The end of a quarter forces me to reflect on what I learned in regards to my coverage area: measurement and attribution. From customer insights (CI) pros and marketers, I saw an increased interest in advancing their measurement approaches. On the attribution front, there is an appetite to learn about specific methodologies, use cases, ongoing attribution management strategies, and attribution applications to marketing/media buys. On the vendor side, I saw more advancement in tools, approaches, and offline and mobile data integration. I predict attribution — and general consumer and marketing measurement — will continue to be a hot topic for marketers and CI professionals well into 2014. Specifically, I expect to see more attribution adoption and usage of attribution to measure customer purchase paths and to learn more about customer behaviors and motivations.
In the meantime, let me recap the Q3 2013 measurement takeaways:
Business intelligence (BI) is an evergreen that simply refuses to give up and get commoditized. Even though very few vendors try to differentiate these days on commodity features like point and click, drag and drop, report grouping, ranking, and sorting filtering (for those that still do: Get with the program!), there are still plenty of innovative and differentiated features to master. We categorize these capabilities under the aegis of Forrester agile BI; they include:
Making BI more automated: suggestive BI, automatic information discovery, contextual BI, integrated and full BI life cycle, BI on BI.
Making BI more pervasive: embedding BI within applications and processes, within the information workplace, and collaborative, self-service, mobile, and cloud-based BI.
Making BI more unified: unifying structured data and unstructured content, batch and streaming BI, historical and predictive, and handling complex nonrelational data structures.
“Figuring out how to think about the problem.” That’s what Albert Einstein said when asked what single event was most helpful in developing the Theory of Relativity. Application integration is a problem. A big problem. Not to mention data, B2B, and other domains of integration. As an industry analyst and solution architect, what I’m most interested in first is how to think about the problem.
Pop Quiz: The Goal of Integration
Which of the following statements best articulates the goal of integration strategy?
The goal of integration is to keep data in sync across two or more siloed applications.
The goal of integration is to improve business outcomes by achieving consistent, coherent, effective business operations.
The correct answer is B. Was that too easy? Apparently not, because most of the integration strategies I see are framed as if the answer were A. Most, but not all — and it’s the ones framed around B that I’m most interested in. Here’s the difference:
A-style integration centers on technology. It begins with data and business logic fractured across application silos, and then asks, “How can integration technologies make it easier to live with this siloed mess?”
B-style integration centers on business design. It begins with a businessperson’s view of well-oiled business operations: streamlined processes, consistent transactions, unified tools for each user role, purpose-built views of data, and the like. It designs these first — that is, it centers on business design — and then asks, “How can integration technologies give us coherent business operations despite our application silos?”
Buy analytics software, hire marketing scientists, and engage analytics consultants. Now wait for the magic of customer analytics to happen. Right?
Wrong. Building a successful customer analytics capability involves careful orchestration of several capabilities and requires customer insights (CI) professionals to answer some key questions about their current state of customer analytics:
What is the level of importance given to customer analytics in your organization?
Have you clearly defined where you will use the output of customer analytics?
How is your analytics team structured and supported?
How do you manage and process your customer data?
Do you have clear line of sight between analytics efforts and business outcomes?
What is the process of sharing insights from analytics projects?
What type of technology do you need to produce, consume and activate analytics?
In 2002, the zeitgeist orchestrator David Bowie opined, “Music itself is going to become like running water or electricity.” A few years later, in 2005, the futurists Gerd Leonhard and Dave Kusek proposed “music as water” in their industry-shaking book, The Future of Music (A Manifesto for the Digital Music Revolution).
The metaphor was simple — music would flow on demand, like a utility, to people's home hi-fis and portable music players. Subscription access to "all" music was the approach that ultimately ended up with no more ownership of physical or even digital copies; CDs, mp3s, and the other ground-bound trinkets would no longer be necessary. Even in my own behavior, I see this change — where once I’d spend time ripping my CDs and loading up my 160GB iPod, now I simply curate music, like my Boxing playlist, in the cloud via Spotify.
Eleven years later, Bowie’s prediction is coming true and streaming is progressing at speed. In metropolitan Argentina 1 in 3 consumers are listening to streaming music - evenly split between mobile and computers (desktop, laptop, tablet). In France 15% of those we surveyed streamed on a computer but a whopping 27% used mobile. In fact this trend to streaming via mobile is likely to be one that will continue worldwide and today in metropolitan regions of Hong Kong and Mexico, as well as South Korea mobile has already considerably overtaken computers as the preferred listening method.
