In today’s world, businesses need to evolve rapidly alongside technological advances or risk falling behind in the race for success; losing out to those prepared enough to adapt when needed. This is a great example of modern natural selection at its finest. At least 40% of all businesses are predicted to fail in the next ten years if we don’t adapt to the new opportunities presented to us by new technologies; opportunities such as becoming insight-driven brands.
Insight-driven brands look to insight and research to inform all business decisions. The difference between insight-driven and data-driven (the previous standard for high-achieving brands), is that insight-driven organisations are not only aware of the insights being drawn from collected data, they actively embed them into every decision that works towards completing business objectives.
There are many well-known examples of insight-driven brands, such as Facebook, Google, and Netflix, who are profiting from their transition into the insight-driven industry; as Forrestor Principal Analyst, James McCormick, states: “Their leaders have a fundamental and emotional understanding of the value of insights in driving their business today — and for developing its future.”
However, in order to join the ranks of the biggest and best brands becoming insight-driven in the next few years, we first need to understand the challenges that insight-driven brands will inevitably face.
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| In order to join the ranks of the biggest and best brands becoming insight-driven, we first need to understand what the challenges are that insight-driven brands will inevitably face. |
1. Insight Management
The first challenge is understanding what to do with the insight once it has been generated. The popular conception is that the toughest part of insights is making them. However the management of these insights and what we do with them next are the bigger challenge.
Businesses often neglect a few of the management processes, specifically the embedding and reusing processes of insight management. Most companies that think they’re insight-driven because they use the insights generated for a few business purposes, are actually only insight-aware. In order to become truly insight-driven we need to gather all of the insights generated and use them in all subsequent processes and decisions across the whole organisation.
A lot of the best insight-driven businesses don’t focus on gathering lots of data every time new insights are needed. The key to an efficient and effective insight-driven business is reusing the same insight; building up a research corpus of data and revaluating it within different parameters, and through that revaluation, gaining new insights that were missed or overlooked during the previous evaluation. But, don’t completely forget to gather new data where possible to add to the existing corpus, because new data will always lead to new insights.
2. Insight-Driven Competition
This may be the most obvious challenge that insight-driven brands risk to face. With many brands looking to become insight-driven, higher competition is a very real challenge. In Forrester’s report, organisations that are currently becoming insight-driven include Alaska Airlines, The Washington Post, and some European football clubs. This highlights that businesses in any industry can become insight-driven.
However, with most businesses adapting in order to survive, sometime in the future we will see many of the same businesses competing against each other with the same consumer-centric strategy: ask the consumers what they want, then make it better and cheaper than the rest. This is obviously going to be a problem unless someone comes up with another evolutionary advance on insight-driven business.
So how do we compete against each other with the same insight-orientated strategy? Focusing on individual departmental strategies, such as marketing or dev-ops, might give us the edge over our competitors. Mack suggests that the best way for companies to compete against each other is to refine their marketing strategy through these specific consumer insights. These insights state that 63% of consumers would rather buy from a company they believe is authentic. In this same study, the best behaviour that our business can have is open and honest communication about products and services.
3. Insight Automation
Market research and other insight industries are slowly incorporating automated machine learning and AI within their insight processes in order to save time and money translating data into insights. With more and more businesses turning to machine learning techniques, the demand for accurate, safe, automated insight is growing.
However, before we put all of our trust into algorithms, machine learning techniques for insight generation is still in the early development stages; this means there are kinks such as algorithmic bias to work out in order to provide genuinely accurate insight for incorporation into our decision-making processes. Algorithmic bias is where a machine learning or AI algorithm develops the same biases as humans when it comes to collecting, categorising, producing, and interpreting data when generating actionable insights. This bias can skew the results that are bound for influencing major decisions, meaning that those decisions are more at risk of being wrong.
So with this in mind, should insight-driven businesses incorporate machine learning or AI algorithms into the insight generation processes? Well, it depends on a few factors: our budget and time constraints, our knowledge, our data, and our mitigation processes. If our organisation has a lot of data to process, and we need to save money and time, then sometimes making a machine do a human’s job might be the better option. But a good knowledge of machine learning algorithms is essential and a bias mitigation process that will either counteract or account for any bias that might occur within the algorithm’s results is also needed. The challenge associated with algorithmic bias for insight-driven brands, is balancing the need for relatively lightning-fast and accurate insights, with the potential for false insights due to algorithmic bias. Should we be prepared to take the risk? Or do we seek a different route for insight generation?
4. Post-Truth Insights
The concept of post-truth has a big effect on the reliability of insights, which means being insight-driven could become very tricky, very quickly. Post-truth, essentially, puts the definition of ‘truth’ up for question. In this world of post-truth, ‘truth’ is now what people believe and is affected by the inherent zeitgeist bias of society. What this means is that consumer intention, what consumers say they will do, and consumer action, what consumers actually do, might not align. With research data collected based purely on consumer intention, how do we know that the insights we are basing business decisions on are actually reflective of reality?
This is one struggle that has been discussed in a lot of different ways. Colin strong for example, explores how we can make surveys more reliable when it comes to bridging the gap between consumer intention and action. However, I believe that a specific research model needs to be created in order to uncover the truth.
5. Insight Budgets
Budgets are always tight within businesses. In order to become an insight-driven business, the insights we gain need to be valuable; they need to have tangible return on research investment. Whether you have a small budget or a large one, there will only be so much you can invest in market research and obtain tangible insights as a result.
One suggestion that could be made, as was explained in our budgeting blog, is that zero-based budgeting could a good technique to adopt. This means that instead of starting out with a budget of, say, £100,000, and that needs to be divided into smaller budgets for other necessary strategies, businesses start out with £0 and then build their budget based only on what they will need to spend. This technique has been proven to improve a business’s chances of gaining RORI.
Is the Future Insight-Driven?
The future promises to be a very turbulent time for insight-driven brands. With technological advances promising faster insights, but society promising more misinterpretable insights, navigating the future could be a minefield. However, with the success of insight-driven brands such as Netflix and Amazon, it has been proven to be the best business strategy for the present.
With the quick progression of driven businesses from data-aware, data-driven, insight-aware, to insight-driven, one question that should be on everyone’s mind is: what is the next stage of the business strategy evolution?
