e-Social + AI DL IoT

Artificial intelligence, or AI as it's called, has been a buzzword for nearly a decade already, yet sometimes it still feels as though we’re just in the early stages of discovering what predictive analytics and machine learning can do for enterprises.

Nowhere is this truer than in marketing and sales functions. According to Forrester, as of 2017 marketing and sales accounted for more than 50 percent of all AI investments.

But when you look at investors who have already sunk serious money into AI projects, only 45 percent have seen any results at all. And among those who are seeing results, 25 percent agree that they’ve become more effective in their business processes. These discouraging numbers paint a vivid picture: Most marketing and sales teams simply aren’t properly equipped to implement AI.

As a marketing leader who has helped companies like Salesforce and Symantec with digital marketing transformations, I’ve seen many "use cases" of how AI is being employed by today’s leading marketers and sales forces. And I’ve learned that often, the best way to kick off an AI initiative and make sure everyone is on board is to show them where others have succeeded.

Here are three ways in which AI has completely transformed enterprise sales and marketing in the 21st century for at least some companies:

1. Predicting outcomes to increase lead generation

Marketing is by nature a very competitive and data-driven endeavor, especially at the enterprise level. Every facet of global, cross-channel marketing relies heavily on a competent knowledge economy comprised of data inputs (and proactive recommendations) gathered at every touchpoint with visitors, leads, and customers.

UntieNots souhaite accroître la fidélité et le panier moyen des consommateurs en magasin grâce à l’IA. Auchan en France et Carrefour en Belgique se sont déjà laissé séduire.

Dans ces temps de lutte contre le gaspillage et la surconsommation, UntieNots pourrait presque faire figure de provocateur. Chaque semaine, les Gilets Jaunes clament leur manque de moyens. Le nombre de travailleurs pauvres ne cesse d’augmenter et le surendettement est un écueil sur lequel s’échouent des milliers de familles. Cette start-up parisienne cherche pourtant à encourager les particuliers à dépenser plus durant leurs courses hebdomadaires.

Sa solution d’ultra personnalisation promotionnelle, baptisée Loyalty Challenge, a pour objectif d’accroître la fidélité et le panier moyen des consommateurs en magasin. Ses algorithmes d'Intelligence Artificielle exploitent toutes les données des cartes de fidélité, de géolocalisation et de navigation web des clients pour créer des campagnes de promotion ciblée en fonction des marqueurs d'affinités identifiés entre un acheteur et une marque. Fondée en 2015, UntieNots travaille déjà avec Auchan en France et Carrefour en Belgique. Cette jeune pousse parisienne est parvenue à combler un réel manque dans le retail.

Lutter contre les « effets d’aubaine »

« Avec mon associé Cédric Chéreau, nous avons accumulé près de quinze ans d’expérience dans le Retail Analytics et nous avons réalisé à quel point les datas pouvaient aider les distributeurs », souligne Zyed Jamoussi qui a travaillé chez emnos, Yves Rocher et PWC « Nous avons alors eu l’idée de profiter des nouvelles technologies qui commençaient à émerger pour révolutionner la fidélité dans le retail ». Les solutions qui existaient jusqu’alors n’étaient pas satisfaisantes pour les enseignes.

Manufacturers are well-versed in gathering data from their operations, but don’t always know how to use it. Data science automation can provide direction. Dr. Ryohei Fujimaki, CEO and founder, of dotData, joins us to predict how machine learning will change data science over the next year.

1. The pressure to achieve greater return on investment from Artificial Intelligence (AI) and machine learning (ML) initiatives will push more business leaders to seek innovative solutions. While substantial investments are being made in data science across many industries, the scarcity of data science skills and resources limits the advancement of AI and ML projects within organizations. In addition, one data science team is only able to execute several projects a year given the iterative nature of the process and the manual work that goes into data preparation and feature engineering. In 2019, data science automation platforms will cover much wider areas than machine learning automation, including data preparation, feature engineering, machine learning and the production of data science pipelines. These platforms will accelerate data science, executing more business initiatives whilst maintaining the current investments and resources.

