L’IA aurait déjà conquis 31% des marketeurs français

Par Élodie C.
le 20/12/2018

 

Salesforce dresse ‘’l’état du marketing’’ en France et dans le monde.

 

source: https://lareclame.fr/salesforce-marketing-2019-211665?utm_source=La+R%C3%A9clame+Newsletter&utm_campaign=ee71d10b5c-la_Rec_Hebdo_COPY_01&utm_medium=email&utm_term=0_b9409b3e9c-ee71d10b5c-429456749

Comme chaque année et pour sa 5e édition maintenant, Salesforce brosse l’état des lieux du marketing mondial : insights et tendances sont passés au peigne fin après avoir sondé plus de 4 100 leaders du marketing mondial, dont 300 en France. L’étude confirme ainsi l’importance du marketing dans l’optimisation de l’expérience client, celle du l’usage des données personnelles et les questions de protection de la vie privée que cela soulève, ainsi que l’adoption de nouvelles technologies telles que l’intelligence artificielle par les marketeurs.

Ainsi, en France, ils sont 31% à utiliser l’intelligence artificielle (29% dans le monde), un usage en hausse de 47% par rapport à l’année passée, et 33% utilisent des assistants vocaux. En revanche, l’Internet des objets (IoT), c’est-à-dire les objets connectés sont les plus largement adoptés par les marketeurs dans le monde.


Toujours en France, ils sont 48 % des spécialistes français du marketing a déclarer avoir une vue complètement unifiée des sources de données clients.

Ces sources d’informations servant à identifier les consommateurs se sont multipliées avec les années, passant de 12 en moyenne en 2018 contre 10 en 2017 et bientôt 15 en 2019. Parmi elles : les CRM, DMP et autres CDP mais aussi et plus simplement les bases de données marketing comprenant les informations clients.

Des informations essentielles pour améliorer la personnalisation : 95% des spécialistes français estiment sue cela booste leur programme de marketing global, mais aussi l’expérience client. Ainsi :
– Près de la moitié des leaders marketing (48%) affirment que leur équipe gère des initiatives d’expérience client dans l’ensemble de l’entreprise, contre 24% en 2017 ;
– la moitié (51%) des spécialistes du marketing partagent leurs objectifs et indicateurs de succès avec les équipes e-commerce. Ils sont 57% à les partager avec les équipes de vente, et 52% avec les équipes de service client, précise encore l’étude.

« Le marketing devient la colle interfonctionnelle de l’expérience client ».

80% des consommateurs déclarent ainsi que l’expérience fournie par l’entreprise est aussi importante que ses produits et services. Le consommateur veut se sentir unique et véritablement compris par les marques, ce que les équipes marketing ont bien compris. Désormais, 54% des équipes marketing parmi les plus performantes ont le “lead” de l’expérience client au sein de l’entreprise (et 45% des marketeurs toutes catégories confondues). Enfin, 69% des business buyers s’attendent à une expérience d’achat similaire à celle proposée par Amazon, comme les recommandations personnalisées.

À noter qu’à l’ère post Snowden et après les multiples scandales sur l’usage des données personnelles des consommateurs/internautes par les géants de la tech, le respect de la vie privée et la confiance du consommateur sont devenus un enjeu fondamental pour le secteur : 51% des professionnels estiment que leur entreprise est plus concernée sur ces sujets que deux ans auparavant.

Enfin au chapitre des indicateurs les plus importants à suivre pour les marketeurs du monde entier, la croissance des revenus truste le haut du classement pour 74% des sondés, suivi par l’efficacité des ventes (64%) et les analytics web (61%). La satisfaction des clients arrive quant à elle en bonne 4e place (60%).

Pour obtenir l’intégralité de l’étude, c’est ici !

Méthodologie

Les données du présent rapport proviennent d’une enquête à double insu menée du 13 août au 23 septembre 2018, qui a généré 4 101 réponses de dirigeants en marketing à temps plein – occupant un poste de gestionnaire ou de cadre supérieur en Amérique du Nord, Amérique latine, Asie Pacifique et Europe. En France 300 responsables marketing ont été interrogés. Tous les répondants sont des panélistes tiers (non limités aux clients Salesforce). Pour d’autres données démographiques de l’enquête, voir page 56.
Les chiffres ayant été arrondis, les totaux en pourcentage ne correspondent pas tous à 100 % dans le présent rapport. Tous les calculs de comparaison sont effectués à partir des nombres totaux (et non des nombres arrondis).

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IBM Watson Marketing Releases 2019 Marketing Trends Report Focused on Emerging Trends Redefining the Profession in the Shift to AI

IBM Watson Marketing Releases 2019 Marketing Trends Report Focused on Emerging Trends Redefining the Profession in the Shift to AI

Top Drivers Include “the Emotion Economy,” Tech-First Marketers, and AI-Powered Personalization, Supported by New Roles in Data and Agile Marketing

With artificial intelligence (AI) continuing to gain usage among marketing professionals, IBM announced a new report from IBM Watson Marketing identifying a new breed of marketers coming to the forefront. Driven by a growing need to meaningfully structure data to enable actionable, real-time decision making IBM’s 2019 Marketing Trends Report provides practical insights for businesses to stay ahead of the forces driving these changes.