Too little data, too much data, inaccessible data, reports and dashboard that take too long to produce and often aren’t fit for purpose, analytics tools that can only be used by a handful of trained specialists – the list of complaints about business intelligence (BI) delivery is long, and IT is often seen as part of the problem. At the same time, BI has been a top implementation priority for organizations for a number of years now, as firms clearly recognize the value of data and analytics when it comes to improving decisions and outcomes.
So what can you do to make sure that your BI initiative doesn't end up on the scrap heap of failed projects? Seeking answers to this question isn't unique to BI projects — but there is an added sense of urgency in the BI context, given that BI-related endeavors are typically difficult to get off the ground, and there are horror stories aplenty of big-ticket BI investments that haven’t yielded the desired benefit.
In a recent research project, we set out to discover what sets apart successful BI projects from those that struggle. The best practices we identified may seem obvious, but they are what differentiates those whose BI projects fail to meet business needs (or fail altogether) from those whose projects are successful. Overall, it’s about finding the right balance between business and IT when it comes to responsibilities and tasks – neither party can go it alone. The six key best practices are:
· Put the business into business intelligence.
· Be agile, and aim to deliver self-service.
· Establish a solid foundation for your data as well your BI initiative.
Forrester is launching new research looking at how firms and companies can better use data and analytics. Please help us make this research better by taking our survey. We want to hear from you whether you use data extensively or not, and your responses will be extremely valuable. Plus you get a free Forrester report (not to mention the warm glow you'll get from helping out).
In addition, we appreciate any efforts to spread the word: Forward this to anyone who uses - or could use - data as part of their job.
On behalf of the Forrester team, thank you very much!
Since 2010, when Forrester asks about organizations’ top software priorities, the number one ranked priority has been business intelligence (BI). Continued economic uncertainty and major industry-changing dynamics like mobility and the shift to digital business put a premium on data and information. The ability to effectively extract, analyze, and interpret vast quantities of data has simply become critical to business strategy decisions. Investments in BI analytics reflect the importance being placed on these technologies.
However, the large number of analytics technologies at differing levels of maturity and adoption has, in many cases, left planners of BI confused as to which technology should be adopted and for which scenario.
As a result, my colleague, Holger Kisker, and I used Forrester’s TechRadar methodology to examine 15 key analytics technologies to identify their usage scenario, current maturity within the enterprise, future trajectory, key vendors, as well as estimated costs for implementation. The technologies analyzed included the following: reporting, dashboards, performance analytics, embedded analytics, web analytics, process analytics, predictive analytics, OLAP, advanced visualization, metadata-generated analytics, location analytics, search/discovery, streaming analytics, nonmodeled data exploration and discovery, and finally text analytics. Forrester clients can read the full report here.
I recently received a direct mail piece from one of my favorite retailers with a massive ad in that proclaimed "We Beat Internet Prices." Now, I am a big fan of straightforward and robust value propositions, but these types of brand exclamations are antiquated and add little value to customers, mainly because they simply reward customers for being good bargain hunters. Instead of simply stating you beat your competitor’s prices, employing strategic pricing and customer engagement initiatives creates real distinct value to your customers by:
Showing them you can execute on your low price promise and not just talk about it. Employing a holistic pricing strategy meets your customer’s price expectations can indicate to your customers that you are truly ‘walking the walk’ when it comes to offering the lowest price.
Building your credibility. Understanding your customers’ needs and offering solutions that facilitate decisions and generate engagement builds credibility. Simply shouting that you match Internet prices does little to build credibility with your customers.
Helping them with real problems. Shoppers don’t need guidance on finding the lowest price -- they need to understand how your brand and solution help them compared to your competition.
Initial business intelligence (BI) ployment efforts are often difficult to predict and may dwarf the investment you made in BI platform software. The effort and costs associated with professional services, whether you use internal staff or hire contractors, depend not only on the complexity of business requirements like metrics, measures, reports, dashboards, and alerts, but also on the number of data sources you are integrating, the complexity of your data integration processes, and logical and physical data modeling. At the very least Forrester recommends considering the following components and their complexity to estimate development, system integration and deployment effort: