Business Value with Predictive Analytics
The market place is fast moving and competitive. To be successful businesses should move faster, stay smarter and have greater forward-looking business insight than their competitors. Numerous flashy computer technologies, management processes promise to improve a company’s bottom line but in reality are expensive and yield minimal positive results. Predictive analytics is different from many technologies because its impact is readily apparent and quantifiable. You can quantify the money you save because predictive analytics improves business processes and increases sales and marketing effectiveness. When you can attribute good numbers to a specific technology such as predictive analytics, you can easily see the value of that technology.
Getting Business Value from Predictive Analytics
Unless a new technology shows you a business value, you’re not likely to use it. Business value can be either qualitative or quantitative. Predictive analytics provides both qualitative and quantitative benefits:
Qualitative. Creating a complete picture of the targeted customer demographic. Understanding what customers need, what they like, and what they don’t like has important value, but assigning a dollar value to this knowledge isn’t easy.
Quantitative. Identifying tangible growth in sales, revenue, or the customer base, which can be assigned a numeric value or percentage. Alternatively, experiencing lower costs, fewer complaints, better customer retention, or fewer problems overall translates to cost savings for an organization. That predictive analytics promotes better decision making is a qualitative statement. Associating increased sales or decreased expenses with predictive analytics provides the quantitative benefits that business leaders relish which is —business people like cold, hard numbers!
The effect that predictive analytics has on a business is determined by how widely and how aggressively the business uses it. Specifically, the wider the area to which a company applies predictive analytics, the greater the potential impact. The use cases for predictive analytics vary, but they can include
- Prospecting. Identifying potential new customers and converting prospects into actual customers.
- Cross-selling and segmentation. Segmenting customers based on past purchasing behavior to better target future promotional offers.
- Customer retention. Analyzing the reasons behind losing customers and enacting cost-effective practices to increase retention and reduce loss.
- Capacity planning. Correctly allocating shared infrastructure resources to allow for appropriate usage without overpaying for unused, excessive capacity.
- Market basket analysis. Determining the right combination of products and services to promote, merchandise, price, and bundle together to maximize sales.
- Market optimization. Determining the optimal marketing campaign by audience segment.
Testing different changes (such as new or different products) and comparing the results against a baseline example.
- Churn analysis.
Determining which customers are most likely to churn and taking steps to prevent them from doing so. Note: Churn rate is most applicable to the subscribers of a service (such as cell phone service) and is the percentage of customers who discontinue their subscription. When discontinuing a subscription, a subscriber generally goes to a competitor.
- Sales forecasting. Forecasting sales for stores based on location, demographics, and past sales.
- Assortment planning. Delivering the right products at the right time to the right stores.
Applying predictive analytics to a business or functional area will have an impact. The degree to which predictive analytics is applied and to what area within the business will determine the overall impact.
Once you’ve determined that you want to use predictive analytics, you need to determine the specific areas where you want to apply the technology. Generally speaking, predictive analytics is applied to business or functional areas where perceived problems or shortcomings exist, or to areas where you hope to realize greater benefits.
Before implementing any changes or applying new processes or tools, you need to collect performance statistics. These statistics form the baseline on which to gauge the impact of the new processes and analytic tools.
As you apply predictive analytics to an area, chart the quantifiable metrics of that area to determine the impact it is having. Compare the new results against the numbers you had before using predictive analytics. Based on the improvements you have quantified, you can determine your improved Return on Investment (ROI). Using the improved ROI allows you to quantify the business value of predictive analytics.
Improving Business with Predictive Analytics
Companies often find that the greatest use of predictive analytics is to increase sales through customer analytics. Companies exist to sell products and services, so deploying predictive analytics to boost sales makes good business sense. Specifically, companies leverage predictive analytics to:
Acquire customers: Identify and obtain new customers for first-time sales in an efficient manner while reducing marketing and advertising costs.
Retain customers: Keep existing customers longer and reduce the likelihood they will shop with competitors.
Grow customer relationships: Expand sales with existing customers to larger and more frequent purchases.
At its core, predictive analytics allows companies to more closely and accurately identify and understand the needs of their customers. With this specific customer information, targeted sales, marketing, and promotional campaigns become less expensive while generating increased revenue. If you want to see an example of targeted marketing, be sure to pay attention to the nature of the commercials that run during your favorite TV show or sporting event. Odds are that the products you see advertised during a football game are not the same products advertised during Saturday morning children’s cartoons. Companies must target their advertising efforts to the audiences most likely to purchase their products or services.
The scope of how predictive analytics is used in various industries is expansive. Almost all industries can use predictive analytics to increase their sales and better understand their customer relationships. So, how can predictive analytics be used in business? Consider these real-life examples of combining predictive analytics with Big Data:
✓ Taking a proactive rather than a reactive position based on real-time data trends and predictions to ensure the leadership position in the market. For example, if a company knows that the demand for a product will increase in several months, that company can fill its inventory for that impending demand.
✓ Suggesting additional products to customers, similar to what they’ve shown interest in or may not feel they need.
Many online sales catalogs alert customers to products related to those in their shopping carts or for necessary support products such as batteries and maintenance items. Increasing sales while helping customers purchase everything they need is a benefit for everyone.
✓ Quantifying and leveraging the actual value of social media comments on the business. Blend the context of Big Data with user sentiment analysis and quantitative data to gain an accurate picture of the environment and market direction.
Predictive analytics puts companies in the driver’s seat interns of knowing how to better understand their customers and proactively meet their customers’ needs. Some industries also use predictive analytics for non-sales purposes.
For example, both insurance and banking and finance industries use predictive analytics to help detect fraud among customers. Some industries use predictive analytics to forecast growth and resource management requirements.
Industries such as health care use predictive analytics to improve patient health.