“It’s not often an entire profession experiences a sea change the likes of which has profoundly impacted all facets of marketing, but we are in that moment,” said Sylvia Vaquer, Co-Founder & Chief Creative Officer, SocioFabrica. “This transformation fueled by AI-powered inferential connections provides a rare opportunity to rearchitect the whole ‘marketing house,’ but it’s critical to have practical knowledge to capitalize on it.”

Also Read: HG Data Audience Extends Technographics to Facebook, Adobe, Twitter, and Salesforce Digital Marketing Platfor

The report’s findings provide deep insights into how CMOs and digital agencies are reimagining the marketing function with the following overview giving a blueprint for what it will look like in 2019 and beyond:

  • In The Emotion Economy, Purpose Creates Brand Loyalty – More than ever, consumers are more likely to engage with brands that are authentic, meaning the brand holds strong convictions and delivers on them, versus experience alone.
  • Marketer 4.0: Emergence of the Tech-Savvy “Martecheter” – Until now the greatest advantages for marketers has been in the order of budget, tools and talent. That model is now inverted, driven by the rapid growth of new skills and customer expectations.
  • AI and Machine Learning Make Hyper-Personalization a Reality – The proliferation of data and compartmentalized marketing stacks has squarely put AI and machine learning-based marketing tools at the center of deep personalization. This will change how marketers make decisions and deploy campaigns as AI analyzes and delivers personalized content with massive scale.
  • Director of Marketing Data Becomes the Hottest New Role – Growth of “marketing data” roles will continue as they drive human and technology connections across their organization. This will enable artificial intelligence and machine learning-based marketing tools to analyze data and customer behavior, make recommendations and predictions, and become smarter based on the data fed into them.

Also Read: Okta Names Shellye Archambeau to Board of Directors

  • Agile Marketing Adoption Accelerates, Driving Marketing Outcomes and Culture – Organizations driven by culture change and agile mindsets will widen their lead having a first-mover advantage, especially where AI-powered marketing technology enables the right set of tools to align and measure the proper objectives and metrics.
  • GDPR Actually Helps Marketers Improve Data Hygiene and Customer Trust – Marketers will increasingly focus on improving data hygiene processes, leading to better targeting and higher quality interactions. With similar regulations already in certain parts of the US and the potential for US-wide legislation, marketers will proactively improve privacy, security, and data management as a catalyst for new business models.
  • Digital Marketing Agencies Transform into “Consulgencies” – As the rankings of AdAge’s 10 largest agencies was cracked – for the first time ever – by The Big Four consultancies in 2017, it serves as a bellwether for the industry including smaller and mid-sized agencies: “Consultancy” and “agency” capabilities will converge, driven by a need to build out deep expertise in AI, data integration, customer experience analytics, mobile apps, and custom solution development.
  • MarTech + AdTech = The Holy Grail of Marketing – Although extensively debated, 2019 will at last be the year marketers lean more into the benefits of programmatic ad spending. By achieving better data connectivity between their martech and adtech stacks they will have real-time understanding of customers and ad spend optimization using AI.
  • Customer Centricity Breaks Marketing Silos and Delivers Happiness – Marketing transformations will increasingly focus on creating differentiated customer experiences. This will be supported by more experimentation using contextualized understanding of aggregated customer data across other areas of the organization such as commerce and digital teams.

“As marketers ourselves, we felt it was critical to dissect the industry dynamics that are reshaping the profession to help our peers set a strong vision for their organizations.” said Michael Trapani, Marketing Program Director for IBM Watson marketing. “By understanding the transformative trends – from team structure, to the advent of AI-powered marketing, to heightened expectations for the discipline – the findings in the 2019 Marketing Trends Report will be indispensable for marketers.”

How AI is redefining personalisation & the job of the email marketer

Personalisation is a word that marketers use every day with no great degree of thought.

Some may conflate segmentation or product recommendations with personalisation. For others, personalisation means nothing more than ‘[first name] [last name]’ in comms.

However, martech integration and the application of machine learning is now enabling more sophisticated personalisation that truly deserves the name. As this AI tech becomes easier and cheaper for marketers to adopt, marketing roles are slowly being redefined.

source: https://econsultancy.com/how-ai-is-redefining-personalisation-the-job-of-the-email-marketer/

All this is easy to observe in the transformation of email service providers into ‘marketing platforms’, ‘personalisation platforms’ and other soubriquets. Though vendor hype may run a pace ahead of what’s happening in the market, the future does seem close.

The idea and reality of personalisation is what I wanted to discuss with Raj Balasundaram, VP Solutions and Strategic Services at Emarsys, a B2C marketing automation platform.

Every marketer has to answer four questions

I began by asking ‘What is personalisation?’

“Every marketer has to answer these four questions,” Balasundaram replied, “Who? What? When? How?”

“That’s fundamentally what personalisation does. Who is the customer? What am I going to say to them? When, or in what context? And how am I going to deliver that message?

“The four questions,” he continued, “need to be answered at an individual level, and they need to be answered every time we contact the person and without thinking about what channel we’re going to send to.”

It’s this concept of lots of individual decisions being made, each considering some aspect of content, time and channel that makes this personalisation different.

