Today’s customers have more choice and buying power than ever before, and they expect unprecedented levels of contextualization and personalization.
The latest wave of technology, led by Artificial Intelligence (AI), may be the first time the sales team has an opportunity to harness a new technology before consumers do, to turn the tables and find new ways to meet and exceed customer expectations.
A recent report from the 2018 State of Sales report by Salesforce, which surveyed sales professionals from around the world, found that high-performing sales teams across the Asia Pacific region will increase their adoption of AI by an average of 271% by 2020.
The industry views AI as the secret sauce they’ve been looking for to reduce the time spent on admin and unearths the next best steps and insight needed to deliver better customer experiences and more sales.
Customer expectations driving sales team change
Customers have never asked more of the sales team. According to the report, globally, more than three quarters of business buyers said they want to work with a partner as a trusted advisor — not just as a sales rep — who can add value to their business.
It is difficult to deliver that value if you’re spending time on the work that isn’t delivering for customers. Sales agents in the region said they spent approximately 62% to 69% of their time in non-sales activities in the last year, according to our report.
No stranger to the increased expectations of sales teams by customers is AirAsia, a leading low-cost airline headquartered in Malaysia. As it revamps its customer care for the digital era, AirAsia has deployed cloud-based solutions as part of its strategy to create faster and more personalized service for its customers.
As a result, the airline’s service agents across eight countries have a single view of all cases from all support channels — web, phone, email, live chat, airport communications — and each guest’s complete history with the airline, allowing them to achieve higher levels of personalized service.
AI is marching toward a tipping point for sales
AI is the next evolution for sales teams using a real-time, single source of truth. AirAsia is leading in this aspect, with the use of AI to pinpoint which areas of customer contact will require more resource allocation and which channels may be phased out over time.
It is critical for local businesses to shift their focus to providing the right data and intelligence to their sales staff, so that they can get closer to the customer and be more efficient in delivering sales outcomes.
Why sales should embrace AI today
It is hard to escape the constant noise around AI and whether we should fear it or embrace it. However, it is clear high-performing sales teams are the ones who use the technology to contact more customers and close more deals.
AI is allowing salespeople to sell smarter in new ways, and delivering more customers to the teams that have already made the switch. According to the report, globally, the majority of sales teams using AI have increased their number of sales representatives since 2015.
New technology will continue to disrupt the ‘way it has always been done,’ and the increasing role of AI in the sales process is no different. It is time for sales teams to embrace AI to stay ahead of the curve to exceed customer expectations.
How can insurers meet increased customer expectations at a lower cost? AI-powered care delivers on a future vision of customer service with an opportunity for savings of 30 percent by, for example, driving customers to digital experiences. In this post, I will explore how to apply AI using an intelligent customer engagement (ICE) framework.
How can your insurance company increase its artificial intelligence quotient (AIQ) with a balanced innovation strategy? In this blog series, I’m exploring the myriad ways in which AI adds value to financial services in general and the insurance value chain in particular. In my previous post, I defined the term AIQ and I revealed and discussed three key ingredients to building a strong AIQ: technology, data and people.
In this post, I’ll take a close look at one of the key areas in the insurance value chain—sales and distribution—and explain how AI-related technologies can add value to this function. But first, I want to reiterate the value of AI and why it’s important to transform your business into an AI business.
Why a strong AIQ is vital for your business—and why you need a strategy first.
Most of what’s written about AI relates to cost-cutting and job losses, but as we saw with the example given in regards to the health industry in the previous post, AI is a much more optimistic story. Its greatest benefits are not only efficiency and productivity, but innovation, improved customer and employee experiences, and the development of new sources of value and growth, especially when it is used to augment human capabilities.
However, to gain these benefits and to identify relevant use cases, it is necessary to develop a cross-enterprise AI strategy that clarifies the strategic goals: the whys, the hows and the whats of the business model leveraging our “AI strategic approach”, as outlined below.
Once the strategic goals have been clarified, the potential use cases for AI can be identified and prioritized according to the impact and estimated implementation effort they have on supporting the achievement of these goals (such as enhanced operational efficiency or improved customer experience) along the Insurance Value Chain:
How can insurers use AI in sales and distribution?
As mentioned in my previous post, there are numerous use cases of AI that can be applied along the insurance value chain. In this post we focus on AI in marketing, sales and distribution, including:
Enabling intelligent customer engagement
Workload balancing / lead allocation for agents
Machine learning insights to support customer segmentation
Automated data extraction from PDF reports and comparison against various policy combinations
Automated demand analysis and generation of new product offerings
Intelligent reporting and visualization
Customer personality and tone analysis
Automated creation of targeted marketing materials and promotions
Enablement of intelligent self-service product research for customers
Automated product recommendations and natural language question answering
When it comes to deciding which AI to employ, insurers need to focus on the things that AI and humans do best together. When AI is combined with human ingenuity across the enterprise, it can help solve complex challenges, develop new products and break into and create new markets.
