Big data and artificial intelligence (AI) power everything that the business does – the global director of digital innovation, Greg Chambers, said: “Artificial intelligence is the foundation for everything we do. We create intelligent experiences. Artificial intelligence is the kernel that powers that experience.”

What Problem Is Artificial Intelligence Helping To Solve?

Marketing soft drinks around the world is not a “one-size-fits-all affair”. Coca-Cola products are  marketed and sold in over 200 countries.

In each of these markets there are local differences concerning flavours, sugar and calorie contents, marketing preferences and competitors faced by the brand.

This means that to stay on top of the game in every territory, it must collect and analyse huge amounts of data from disparate sources to determine which of its 500 brands are likely to be well received. The taste of their most well-known brands will even differ from country to country, and understanding these local preferences is a hugely complex task.

How Is Artificial Intelligence Used In Practice?

Coca-Cola serves a large number of its drinks every day through vending machines. On newer machines, typically the customer will interact through a touch-screen display, enabling them to select the product they want and even customise it with “shots” of different flavours. The company has begun fitting these machines with AI algorithms allowing them to promote drinks and flavours that are most likely to be well received in the specific locations where they are installed.

The vending machines can even alter their “mood” depending on where they are located – with machines in a shopping mall displaying a colourful, fun persona, those in a gym more focused on achieving performance, and those in a hospital appearing more functional.

Coca-Cola also uses AI to analyse social media and understand where, when and how its customers like to consume its products, as well as which products are popular in particular localities. With over 90% of consumers making purchasing decisions based on social media content, understanding how its billions of customers are discussing and interacting with the brand on platforms like Facebook, Twitter and Instagram is essential to its marketing strategy. To do this, Coca-Cola analysed engagement with over 120,000 pieces of social content to understand the demographics and behavior of its customers and those discussing the products.

Another application of AI was in securing proof of purchase for the company’s loyalty and reward schemes. When customers were asked to manually enter 14-digit product codes printed on bottle caps into websites and apps to verify their purchases, uptake was understandably low due to the unwieldy nature of the operation.

To encourage more customers to engage with these schemes, Coca-Cola worked to develop image recognition technology that allows purchases to be verified by taking a single smartphone picture.

What Technology, Tools And Data Were Used?

Coca-Cola collects data on local drink preferences through the interfaces on its touch-screen vending machines – over 1 million of them are installed in Japan alone.

To understand how its products are discussed and shared on social media, the company has set up 37 “social centers” to collect data and analyse it for insights using the Salesforce platform. The aim is to create more of the content that is shown to be effective at generating positive engagement. In the past, the process of creating this content was carried out by humans; however, the company has been actively looking at developing automated systems that will create adverts and social content informed by social data.

It also uses image recognition technology to target users who share pictures on social media inferring that they could be potential customers. In one example of this strategy in action, Coca-Cola targeted adverts for its Gold Peak brand of iced tea at those who posted images that suggested they enjoy iced tea, or in which the image recognition algorithms spotted logos of competing brands. Once the algorithms determined that specific individuals were likely to be fans of iced tea, and active social media users who shared images with their friends, the company knows that targeting these users with adverts is likely to be an efficient use of their advertising revenue.

For purchase verification, off-the-shelf image recognition technology proved to be insufficient for reading the low-resolution dot matrix printing used to stamp product codes onto packaging. So, Coca-Cola worked to develop its own image recognition solution using Google’s TensorFlow technology. This used convolutional neural networks to enable machine recognition of codes that could often appear differently depending on when and where they were printed.

What Were The Results?

Analysis of the data from vending machines by AI algorithms allows Coca-Cola to more accurately understand how the buying habits of its billions of customers varies across the globe.

It uses this to inform new product decisions – for example, the decision to launch Cherry Sprite as a bottled product in the United States was taken because the data showed that this was likely to be a winning initiative.

Computer vision analysis and natural language processing of social media posts, as well as deep learning-driven analysis of social engagement metrics, allows Coca-Cola to produce social advertising that is more likely to resonate with customers and drive sales of its products.

Applying TensorFlow to create convolutional neural networks enabled scanners to recognise product codes from a simple photograph, increasing customer engagement with Coca-Cola’s different loyalty programs around the world.