Balasundaram simplifies it for me: “The machines don’t segment, they don’t personalise, all they think of is an event. So, ‘here’s Ben, what do I need to do with him?’ It’s a singular transaction, rather than putting a list together or using smart content blocks for example.”

Essentially, this is marketing automation but with many more variables. Rather than designing a handful of pathways which a consumer might be funnelled down (e.g. welcome campaigns, loyalty campaigns etc.), the technology uses statistical analysis of the information that the marketer has about the individual to decide what the best action or option is in any instance.

question mark

“It’s not the channel that surprises [customers], it’s the content…”

Balasundaram remarks that this tech is effectively bringing an end to siloed marketers. He says that “Whereas currently [marketers] have already decided what they’re going to do – ‘I want to send an email, I’ve already decided Ben is in this particular group, and I’ve already decided what the content will be’ – this is not what personalisation is about.”

What Balasundaram is referring to is channel agnosticism. And while some marketers may think this ignores the fundamental difference between media channels and content formats, Balasundaram is also advocating for a return to a more strategic way of thinking.

“It’s not the channel that surprises [customers], it’s the content that surprises them,” he says. Though he does point out that millennials are more likely to be delighted by personalised direct mail simply because they may never have received it before.

“The content [or message] should be created well before we decide to go down an email route,” he continues, “and this takes away the need to do segmentation – I already know what to say to Ben, and I’m finding the right moment to say what I want to say, and that is vastly different to the way marketers work. It’s a different way of thinking.”

channels

“If you start segmenting people, you’re really not personalising…”

Balasundaram can sound quite dogmatic – “[Email marketers] have been doing the same thing over and over again, and it clearly doesn’t work, people know it’s a mass email. Even the common consumer knows it’s mass emailing,” he says. But he is also realistic and recognises that common practices in marketing are influenced by the technology available. Take this soundbite for example:

“If you start segmenting people, you’re really not personalising. But I don’t think there’s a difference between personalisation and segmentation, they are one and the same – the reason we did each is purely down to the level of tech we have or the limitations we have. Now the tech is taken care of, should we really go back to segmentation?”

This was the part of our discussion where we got to the crux of the matter and Balasundaram’s most illuminating point.

“So far,” he says “marketers have been concentrating on the operational part because to get a campaign out the door, it will take them two or three weeks to arrange the data, all the coding, segmentation – which is internally focused, operationally focused. And they actually end up not concentrating on the most important thing, the creative part.

“[This] was not the marketers fault, the tech didn’t help them out, but now the whole work paradigm will change simply because of the fact all we expect marketers to do is write content for their end consumers. The tech forces marketers to think about consumer perspective every step of the way. When an email goes out and the marketer looks at it and says ‘yeah, I know it’s not perfect, but this is the best I can do’ – that will change, because marketers have fewer excuses now. The tech has caught up to a point where you can go individual to individual.

As an addendum, Balasundaram says “You can even generate the content using AI”, referring to tech such as subject line optimisation which is rapidly being adopted by big brands that send do a lot of marketing messaging.

pepper

“A pure email marketer probably won’t exist in the next five years.”

The shift of mindset to customer-centric campaigns, away from operational-centric campaigns is what Balasundaram describes as “taking the [channel] silo away, putting everything into a common pool and finding patterns in it.” From a tactical point of view, this could entail using push notifications for users that don’t open emails, or search retargeting for those that unsubscribed from email, perhaps with an incentive to return (such as free delivery).

Typically, Balasundaram tells me, Emarsys will work with an inactive part of a client’s customer database when that client first trials their machine learning tech. He says they may look at “churning customers, or customers about to leave or not responding…then apply AI personalisation techniquesand…it usually takes about 6-8 weeks for the algorithms to learn a bit more about the customers but then they’ll eventually see the results.”

When I ask what this means for the marketer in the long run, Balasundaram is punchy. He says “A pure email marketer probably won’t exist in the next five years. They need to think about email marketing in terms of a bigger business strategy. If they’re going to be pure email marketers, it will be difficult – if you don’t see the customer as part of the bigger picture, it’s never going to work.”

He continues, “Marketers will have more time to think about business strategy and tactics, and the components required in creating the content. They can spend more time… creating rather than deploying. Instead of thinking about improving clickthrough rate, they can be reporting on revenue. [It’s about] revenue over operations.”

6 Current Wins for AI in Marketing

  1. Salesforce’s State of Marketing report included the fact that 60 percent of marketers are counting on AI implementations like Salesforce Einstein to have a “substantial or transformational” impact on programmatic and media buying for the year ahead and at least through 2022.
  2. A CapGemini survey of 1,000 organizations found that 83 percent agree AI has already created new jobs and 75 percent have seen a 10 percent uplift in revenues directly tied to AI initiates. Meanwhile, 73 percent said that AI has boosted customer satisfaction scores and 65 percent said AI-based decisions have reduced customer churn rates.
  3. Deloitte’s 2017 Global Contact Center survey found that 56 percent of technology, multimedia, and telecommunications companies will be investing in AI this year, especially for “sentiment analysis” of social media data. Sentiment analysis uses AI algorithms to surface clues of underlying opinions in posts, social video, and comments. The sentiments can then be categorized and segmented based on audience types for more accurate targeting.
  4. Voice analytics are helping marketers be more collaborative and persuasive in both internal and external meetings. Voice patterns, unexpected pauses, and previously unintelligible crosstalk can be unraveled by programs incorporating AI and machine learning. An example is a company that discovered their most persuasive speakers instinctively used a talk-to-listen ratio of 43:57. In their most successful interactions, they spoke 43 minutes for every 57 minutes of listening.
  5. Oracle researchers reported that 36 percent of brands have already implemented AI chatbots for customer interactions and market research. A full 80 percent of brands said that they either have chatbots now or intend on implementing them by 2020.
  6. Mass customization sounds like an oxymoron, but AI is making relevant content work at scale. Google is constantly refining its many AI projects but has been particularly successful at dialing up viewable impressions for premium placements. In advertising for Google’s own Pixel phone, they used AI to boost impressions on premium inventory by 3X, while reducing the viewable cost per thousand (CPM) by 34 percent.

How Machine Learning and AI Can Improve Travel Services

A shopping machine: AI and the future of retail

Bad shopping experiences are a common problem, whether it takes the form of a badly-structured website or an ill-equipped customer services team

source: https://www.information-age.com/shopping-machine-ai-future-retail-123464630/

A shopping machine: AI and the future of retail image

Despite spending vast sums on digital tools and solutions, it can still be a struggle for retailers to provide a clean, seamless buying experience for customers.

Thanks to mobile technology and the Internet of Things (IoT), customers are more connected than ever before – meaning retailers have a range of channels to interact through (online, mobile, wearables, SMS, social media and so on).

Yet retailers are often communicating inconsistently across these channels, creating a fractured and frustrating experience for consumers.

While there is no one magic bullet to solve this problem, the recent advances in AI technology offer retailers a powerful tool to provide customers with a superior shopping experience.

Birth of the retail chatbot

One key application of the latest AI technology is the virtual assistant, or ‘chatbot’.

With AI assistants or chatbots, retailers have the option to create a much better experience through voice interaction, giving users a ‘conversational commerce’ experience.

>See also: Retail: the next big industry impacted by AI

The spoken interaction of a chatbot has a number of benefits. First, most people prefer to chat as it’s far more natural than typing a query into Google; so chatbot interactions are easier and more comfortable for customers.

Imagine being able to just ask your phone to order some washing powder and, thanks to AI, it knows exactly which brand you prefer and is able to comparison shop for the cheapest option in seconds without you needing to be involved.

Chatbots are both intelligent and context aware, meaning that in conversation, they are better able to engage customers, both in-store and online.

This enables retailers to cross sell additional products based on real time customer feedback; as well as factors such as a customer’s purchasing and browsing history, location and immediate needs.

All of this combines to produce a more engaging experience – and engaged consumers return more frequently to buy more products because retailers are finally able to talk ‘with’ customers not ‘at’ them.

Omni-channel AI

The utility of chatbots means they are a wonderful customer engagement tool in multiple scenarios that cannot be ignored by retailers. Yet in the same way that online commerce didn’t kill the brick and mortar store experience, chatbots will not kill the online shopping experience.

The question then becomes, not whether chatbots should be used, but how they are best integrated into the omni-channel experience.

There are certain tasks where chatbots are perfect for interactions must be quick and efficient; tasks like getting receipts, shipping notifications and live automated messages are ideal from a customer service perspective.

>See also: Using AI to transform e-commerce

But bots are less suited for deep-dive research on a given product. Any task which involves typing out numerous responses to questions rather than simply pressing buttons renders the chatbot annoying rather than helpful.

For example, research has found that 93% of millennials read product and customer reviews before purchasing a product. These types of interactions are ill-suited to chatbots. However, retailers can still rely on AI in a different form to come to the rescue – the cognitive website.

The self-aware website

Along with mobile, chatbots and social media, the website still has a key role for retailers. However, the old model of a static site is rapidly becoming outdated.

In the digital age, websites need to be able to deliver a personalised, contextual and relevant experience to each individual consumer. By deploying cognitive websites which ‘learn’ about the customer, brands will be able to provide the detailed background needed ahead of a big purchase which is optimised to result in a purchasing decision.

>See also: 3 ways artificial intelligence is transforming e-commerce

The caveat is that like all forms of marketing, it’s easy for retail AI to go too far. Just because someone has been looking at a friend’s baby pictures on Facebook, doesn’t mean they want to purchase nappies – an AI tool making the wrong inference can easily come off as creepy.

Ultimately, the utility of the various AI tools is too great for retailers to ignore, and chatbots or cognitive websites will be the future of consumer interactions.

The trick is to understand which type of AI to apply, when and where. At the heart of this adaption is understanding the various AI tools that are available and which will work best for what type of customer interaction.

Once this is understood, retailers can help to blend these various AI tools into a unique and seamless experience for the consumer.
Sourced by Frank Palermo, executive vice president – global digital solutions at VirtusaPolaris

Artificial Intelligence in Insurance – Three Trends That Matter

 

Change is here, more is coming. The insurance market is dominated by massive national brands and legacy product lines that haven’t substantially evolved in decades. Sound familiar?

People are already placing bets. Insurance is an industry that venture capitalists consider so ripe for disruption that the founders of Lemonade, a New York-based insurtech company, raised one of the largest seed rounds in history simply by talking.

It’s not just the venture crowd. Warren Buffett has gone on the record saying that the coming of autonomous vehicles will hurt premiums for Berkshire-owned Geico.

There’s good data suggesting this is true. Buffett may have been referring to a 2015 KPMG report which predicts that “radically safer” vehicles, including driverless technology, will shrink the auto insurance industry by a whopping 60% over the next 25 years. And auto insurance is more than 40% of the insurance industry. But aren’t there massive upsides for insurance carriers resulting from business process automation?

(For readers with a strong interest in other financial applications of AI, please refer to our full article on machine learning applications in finance.)

Artificial Intelligence in Insurance – Insights Up Front:

Trends that business leaders should know about. In this article we look at three key ways that AI will drive savings for insurance carriers, brokers and policyholders, plugging into existing transformations within the insurance industry:

  1. Behavioral Policy Pricing: Ubiquitous Internet of Things (IoT) sensors will provide personalized data to pricing platforms, allowing safer drivers to pay less for auto insurance (known as usage-based insurance) and people with healthier lifestyles to pay less for health insurance
  2. Customer Experience & Coverage Personalization: AI will enable a seamless automated buying experience, using chatbots that can pull on customers’ geographic and social data for personalized interactions. Carriers will also allow users to customize coverage for specific items and events (known as on-demand insurance)
  3. Faster, Customized Claims Settlement: Online interfaces and virtual claims adjusters will make it more efficient to settle and pay claims following an accident, while simultaneously decreasing the likelihood of fraud. Customers will also be able to select whose premiums will be used to pay their claims (known as peer-to-peer (P2P) insurance).

Insurance as a global marketplace tends to be associated with public distrust (one Australian poll ranked sex workers as more trusted than the insurance industry), and this may present unique challenges to technology innovations – through AI or otherwise.

Therefore, a key concern introducing new technologies will be in convincing the public that automation isn’t simply a Trojan horse for denying their claims — a worry that 60% of consumers have expressed about purchasing coverage via chatbot, according to a recent survey by Vertafore.

Three Current AI Application Trends in Insurance / Insurtech:

We’ll take a look at all three major AI insurance trends one by one, examining at the current state of the technology, the changes underway, and the potential resulting shifts in the industry. We’ll begin with “behavioral pricing”:

1 – Behavioral Premium Pricing: IoT Sensors Move Insurance From Proxy To Source Data

IoT data is opening a slew of  are three key ways that IoT data will enable personalized insurance pricing:

  • Pay What You Risk: Telematic and wearable sensor data enables lower premiums for less risky behavior, including driving less and exercising more
  • Bundle Policy and Loss Prevention Hardware: Smart home companies will offer policy discounts to users of sensorized loss prevention technology, enabling cross-selling of devices and insurance
  • Verify and Settle Claims: IoT data markets will enable carriers’ faster access to verified risk management information, rather than relying on costly assessments and audits

Hypothesis: IoT disrupts insurance the same way that data science has been disrupting finance: moving analysis from proxy to source data.

In the old world: Financial models were once dependent upon statistical sampling of past performance to forecast future outcomes.

Today: Data science has enabled predictions based on real events, in real time, using large datasets rather than samples to make the best guess.

In the old world: Insurance carriers relied on risk pools constructed using statistical sampling.

Today: IoT sensors allow insurance carriers to price coverage based on real events, in real time, using data linked to individuals rather than samples of data linked to groups.

Big picture: In each industry we are moving from proxy data (about categories) to source data (about individuals).

See a pattern? Whether the asset is a stock portfolio or an ‘09 Honda Civic, a bond or a cargo ship, the shift in how the value of the asset is forecasted is driven by the type of data that technology can offer analysts.

Here’s an example: Usage-based or pay-per-mile car insurance demonstrates this logic. Telematics sensors allows real-time tracking of an underlying asset (cars) allowing for the roll-out of a new product line in the related insurance market (auto insurance) by personalizing the risk of the event being insured (a car accident).

What does this actually mean? Safer drivers can pay less for policies, and any driver can pay by the mile. Policyholders aren’t part of a risk pool any more — they are paying what they risk. This is a fundamentally new type of insurance product, enabled by the underlying technology of telematics.

The only catch? You have to install a telematics sensor in your car. And you have to drive safer than average, and less miles than average. For some, it’s a great bargain. For others, not so much.

This is why insurance companies are becoming hardware companies: sensors.Take Neos Ventures, a company that provides smart home monitoring and emergency assistance IoT along with a home insurance policy. The idea is that if Neos can provide tech that makes gas leaks, water damage and home intrusions less likely, then they’ll be able to pass along those savings in the form of lower premiums to their customers.

Neos Insurtech Chatbot

The only catch? You have to install Neo’s camera and sensors in your home.

Hypothesis: To succeed in the next decade’s markets, insurance companies will have to rapidly move from pricing based on the likely behavior of categories to pricing based on the actual behavior of individuals. This is how consumers will experience the move from proxy to source data.

Wearables and GPA are likely to drive the change. As Vikram Renjen, SVP of Insurance for Sutherland notes: “With supplemental GPS data, wearables could monitor and report on compliance to the rehabilitation protocol of a disability claimant. Improved compliance would shorten the time until return to work.” (This is about workplace comp).

Surveys show consumers want this change. Consumers have shown willingness to turn over facial and even biometric data for cheaper products, with one survey by Troubadour Research & Consulting finding that nearly half of consumers would turn over data from wearables to insurance companies. BioBeats and Fitsense are two startups tackling wearables data for health insurance, with a focus on personalizing employee health plans.

There’s still a lot of uncertainty on the back end of usage-based insurance. A 2017 report from the National Association of Insurance Commissioners noted: “…UBI is an emerging area and thus there is still much uncertainty surrounding the selection and interpretation of driving data and how that data should be integrated into existing or new price structures to maintain profitability.”

But most customers who tried it seem to have loved it. A 2016 survey by JD Power & Associates found: “…UBI participants provided more positive recommendations and more often indicated that these recommendations resulted in a friend, relative or colleague purchasing from their insurer compared with those customers who did not use a UBI program.” Some insurers offer discounts for participation in usage-based insurance programs to collect thousands of miles worth of monitored driving data. They can then use this data to benchmark their own risk scoring models on other business lines.

And roughly a fifth of the market isn’t even interested. 21% of customers declined to participate in a UBI program when it was available and 81% of those respondents did so because they didn’t want their driving monitored, didn’t think they’d save money, or didn’t think their premiums would decrease. People with long commutes, who frequently drive long distances or who savor speeding on the open road would hardly benefit from their insurance company tracking their behavior.

EY IoT for Insurance

Just because some carriers are getting sensor data doesn’t mean they are using it. As we’ve seen with many enterprise applications of machine learning, the reliability, richness and latency of the source data – alongside the proficiency of the analytics – becomes vital. That’s where platform marketplaces like Next Generation Platform (NGP) by Octo Telematics comes in, providing auto insurance carriers with an Application Platform Interface (API) for driver behavior scores, crash and claim analysis alongside specialized risk analytics for fleet managers and car rental companies.

Legacy players are slow to change. The 2017 Excellence in Risk Management report found “…an apparent lack of awareness among many risk professionals on existing and emerging technologies including telematics, sensors, the Internet of Things (IoT), smart buildings and robotics, and their associated risks.”

Markets could start moving fast as consumers trade IoT data for lower premiums. As managing director of Corporate Finance for KPMG Joe Schneider wrote, detailing the shift in the auto insurance industry: “Once the massive market disruption begins and traditional insurance business models are flipped upside down, we expect significant turmoil.”

Plug N Play Insurance Startup Video

It’s all about the sensor data. Anyone trying to benchmark legacy players versus newcomers should answer this question: How well are a company’s business lines positioned to take advantage of sensor data originating from their policies’ underlying assets?

With any new tech there are risks, which can be a good thing. Sensor data decreases risk in many ways, but of course it also introduces some novel vulnerabilities. Pretty much anything with a sensor may be vulnerable to hacking, and anything vulnerable to hacking may trigger penalties under data breach laws. Such vulnerabilities may allow carriers to develop new business lines that underwrite emergent risks, as the bull market for cyber insurance is already demonstrating.

2 – Customer Experience & Coverage Personalization: AI Interfaces Allow Better Customer Onboarding

Here are the three key ways that AI will enhance the insurance buying experience:

  • Chatbots Will Recognize You: Advanced image recognition and social data can be used to personalize sales conversation
  • Platforms Will Verify Your Identity: Automated personal identity verification can speed authentication necessary for quoting and binding
  • Carriers Can Customize Your Coverage: Machine learning can allow fully online or app-based shopping experience

(Readers with an explicit interested in conversational interfaces may want to read our full article about 7 chatbot use cases that are working now.)

You can now buy insurance with a selfie. In January 2017, the life insurance startup Lapetus made headlines by offering a service for people to buy life insurance using a selfie. Since habits such as smoking cigarettes are strong predictors of lifespan, Lapetus can use facial analysis to rapidly assign risk scores without a lengthy or onerous medical examination. The SMILe (smoker indication and lifestyle estimation) approach is explained on the company’s “about” page:

Lepatus Facial Recognition

Everything is numerically larger in China. Image recognition is also at the core of insurtech startup Zhong An’s business model. Zhong An is the first online-only insurance provider in China, and since 2013 has sold 7.2 billion insurance products to 429 million customers. Because they only ever meet their customers online, they rely on machine learning to prevent fraud and ensure a personalized customer experience.

Successful e-commerce is all about the customer. The most personalized customer experience is the one most directed by the customer. That’s the thinking behind Allianz1, a web interface in the Italian marketplace that allows buyers to create their own coverage products by mixing and matching from Allianz thirteen distinct business lines.

Insurtech likes chatbots. According to an Accenture survey, 68% of respondents in the insurance industry use chatbots in some segment of their business.

Chatbots like branding and human names. Famous insurance chatbots now include Geico’s Kate and Lemonade’s AI Jim, who settles claims. There’s also chatbots from Next, who sells commercial insurance to personal trainers via Facebook Messenger and Trov, who sells on-demand to individuals for personal property coverage.

3 – Faster, Customized Claims Settlement: AI Settles Claims Faster While Decreasing Fraud

Speed and success in settling claims is a critical factor for insurance business efficiencies, as well as for Here are two key ways that AI will improve customer satisfaction after filing a claim:

  • Speed in Settling Claims: This time-to-settle metric will end up being important for which business lines customers prefer using.
  • Decrease Likelihood of Fraud: This decreasing-fraud metric will end up being important for which solutions insurance companies prefer using.

AI’s advantage seems to be most obvious in claims settlement. Lemonade’s AI Jim made headlines in January 2017 by purportedly settling a claim in less than three seconds. This time-to-settle is the performance metric that customers care about most, according to surveys by JD Power & Associates, whose No. 1 ranked insurer clocked in at eleven days.

That’s a delta of several orders of magnitude. That means the No. 1 ranked insurer’s claims department took 316,800 times longer to settle a claim than Lemonade’s AI Jim. Again, time-to-settle is consistently the metric that customers most care about.

Most insurance executives already understand that AI will drastically change their industry. An April 2017 Accenture survey found that 79% of insurance executives believe that: “…AI will revolutionize the way insurers gain information from and interact with their customers.”

Zhong An insurtech

AI will likely bring faster claims settlement with decreased fraud.These two areas of focus are potentially among the biggest “low hanging fruit” opportunities for AI in insurance. Since there is limited digital information flow between insurance companies and hospitals in China, Zhong An relies on AI solutions to process vast quantities of paper information on policyholders. As Wayne Xu, chief operating officer of Zhong An explains: “We have been using machine learning to do fraud detection, to process hard copies and digitise information.”

This is a massive savings opportunity – with or without chatbots. Insurance carriers routinely report $80 billion in fraudulent claims. The most common form of insurance fraud is identity theft, where insurance and identity data is stolen for the purpose of filing a claim without the knowledge or consent of the policyholder. Fraud is already being detected in data security and payment / transaction fraud, and similar applications will continue to make their way into the insurance industry.

Fraud detection is the one AI tech trend that no one has ignored. That’s one reason why fraud detection is among the fastest areas of tech adoption in the insurance industry, with over 75 per cent of the industry reporting to have used an automated fraud detection technology in 2016. Shift Technology is one startup helping insurance companies prevent fraud,  recently crossing 82 million claims analyzed.

Conclusion: Benchmarking AI Solutions in Insurance

Customers evaluate the performance of insurance products when they need to be paid, not when they make their purchase. Unlike other products or services, customers are only able to form a judgment about the value that an insurance carrier delivers when the event being insured against takes place. Therefore, as Alex Polyakov, CEO and founder of insurtech company Livegenic writes: “The most important metric in insurance, hence, is customer satisfaction measurement of the customer post claim.”

That should be the core metric for disruptive AI. Since many such as Lemonade and Next are only a few years old, we currently lack sufficient data to determine whether these companies will be able to deliver a superior customer experience at scale. There is no denying that much of the customer journey with insurance companies is “stodgy”, and potentially in need to major refinement and streamlining. Time will tell how those changes will manifest for the customer experience.

Buying insurance or filing a claim with only a few clicks has undeniable appeal. So much so that Mike LaRocca, CEO of State Auto Financial (STFC) had this message for fellow insurance executives in January, 2017: “The power of change is coming, and if we fail to see it, we could be dead too.”

There seems to be a consensus: The status quo insurance business’ days are numbered. The April 2017 Accenture survey found that this opinion is widespread: ”Insurance executives believe that artificial intelligence (AI) will significantly transform their industry in the next three years.” Whether telematics, autonomous vehicles, chatbots or customization platforms, the market will likely move towards firms that are able to best harness AI to improve the customer on-boarding and claim management process.

The Role Of AI In Customer Experience By Will Thiel

  • Role of artificial intelligence in CX

“By 2020, 85% of customer interactions will be managed without a human” – Gartner

Today’s customers live in an omnichannel world. But most companies still force these evolved customers onto engagement paths that are steeped in legacy and instantly feel outdated.

source: https://www.pointillist.com/blog/

Artificial intelligence can be successfully employed to provide an intelligent, convenient and informed customer experience at any point along the customer journey. This will result in re-imagined customer experiences and end-to-end customer journeys that are integrated and more personal, so that they feel more natural to customers.

In this post, I will lay out why artificial intelligence is a game changer in CX, take a look under the hood at how AI is applied to CX, and explore use cases for how leading edge companies are already reaping benefits from AI applications in customer experience.

The Need For AI In Customer Experience

Customer experience is a competitive driver of growth when successful and the greatest source of risk when failing. Data insights are one of the primary tools for CX enhancement. CX datasets are messy, however, and the customer behaviors are chaotic. The rules are undefined and the success criteria are ambiguous. CX is the nightmare dataset for an AI developer.

AI application in CX

At the same time, this complexity is precisely the reason why AI can unleash so much value across the customer experience. Salespeople, call center agents and employees in other customer-facing roles cannot be expected to understand a customer’s entire history and derive their own insights from it in real time.

Automated systems cannot be hand-programmed with rules to handle every conceivable customer history. Delivering a coherent experience across all enterprise touchpoints requires finding patterns across an overwhelming number of data points. This is prime stomping ground for AI.