Data analytics for better customer engagement
In sales and distribution, insurers can use data analytics to improve customer engagement. I will discuss intelligent customer engagement further in the use case below.
Virtual assistance (VA)
Accenture’s virtual advisor Cathy (Cognitive Agent to Help You) is a self-learning virtual agent that responds to customer queries by extracting information from a back-end database. Cathy is always learning more as it consumes human-agent interactions and stores knowledge on its database, enabling it to make automated product recommendations based on customer profiles. If a more complex customer request arises, Cathy seamlessly transfers the request to a human agent.
Machine learning
Insurers can boost their sales and distribution by using machine learning to analyze customer personality and tone. Machine learning makes selling (and buying) insurance easier than ever—virtual agent Amelia, for example, can give customers a motor insurance quote immediately, without the need to speak to a human being.
What are the benefits of AI for sales and distribution in insurance?
When humans and machines work together, they create the opportunity for growth and innovation. Within insurance sales and distribution, AI-related technologies can help to enable:
Increased lead generation—data analytics helps insurers identify and reach potential customers. The insight derived from data analytics can drive and constantly improve the sales team’s effectiveness at generating leads.
Efficient leverage for cross- and up-selling effectiveness—AI such as data analytics and VA gives insurers invaluable knowledge about their customers, making it easier to convince them to buy a comparable higher-end product (up-selling) or a product that is related to the ones they already have (cross-selling).
Increased service quality—self-learning virtual advisors like Cathy interact with customers and absorb information about their needs. This valuable feedback drives personalization of products and improves the quality of services.
Use case: intelligent customer engagement (ICE)
With intelligent customer engagement, insurers can strategically deflect issues that need to be addressed from human agents to machine chatbots. They can predict why customers are calling and approach them proactively. Humans now take on the new role of knowledge engineer: they take over where the AI ends and curate the knowledge corpus over time.
In our future vision for insurance, AI-powered care gives insurers the opportunity to save 30 percent of their customer service costs by:
Driving customers to digital experiences;
Providing conversational interactions that increase digital adoption and containment;
Leveraging AI to automate and deliver consistency across channels.
Insurers are looking to better connect with their customers, and to strengthen relationships with experiences that delight them—while reducing the cost to serve. Technology enables them to do this by, among others, shifting the mix of customer contacts:
It’s time to put your AIQ to work
When you combine human ingenuity with AI—such as data analytics, virtual assistance and machine learning—to improve the sales and distribution function, you will see results improving.
AI presents the opportunity for business transformation by enabling intelligent processes in the value chain and intelligent products and services in the market. Success will depend on how well your organization can harness the combined power of technology, data and people.
In my next post, I’ll look at how you can use AI to augment underwriting and service management. Get in touch to find out how you can boost your company’s sales and distribution function, as well as others within the insurance value chain, or download our report on How to boost your AIQ.
This article was originally posted on accenture.com
When you find yourself stranded in a city looking for a hotel room, you need a mobile app that tells you current availabilities for that day, not the best deals in three months. Artificial intelligence (AI) is making this far easier, sifting through and making sense of reams of data to get you the information you need right when you need it.
“We leverage AI to find a much faster way of coming to data-driven decisions to improve the experience for our customer,” says Ben Harrell, chief marketing officer of Priceline. “We are able to dig through so much data and provide our customers with the best travel deals, and machine learning and AI give us a better way to tailor the results to meet each customer’s needs.”
AI is becoming the X factor for marketers today, and many companies, like Priceline, are already embracing it. Case in point: Priceline just promoted one of its data analysts to be the company’s CMO. Harrell, who started out on the financial analytics team leveraging data to find actionable insights, has made the shift to the marketing side. He now uses his analytical skills to drive business strategy and develop marketing campaigns.
Forbes Insights research confirms that marketers recognize the key role of AI: 84% of those surveyed say AI is important to the future of their company and that it’s five times more important than other technology solutions such as the Internet of Things and cloud computing. Their thinking reflects what IDC is seeing in the market worldwide – it estimates that by 2019, 40% of all digital transformation initiatives will use AI, and that by 2021, 75% of all enterprise initiatives will.
Here we take a look at how AI is impacting marketing today and the role AI can play in increasing productivity, improving speed to market and creating stronger relationships with customers.
