Every business wants to be ‘agile’ in today’s hyper-accelerated world. But what does that mean? Fast, iterative and adaptive agile research is a non-negotiable for companies moving into the next era of innovation work, says Nick Coates.
The Agile Manifesto was developed by frustrated software developers in 2001. Instead of document driven and heavy processes, it encouraged rapid and flexible responses to consumer input. In recent years, this has stretched to the area of consumer insight, and agility has now become an urgent imperative in the research process.
Fast, iterative and adaptive agile research is a non-negotiable for companies moving into the next era of innovation work.
Instead of following traditional research processes that have not been challenged or revised for many years, researchers need to generate consumer insights quickly, learn from those insights, and then decide on the most impactful next step – depending on where the results take them and not on what has been continued year after year as a matter of ‘best practice’.
Ultimately, agile research should help innovators get to market faster and with better products.
Despite the many strides made with agile research, an Ipsos global survey found that only 24% of consumers felt that brands deliver regular innovations and new products. Innovation remains an elusive concept, with 94% of global executives reporting they are dissatisfied with their organisation’s innovation performance. Researchers need to do better to help facilitate effective innovation for our clients.
Ipsos believes that the journey to agile research will be characterised by four major trends:
- Quality and speed.
- Social intelligence will play a larger role
- Artificial intelligence will help facilitate iteration
- Modular innovation approaches will be more popular
Let’s examine each of these trends in detail
- Quality and speed
Speed is a key concept of agility. To deliver speed, many types of innovation research – including idea, concept and package testing – have become automated and/or standardised. This is ideal if speed is the only requirement, but these solutions often means research outputs lack quality. Some of the issues that arise from automated solutions include unrepresentative samples, device specific solutions, unproven measures of success and limited analysis and ways of interpreting the data.
Solutions need to be fast and high-quality. For example, idea, concept, and package testing results must be compared to competition to be meaningful and benchmarking is key.
So, how do we ensure quality and speed?
- Real-time systems in place for assessing respondents. Are they real, are they speeding through the interview, are they providing inconsistent answers? Systems should pick up these faults to correct them in real-time.
- Device agnostic surveys to maximise coverage and respondent reach
- Validated success measures should form the basis of all agile idea, concept and package testing. (For example, at Ipsos, we use Relevance, Expensiveness and Differentiation for our rapid innovation testing, measures that have been tested and proven).
We are fortunate enough to have research and development (R&D) to provide device agnostic tools as well as validation of the measures we use in our agile research.
Finally, we expect more diagnostics and guidance from the research solutions that are employed. Solutions should include success drivers, forecasting and profiles, to name some examples that will help to manage innovation portfolios.
- Social intelligence and product development
There is huge scope for the role of social intelligence in research practices, one example being product development. It’s fast, it’s flexible and it’s cost-efficient. Social intelligence is already being leveraged to identify innovation opportunities.
While marketers typically rely on surveys, focus groups and desktop research to uncover new trends, social intelligence is becoming a new agile alternative. Social intelligence accelerates innovation because you do not need to ask consumers any questions. Using text analytics, you can analyse large amounts of data and have access to real-time information.
- Artificial intelligence will help facilitate iteration
Agile research is not only intended to be fast; it should also be iterative. During rapid concept tests, for example, results from the fieldwork should ideally inform real-time changes to the survey to glean better information based on what has already come up.
Rapid prototyping is another possibly, whereby prototypes are evaluated by consecutive groups of consumers, immediately followed by a work session with R&D to merge the results on-site and in real-time. This then directs the next step – being suggestions from the consumers themselves about further optimisation. This has the potential to happen in one day – merging quantitative rating scales with qualitative explanations.
Iterative approaches such as these are essential to facilitate speed, collaboration, continuous learning and of course, agility. Artificial Intelligence can automate certain research processes, which is why the role of AI is so important. An example would be a programme that creates new questions depending on the replies received from respondents. This allows an intelligent drill down for what non-useful information might otherwise be, should the question not be satisfactorily answered in the first instance.
- Modular innovation approaches will become more prevalent
Traditional innovation processes have always followed predefined sequences with yes/no outcomes at the end of each stage. We are starting to see these linear processes giving way to modular approaches. Research and learnings from different sources and studies are merged together and, if appropriate, traditional steps are eliminated because they don’t add value. This agile approach is quicker, easier and more learnings-driven than many traditional market research approaches and, ultimately, will help the marketer get to market faster with a better innovation.
Moving agile to the next level
Agile research promises to help marketers move more quickly, more efficiently and more intelligently than ever before. However, agile research as it exists today is just the beginning of what will be a huge change in how we conduct innovation research and it is something that the research industry should be especially excited about. We expect to see agile research evolve to deliver higher quality research, more (automated) iterative processes and more holistic learnings. The result will be faster, deeper insights that will help marketers achieve greater innovation success.