2. Transparency and interpretability will become even more important than accuracy. Traditional data science approaches are "black boxes" that result in less actionable business outcomes. Given the current regulatory climate, as it relates to profiling (GDPR, etc.), businesses are demanding increased transparency along with actionability. Current data science processes focus primarily on accuracy and not transparency. In 2019, we will see an emergence of new tools that will enable data scientists to have greater transparency, while sacrificing little in the way of accuracy. This shift to a more “white box” approach to data science will deliver more transparent and accurate models.

The birth of the field of AI in the 1950s predetermined a new milestone in scientific thought: Since then, it became possible to use computer programs for solving mathematical, economic and other problems, which were relying before on human intelligence. The further development of programs and their growing complexity required more work on code, rules and decision-trees. At that point, realizing the need for more advanced data processing approach, researchers came to what is known today as “Machine Learning” — the ability of computer systems to learn without being explicitly programmed.

Today Machine Learning is an integral part of search engines, navigations systems, email providers and social media networks. But in the recent few years, the mobile app market has seen the biggest growth in the use of Machine Learning for customizing the app experience, boosting sales and providing app security.

A significant part of today’s mobile applications is connected to some extent to Machine Learning for the purpose of better user experience and app functionality. The examples of ML in the app include Google Maps (traffic predictions, ‘find parking’ feature ), Netflix (classification of the content by genre, actors, reviews, length, year and so on, personalized recommendations), Flo (period predictions and tracking), Uber (estimated time of arrival, cost of the ride, real-time information on maps), MSQRD (in-app face detection), Tinder (‘smart photos’ feature, personalized recommendations), and many others.

Why do some mobile in-app ad campaigns succeed, while others fall flat? In part, it’s because the creatives used are just ineffective. Too often, ads go unseen — and unclicked.

In the second quarter of 2018, Moat’s average valid and viewable rate was around 60 percent, while the average viewable rate noted by IAS in the same time frame was less than 50 percent. That means two of every five ads will never be seen in full by a real person.

The average click-through rate of an in-app ad is just over 1.5 percent. While it’s better than the average CTR for mobile web ads (1.12 percent), it still means that a lot of ads are not leading to sales, app downloads, and sign-ups.

So, is there a better way? What can advertisers do to make sure their ad spend yields real results?

This is one application where artificial intelligence (AI) and machine learning (ML) can help in a major way. By applying advanced analytical insights to the art of ad creatives, mobile marketers can be sure their ad campaigns are more appealing and thus more effective.

For too long, online consumers have been pitched the same kinds of clothes, the same types of opinions and the same sort of songs and over again, thanks to a like, an ad click or a Google search. We’ve been living in topical bubbles where our interest data is too often used to maintain our sensibilities rather than expand them. The fake news phenomenon is one of the biggest ramifications of these bubbles, but algorithms don’t just impact our political leanings, they also influence our purchase decisions and almost everything we do with tech.

What’s more, an internal conflict among consumers puts businesses in a precarious position. On the one hand, 53 percent say they are concerned by data-driven ad retargeting and widespread support for new privacy legislation in GDPR and the California Consumer Privacy Act of 2018 makes it clear that people are wary of how marketers use their information. On the other hand, the majority of millennial and Gen X consumers are willing to exchange data for more relevant experiences from brands. Marketers must strike the right balance: customize what people see without over-personalizing.

This balance is key to applying artificial intelligence, which should be used to inspire discovery instead of insulating people from new things. Technology can help us stretch beyond our comfort zones, and while many brands aren’t there yet, a handful of innovative players are beginning to approach marketing with this lens.

72 % des entreprises qui utilisent l’intelligence artificielle pour améliorer leur service client en verraient déjà les bénéfices…

À l’heure où les consommateurs sont de plus en plus réceptifs à une expérience client automatisée, une nouvelle étude* réalisée par ServiceNow et Devoteam analyse la révolution impulsée par l’intelligence artificielle (IA) dans la fourniture de services.

