6 Ways Companies Use Predictive Analytics Across Industries

6 Ways Companies Use Predictive Analytics Across Industries

Can we really know what outcomes are likely? It may not be as far-fetched as it sounds. Predictive analytics can give us a highly accurate “Crystal Ball,” allowing us to see into the future, leveraging insights gleaned from large data sets and advanced machine learning (ML) algorithms.

Predictive analytics is the use of data, algorithms, and ML techniques to assign ‘scores’ to various user segments based on historical data. Its goal is to assess a likelihood of future events — such as a purchase or customer churn — so that a specific action can be taken. Using predictive analytics, we can know with a high degree of certainty the outcomes for future customers and business activities.

Boost Marketing Results With Predictive Analytics

Marketing teams can reap rich rewards from predictive scoring, but its benefits also extend to the supply chain, operations, product, maintenance, finance, and customer service teams as well. All of these functions can benefit from using predictive techniques to improve business results and here are some examples of how they’re used.

  1. Identification of Customers Likely to Churn

    In modern growth marketing efforts, churn is a crucial statistic. The old axiom rings true, “It’s cheaper to keep an existing customer than to find a new one.” Predictive retention models can identify which customers are most likely to churn — and companies can respond by reaching out to them with education on product benefits or other promotions. Predictive scoring can also identify a set of behaviors in customers who are less likely to churn. Messaging likely churners and steering them to adopt behaviors of customers who are less likely to churn is a valuable outcome for any business.

  2. Recommendations for eCommerce Cross-selling and Upselling

    If you’re a retailer selling a variety of products, predictive scoring can help you tailor your ‘recommended for you’ product placements by analyzing historical customer data and applying customer profiles to offer look-alike targeting for optimal conversion. For example, someone who has purchased hiking boots might be shown advertising for other outdoor gear — while someone who has bought kitchenware might be shown ads for kitchenware.

  3. Predictive Maintenance and Quality Fulfillment with IoT Monitoring

    If you are responsible for customer support or hardware maintenance and repair — perhaps monitoring through IoT devices — knowing which components are most likely to fail would allow you to be proactive rather than reactive, thus avoiding unscheduled downtime and planning for replacement or maintenance of parts based on real-time data rather than schedules. Information gathered on product lifecycle and failure rates can be valuable for product and engineering for future development, and can be used to inform pricing strategy on warranties and service contracts.

  4. Prediction of Features for Product Development

    Predictive scoring can be used in industries like gaming to help determine which features — or which interfaces — to include for upcoming releases. A recommendation engine might give you a score for features with various attributes. Information such as what competitors are doing can also be incorporated. This can be useful for companies with limited resources who want to make release decisions quickly and efficiently.

  5. Inventory Forecasting and Pricing Strategy

    Predictive scoring can be used for valuable combinations of data in retail: a real-time, single view of inventory across a large number of SKUs in a wide variety of categories and locations as well as an additional layer of geographic sales data. The layers of data can be combined to identify buying patterns that can inform pricing and promotional strategy as well as inventory.

  6. Risk Calculations

    Predictive scoring is also useful for both the banking and insurance industries: to calculate risk. Predictive scoring can evaluate prospect lists and provide an indication of the relative insurance risk of a potential customer. The predictive model could examine hundreds of risk indicators, narrowing characteristics down to small details, and predict expected losses. For homeowners’ policies, data inputs might include such factors as:

    • exposure to fire
    • exposure to wind
    • proximity to open water
    • elevation

Predictive analytics improves efficiency and adds value

The business world is highly inefficient — with processes that are wasteful and not terribly effective. Applying empirical data to business processes has numerous benefits, helping identify valuable customer segments and much more. With the rise of predictive analytics, companies will be able to delight their customers with experiences tailored to their preferences, operating within an efficient and aligned organization. To learn more, download Harness the Power of Prediction to Boost Campaign Response.

Rob Glickman
Rob Glickman
Rob serves as Head of Marketing, Data Business at Arm. Previously, he was VP of audience marketing at SAP where he led a global team of marketers chartered with driving modern marketing demand generation programs. He brings nearly 20 years of marketing experience ranging from lean startups to large enterprises, including running product marketing for Symantec, eBay and PayPal, where he held various marketing leadership roles globally.
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