State Of Play
Despite the majority of marketers recognizing the importance of AI, these are still the early days. Less than a third of marketing executives consider AI a significant part of their business or say that AI is fully deployed. The majority are either in discussion, experimenting or in pilot project stages.
Why this lack of progress? Companies appear to be challenged by two major issues: talent and data. Three out of five marketing executives in the Forbes Insights survey say that the availability of personnel with the needed expertise is a moderate to severe challenge in fully implementing AI solutions. This aligns with Gartner’s 2018 CIO survey, which found that the lack of specialized skills in AI is a significant pain point for many companies. Given these trends, it’s easy to see why talent may become one of the biggest barriers to the widespread adoption of AI.
Stitch Fix has found creative ways to attract leading talent. Think: state-of-the-art algorithms and sophistication AI.STITCH FIX
Some companies have found creative ways to attract talent. Stitch Fix, a popular personal shopping service, relies on AI to run its business. To attract data scientists, the company has created a state-of-the-art algorithms tour on its website, which not only describes how the company uses algorithms but also displays its sophistication with AI.
The second AI implementation challenge revolves around data. For AI to be effective, companies need a large amount of good, quality data, something more than half of the marketing executives in the Forbes Insights survey say is not readily available.
This resonates with Priceline’s Harrell, who says, “Often, we don’t have sufficient data in exactly what we are looking for in the moment we need it. Machine learning and AI help us to try to bridge the gap, without which it would be practically impossible to effectively leverage the data we do have.”While there is no secret recipe for overcoming these two key challenges, the answer is the traditional formula for marketing success: Encourage management support for these critical initiatives and develop strong relationships with the technology side of the house, so the marketing and IT teams are working toward the same goals.
3 Ways AI Benefits Marketing
When looking at AI’s impact on marketing, we see three key benefits:
1. Increasing Productivity: This is the low-hanging fruit when it comes to the promise of AI, but that doesn’t make it any less critical for marketers. Think about some of the activities marketers need to accomplish every day—reaching customers across channels, targeting and retargeting advertising, and doing direct marketing like email. Artificial intelligence is what makes all of this truly possible on the scale needed to drive success. By enabling the analysis of millions of touchpoints to learn what kinds of messages work in which channels and when it’s most effective to reach customers, marketers, with the help of AI, can deliver the right message at the right time.
Staples is just one company already using AI to push productivity boundaries with its omnichannel approach. By using the natural language processing and machine learning capabilities of IBM’s Watson, they have created an intelligent version of their iconic Easy Button. The physical button itself, found in many offices, is fitted with sensors and speech-to-text functionality so business customers can use it to easily order supplies using voice, text or email. To provide customers with anytime and everywhere service, Staples is also using its Easy Button software platform to support all of its chat experiences across channels, including its website, its mobile app and third-party messaging platforms like Slack and Facebook Messenger.
2. Improving Speed to Market: The promise here is similar to productivity gains. By sorting through billions of pieces of information, AI enables marketers to better understand their customers, which can then guide the design and launch of new products and services. Depending on industry, how this improvement looks will vary. In manufacturing, for example, companies improve time to market when they use AI to automate their analysis and production processes. In another industry, analysis of large data sets may reveal or validate new customer segments.
With the help of machine learning, American Express, a company that handles more than $1 trillion in transactions annually, created a real-time recommendation engine to better keep up with changing customer behaviors and methods of access, such as mobile technology. Using its large data set and a learning algorithm, the system can now identify new customers and recommend new services for existing customers and vendors much faster than what was previously possible.
3. Increasing Customer Engagement: This is the perfect convergence of marketing and artificial intelligence. Even as consumers balk at privacy concerns, they continue to demand more personalized communications across channels. They want individualized and relevant content delivered at the right time on their preferred device.
AI is increasingly allowing companies to do just that. Netflix’s recommendation engine, for example, is tuned for hyper-specific categorization and can match titles to the exact people who are interested in seeing them. Strong recommendations ultimately result in increased viewership, lower churn and (with the help of a larger user base), more data to strengthen its algorithm. Netflix’s AI-assisted recommendation system is estimated to save it $1 billion per year.
“We leverage AI to find a much faster way of coming to data driven decisions to improve the experience for our customer.”
For companies that get it right, like Netflix, the rewards stem beyond cost savings. According to Boston Consulting Group, “Brands that create personalized experiences by integrating advanced digital technologies [enabled in part by AI, mobile and cloud technologies] and proprietary data for customers are seeing revenues increase by 6%-10%—two to three times faster than those that don’t.”