Cette étude révèle que près plus d’un tiers (30 %) des entreprises européennes (38 % des entreprises françaises) ont mis en place des technologies d’IA pour le service client et que 72 % de celles-ci en constatent déjà les bénéfices, notamment des gains de temps pour leurs collaborateurs, le traitement plus efficace des tâches volumineuses ou encore l’offre d’un support disponible en permanence.

« La majorité des entreprises offrent une expérience omnicanale à leurs clients mais nombre d’entre elles peinent à suivre le rythme de croissance de la demande des consommateurs pour des services sur ces différents canaux.

Les pionniers recueillent les bénéfices de l’utilisation des technologies d’intelligence artificielle pour faire face à des tâches et demandes courantes, ce qui permet à leurs agents de ne plus être dans la réaction mais plutôt dans un engagement proactif. »

The world has gone chatbot mad of late. Put the word into Google and you’ll be inundated with articles telling you which chatbots will best meet business needs, listing the best chatbots this month, or simply explaining what they are. For anyone unsure, these are the live chat services you’ll find on many customer-facing websites, where the helpful responses made not by a human customer service representative, but by a robot.

Banish from your minds all thoughts of the robot from movies such as Lost in Space. The chatty little robot behind the chatbot is a piece of software, driven by artificial intelligence (AI). The chatbot-as-customer-care-rep has a database of questions, prompts, answers and responses to fall back on. These will have been written by a person, although give it time and chatbots will probably start writing their own material.

There’s plenty of scope for robots to help out in warehouses, as Ocado’s many investments in such technology clearly indicate. But what about AI and chatbots? One of the reasons for the popularity of chatbots, particularly on retail sites, is they can act as a frontline of customer care help, leaving your human staff to do more valuable work. But you probably don’t need a chatbot in your warehouse. There are other opportunities for AI to make meaningful inroads into delivery operations, however. Some of which your company may even be using already.

Chaque jour, votre entreprise reporte l’exploitation du marketing basé sur l’intelligence artificielle, vous êtes perdant face à des entreprises qui explorent les avantages de cette nouvelle technologie révolutionnaire.

L’intelligence artificielle alimente les éléments des modèles marketing et des systèmes de prédiction des grandes marques comme Netflix, Google, Amazon et d’autres marques grand public. Au cours des deux dernières années, IA a fait des progrès dans le domaine du commerce électronique, en particulier en aidant les équipes de marketing numérique à augmenter presque tous les aspects du parcours numérique de leurs acheteurs et de leur parcours d’achat.

La meilleure partie ? Les systèmes d’IA et les avantages concurrentiels qu’ils offrent constituent encore une force relativement inexploitée au sein de l’industrie. Les marques qui explorent et investissent dans l’intelligence artificielle ont tout à gagner, tandis que leurs homologues font comme si de rien n’était et adhèrent au statu quo. Les outils d’IA qui n’étaient auparavant abordables que pour les géants de l’entreprise sont maintenant abordables et efficaces pour les petites et moyennes entreprises.

Voici trois façons grâce auxquelles l’intelligence artificielle peut vraiment révolutionner l’expérience d’achat en ligne et du commerce électronique pour votre consommation finale:

Personnaliser l’expérience d’achat en ligne

Les systèmes d’IA ne peuvent pas encore construire votre site Web à partir de zéro, mais les applications d’apprentissage automatisé sont capables d’améliorer l’expérience des visiteurs de votre site Web d’une multitude de façons personnalisées. Les algorithmes sont capables de digérer des données clients complexes et de fournir un contenu pertinent aux individus au bon moment. Avec l’IA, vous pouvez améliorer :

L’expérience de navigation. L’IA ajuste le traitement de chaque élément de données client (emplacement, démographie, appareil, pages vues, produits consultés, articles cliqués, temps passé sur la page, etc.)

