting a bigger seat at the executive table with direct and higher accountability for their investments, performance and business impact.  Meanwhile, the customer journey continues to fragment across devices, channels and conversion points, leading to a data source explosion that can quickly move from dozens to hundreds or more.

When you combine marketing’s high numbers of data silos, fast and frequent changes in sources and formats and increasingly high stakes and budgets– you have perfect storm conditions on the data and business sides for new ideas and rapid innovation.  In response, a new kind of platform has emerged and matured in response– one specifically designed to connect marketing at the data layer– across every source and format– and across every performance, outcome and investment across the customer journey.

It’s called Marketing Intelligence– and its being driven by these data challenges in coordination with the movement of marketing to a broader set of KPIs that tie marketing directly to the short and long-term health of the business:  ROI, Loyalty and Growth.

It makes sense for marketing to take a central seat at the table to manage investments, performances and outcomes in a connected, holistic way. Embracing data at the engagement and automation levels has been common within marketing for the last ten years. As has been the goal of the customer-centric view and the seamless customer experience.  But, connecting marketing at the data layer is another order of magnitude.

To get there, AI and machine learning are stepping in to play a key role.  In order to optimize and report on ROI, growth and loyalty, marketers need to connect, unify, analyze and act on a large set of data that change frequently.  These sources include media and advertising, email and marketing automation, social data, web and mobile analytics, CRM and customer data, sales data, competitive data, brand health survey data…(add your next data sources here).

In this post, I’ll share three ways that AI and machine learning are making the difference for marketing intelligence deployments at scale to help marketers hit their goals. While these are based on our approach at Datorama, my goal is to share their intent in more general terms along with the challenges they’re being used to solve and what it means for the business.

  1. Centralizing data across your touchpoints: According to the latest Chief Martec arTech Landscape, there are 5,381 different solutions available to marketers today – all of which generate data and insights. From these, marketers pick the combinations that help them manage campaigns and engagement data to dayThe foundational layer AI can play for marketers today is to create a panoramic view of all your customer touchpoints in one place– replacing the spreadsheets, reports, one-off dashboards or multiple systems needed to go to get a holistic view of marketing. What smart AI can do at this level is advance you into a “post-API” world of unified marketing intelligence, where any data you need can be added to the picture immediately by the marketing organization itself.  To get this done, AI-powered data integration can now transform any data reporting source in the form of a feed or file into a continuous data connection that leverages, email, FTP or other storage locations to continually get updates from.  Where the smart tech comes in is AI’s ability to recognize what these sources are and how they should be mapped into a data model automatically– simply by looking at the file input.  This means you can drop in a file from your email or social listening systems for instance and see them automatically identified, harmonized and continually collected with all your email, social and other data.  In conjunction with popular API connection capabilities, you have a hybrid approach to accommodate any data source with no coding or API requirements. Speed, agility and comprehensive data coverage are the result.  If you’d like more detail on this, this has been a popular topic on our blog.
  2. Organizing your data to mirror the way you think about your business: While centralizing data is step 1, step 2 is getting it organized– and with AI you can have it self-organize for you to reflect the ways you think about your business. When you want to measure and monitor goals and compare and contrast performances– you want your data from all of its different sources to line up by region, product, or the teams you support. On the marketing side, you want to be able to drill from the highest level of each ROI, growth or loyalty KPI down to every level beneath it. And you need to be able to do this across programs, campaigns, channels, content, audience and customer segments. The new approach is to use dynamic data models that crowd-source knowledge from across customers to provide in-the-box understanding of how marketing data should be related together. Paired with AI and machine learning, integrated data can be automatically mapped into the model with no need to create or maintain a model from scratch. Together with point #1, these AI and machine learning applications dramatically improve initial time to value as well as provide ongoing agility as marketing’s data continually changes and scales.  More detail on this approach is on the blog
  3. Deriving the optimization paths across your customer journeys, every day:  Whether the marketing budget is $1M, $100M or $1B a year, CMOs in central roles within the business are being asked to make performance and results much more predictable. Outputs have much more visibility and peers are counting on marketing to deliver.  But in order to get to meet goals, smarter decisions need to happen on a more frequent basis, across the entire customer journey. The sheer scale of data in a pre-AI world has limited marketers’ ability to see opportunities and take action. But, it stands to reason that if results need to be predictable, marketers need a pipeline of insights to become more predictable as well. It should be as easy to get new ideas on improving marketing as it is to browse a feed of stories on your favorite social networks. This is where AI is entering now. Across the millions or billions of rows of data marketers are expected to make sense of, the bots are being released to find not only what’s moving and shaking in their performance– but also answering why it’s happening and what they can do about it. These are the optimization paths CMOs and their teams can use to flip that switch to always-on optimization.  This is the next chapter in AI-powered insights. Last week we shared news of our work in this arena with the launch of Genius.

Marketing is being elevated in importance within the modern business. On the one hand, that presents a growing opportunity to transform the businesses, answering questions using unprecedented methods in analytics and optimization. On the other hand, it poses a nebulous challenge in understanding data in all its varieties and volumes. Marketing Intelligence and its use of AI and machine learning is now capable of connecting the big picture, providing the zoom functions to move through its granularities, and signaling the points of interest to make faster more frequent optimization.  Marketers today have a great opportunity to help lead their businesses. AI is here to help them get it done– and it’s just in time.