Key Challenges, Learning Points And Takeaways

• If you sell hundreds of different products across multiple countries, perceptions and customer behaviour can vary greatly from market to market. Understanding these differences helps tailor specific messages for different markets, rather than relying on a one-size-fits-all approach
• When you’re dealing with global brands, user data from social media or generated through your own systems (such as vending machines) is vast and messy. AI provides a viable method of structuring this data and drawing out insights
• Computer vision technology such as image recognition tools can analyse millions of social media images to help a brand understand when, how and by whom its products are enjoyed
• As well as making marketing decisions, brands that are fully invested in AI are beginning to use it for designing new products and services

This is an edited extract from Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems, by Bernard Marr, with Matt Ward (published by Wiley, April 2019).

About the authors: Bernard Marr is the founder and CEO of Bernard Marr & Co and an internationally best-selling business author, futurist, keynote speaker and strategic advisor to companies and governments. He is one of the world’s most highly respected voices and a renowned expert when it comes to topics such as artificial intelligence and big data. Marr advises many of the world’s best-known organisations on strategy, digital transformation and business performance. He is the author of Big Data in Practice: How 45 Successful Companies used Big Data Analytics to Deliver Extraordinary Results and Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance, both published with Wiley.

Matt Ward is the research lead for Bernard Marr & Co. Matt has a background in investigative journalism and spent the last few years working closely with Bernard Marr on the latest technology topics. Matt is an expert and experienced writer in the field of business technology and artificial intelligence, where he has worked with companies such as IBM, Intel, Citibank and NASA.

 Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, , and Cyber Security & .

The post How Coca-Cola is using AI to stay at the top of the soft drinks market appeared first on AI News.

A total of 307 digital commerce organisations were surveyed for the study. These companies are currently using or piloting the technology to understand the adoption, value, success and challenges of AI in digital commerce. Organisations participated in this study were from the US, Canada, Brazil, France, Germany, the UK, Australia, New Zealand, India and China.

Among the respondents, three-quarters said that they are seeing double-digit improvements in the outcomes they measure. Customer satisfaction, revenue, and cost reduction are the most common metrics used to measure the business impact of AI. For customer satisfaction, revenue and cost reduction specifically, respondents cited improvements of 19, 15 and 15%, respectively.

Moreover, the study also reveals lack of quality training (29%) and in-house skills (27%) are the top most challenges in deployment of AI in digital commerce.

In order to overcome difficulties while implementing AI, organisations must consider following the ‘four pillars of marketing success’, according to Brian Baumgart, CEO of Conversion Logic. These are data, systems, algorithms and people:

• Data: the organisations should be able to aggregate and normalise data into one context. This means that everything is a line probability, all the time variables are the same, and everything has generally a comparable meaning of time and place
• Systems: Parties, such as marketers or people or vendors who support them, should have a flexible architecture. The organisation should be able to add new data sources from different places easily, quickly, and readily
• Algorithms: The system as a whole should be modular and scalable. By this, it means that the process of plugging in different algorithms and different data sources to do different things should be easy
• People: Having right people at hand, or expertise, is utmost important, as these are the people who are driving all of this

The post AI in digital commerce is generally considered a success, says Gartner appeared first on AI News.

For the new push, Gumtree teamed up with the AI innovator to revitalise their CRM strategy to ensure continued engagement from customers. Gumtree was seeing a decline in email marketing engagements and adopted different steps to resolve the issue internally.  However, nothing came to the rescue and finally Gumtree approached Phrasee.  As a solution, Gumtree aligned its existing technology – Salesforce Marketing Cloud – with Phrasee’s AI technology for marketing language generation.

The Phrasee Salesforce AppExchange app offers a straight-through integration for building and distributing split tests using subject lines generated by Phrasee.

Commenting on the effectiveness of Phrasee’s AI technology, Matt Button, Gumtree Head of CRM, said: “Despite the fact Gumtree is used by 33% of the UK digital population, email marketing open rates had been steadily declining. We needed to reverse the trend – and fast. We identified that we needed to revitalise our CRM strategy and bring in something to get our customers’ attention in order to actually make them want to open our emails.”

