Competitive success (and failure) is increasingly determined by the quality of deep customer / market intelligence insight combined with the ability to ensure this insight actually gets to all the people that should act on it. Most Market and Customer Intelligence (MI) teams find themselves frustrated in their attempt to deliver on these values due to time-consuming manual processes and the challenge of putting this data safely into the hands of more people. The solution: one-click reporting automation and self-service visualizations.
Given the critical – and rapidly growing – importance of deep customer and market insight, the key ambition and key challenge for Market Research / Customer and Market Intelligence (we will refer to these collectively as “MI”) teams in large organizations is twofold:
- Creating deep insight and recommendations, not just reports. The frustrating reality for many MI teams, is that the apparently simple act of producing “the basics” (e.g. periodic reports) is so labor-intensive, that very little time is actually left over for analysis and the creation of deep insight and recommendations. Their key challenge is to (re-)deploy valuable time and human talent that is currently spent on “production” towards the creation of deep insight and recommendations for key decision-makers.
- Distributing insight more widely – safely. Once the first challenge is addressed and deep insight is created, it is critical that its value and impact to the business is fully exploited through wider access to regular (non-expert) business users. And, in a way that leaves zero scope for misinterpretation of this rich and (statistically) complex data. Deep insight is worthless if it remains within a small circle of experts and does not get to everyone on the “front line” who can and should act on it.
1. Escaping the ‘hygiene factor trap’ – creating space for insight
Most MI teams aspire – and are expected by the business – to be expert consultants. Reality is unfortunately very different for many MI team. These teams find themselves in a “no win, taken-for-granted-hygiene-factor” trap they find it difficult to escape from. They are forced to spend all their time on producing a standardized output that is “simply expected” and taken for granted by their internal stakeholder (i.e. a so-called hygiene factor). Worse, the only opportunity they have to be noticed is a negative one (i.e. when there is an error, or a deadline is missed). Their colleagues in the IT support teams will recognize this situation. Nobody calls IT support to thank them for the fact that “e-mail is working again today”. No, this is simply expected and they are only noticed and contacted (negatively) when they fail to deliver to these expectations.
Of course, this situation is not unique to MI. Indeed, other departments that are responsible for periodic reporting on critical data, such as finance, face the same challenge. Not surprisingly, the basic solution is also the same: automation, automation, automation.
There is no reason why valuable human time and talent should continue to be spent on routine, repetitive, manual activities that are ideal scenarios for the application of technology.
Similar to the earlier mentioned departments, the objective of the MI team should be to automate all standard reporting and updating activities, so that human time and talent can be released for complex, value-added analysis. In other words, turn the “80/20” to “20/80”. Move from 80% time spent on production and 20% on analysis to 20% production and 80% analysis through effective automation.
It is in executing this objective that MI does face a unique challenge, compared to other areas, such as finance or sales. Specifically, the particular nature of its (survey-based) data. “Regular” (transactional) business data, such as sales and financial data, simply require the application of basic arithmetic rules and can therefore be managed by almost any “generic” reporting or business intelligence tool. MI data, on the other hands, requires the application of statistical techniques. Think for example about the challenge of “significance” and sample size considerations, the correct application of weights across calculations, managing multiple-choice results or determining the statistical correlation of image and satisfaction items on purchase decision.
MI data therefore not only requires an effective automated reporting solution, but one that is specialized and natively supports MI data and processes.
Slideworx, we call this concept “one-click reporting”: once a “story” (i.e. interactive report and presentation template) is created, it should be a matter of a click of a button to update or regenerate it for other countries, products, divisions etc. Allowing highly skilled MI professionals to apply their time and talent to the type of deep, “drill-down” analysis that has a real, noticeable business impact.
2. Safe self-service – zero risk distribution of MI data to regular users
Once time and space is created to generate deep insight, the next problem is how to get this into the hands of more people across the business, effectively and safely. This challenge quite naturally leads MI teams to consider some type of “self-service” concept. The big, and widely underestimated, challenge, however, is getting self-service right. Specifically, the challenge of ensuring that such self-service is both meaningful in providing real self-service (i.e. empowerment) and does not create more questions (for the MI teams) than answers (for the business users).
Unfortunately, many Business Intelligence (BI) solutions have erred in one or both of these areas:
- Self-service anarchy – Many BI solutions approach self-service as an extreme form of “data to the people” individual empowerment without properly considering the governance needs required to make self-service work in large complex corporations. Empowerment without clear rules and a common “vocabulary / language” (e.g. KPI definitions) is a recipe for chaos.
- Fake self-service. Other BI solutions restrict the self-service flexibility for non-expert users to such a degree that it becomes practically meaningless and does not actually empower them and/or reduce their dependence on (“bottleneck”) experts for simple tasks / questions. For example, these tools give non-expert users access to a more interactive version of standard reports but do not allow them to change these in any meaningful way to create their own reports or presentations.
The above challenges are true for any BI application in a large organizational context. They are doubly true when considering MI data.
Similar to the earlier points made regarding the statistical nature of (survey-based) MI data, the complex considerations required to reach correct conclusions means that that survey-based data cannot simply be managed by anyone with basic numeracy (e.g. add, subtract, divide, multiply). As a result, many MI teams are naturally cautious about – if not outright fearful of – providing non-experts with a meaningful level of self-service empowerment. Simply putting MI data into the hands of more people without the proper safeguards would create more problems than it would solve. Problems that MI would probably be held responsible for as ultimate guardians of this data and analysis. Instead of being the catalyst of optimal, widely distributed and adopted organizational insight, this turns the MI team into the bottleneck.
Imagine for example, a meeting where different people arrive with different, most likely not clearly defined or documented, methodological assumptions and results and ways of calculating key performance indicators (KPI) (e.g. “overall product satisfaction”), leading to completely different conclusions. This meeting is unlikely to focus on reaching a decision on action. Instead, all time will probably be spent arguing about “who has the right data”. Worse, after a few of these meetings, it is likely that all trust in these studies, data and conclusions will be eroded. This is why the word “safely” is so crucial.
The key to successful self-service is creating real empowerment for business users, within a clearly defined, water-tight “safe space” defined by the MI team.
To achieve this, we have taken an approach that is very different from mainstream BI players. Instead of focusing on “charts”, the whole Slideworx system is built around what we call (analytical) “visualizations”. Visualizations are effectively analytical graphical modules that provide the business users with a high degree of customization and analytical flexibility within clearly defined parameters and with application of the calculation rules defined by the MI team within the underlying cross tabulation tool and data engine. Each visualization is optimized for a specific type of analysis (e.g. structure, frequency, scatterplot etc.) and for optimal non-expert simplicity and usability. A visualization can either be used for dynamic analysis “on screen” or to produce fully customized Interactive Stories (i.e. reports or slide presentations) or exported as fully formatted corporate documents.
In other words, “anyone” can autonomously create their own analysis and reports / slides either from scratch or reusing official reports and presentations created by the MI team at zero risk of errors, inconsistencies or misinterpretations.
The combined result of “one click reporting” and “safe self-service visualizations”: space for the MI team to fulfill their role as consultants – creating deep insight and recommendations to the business – and the ability to distribute this for maximum business impact with complete peace of mind.