Can midsize companies stay afloat or even grow in this challenging time? The answer is yes. As a former VP of a fast-growing startup company that was hit by – and survived – the 2001 recession, I know this is a terrifying time. But I am here to tell you that as unlikely as it may seem, midsize companies have the opportunity not just to survive but thrive. And analytics can play a key role in helping weather this crisis.
2019 research by the National Center for the Middle Market on the power of analytics shows that analytics helps drive stronger middle-market performance by providing insights and make stronger decisions from data. This has never been more critical than it is now, as midsize companies need to adapt to address the unique situation we find ourselves in. Let me give you an example of a midsize business using analytics to navigate the current economic climate. And while this example is from a US company in a specific industry, it is entirely applicable to businesses in many countries and industries worldwide.
Twiddy & Company is a vacation rental company in the North Carolina Outer Banks (OBX) representing the owners of privately held vacation homes. With stay in place orders and an OBX ban on visitors, revenue is currently flat. Yet Clark Twiddy, CEO of Twiddy & Company, isn’t panicking. The company made an investment in analytics on the tail end of the financial crisis in 2008 after realizing they needed better information from which to make decisions affecting their bottom line. Now they’re using analytics to survive and ultimately thrive.
“The companies that will survive and profit on the other side of this crisis are those that understand their customer and the data in their business,” Clark told me recently. Right now Twiddy is using analytics to forecast what demand will need to be for vacation homes to obtain 0% vacancy in the upcoming season. Models show they’ll need a 1.8 to 2.9 demand multiplier to reach their goals.
Armed with this analysis, Twiddy can make shrewd advertising investment decisions. Knowing which weeks are predicted to have slow uptake advises their marketing strategy.
“Having this intelligence means we don’t have to stop marketing altogether but can make judicious decisions on when and where to advertise,” said Clark. “We’re seeing many companies shut off advertising dollars completely, but this is an emotional response. Having these multiplier models helps us allocate capital much more efficiently in this tense environment. The data show us where to turn off ad dollars when there is not requirement for a multiplier effect and when to increase investment where we see 2x or 4x multiplier needed. Companies that can allocate scarce capital the most effectively and efficiently are positioning themselves on the other side of the curve to be the first movers to recapture that demand.”
Getting through the adoption path and the hurdles
When Twiddy & Company started its analytics journey, no one in the company had any business analytics experience. They were domain experts but not modelers. But any company can realize success with analytics. It just begins with a realistic analytics adoption path. Key steps to consider:
- Poor or dirty data quality is a critical issue. Research from Gartner on data quality found that in 2017, poor-quality data had an annual average cost of $15 million. Data quality enables you to standardize and improve new and existing data so you can trust the information you’re going to use as the basis for your business initiatives. The first step is to identify all your data sources and ensure the data is cleaned and data management is accessible from a single, common control point. It is also important to be able to integrate with third-party applications, which makes it possible to merge all data into a single system before analyzing structured data.
- If you do not have anyone with analytics experience on your team, consider working with a consulting firm or a partner who can help get you started.
- Look for flexible analytics deployment options that fit your priorities, whether they be on-site, private hosted cloud, public or community cloud, or software as a service (SaaS).
- Evaluate analytics that can ultimately be used not just by business analysts, developers or data scientists with experience using tools such as R and Python API, but also by novices and business users through solutions such as visual analytics.
- As you plan for the long game, consider analytics that natively embed artificial intelligence capabilities in their software to provide you with more intelligent, automated solutions.
I’ll leave you with some sage advice from Clark: “So many midsize companies are in firefighting mode right now, but the antidote to anxiety and fear is a disciplined approach to data analytics. Whatever position we are in on the other side of this curve, it will in no small reason be because of our use of analytics. Our survival will be our ability to understand how to use resources with the greatest effectiveness, and every one of those decisions is enabled by data and interpretation and knowing how to use it.”