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Contributed on August 9, 2018

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Smart marketers are leveraging artificial intelligence to bolster customer experience

Blog: Capgemini CTO Blog

The share of customer-service interactions handled entirely by artificial intelligence (AI) will have multiplied by five, globally – to 15% of total interactions – between 2017 and 2021. And, next year, at least 40% of such communications will involve an element of AI, according to the research firm Gartner.[1] What’s more, by 2020, more than 40% of all data analytics projects will relate to an aspect of customer experience (CX).

Data and analytics are an increasingly critical tool across marketing, sales, e-commerce, customer service, social media management, and field service departments.[2] However, few of these teams command an accurate understanding of their customers’ level of brand trust and likelihood to remain loyal.

Marketing leaders are responsible for predicting purchases and changes in customer tastes and preferences. Not surprisingly, therefore, more and more marketing executives are relying on AI technology to craft a truly personalized experience for customers.

Not only can artificial intelligence help marketers drive efficiency, but it also can help them make fact-based decisions about their audience, channels, content, and the timing of messaging across the product lifecycle to create a compelling and seamless CX.

Marketing leaders are strategically using AI to:

• Anticipate customer purchases and buying patterns
• Optimize media buying
• Price and place products
• Gain insight into the customer lifecycle to target social media strategically.

Anticipate customer purchases and buying patterns: Commercial purchases of big data technology are expected to maintain a compound annual growth rate of 11.9% through 2020 when revenues will exceed $210 billion, according to IDC’s semiannual report.[3] .

Almost 90% of people follow daily routines that a few mathematical equations would be able to predict, postulates an MIT Media Lab study. Predictive marketers who leverage such AI-enabled algorithms, achieve 2.9% higher revenue growth than traditional marketers, reports Forrester Research.[4]

While the predictive use of AI is still evolving, the technology’s machine learning and deep learning sectors are being developed, which could beef up capabilities even further.

Optimize media buying: Media buying is the purchase of advertising from a media company such as a television station, newspaper, magazine, blog, or website. Media buyers negotiate and monitor advertising space on behalf of clients in an attempt to reach the highest number of people in a target audience at the lowest possible costs. The analytics systems are so advanced these days that they can predict consumer behavior at any given time of year. Leveraging large sets of data and algorithms, AI could dramatically change the media-buying process, reallocating budgets accordingly.

Price and place products: When done right, product pricing can be the most useful part of a brand’s strategy.In product placement, AI technology can aggregate vast amounts of data to compute past sales – including spikes and drops. Using complex machine learning algorithms, AI can predict the places where a particular product must be placed to target the sought-after audience.

Insight into the customer lifecycle: Closed-loop analytics enable insight into the entire customer lifecycle by actively monitoring comments on social media and then determining customers’ market needs and concerns.

Analysis includes a look into social media comments, suggestions, complaints, etc. Leveraging smart, closed-loop analytics, marketers can quickly assess and categorize customers’ social media data to better understand user needs. Artificial intelligence can even drive conceptual messaging to creatively reach the right customer at the right channel, at the right time. In today’s highly competitive marketplace, shoppers have more choices than ever before. Businesses that offer suggestions to consumers based on their purchasing record can simplify the shopping experience and drive sales.

Now more than ever, it makes strategic sense to leverage big data and AI to make the customer experience a top business .

Tap into Capgemini analytics and data science expertise

To find out how Capgemini’s analytics solutions and services drive innovation, enable agile new ways of working, and increase business value, contact Gunjan Aggarwal or click here to learn more about our Smart Analytics platform for financial services.

As Capgemini’s practice leader for Analytics and Artificial Intelligence, Gunjan Aggarwal helps clients face today’s dynamic business environment by offering innovative thinking and sound technology advice for measurable results. A digital native with years of information technology and systems integration experience, Gunjan is an expert when it comes to building standardized and custom data science solutions.

[1] The Economist, “Customer service could start living up to its name,” March 31, 2018, https://www.economist.com/special-report/2018/03/31/customer-service-could-start-living-up-to-its-name

[2] Gartner website, “Gartner Says 25 Percent of Customer Service Operations Will Use Virtual Customer Assistants by 2020,” Susan Moore, February 19, 2018, https://www.gartner.com/newsroom/id/3858564

[3] Arrow ECS eMagazine, “Big data market to climb to $210 billion by 2020,“ April 24, 2018, http://ecsnamagazine.arrow.com/big-data-market-to-climb-to-210-billion-by-2020/

[4] Forrester Research/EverString report, “How Predictive Marketing Analytics Boosts B2B Business Performance, December 2015, http://pages.everstring.com/rs/246-GSV-300/images/EverString_Predictive_Marketing_Analytics_TLP.pdf, accessed July 2018.