The End of BI for Marketing

by   |   May 1, 2015 5:30 am   |   0 Comments

Opher Kahane, CEO and co-founder, Origami Logic

Opher Kahane, CEO and co-founder, Origami Logic

Marketers are struggling to measure, report, and analyze their marketing performance in terms of business impact like never before. While it has become painfully clear that manual, error-prone spreadsheets simply don’t cut it in today’s complex media and consumer landscape, figuring out how to turn marketing data into intelligence still stumps most marketers. Marketers want easy access to all of the diverse data their campaigns are generating – in a centralized hub with reporting, visualization, and exploration tools that help inform marketing decisions. Sounds like what a business intelligence (BI) solution could offer, right?

Wrong.

When marketers try to automate their efforts with generic BI tools, they quickly realize that the tools are too rigid and complex to manage evolving marketing needs. Marketers simply cannot expect old tools to solve a new problem. Even modern, cloud-based BI products that claim to have out-of-the-box capabilities geared toward marketers can’t manage the marketing data mess.

Outpacing Technology

Marketers move at a rapid pace – everything is always changing, and BI solutions simply are not equipped to manage the modern marketing data environment. It takes so much time, money, and technical investment to get a BI solution properly deployed that by the time it is ready, the marketing organization needs something completely different.

Taking a step back, it becomes easy to see why BI tools don’t fit smoothly into the marketing intelligence puzzle. The entire BI technology stack – BI and visualization tools; data warehouses; data marts and data cubes; extraction, transformation, and load (ETL) tools – was designed to deal with highly structured, slowly changing data coming from a small number of internal data sources.

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Generic BI relies on an extremely rigid data structure in which data is stored in a data warehouse (or data marts). Using a top-down design process, understanding the detailed reporting and visualization needs of end users comes first. Only then can a structured data schema, which optimizes performance and provides the interface between the essential BI building blocks, be created. Unfortunately, the resulting structured data schema is incredibly inflexible and tied so closely to the original needs that it is quickly outdated. Each end-user requirement change or new data element throws everything out of whack and requires a complete redesign of the schema. This leads to a never-ending investment in technical resources.

BI may work in certain business silos, but nothing about today’s marketing environment screams slow moving, small scale, and unchanging to me. Marketers are trying to get an up-to-the-minute understanding of extremely diverse data sets in incredibly high volume from an exploding number of marketing channels and systems, all of which represent data in a different manner. There are three core elements of the marketing intelligence mix driving constant change:

  • Metric madness. New data are endlessly added, and associated metrics need continuous updates to keep up. Social media messages and the metrics associated with them are a prime example. Marketing is a 24/7 effort.

 

  • Engagement evolution. Underlying data changes as marketing platforms advance. For example, Facebook, Twitter, and DoubleClick data forms continuously change as new ways to interact – such as Twitter real-time video – are introduced.

 

  • Marketing malcontents. The questions marketers ask of their data and the way they want to organize it is always in flux as they try to find the best ways to align it with their unique business environment.

 

The rate of change required by the modern marketing environment vastly outpaces what BI-based solutions can absorb. Even if a BI initiative were able to get off the ground quickly enough to meet the initial scope, every minor change from then on would require IT support. Inevitably, most marketers with a BI system end up asking IT to run ad-hoc reports. That doesn’t sound like an agile, independent, data-driven marketing organization to me.

Simplifying the User Interface

Another challenge for marketers trying to use BI solutions is that marketing analytics need to span across the quantitative results and creative components. Marketing is both a science and an art, so marketers make decisions with the left and rights sides of the brain. To do so, they need highly structured performance data as well as highly unstructured marketing asset/creative data available at all times.

Usability becomes a huge barrier for BI success in marketing organizations. Marketing engagement channels and associated metrics are always changing, and marketers cannot afford to wait months for a BI system update every time Facebook releases a new ad unit or Google upgrades the Google Analytics API. Changing a schema for every new campaign, AdWords account, product launch, etc. seems unmanageable, and rightfully so.

For broad adoption by marketers, solutions need to be extremely easy and intuitive to use. Asking marketers to learn SQL, for example, is a fool’s errand. They should not need to have a deep understanding of data models and schemas, or become Excel and data cube pivot experts either. Marketers crave simplicity. Therefore, BI technology stack is not a good match for marketing organizations, and the end of BI for marketing is inevitable.

Marketers trying to turn a BI tool into a marketing intelligence solution will miss the mark. They likely will spend millions of dollars, wait a long time to see it put into action, and need to pull in more IT support to manage critical updates. However, if you meet marketers with a ton of patience, no looming deadlines, unlimited IT budget/resources, and a slow, simple trickle of standardized marketing data, they might be good candidates for BI.

Opher Kahane is the CEO and Co-Founder of Origami Logic. Opher is a serial entrepreneur and executive, now on his third industry-disrupting start-up. His previous two start-ups, ClassX, a VoIP pioneer, and Kagoor Networks, a VoIP analytics pioneer, were market successes. ClassX merged with VocalTec and went public on the NASDAQ in 1996 and Kagoor was acquired by Juniper Networks in 2005. Prior to founding Origami Logic, Opher was General Manager and SVP of Juniper’s $1B routing division.


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