A Crystal Ball Into Your Company’s Future?

Gain more insight and make better decisions by coupling business relevant information with predictive analytics

Business intelligence provides valuable insight into the state of affairs within an organization. The information is critical to decision-making. But when combined with predictive analysis, synergies can be leveraged to improve business and operations. Many industry analysts like to make an analogy between BI and predictive analytics by citing a quote from the famous hockey player Wayne Gretzky, who said: “A good hockey player plays where the puck is. A great hockey player plays where the puck is going to be.”

Comparably, BI tools help users know what has happened and what is happening, while predictive analytics tools help to elicit more from this information by providing an understanding of why these things happened and in predicting what will happen.

For example, BI tools can report which sales region had the highest sales, how many widgets were sold in stores in different ZIP codes, the average spending per online customer vs. in-store customer, and how many customers stopped doing business with your company last year. All of this information is essential for developing new product and services, allocating resources, investing in marketing campaigns, and so on.

Predictive analytics tools, though, can give deeper insight into why these things happened. For example, knowing the average customer spends $100 per visit to a store is one thing. Knowing that a certain 20 percent of the customers are responsible for 80 percent of all revenues and that they are more likely to buy particular products bundled together is much more valuable. Also, identifying which products influenced the purchase of others or the strength of the relationship between products purchased together would give more insight into specific buying patterns. This added level of analysis can yield valuable results. It helps you understand how that prized segment of your customer base would respond to very targeted promotions.

Similarly, knowing that the average response rate to a direct-mail marketing campaign is, say, 4 percent, an organization can decide how often to run these campaigns factoring in mailing costs and the revenue generated by a campaign’s sales. Knowing the types of customers and being able to correlate that with what they purchased and when they are likely to purchase again would allow an organization to target those customers at the right time with the right offerings.

This would allow the company to increase the effectiveness of their marketing promotions while ensuring customers are offered a product or service they would actually be interested in. That’s the difference between BI and the power of BI combined with predictive analytics.

 

Predictive analytics helps organizations look forward and make educated decisions that anticipate the future needs of customers. It combines known information about customers, sales, operations, or finances, with critical insight that helps solve problems, achieve business objectives, and uncover hidden patterns not easily identifiable through reports or dashboards. The combined knowledge is used to take actions that can improve business.

A traditional example of predictive analysis’ use would be to identify trends like poor customer service or customer dissatisfaction and correlate complaints to customer churn. Having insight into why customers are leaving or why they stay, an organization can take action to retain them. For instance, by surveying customers, an organization might find that 30 percent of their customers consider the price of the service to be the most important factor in choosing a company. Another 30 percent might love to receive perks and consider such offerings a distinguishing factor that keeps them coming back. And the rest might simply feel that timely and courteous service is essential.

Having this level of insight into customer likes and dislikes can help an organization make predictions about the future actions of these customers. Correlating this information with actual customer actions allows an organization to take action. For example, having identified a segment of the customer base that attaches importance to pricing, an organization might offer discounts or reduced rates if the customer signs a multi-year contract. Those who love perks might be offered free shipping, a free music download, or an extra day at a hotel.

In another area, an organization might use predictive analytics to cross-analyze sales data and marketing spending, perhaps finding that 80 percent of the sales in response to direct mail or e-mail campaign come from 20 percent of its customer base. By selectively targeting this group of 20 percent in future campaigns, the organization can significantly increase the ROI of these campaigns.

With such success from traditional predictive analytics usage, organizations are looking to expand its influence to more areas of operations and to more users. In particular, predictive analytics is increasingly being used to help identify key influencers in customer satisfaction, employee retention rates, customer churn, and other areas.

For example, Human Resources (HR) might use predictive analytics to help select job applicants. Specifically, employers want to predict which job applicants are going to make a commitment to their job. Predictive analytics can be used to show which personality traits are better predictors for worker productivity and turnover. Predictive analytics might also be used to retain talented employees by helping predict if an employee is likely to leave based on the types of services they consume from the company such as training, taking advantage of 401k plans, or the number of vacation days taken. Armed with this information HR managers can target top performers with programs designed to increase their investment in the company and hence their likelihood of staying.

If you are ready to learn more about the benefits of Predictive Analytics combined with Business Intelligence, contact Axis Global Partners today.

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This entry was posted in Business Intelligence (BI), May 2011, Newsletter and tagged , , , . Bookmark the permalink.

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