CMO, cognitive technologies

Analysis

How cognitive technologies can help marketers reach customers

Technology advances are now allowing us to automate tasks traditionally assumed to require human intelligence. This includes activities as diverse as recognizing handwriting or identifying faces, planning, reasoning from partial or uncertain information, and learning. They’re done via computer vision, natural language processing, speech recognition, and robotics, among others, and they’re collectively known as cognitive technologies.

The new capabilities cognitive technologies hold are a huge opportunity for marketers in particular. Private investment in the artificial intelligence sector, which includes cognitive technology, has been expanding 62 percent a year on average for the past several years, according to one estimate, and is expected to continue1.

Applications for cognitive technologies that can help marketers often fall into three main categories:

  • Product: Product applications embed the technology in a product or service to provide end-customer benefits. For example, Netflix uses machine learning to predict which movies a customer will like. This feature has had a significant impact on customers’ use of the service; it accounts for as much as 75 percent of Netflix usage2. The Roomba robotic vacuum cleaner created an entirely new product category, achieved sales of 10 million units, and spawned several competitors3.
  • Process: Process applications are embedded in an organization’s workflow to automate or improve operations. Automation tends to be internally focused; however, speeding up or improving internal processes can be a boon for marketing executives trying to analyze complicated or massive amounts of data that support the needs of always-on, agile marketing activities. One estimate says that by the end of 2015, 55 percent of US digital ad spend will be traded programmatically4.
  • Insight: Many companies are using cognitive technologies to generate insights that can help reduce costs, improve efficiency, increase revenues, improve effectiveness, or enhance customer service. Intel is using machine learning to improve sales effectiveness and boost revenue. One approach it takes is automatically classifying customers using a predictive algorithm into categories that are likely to have similar needs or buying patterns. The resulting categories can be used to prioritize sales efforts and tailor promotions. The company expects this strategy to result in $20 million in additional revenue when rolled out globally5.

The three Vs framework

In order for marketers to identify potential opportunities, they will need to evaluate the business case for investing in these technologies in an individualized, case-by-case way. This means looking across their business processes, their products, and their markets to examine where the use of cognitive technologies may be viable, where it could be valuable, and where it may even be vital.

  • Viable: A surprising number of tasks can now be performed by cognitive technologies. Examples include first-tier telephone customer service (which can also simultaneously capture customer data for later analysis), processing handwritten forms, or examining data sets too big to be understood by human experts and too unstructured to be analyzed by traditional analytics
  • Valuable: Some tasks are performed by experts that don’t require deep expertise, and may be a good candidate for automation. For instance, market researchers who scan open-ended response surveys looking for similar key phrases, or outliers, are using their reading skills more than their analytics skills. It may be valuable in this scenario to use natural language processing techniques to automate the process of reading, extracting, and compiling similar responses
  • Vital: Processes that require human perception at a very high scale may be unworkable without the support of cognitive technologies. Twitter uses natural language processing to help advertisers understand when, why, and how its users post comments about television shows and TV advertising

How to apply the three Vs of cognitive technologies

Even with the three Vs framework, it is not a simple or straightforward matter to adopt cognitive technologies. These technologies are still evolving, leading practices are scarce, and trial and error is very much a reality, especially when starting out. That said, marketing executives can be systematic when applying the three Vs framework to any given scenario:

  • Create a process map of your main business and marketing processes to reveal workflows where cognitive technologies may have applications, such as reviewing documents, compiling market data, processing forms, identifying patterns, and market planning and scheduling
  • Review your staffing model to identify roles where cognitive skills and training may be underutilized or where expertise is in short supply
  • Perform a data set inventory to uncover operational data sets, such as customer data and sales data, that may be under-analyzed and insufficiently exploited
  • Conduct a market analysis to reveal opportunities where improvements in performance or automation features are valuable to existing or new market segments and can differentiate your company’s offerings
  • Give preference to value- and growth-building opportunities. When prioritizing investments in cognitive technologies, companies should generally favor opportunities to create new or better products or services rather than simply to cut costs

Future challenges

Even with the ever-growing improvement of these technologies, for the foreseeable future humans will be very much “in the loop”—not only to develop, customize, and train the systems, but also to oversee, guide, and improve them. Indeed, a promising approach for cognitive systems is designing them to work hand-in-hand with people, leveraging the strength of each.

These opportunities also come with challenges and risks. Costs and timelines may be unpredictable, knowledge of the rapidly changing landscape of cognitive technology vendors is likely to be in short supply, and marketing executives may need to redesign tasks, jobs, management practices, and performance goals when they implement cognitive technologies.

The greatest potential for cognitive technologies is to create value rather than to reduce costs. Using the three Vs framework, organizations can begin to explore where cognitive technologies will benefit them most.

To explore this topic further, read more about cognitive technologies on Deloitte Insights, or join Deloitte’s cognitive technologies course.

Published on October 19, 2015.

Sources:

1. Kevin Kelly, “The three breakthroughs that have finally unleashed AI on the world,” October 27, 2014, http://www.wired.com/2014/10/future-of-artificial-intelligence/.

2. Steve Donohue, “Recommendation engine drives 75 percent of Netflix traffic,” FierceCable, April 9, 2013, http://www.fiercecable.com/story/recommendation-engine-drives-75-netflix-traffic/2012-04-09.

3. Boston Dynamics, “BigDog—The most advanced rough-terrain robot on Earth,” http://www.bostondynamics.com/robot_bigdog.htmlScience Daily, “Robot caregivers to help the elderly,” May 5, 2014, http://www.sciencedaily.com/releases/2014/05/140505104221.

4. Staff writer, “US programmatic ad spend tops $10 billion this year, to double by 2016” eMarketer, October 16, 2014, http://www.emarketer.com/Article/US-Programmatic-Ad-Spend-Tops-10-Billion-This-Year-Double-by-2016/1011312.

5. Derrick Harris, “How Intel is betting on big data to add tens of millions to its bottom line, GigaOm, November 18, 2013, https://gigaom.com/2013/11/18/how-intel-is-betting-on-big-data-to-add-tens-of-millions-to-its-bottom-line/.

6. John Still, “Is artificial intelligence the next step in advertising?,” The Guardian, July 27, 2015, http://www.theguardian.com/media-network/2015/jul/27/artificial-intelligence-future-advertising-saatchi-clearchannel.

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