3 Building Blocks for Successful Application of AI in Customer Experience

The successful application of AI in customer experience requires 3 fundamental capabilities:

    1. Data Unification
    2. Real-time Insights Delivery
    3. Business Context

data unification

Data Unification

Data unification is a must for any type of behavioral analytics. AI thrives on information—the more the better.

The new generation of data unification tools make this daunting task cheap, fast, and relatively pain-free. Customer journey analytics platforms provide this service for a fraction of the cost of the dedicated data services providers of yore—even delivering a level of data integration free of charge.

The tedium of pulling together dozens of data sources is now just background noise. Expect timelines of days not weeks, with simpler data sources integrated within 1-3 days.

It’s a far cry from the expansive data engineering initiatives that likely still haunt your dreams.

Real-time Insights Delivery

For AI to impact the customer experience, insights need to be conveyed in the moment through the customer’s chosen touchpoint. Integrating with these touchpoints is the key to in-the-moment engagement.

Most leading SaaS platforms have APIs and consider 3rd-party integrations to be a critical component of their value proposition. The world would be a beautiful place if all touchpoint data was available through APIs.

The truth is that, in addition to elegant SaaS data streams, most enterprises must rely on myriad on-site, home-grown and legacy touchpoint data sources—product interfaces, payment platforms, point-of-sale systems, customer care, etc.. This reality creates a challenge for delivering real-time insights and are still very much a custom affair.

Customer journey analytics platforms are now filling this gap with a host of APIs options and development kits to deliver comprehensive, real-time touchpoint integration with minimal investment.

Business Context

For a simple, isolated interaction, AI is able to deliver results by simply knowing that an email is an email and a campaign is a campaign. Our web analytics and CRM platforms take advantage of this inherent luxury.

But in holistic, cross-channel journey analytics, the idea that touchpoints of a similar category will be the same across enterprises is an antiquated notion.

Customer journeys are as unique to individual businesses as fingerprints. Every company has their own set of touchpoints and a distinct method for employing those engagements in their customer experience.

For AI to deliver value, it must be given some context. By context, I mean more than simply designating a certain interaction as an “inbound call” and another as “order fulfillment.” AI must know the significance of these events in shaping a customer behavior. That requires an awareness of both the journey that these touchpoints helped to shape and the KPIs which were subsequently impacted by that customer behavior—whether related to revenue, profitability, customer lifetime value, customer satisfaction or other factors driving high-level business performance.

Armed with that information, AI systems can do more than find the “next best action” or the optimal audience. With proper business context, an AI can find touchpoints and tactics which actually shape the customer behaviors behind the business’s primary measures of performance.

Three Ways AI Is Being Applied to Improve Customer Experience

Now that we understand what it takes to successfully apply artificial intelligence in customer experience, let’s delve into some of those applications to see how AI is unleashing disruption across various aspects of customer experience by unifying data, providing insights in real-time, and incorporating critical business context.

1. Customer Service Gets A Gigantic Makeover

AI’s biggest impact undoubtedly will be to transform customer service by making it automated, fast and hassle-free. As I previously mentioned, salespeople, call center agents and employees in other customer service roles cannot be expected to ingest and understand a customer’s entire history prior to each conversation. But, artificial intelligence is now making it possible.

Here’s how AI applications are giving customer service a makeover:

Chatbots

Chatbots are AI-based conversation agents that are being used in many different customer-engagement scenarios. They are designed to simulate human interactions and provide immediate, personalized responses 24*7. This eliminates frustrating delays and errors in customer service, particularly for handling customer complaints.

Virtual Assistants

Virtual assistants utilize AI to obey commands or answer questions. Online retailer Spring was one of the first to start using Facebook’s Messenger Bot store to offer a personal shopping assistant. It helps shoppers find what they are looking for by engaging them in simple conversations.

virtual assistants

2. Predictive Personalization – Going From One-Click to Zero-Clicks

Artificial intelligence is helping businesses create experiences that naturally integrate with consumers’ everyday lives.
Consumers will no longer change their pattern of communication when interacting with brands in order to satisfy their needs. Intelligent prediction and customization will make customers feel as if every product or brand experience was tailored just for them.

Companies will be able to assess individual shopper inventories and consumer behaviors to predict and deliver goods to homes before they even realize they are running low. Self-driving cars will use their knowledge of preferred routes and in-vehicle entertainment drawn from past behavior to optimize daily commutes and long roadtrips. Even asking for help will become easier as AI infused with emotions will make customer experience interactions smoother and streamlined across channels.

3. AI-enabled Customer Analytics Discovers High-Impact Customer Insights

Optimal customer experience is achieved when a business remembers a customer and treats them with attention, respect and consideration throughout their unique customer journey.

Mining insights across billions of unique customer journeys using traditional analytics methods and tools is a laborious and slow process, which tends to confine it’s usage to a small set of pre-defined problems.

The power of AI-enabled customer journey analytics is that it can sift through a much, much larger and more complex data space and thereby uncover many more business opportunities—even opportunities you didn’t realize you should look for. As a result, you can spend your time prioritizing these insights instead of hammering away at the underlying data.

AI-enabled customer journey analytics finds every single relationship in the data that exists(without expressly being told to look for it). It can predict the likelihood of future behaviors with high accuracy, while simultaneously finding the drivers and inhibitors of customer performance.

AI-enabled customer analytics