COVID-19 has rocked the business world. Priorities have changed and consumer behaviours and attitudes have shifted. Business has slowed; budgets have tightened. The consensus: now is not the time for assumptions and ineffective spending. It’s time for smart, evidence-based decisions. To survive the predicted post-COVID slump, businesses need data analytics and business intelligence to light the way.
The good news is many businesses are on board. According to Sisense, who surveyed more than 500 US data professionals and business executives, 55 per cent of businesses have started to use data to improve efficiency, 47 per cent to support customers and 45 per cent to predict future outcomes.
“As businesses have sought to save money and cut expenses wherever possible to weather the COVID downturn, budgets for business intelligence and data analytics projects have largely remained stable.” — Louis Columbus, Forbes.
Time to get data smart
More and more businesses are waking up to the power of data. According to Sisense, 49 per cent of companies surveyed also use data analytics more or much more than before the COVID-19 crisis. And small businesses, perhaps not so surprisingly, are leading the way thanks to their often centralised data solutions. Larger enterprises meanwhile are still struggling to change course and implement data storage infrastructure and analytics software, and un-silo data.
However, there is still one critical issue that could derail both small and big business alike. Data quality.
Collecting and analysing customer data allows data scientists like me to identify who your most important customers are, what motivates them to purchase, and where and how they engage with brands. But the problem many businesses face isn’t the collecting data, but rather drawing accurate insights from it. Poor data quality—such inconsistent variables and irrelevant data—clouds our ability to understand your data and by extension, clouds your understanding of your customers.
What data should you gather?
More data doesn’t always equal better data; quality is as important as quantity. The data you gather should:
- Collect the same variables consistently. Gaps in your data make it difficult to draw accurate insights on seasonal trends, regional/location behaviours, and demographic distinctions.
- Focus on variables that are meaningful to your business. For example, if you regularly use short-term coupon codes and promotional redemptions, it can be difficult to measure any relationship between the coupons and/or promotions and your revenue—the time frame is simply too short. Therefore, collecting data on these short-term coupons and promotions will likely have little use or value to your organisation.
- Reflect your industry. For example, a cinema that knows what genres its customers enjoy knows which movies to promote and to whom. But a cinema that also knows what snacks its customers favour has more edge in its ability to promote to its target audience. We live in a world where data is bought, sold, and traded. If you seek third-party data, seek out sources that make sense to your industry.
Data is meaningless without good analytics
Collecting data is only the start. Data is very much a raw material; the timber and concrete that, with the right tools and expertise, can be utilised to build powerful insights. Opening a spreadsheet and calculating the general statistical trends in your data is only the first level of analysis (though an insightful start). Modelling the data to find true cause and effect relationships, using reductive analytics to group variables to identify underlying themes, clustering customers into data-driven segments to better understand different types of customers—these are all practical, evidence-based insights that require the right tools and the right skills.
Accessing powerful analytics such as these requires investment, both in people and systems. And moving from intuition to data-driven decisions can be uncomfortable, especially for marketers, at least at first, but it can pay off in the long term. How well it pays off is proportional to the quality and quantity of data available. In other words, there is great value in historical data.
The power of historical data
Meaningful insights require both quality and quantity of data. Whether you are predicting seasonal changes or quarterly changes, you need data. The longer you collect data, the more valuable it becomes.
But what about Covid-19? Socially and economically, the pandemic has spun the world off its axis. What good is historical data in these unprecedented times?
Although Covid-19 is the first global disaster coupled with an economic crisis in the data age, it has not struck us blind, nor rendered your historical data useless. With or without Covid-19, we live in an era of rapid change. Many businesses have already learned how to adapt to remain relevant to customers. Having access to historical data simply makes it easier to pivot in response to market conditions.
Thanks to Covid-19, budgets are tighter, stakes are higher. And businesses need effective and focussed marketing. With historical data, you know which consumers are most likely to venture out and return to old habits first. You know who your most loyal customers are; the ones who will first grace your threshold. You know what advertising campaigns draw in new customers and what campaigns fall flat. You know that brand awareness may be the start but that raising awareness with the right themed messaging is more successful at converting consumers.
With historical data at hand, you can see how your customer behaviours have changed from the norm and adjust accordingly. If you’re only just gathering your data, you have nothing to compare it to. Which brings me to my next point.
If you’re not collecting data, start now. The sooner you start collecting, the sooner you’ll start to see results.
So now what?
It is no longer the visionaries or the innovators that need smart data to succeed. If businesses are to survive, recover, and build resilience in this new reality, they need to make all their decisions data driven. Assumption and gut feel are not enough in the current climate, especially when all the competition is going on evidence. But where there’s data and will to use it, there’s a way.
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About the author
Jack Perkins – Data Scientist
Jack is part of the team behind Perceptive’s analytical models, making him one of the primary drivers of our client’s insights—a skillset that is built around experience in database design, dashboard creation and analytics-oriented programming; not to mention a Masters in Computer Information Systems.
Predictive or descriptive, qualitative or quantitative, Jack is the man at Perceptive who makes the data dance.
 Sisense, State of BI & Analytics Report 2020: Special COVID-19 Edition, 2020.
 Louis Columbus, How COVID-19 Is Changing Analytics Spending, Forbes, May 10, 2020.