Driving Business Growth with Predictive Analytics

The rate at which data is being generated is accelerating. Exponentially so. For example, more than 500 hours of video are uploaded to the popular online video sharing and social media platform, YouTube, every minute. At common rates of video file size, that translates to 4,860 terabytes a day.¹

Businesses not only have their own internal data, but they can also readily access or gather many types of external data. As the data explosion continues to pick up pace, it is not only incredible to think of the sheer amount of data being generated every moment, but also very exciting to consider how all this data could be put to many good and profitable uses.

The Challenge

More data does not equate to better data. Nor does it mean businesses are leveraging it more thoroughly. Most companies actually use a small amount of the data they collect and store.²

Clearly, there are challenges involved in putting data to use in ways that benefit businesses. Common challenges include storage and infrastructure issues, lack of expertise in handling and analysis, and developing viable business strategies with the data at hand.

The vast amount of data generated due to the data explosion is making it even harder to find the data that really matters. 

In the coming years, the real challenge for businesses will be how to wade through all the data being generated and extract accurate intelligence from it. Having said that, deriving accurate intelligence or facts from data will not be enough. Data will need to be gathered and analyzed in a way that enables strategic benefit to businesses on a practical level. Also, keep in mind that most data has a limited lifespan and becomes less useful or relevant over time. 

Love it or hate it, data is everywhere, and it isn’t going away. Despite the abundance, even overabundance, of available data, relatively few businesses are truly being smart with their data. Too often companies lag, failing to exploit the power of their data and apply basic strategic frameworks that will grow their businesses.
 

The Opportunity

Even though these challenges are very real, within the data explosion lies extraordinary opportunity. The most successful businesses of the 21st century will likely be those that embrace the intelligent use of data and analytics. We’ve seen firsthand many times how incremental data-driven improvements in business operations and strategies yield disproportionately high returns at scale. To adapt an old cliché, even a little data can go a long way. 

Fertile Area to Drive Business Growth

One of the most fertile areas for furthering the use of data to drive business growth lies in developing predictive applications relevant to your interactions with your customer base. Doing so requires understanding and linking category or market dynamics to your customer data platform.

Customer Value and Market Dynamics

Definitions of customer value shouldn’t merely be equated with the revenue a customer provides at present but should also include dimensions such as customer loyalty and potential lifetime value, constructs reflected by the overall value your brand or company delivers to the customer. Factoring in related market dynamics and being able to accurately identify what drives customer value are essential components of the successful use of data to grow your business. 

Linking It All Together to Provide Predictive Utility

As you develop your understanding of the aforementioned dynamics, you can apply predictive analysis and customer modeling to project future customer value as well as identify additional opportunity.

The great news is that you can start to link this intelligence to your customer data platform and segment your customer base. Segmentation might be based on measures of current value versus future value as well as opportunity, for example.  Predictive analysis and customer modeling can be used to help you understand future customer value and to identify those customers or customer segments that are the most likely to grow. You can also identify customers who need investment versus customers who may have peaked and for whom your focus would be on customer retention or profit maximization. 

Multi-Dimensional Strategic View

Once this strategic framework is developed, it can be refined periodically based on additional customer data and updating of your customer models.

Other constructs to understand might include:

• Average Customer Value and Market Share
• Drivers of Customer Satisfaction and Advocacy
• Barriers to Customer Growth
• Competitive Pressures or Environmental Influences
• Emerging Technology or Threats

Using classic market research and advanced analytics, you have a powerful way to stay on top of the dynamics at play in both your respective category and among your specific customer base.

The Value of Predictive Analysis Over Time

So, what is it about predictive analysis that makes it so valuable? 

Predictive analysis can help you:

• Gain insight into where your strategic focus priorities should be and with whom
• Set a replicable measurable framework in place that will be used to track growth as well as identify opportunities and risks
• Gain insight into developing supporting tactical deployments that will optimize and grow your business
• Build a loyal customer base with a higher per capita lifetime value

Exciting database applications exist in identifying customers who are at risk for churning out of your business before it’s too late, developing lifetime loyalty among your customer-base, and strategic account-based marketing.

Interested in learning more about predictive analytics and how to use data more effectively for your business? TRC can help you find the right data to move your business forward. Contact us to discuss your goals and needs. 
 

References

1. How To Understand the Data Explosion, Overberg and Hand, The Wall Street Journal, December 8, 2021, How to Understand the Data Explosion - WSJ
2. Big Data Overload: Why Most Companies Can’t Deal With The Data Explosion, Bernard Marr, Forbes, April 28, 2016, Big Data Overload: Why Most Companies Can't Deal With The Data Explosion (forbes.com)