Separately, global technology firm Datorama has furthered its marketing intelligence platform by adding a new complementary solution – LiteConnect – which uses AI to easily convert any spreadsheet or data file into a interactive, marketing-specific dashboard in just the blink of an eye. Once a file is uploaded through LiteConnect, it automatically converts the file into an expert-level, interactive dashboard through the use of AI.

Targeted particularly at marketers, the patent-pending solution offers a quick and hassle-free entry point to technology-assisted analysis helping them get away from manual reporting methods.  LiteConnect is designed to offer marketers the added flexibility of analysing data with minimal hurdles.

The post How Gumtree has boosted customer engagement through marketing language generation appeared first on AI News.

What is the customer appetite?

In a recent survey by SugarCRM and Flamingo, three-quarters of the respondents said they’re comfortable using chatbots and think chatbots improve the online experience. There is tremendous potential for the intersection of voice technology and CRM, with voice- activated assistants such as Siri and Alexa paving the way. People are interacting with these devices in ways you could not have imag­ined even a few years ago, and every connection becomes a data point. It’s going to have a huge impact on customer engagement and the foundation that CRM sits on.

But AI needs to add value

Machine learning is only useful if it’s benefitting the customer experience and improving the way employees work. For example, if a customer wants to carry out a simple task e.g. checking an order status, then a human is typically not essential to the delivery of this information. This is where AI tech should come into play – automating jobs where humans aren’t needed.

Because as much as 98 per cent of all customer interactions are simple queries of some kind, bots can be immensely valuable for scaling and streamlining engagement. You don’t want to be delighted by the answer; you just want the answer. That’s the value of AI: the ability to learn without the human on the ordinary stuff.

Consumer-facing businesses are routinely rolling out this technology – for example, RBS’s chatbot ‘Luvo’ or O2’s Aura which has the ability to solve basic customer issues; and therefore has the potential to reduce the need for as many customer services employees. Even more complicated sectors are experimenting with AI with UK start up Habito providing customers with the world’s first ever mortgage advice chatbot- disrupting what is conventionally thought of as a lengthy procedure.

But chatbots have limitations as they do not have the capabilities to understand complex issues or emotional signals such as tone of voice. This is where human employees are still needed, to maintain customer relationships and avoid frustrating customers – or causing them to take their business elsewhere.

It’s fair to say, to date, the most noise around AI has been the role it can play in customer service. But there is exciting potential in marketing, too – particularly when it comes to analysing customer data, and optimising sales and marketing strategies based on the data. And most marketers are feeling positive about AI; research by eMarketer found 75% of marketers felt confident about adding AI to their marketing and sales efforts.

Quality data management is crucial for driving meaning from AI 

Artificial Intelligence, machine learning and predictive technologies all hinge on the quality of the data set they are interpreting and learning from. The whole purpose of this technology is to study patterns of behaviour from data, and construct algorithms that can learn from and make predictions, boosting efficiency and cutting down on manual processes.

The ultimate aim is to reduce the investment and resource needed to programme machines – it’s called machine learning for a reason. Customer Relationship Management (CRM) systems can be at the heart of this, driving insight and valuable learnings from rich, robust data.

As CRM systems become more adept at consuming large amounts of data, and leverage machine learning algorithms to generate insights more quickly, they will allow every user to better know every customer, and to anticipate and predict customers’ needs more effectively.

Collecting a variety of unstructured data, including social media posts, emails, and call centre recordings, and combining this behavioural data with transactional data, CRM systems will be able to deliver deeper insights on customer preferences, which deepens the customer relationship. Social data in particular can help an organisation learn from and engage with customers at a more holistic level.

We are already seeing this become a reality with the launch last year of Sugar Hint. It helps marketers gather a wealth of relationship intelligence about businesses and individuals from just a name and email address. It eliminates the need for lots of manual research and data entry and gathers customer intelligence from a broad range of social data sources so users can quickly and efficiently learn more about their prospects to establish a productive relationship.

While chatbots were the first noticeable manifestation of AI in action, innovations such as relationship intelligence are the next step. They will revolutionise the way we interact with our customers – telling us things we don’t already know about them or that would take hours to discover manually. Down the line, through AI, businesses will be able to obtain intelligent recommendations for best actions, priorities and likely outcomes and to use this insight to generate marketing that truly resonates. By combining AI and CRM, all of our interactions will become more meaningful and effective.