Intention d’entrée/sortie. Au lieu d’inciter les utilisateurs à utiliser des superpositions d’intention de sortie, vous pouvez personnaliser vos fenêtres contextuelles pour qu’elles soient incroyablement pertinentes pour chaque personne. Vous pouvez également vous assurer que le contenu de widgets Web spécifiques corresponde à ce que les contacts voient ailleurs, comme dans un e-mail.

De nombreuses marques de commerce de détail à succès mettent à l’essai ou utilisent des tactiques de personnalisation Web axées sur l’intelligence artificielle pour augmenter les taux de conversion, saisir plus d’informations sur les contacts des utilisateurs et faire en sorte que les clients continuent de s’intéresser à leurs produits et à leurs contenus.

Bottom Line: AI and machine learning are enabling omnichannel strategies to scale by providing insights into the changing needs and preferences of customers, creating customer journeys that scale, delivering consistent experiences.

For any omnichannel strategy to succeed, each customer touchpoint needs to be orchestrated as part of an overarching customer journey. That’s the only way to reduce and eventually eliminate customers’ perceptions of using one channel versus another. What makes omnichannel so challenging to excel at is the need to scale a variety of customer journeys in real-time as customers are also changing.

89% of customers used at least one digital channel to interact with their favorite brands and just 13% found the digital-physical experiences well aligned according to Accenture’s omnichannel study. AI and machine learning are being used to close these gaps with greater intelligence and knowledge. Omnichannel strategists are fine-tuning customer personas, measuring how customer journeys change over time, and more precisely define service strategies using AI and machine learning. Disney, Oasis, REI, Starbucks, Virgin Atlantic, and others excel at delivering omnichannel experiences using AI and machine learning for example.

In the predictions game, it's time for us to bat clean-up once more. Following Big on Data bro Andrew Brust's roundup of AI-related predictions from a cross section of industry executives, now it's our turn. We'll focus mostly on what this all means to the database, a technology that after Y2K was thought to be entering its finished state.

In 2019, we view the AI and the cloud as being the great disruptors.

Let's paint the big picture first. At Ovum, we've long forecast that by 2019, half of all new Big Data workloads would run in the cloud. According to our latest data, that scenario is already bearing out, with our surveys showing roughly 45% of respondents reporting running at least some Big Data workloads in the cloud.

The cloud's impact on databases is that it is redefining the basic architectural assumptions on how to design them and manage data. On-premises, it was all about threading the needle in sizing just enough capacity to be fully utilized, but not too much capacity to trigger software audits or result in excess license charges. And for Big Data, it was all about bringing compute to the data because the network overhead of moving all those terabytes was not considered particularly rational.

Enter the cloud, commodity infrastructure, cheapening storage, faster network interconnects, and most of all, virtually limitless scale, and for database vendors, it was back to the drawing board, such as separating storage from compute. Add some fuel to the fire: our belief that the best way to realize value from cloud database deployment is through managed Database-as-a-Service (DBaaS) where patches, upgrades, backups, failovers, and provisioning and handled by the cloud provider, not the DBA. And that sets us up for our first prediction, which by the way, happens to be buzzword-compliant.

Big data has played a fundamental role in the evolution of content marketing. The Editorial Staff atBusiness.com states that big data has helped by:

- Providing deeper insights into customer mindsets by tapping data from social networks, shopping activities and other third-party data resources
- Analyzing the best times to distribute content for ROI
- Tracking various factors that influence marketing campaigns with data driven tracking tools

Some of the most recent advances in big data have been especially significant. Predictive analytics has been especially valuable.

But how significant is predictive analytics in content marketing? Martech Advisors reports that companies using predictive analytics in their campaigns are able to get more traction with 77% less content.

How is Predictive Analytics Helping with Content Marketing?

With the increased popularity of digital marketing, many businesses and companies choose to invest a lot on content creation. Creating excellent content helps you connect with your audience and make a great positive impact on their mind. It is especially effective when you use big data insights to learn more about them.

Having right writing skills is definitely great for a good start but it is not enough. In this competitive world, everyone needs to use data-driven tools to create and curate great content. If you really want to produce content that can stand out in the crowd, then you must be well-equipped with the right tools and understand how big data helps.

