The greatest competitive edge any company can have is knowing more about its operations and the market dynamics affecting its operations than its competitors. Competing with greater intensity and the ability to get more done in less time is what differentiates market leaders from every other company in a given industry.
The State of Predictive Analytics
- By 2020, predictive and prescriptive analytics will attract 40% of enterprises’ new investment in business intelligence and analytics.
- Forrester forecasts a 15% compound annual growth rate (CAGR) for the predictive analytics and machine learning market through 2021, according to their study The Forrester Wave™: Predictive Analytics And Machine Learning Solutions, Q1 2017.
- Between 2017 and 2019, spending on real-time predictive analytics is growing three times faster than any other type of analytics, according to a recent Gartner study.
- 69% of decision-makers believe analytics will be crucial for business success in 2020, with 15% considering it essential for operating their businesses today.
- According to a recent McKinsey Global Institute study, a leading aerospace manufacturer is using manufacturing intelligence to track and improve overall equipment effectiveness (OEE), reaping a $415M savings. Of that, 60% can be attributed to advanced analytics and manufacturing intelligence.
The bottom line is that market leaders across all industries, and especially in manufacturing, need to know how to make predictive analytics pay. They’ve moved beyond managing their businesses using descriptive and predictive analytics. Predicting when the best time is to define specific prices for individual customers, launch new products, discontinue legacy products and offer promotions, cross-sells and upsells is driven by insight and data.
A recent McKinsey Global Institute article includes in-depth analysis of how and where analytics is becoming the catalyst for the delivery of valuable insights thanks to machine learning. The following graphic from the study shows where predictive analytics and machine learning has the greatest potential to drive revenue growth:
The areas of optimizing pricing and scheduling in real-time, preventative maintenance in the manufacturing and energy industries, optimizing merchandising strategies (including upsell and cross-sell) and enabling manufacturers to become more customer-driven are all in the higher potential region of the graphic.
The following are the five strategies that drive revenue growth based on insights and intelligence from predictive analytics:
1. Fine-Tuning Pricing
Using predictive analytics to fine-tune pricing to specific customer segments helps companies attain greater margins based on their customers’ willingness to pay. This, in turn, grows sales. McKinsey found that 75% of a typical company’s revenue comes from its standard products, and that 30% of the thousands of pricing decisions companies make every year fail to deliver the best price. With a 1% price increase translating into an 8.7% increase in operating profits, assuming there is no loss of volume, pricing has significant upside potential for improving profitability. Being able to track price elasticity and overall costs of sales in real-time is now possible with predictive analytics and cloud-based ERP systems.
2. Combining Predictive and Geoanalytics
Combining predictive and geoanalytics to maximize the marketing spend on launching new biopharma products increases product revenues. At the same time, optimizing selling strategies and go-to-market plans using geoanalytics are starting to happen in the biopharma industry. Boston Consulting Group (BCG) found that biopharma companies typically spend 20% to 30% of their revenues on selling, general and administrative tasks. If these companies could more accurately align their selling and go-to-market strategies with the regions and territories that had the greatest sales potential, go-to-market costs would be immediately reduced. Retailers are continuing to use the combination of predictive and geoanalytics to better define which potential expansion locations offer the greatest growth potential.
3. Creating Responsive Customer Service
Predictive analytics is making it possible for marketers to create more responsive customer service, leading to greater upselling and cross-selling opportunities. Analytics is revolutionizing how companies attain greater customer responsiveness and gain greater customer insights. A Forrester study found that 44% of B2C marketers are using predictive analytics to improve their responsiveness. In addition, 36% are actively using predictive analytics to gain greater insights to plan more relationship-driven strategies. Automating cross-sell and upsell within Configure-Price-Quote (CPQ) applications has revolutionized multichannel selling. Cloud-based CPQ systems can scale the impact of predictive analytics across selling networks globally faster and more accurately than any on-premise alternative.
4. Anticipating Customer Demand
66% of sales professionals report that using predictive analytics increases their sales velocity. The main catalyst behind this is anticipating what their customers want before they ask for it. Salesforce Research also found that sales teams that rely on predictive analytics to plan account-based marketing strategies are 2.4x more likely to seek out new ways of excelling with analytics apps. 79% of sales team globally running Salesforce are using some form of analytics, with the highest performers using predictive analytics to plan and complete complex sales cycles.
5. Going All-In On Predictive Analytics
Forrester Consulting found that marketers who rely on predictive analytics are 2.9x more likely to report revenue growth at rates higher than the average for their industries. Predictive marketers are also 2.1x more likely to have a commanding leadership position in the product/service markets they serve. In addition, they’re 1.8x more likely to consistently exceed their goals when measuring the value that their marketing organizations contribute to the business. Forrester Consulting also found that manufacturers that choose to use predictive analytics to define new product prices are 67% more likely to attain profitability on the new product line in 6 months or less.