Achieving long-term pertinence as a brand in an era of consumers’ decreasing attention span, rapidly shifting desires and global competition is easier said than done. How can the ‘IT’ brands of today use AI analytics to ensure they will still be on top tomorrow?
When it comes to brand longevity, the times, they are a’changin
The average expectancy for a brand’s lifespan is declining more rapidly than visitors at the local gym in the first months of the new year.
In 2020, the average lifespan of a company on the S&P 500 Index was just over 21 years, compared with 32 years in 1965.
When looking at newly created brands, the picture becomes even more alarming when referencing the same report from Statista: As of 2019, the average lifespan is about 10 years before the company is bought, acquired, or liquidated.
Furthermore, companies are held increasingly accountable for their stance on social and political issues. In the Edelman Earned Brand study from 2021, nearly two-thirds of consumers worldwide would buy or boycott a brand solely because of its position on a social or political issue, which was up 13% from the year prior. If a brand takes a hands-off approach to such influences and doesn’t attend to the consumer zeitgeist, they are likely to be blindsided by an issue, resulting in a revenue loss.
In this environment, brands have a hard task in front of them: The need to simultaneously figure out how to carve out a unique lasting image and adjust strategy with the rapidness of social media trends and global events.
How did we get here?
While watching the World Cup, viewers during halftime were occasionally shown throwbacks of situations and players from somewhat twenty years ago. While the focus of the broadcast was great goal positions and players who cemented their legacy, an immediate observation was that today’s greats look nothing like the players back then. The overall differences in physical strength and body fat percentage are visible even from a single photo.
This is hardly surprising, as competition in the sport and the desire to use everything at players’ disposal to maximize their potential has led to advances in nutrition and sports science and enhanced the physical and mental capacity of modern players.
Business is evolving in the same way: strategies need to undergo an evolution at the pace of consumers to stay on top.
Many companies, having grown complacent from short-term success, fail to address the four driving social analogs that no company can escape: the environment, consumers, competition, and government.
Even today, business plans tend to be written with backwardation as a driving principle: if it worked last year, it will work next year. This strategy fails to account for the future of the market, and how rapidly the ground beneath brands is shifting.
A clear example: The worst market selloff in a decade made 2022 radically different than 2021. With a near-recession, a housing crisis in the US, and a war at Europe’s doorstep intensifying, it is a safe assumption 2023 will deliver an even bigger shift to what worked well over the last five years.
Predictive vs. Prescriptive Analytics
The method described above of using past data and historical models is also called predictive analytics. The problem with this approach, as already discussed, is that it’s only accurate in highly inelastic markets and can lead to ineffective strategies when companies are faced with something new, like a pandemic, an innovative competitor, or a complex macroeconomic event like the war in Ukraine.
Prescriptive analytics, on the other hand, employs a holistic approach and bases its findings on simulations of what will happen rather than what has happened.
Predictiveness is still used to calibrate data points and form parameters around what information is available. However, the approach goes one step further by building digital twins of the environment they operate in, companies generate insights that pull from the social analogs to answer what-if questions and reveal patterns in human behavior.
Prescriptive Analytics? I’ll Drink to That: A Case Study
One recent use for prescriptive analytics came into the market during the initial COVID-19 outbreak.
At the beginning of the pandemic, companies within the alcoholic beverage market were unsure of what to expect as no one could go to the bars. This disruption led to the emergence of a “do nothing” category which ended up costing the industry a whooping $80 billion over the next two years due to a lack of on-premise availability.
Enterprises needed to restore profitability and by using prescriptive analytics, they found their most profitable target group during the subsequent period of social recovery: Young party-goers who would be looking to seize the opportunity to go out again.
While they also were able to find the segment most likely to see a quick return in sales, brands could also identify potential threats in consumer behavior and competitors.
Simulations showed that it was likely that older, on-premise customers who historically had higher consumption levels would be much more likely to stay at home longer. Additionally, the emergence of the recreational cannabis industry represented a significant potential competitor, and data from past years could now adequately model for such a competitor because it didn’t exist at that scale.
By utilizing prescriptive analytics to model their markets, companies within the alcoholic beverage market could adjust their forecasts and optimize business decisions in the near time. Using these tools, organizations can address highly impactful social analogs and accurately plan for dynamics that have never existed before or may never even happen.
The ability to simulate any challenge within a business environment brings a level of preparedness historical data simply cannot match.
Adjusting to the Adjustment: How Can Companies Adapt Faster?
If creating a digital twin of the entire market a company operates in sounds complex, it’s because it is. It would be the equivalent of a firm electing to develop a ChatGPT platform from scratch: both extremely time-consuming and less effective than adopting a readily available solution.
Scios’ prescriptive analytics platform Market has been actively used by enterprises over the past decade to utilize the complexity of consumer behavior for long-term brand success. We have had real-life expertise and exposure to perfect simulations and AI deployment in a corporate strategy environment, helping businesses to address everything from new product launches to the 2020 COVID pandemic.
If you feel that your in-house strategy is no longer sufficient to cover all the bases when it comes to market research, you’re probably right. Contact us to discuss how your company can utilize AI to maximize its longevity and efficiency.