Content development is not something that you can take lightly. It is an art and one needs to master in this. Nowadays, there are many tools available in the market that can help you in mastering this art. Using big data content creation tools can help you in content research as well as in collaborating with other writers to create a masterpiece. Even you will need tools for SEO (Search engine optimization) and analytics to make sure your content is perfect.

Moore Stephens estimated the size of the marketing technology or martech industry around $24 billion in 2017. It follows that AI would find its way into the martech world. Numerous companies claiming to assist organizations in their marketing; we wrote a report on marketing and AI detailing this connection.

As of now, numerous companies claim to assist marketers in aspects of their roles from customer profiling, to analytics, to chatbots. As marketing can be applied to every industry, the goal of this report to allow business leaders in any industry to garner insights they can confidently relay to their executive teams so they can make informed decisions when thinking about AI adoption.

We researched the space to better understand where AI comes into play in the martech industry and to answer the following questions:

What types of AI martech applications are currently in use in industry?
What tangible results have AI martech applications driven in industry?

La conférence Microsoft Ignite 2018 prend place du 24 au 28 septembre 2018 à Orlando, Floride. L’occasion pour la firme de Redmond de dévoiler de multiples nouveautés pour le Big Data, l’intelligence artificielle, et le Cloud.

Comme chaque année, la conférence Ignite est l’occasion pour Microsoft de dévoiler de nombreuses nouveautés pour ses différentes gammes de produits et services. Parmi les différentes thématiques abordées, la cybersécurité était sous le feu des projecteurs.

Les cyberattaques contre les entreprises de tous les secteurs sont de plus en plus fréquentes et redoutables. Afin d’aider les organisations à lutter contre ce fléau, Microsoft vient de dévoiler plusieurs nouveaux services de cybersécurité.

La nouvelle application Microsoft Authenticator permettra de se connecter aux applications connectées Azure AD sans mots de passe afin de supprimer le risque de voir ces mots de passe tomber entre de mauvaises mains. De même, la nouvelle solution Microsoft Threat Protection utilise l’intelligence artificielle pour détecter et éliminer les cybermenaces. Enfin, Azure Confidential Computing va protéger la confidentialité et l’intégrité des données sur le Cloud Azure.

Sales reps spend a lot of time doing repetitive, tedious tasks in their quest to make their quotas and boost revenue.

These often include manually following up with leads and closing new business.

Screen Shot 2018-09-18 at 12.54.26 PMBut what if your sales team could spend time only on the strategic, high-value tasks that really move the needle, instead of all these day-to-day chores?

That's the promise of Exceed.ai, an AI-powered sales assistant that claims to automate lead qualification and nurturing at scale, in the process offloading a lot of the chores that sales reps dread.

We spoke with Ilan Kasan, cofounder and CEO of Exceed.ai, to learn more about how the tool uses AI to improve marketing and sales.

1. In a single sentence or statement, describe Exceed.ai.

Lead qualification and nurturing at scale using AI and automation. Exceed.ai is an assistant to your sales team that engages leads in unlimited conversations, delivering timely, human-like, personalized nurturing and qualification.

2. How does Exceed.ai use artificial intelligence (i.e. machine learning, natural language generation, natural language processing, deep learning, etc.)?

We use AI to engage leads in two-way email conversations.

We use natural language understanding to read emails to understand what the lead is saying and their responses to our qualification questions. We use natural language generation to respond to leads: handle objections, answer questions and ask qualification questions.

We use machine learning and AI to optimize and personalize each conversation with each lead to ensure more leads are push through the pipeline.

FMCG retailers today have more data available than ever before, so how can marketers maximize its potential to grow share of wallet? With AI delivering 2x uplift in offer redemption, it’s time to get serious.

It wasn’t long ago that one of the main challenges that retail marketers faced was the collation of relevant information about customers and their shopping patterns. Now, with the explosion of data sources – from loyalty data to social interactions, our recent survey found that 40% of retailers face the challenge of integrating data across multiple marketing channels. So, whether you’re in grocery retail, health & beauty or pet specialty, it’s time to fully embrace the marriage of retail marketing and AI.

