The general trends in the business intelligence (BI) marketplace are well known: deployment to front-line operations, replacement of descriptive with predictive analysis, extension to unstructured data sources, greater use of hosted services and more advanced visualization. All of these certainly apply to marketing departments. Are any BI trends more unique to marketing? Let me propose two that are definitely happening and another that's a bit more speculative.
One clear development is the consolidation of Web and nonWeb analytics. An observer from Mars might ask why these were ever separated in the first place, but it wasn't done on purpose: the newness of the Web and the technical difficulties of capturing Web behavior ensured that initial Web analytics systems would be standalone products. Only now that a generally accepted approach has evolved for acquiring Web interaction data - essentially, building tracking codes into Web pages - is it possible to shift the focus from data capture to true analysis. (Not that the data capture problem is fully solved: page tags are inadequate for many types of Web data, including user-generated content, rich media content and some dynamic Web pages. We can still expect more innovation in that area.)
In merging Web and nonWeb analytics, the biggest challenge is customer data integration (CDI): linking Web identities with offline identities. Precious little can safely be inferred from conventional matching techniques because there is often no connection between the text of an email address and a postal address. More likely, marketers will rely on registration and similar direct data sources to link online identities with their real-world counterparts.
Once customer identities have been linked, marketing intelligence systems will need methods to handle much richer sets of interactions across time and multiple channels. The simple one stimulus:one response model that seeks to attribute a particular customer behavior to a particular marketing contact has become hopelessly outmoded in a world where many contacts are recorded before, during and after each business transaction. What's new is not that there are many contacts, but that they're actually recorded and therefore available for measurement and optimization.
BI vendors are just beginning to offer analytical tools that can show the relationships among multiple, cross-channel contacts and business results. This is where techniques such as visualization, time-series analysis and simulation come to the fore. It also requires deeper integration with financial information to make sure the analysis can focus on meaningful business results, not just whichever behaviors are easily observed.
The second clear trend is greater interest in marketing performance measurement. One explanation is that the rapid expansion of the digital channels (Web, email, mobile) has marketers looking for new ways to allocate resources because the old rules of thumb didn't include the new channels; marketers need different approaches to determine the appropriate mix. And while many marketers would probably be content to make the allocations based on their personal instincts, chief executives and finance departments are pressing for less subjective approaches.
One practical consequence of reporting across many efforts in many channels will be a greater need to aggregate details from hundreds or thousands of marketing programs into meaningful summaries. This implies technologies such as balanced scorecards and hierarchical reporting structures. These will have to show major trends, performance against plan and significant variances while still allowing users to drill down into details on demand. The techniques that BI vendors have developed to serve areas outside of marketing should generally prove sufficient for these applications.
The complex interrelationships among multichannel contacts will also push BI vendors to deploy marketing measurement systems that go beyond reporting of past results to predictions of future behavior. These predictions are not simple statistical forecasts or probability models, but multifactor simulations that estimate how each customer contact affects later activities and that also show the impact on those activities from external factors such as competitor behavior and business conditions. Only when extraneous activities are factored out using a reliable, consistent method that is credible to managers outside of marketing can performance measurement systems really report on the impact of specific marketing decisions.
In other words, both the increasing variety of marketing channels and the increasing pressure for marketing performance measurement are pushing BI vendors toward more powerful analytics in general and customer behavior simulations in particular. Simulation, in turn, is closely connected with business process management, so it may push BI vendors to extend their products in that direction.
So far, there's been little concrete evidence of this movement, so I still consider it speculation rather than a confirmed trend. But it's hard to imagine marketers finding any tool other than simulation that can organize their information to be comprehensive, comprehensible and actionable. Of course, they always have the option to do nothing, but this seems unlikely. The pitfalls of using disconnected, short-term measures are too obvious to ignore. We can expect sophisticated business simulations to play an increasingly central role in BI for marketers.
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