How Intel's CIO Drives $1 Billion Of Value Through Analytics

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By Peter High, President, Metis Strategy

This article is by Featured Blogger Peter High from his Forbes.com Column.

From the beginning of her tenure as Intel’s CIO, Kim Stevenson has pushed IT to be a source of innovation for the company. One of my favorite stories from Implementing World Class IT Strategy was a vignette that described how Stevenson polled her new peers in the leadership team about IT’s performance upon taking the job. She discovered that they were largely satisfied. When she dug into the details more deeply, however, she discovered that IT was not being judged accurately. She had the gumption (it would be proven to be foresight) to note that the company needed to set the bar higher in its evaluation of the IT department.

Since then, Stevenson has ensured that the bar has remained high. One of the most ambitious projects that her team has led has been a big data analytics platform that has changed the way in which the company markets and sells its products. Greg Pearson, Senior Vice President, General Manager, Sales & Marketing at Intel confirms as he notes, “We have truly moved beyond looking at big data analytics as a technical project, it is essential to how we run our business.”

Stevenson estimates that it has already produced $1 billion in revenue and productivity gains to date. For this reason, she is the winner of Forbes CIO Innovation Award for 2016.

Peter High: Please describe the big data analytics platform that your team developed.

Kim Stevenson: This project changed the way Intel markets and sells, delivering USD $1 billion from incremental revenue and productivity gains. The solution delivered a personalized customer experience by translating big data analytics from sales and marketing engagements into new revenue opportunities. It leverages visibility into multiple stages of marketing-to-sales pipeline improving sales closure rates. This platform has driven better decision-making in pricing and demand forecasting for manufacturing.  This project has changed business processes for multiple groups at Intel; it has changed the way sales, marketing, distribution and demand forecasting teams work together.

High:  Is this idea new to Intel and the industry?

Stevenson:  Yes.  The project started with a proof of concept in 2014.  We used a three-phase approach to build the platform solution.

In phase one, we determined what data sets could be used for aggregation.  In phase two, we built a predictive analytics engine that modeled and learned from the aggregated data. Finally, in phase three, we optimized the engine to include feedback from the sales organization to create the account rankings.

After we identified the data sources to access, we filled out and scored the profiles of the reseller channel customers.  We added static business rules—which we created based on characteristics of known high volume customers—to increase profile accuracy.

The initial rankings were the goal of the 2014 proof of concept and provided to the sales teams.  After the sales organization engaged with the ranked accounts, they collected and integrated feedback into the big data platform to create new static business rules. This activity demonstrated value to SMG and produced more accurate rankings.

In 2015, the project was automated and scaled to include a ‘what if’ predictive modeling system with a visual user interface for factory planning, price optimization and demand forecasting.  Information visualization helps the sales people focus on accounts that yield the most sales.

The ability to make better, faster decisions starts when business partners working alongside IT, embrace the power of analytics.  Together, the SMG and IT teams utilized big data analytics in a way that created millions in revenue generation, productivity and cost reductions for Intel.

High: Please describe the way in which this award has led to enhanced revenue?

Stevenson: This project created an automated, scalable and personalized big data analytics platform that changed Intel’s approach to sales.  The project leveraged disparate data from marketing campaigns, Intel.com document searching and downloads, sales pipeline and sales billings to personalize our interactions with our reseller channel customers.   A master data management platform was developed to interconnect the big data and build an intelligent customer profile.  The final part of the project was a visualization platform to do ‘what-if’ predictive modeling to forecast demand to manufacturing and optimize pricing.

In phase one, we determined what data sets could be used for aggregation.  In phase two, we built a predictive analytics engine that modeled and learned from the aggregated data. Finally, in phase three, we optimized the engine to include feedback from the sales organization to create the account rankings.

After we identified the data sources to access, we filled out and scored the profiles of the reseller channel customers.  We added static business rules—which we created based on characteristics of known high volume customers—to increase profile accuracy.

The initial rankings were the goal of the 2014 proof of concept and provided to the sales teams.  After the sales organization engaged with the ranked accounts, they collected and integrated feedback into the big data platform to create new static business rules. This activity demonstrated value to SMG and produced more accurate rankings.

In 2015, the project was automated and scaled to include a ‘what if’ predictive modeling system with a visual user interface for factory planning, price optimization and demand forecasting.  Information visualization helps the sales people focus on accounts that yield the most sales.

The ability to make better, faster decisions starts when business partners working alongside IT, embrace the power of analytics.  Together, the SMG and IT teams utilized big data analytics in a way that created millions in revenue generation, productivity and cost reductions for Intel.

High: Please describe the way in which this award has led to enhanced revenue?

Stevenson: This project created an automated, scalable and personalized big data analytics platform that changed Intel’s approach to sales.  The project leveraged disparate data from marketing campaigns, Intel.com document searching and downloads, sales pipeline and sales billings to personalize our interactions with our reseller channel customers.   A master data management platform was developed to interconnect the big data and build an intelligent customer profile.  The final part of the project was a visualization platform to do ‘what-if’ predictive modeling to forecast demand to manufacturing and optimize pricing.

Intel IT partnered with Intel’s Sales and Marketing Group (SMG) to develop a predictive analytics engine to create a ranking system for our reseller channel customers.  The new platform identifies customers with the greatest potential for high-volume sales so the sales teams can create better engagements in order to help them grow the reseller channel business.

In the past, the sales organization focused on the largest accounts and classified customers with static business rules.  However, Intel’s reseller channel customers buy from distributors, which meant the account size couldn’t always be determined.  The sales organization needed assistance prioritizing which reseller customers should receive the most support, determining the optimal time in the customer’s buying cycle to contact them and deciding what products or support to offer.

High: How much has revenue grown due to this idea?

Stevenson: Intel IT team utilized big data analytics to provide valuable customer insights, delivering nearly $1B in incremental revenue and productivity gains for Intel in 2 years. Through the innovative use of advanced analytics and machine learning capabilities, the team:

  • Increased revenue by $185M through personalized campaigns
  • Decreased wafer production time significantly through “what-if” analysis modeling and optimizing the long range business planning cycle has avoided $100M costs annually
  • Forecasted the right product mix and demand to drive $265M in revenue up-lift over the last two years
  • Customized our reseller customer engagement to deliver 2500 new customers and ~$200M incremental revenue
  • Increased marketing campaign returns; delivering $180M in converted opportunities and $700K in annual savings through digital marketing efficiency
  • Personalized our engagement with our channel customers to deliver the right products at the right time; 41% of products recommended by the platform now turn into actual sales, higher than Intel’s internal sales agent’s rate of 30%.
  • Identified three times as many potential high-volume resellers in the Asia-Pacific region compared to what the sales organization could identify using manual methods.

Originally published on Forbes.com