With 90% of retailers currently executing personalized marketing campaigns, it’s clear that delivering these campaigns efficiently is a struggle, which leads to the question – how effective are those campaigns?

Retail marketing and AI: They just belong together

Personalized marketing campaigns can only be successful if the retail marketer has a 360° view of the customer, and the ability to recommend the most relevant offers to a given customer at a given time. There is little point having all the data if the marketer isn’t able to tap into it efficiently. This is where AI and machine learning play an essential role – there is no such thing as too much data with these technologies – the more data they have, the better they learn and perform.

What would make retailers change their marketing systems?

In our survey of U.S. retail marketing executives, 67% said they would upgrade or change their systems if they could achieve faster, easier aggregation of customer data for targeting and analysis. Recognizing that systems can offer more than just data aggregation, 44% would upgrade or change if they could reduce the effort needed to manage marketing campaigns and optimize ROI.

73% des consommateurs affirment avoir déjà interagi avec une IA et plus de moitié ont été satisfaits de cette expérience. C'est ce qui ressort de l'étude de Capgemini, réalisée auprès de 10 000 consommateurs et plus de 500 entreprises répartis dans 10 pays*. Cependant, pour séduire les consommateurs, un équilibre entre IA et interactions humaines est privilégié par 55% des interrogés. Pour 64%, il faudrait même que l’IA devienne plus humaine.

Les consommateurs apprécient l’équilibre entre IA et êtres humains.

Selon le rapport du Digital Transformation Institute, la disponibilité permanente de l’IA et le contrôle qu’elle permet sur les interactions, sont des points positifs pour 63% des consommateurs, déjà initiés à cette technologie. Mais alors que l’IA prend de plus en plus de place dans leur quotidien, il s’avère que plus celle-ci est dotée d’une intelligence proche de celle de l’Homme, plus les consommateurs se sentent à l’aise. Cette humanisation de l’IA pourrait même être un facteur de fidélisation puisque 49% des interrogés affirment qu’ils se sentiraient plus proches des organisations offrant des interactions IA plus humaines et donc seraient aptes à dépenser davantage.

Mais attention, une IA plus humaine ne signifie pas à l’apparence semblable à celle d’un humain. Ce critère semble au contraire déranger plus de la moitié des consommateurs, qui préfèrent une IA à la voix humaine ou même capable d’interpréter les émotions. De cette manière, un juste équilibre entre IA et humain paraît correspondre aux interrogés de tous âges, bien que 66% souhaiteraient malgré tout être avertis lorsque l’IA est utilisée par les entreprises dans les interactions.

Acobot LLC, an artificial intelligence startup, announced a new release of its artificial intelligence Aco, featuring the capability of self-learning from free-form text such as web pages and creating conversational AI chatbots, which are ready to work as virtual agents for customer support automation, without any further training by a human.

Conversational AI and chatbots are often mentioned as today and tomorrow in the tech circles. For businesses, the efficiency and cost savings derived from the chatbot’s interacting with customers is attractive. As a result, more and more businesses include a chatbot with their website in the recent years. However, lacking in natural language processing (NLP) support, most of those chatbots only accept clicks or match answers by keyword, resulting in poor performance.

As for the chatbots powered by AI/NLP, their performance is decided by not only the algorithm but also the volume of knowledge they possess, i.e. the data or contents on the subject they work with. Like all other AI applications, the more data a chatbot has, the better it may perform.

Today coding and scripting is no longer a necessity for creating a chatbot because of the emerging of a number of conversational AI aPaaS like BrainShop, another product of Acobot LLC. However, it still requires considerable efforts and resources to manually prepare business-specific data, the knowledge that AI can understand. The turnaround for preparing such data ranges from one month to half a year. The costs often go beyond the budget of small businesses and many projects fail because insufficient data lead to poor performance – the chatbots can answer only 20% or less of questions – and consequently poor user experience.