The Time To Act Is Now
For those who haven’t yet begun, now is the time to start developing your AI initiatives. For those who are just starting, it’s time to ramp up your efforts. Artificial intelligence brings value to the entire enterprise and has moved far beyond the confines of the IT department—it’s now delivering significant benefits to marketers who know how to take advantage of the technology, and it will continue to do so in the future. In fact, marketers agree that companies that don’t develop their AI capabilities now will be at a significant competitive disadvantage in the next five to 10 years.
At the end of the day, marketing is meant to improve the customer experience, and AI undoubtedly helps in this regard. “There is the adage: Marketing is getting the right message to the right person at the right time in the right place,” says Priceline’s Harrell. “Ultimately, the most important piece is to help customers find what they’re looking for and have a better experience.”
Harnessing its influence can make it into your company’s superpower
Artificial intelligence and machine learning continue to increase the stakes in the analytic, predictive and executional arms race needed to create and keep customer relationships. Marketing is at the center of this change, and several existing applications promise to irrevocably change the landscape with step-level superpowers.
With great power comes great responsibility, and marketers must be ready to change and adapt to the new landscape if they want to avoid being the haphazard hero who lost the instructions to their supersuit. By recognizing the four ways AI and machine learning will enable change in industries and organizations, the savvy marketer can avoid costly missteps as they learn how to harness the awesome power of an enhanced world.
Follow the money
Chances are you are already using AI and machine learning to buy media. Complex and high value, programmatic media buying is the most mature AI marketing application. eMarketer estimates programmatic will drive $39 billion in total 2018 media spend and almost 80 percent of U.S. display ad traffic. But just as AI and machine learning enable programmatic, they also enable ad fraud, estimated to cost the industry about $19 billion (roughly 20 percent of total spend).
Solutions: New channels and using AI to fight AI
Companies like Uber are using AI to detect subtle patterns in time, location and sequencing to identify and shutdown systematic fraudsters. Look for more tools to help detect and discourage basic click and attribution fraud.
Beyond display, AI and machine learning applications are improving conversion rates in call centers, direct mail, voice assistant and chat by focusing on delivering the right message, in the right channel, at the right time.
Speed up to keep up
Several existing applications promise to irrevocably change the landscape with step-level superpowers.
AI and machine learning significantly reduce the time to create, deploy, test and revise personalized campaigns at massive scale. This automation pulls team members out of their channel silos, allowing them to focus on products and segments across the lifecycle. As test and learn speeds up, the time required to create, approve and tag content will become the bottleneck for some and competitive advantage for others.
Solutions: Agile and content management
Marketing will need to become faster and break down existing channel-based organizational silos by adopting the Agile methodology or similar approaches.
Consider investing in content workflow software to speed the development and approval process, too. Then expand that to ensure all content is categorized and tagged to support AI-based retrieval, attribution and optimization.
Branded relationships
Automated voice and chat marks a significant turning point as AI moves from back-office predictor to frontline transacting and brand voice. While great in theory, brand marketers must manage the increased risk from an out-of-control automated experience by setting clear policies. This is important since branded relationships will become even more necessary to stay top-of-mind when customers order by voice without visual reminders.
Solutions: Define guidelines, have less shelf space and organize new creative types
Define or tighten up policies and guidelines to set parameters for AI, including how and how often AI connects with the prospect/customer. Voice- and logic-based creative will rise in importance with a new place at the table for decision science to optimize the long-tail “choose your own adventure” narrative. And a shift from eyeballs to voice/chat could mean fewer, more curated search options with less shelf space for unfamiliar brands.
Superpowers need data
Personalization and the AI engines behind them require large amounts of data to best optimize outcomes. Beyond cookies and account data, leading brands are in a race to capture and leverage big data as a competitive advantage, with companies like Google, Amazon and Facebook holding many of the cards.
Solutions: Create hit lists, data strategies and opt-in and permissions
Identify a list of highly valuable data points to drive efficient conversions. Develop a comprehensive strategy for collection, storage and retrieval. Because of GDPR, customer opt-in should definitely be part of this hit list.
In the end, you can always ask: Use surveys, feedback forms and sales dialogue to simply ask direct questions on rich topics like motivation, intent and purchase barriers.
Stay human
While AI and machine learning are an awesome addition to the marketer’s toolkit, it doesn’t replace human storytelling. Since AI can only optimize in the parameters it’s been given, it is not intuition. Applied correctly, however, high-powered optimization provides a significant advantage for marketing teams to understand and engage customers.
Ajay Agrawal says, “The organizations that will benefit most from AI will be the ones that are able to most clearly and accurately specify their objectives.” Be deliberate and focus on minimum viable outcomes in high-value processes.
Derek Martin helps clients increase customer value and accelerate digital transformation.