The post Finding the meaning in AI for the customer experience appeared first on AI News.

The use of chatbots – or virtual customer assistants (VCA), as Gartner puts it – will go up from just under 2% of operations as of 2017.

The figures come from Gartner’s Customer Experience Summit, taking place in Tokyo. Gene Alvarez, Gartner managing vice president, argues chatbots should be more than informative, and instead enrich the user experience.

“As more customers engage on digital channels, VCAs are being implemented for handling customer requests on websites, mobile apps, consumer messaging apps and social networks,” said Alvarez. “This is underpinned by improvements in natural language processing, machine learning, and intent-matching capabilities.”

This will have a knock-on effect on organisations’ strategies, Gartner added. By 2019, one in five brands will abandon their mobile apps, but not the medium altogether. Instead, they will opt for greater presence in Facebook Messenger and WeChat.

What’s more, Gartner said that by 2020 30% of all B2B companies will employ artificial intelligence to augment at least one of their primary sales processes, and that more than 40% of all data analytics processes will relate to an aspect of customer experience.

By the same year, augmented, virtual, and mixed reality immersive solutions will be adopted by 20% of large enterprises as part of their move towards digital transformation.

The post Gartner argues how customer service is becoming more chatbot-centric appeared first on AI News.

Rubikloud deploys AI applications to help retailers generate extensive revenue through the Rubikloud platform that enables automated OLAP data integration with legacy applications and data warehouses. Automated instructions are delivered by the platform to execution layers including ERP, supply chain system and tools related to marketing automation. Retailers who deploy Rubikloud’s Promotion Manager or Lifecycle Manager realise material uplifts to promotional revenue, inventory stock out rates, and loyalty revenue.

Kerry Liu, CEO of Rubikloud, said: “Rubikloud is planning greater global expansion as traditional retailers realize what’s at stake if they don’t integrate AI now. The holiday season has the potential to make or break retailers’ yearly revenue, leaving no margin of error for inventory stock-outs or disappointed loyal customers. But as the stakes rise, legacy tech providers are falling short in developing retail AI core-applications.”

Including the $37 million, Rubikloud has secured total of $45 million in venture financing thus far. The most recent financing round was led by Intel Capital.

Stacey Shulman, chief innovation officer in the Retail Solutions Division at Intel Corporation, said: “The first three levels of Retail that AI will impact and transform will be the supply chain, corporate head office, and front of store. Combining Intel’s focus on retail, IoT and in-store devices with Rubikloud’s intelligent decision automation will further position the two companies to continue as retail AI leaders.”

The post Rubikloud secures $37 million for expansion of retail AI solutions appeared first on AI News.

The platform works by effectively decoupling content management systems from the user-facing experience. It works by adopting algorithmic delivery models, and using machine learning and natural language processing to gain insights on a user’s content history, geo-location, time of day or expressed interests, to offer unique content experiences to each user, thus addressing user’s increasing demand for content diversity.

Newsroom AI has been testing the technology for more than 10 months with a range of digital publishers, resulting in up to 400% increases in time spent on site and up to six times increase on average revenue per user. The platform has a built-in exchange module that enables editorial teams to immediately trade content with like-minded publishers. It also offers rich functionality such as swiping between sections, articles or the instant loading article pages, features synonymous with mobile apps.

Mihai Fanache, founder, and CEO at Newsroom AI, said: “We help publishers save resources on covering topics that are not within their newsroom’s area of expertise – such as local news and stock markets, or become a distribution platform for independent reporters or specialised bloggers, with one simple RSS integration.”

Publishers would be able to accelerate their product development cycles from months to just within 48 hours with the help of this platform, the company added.

The post Newsroom AI launches customisable newsfeed platform for news publishers appeared first on AI News.

The same concept applies to your customers. Engaging them on a personal level is the best way to show them you care about them, that you want to build a long-term relationship with them.  Sounds easy, right?   But getting to know the interests, passions, and attitudes of millions of customers is a complex problem.  Fortunately, there has never been a better time for building robust Customer 360° profiles at digital speed and massive scale.  Sophisticated graph algorithms and artificial intelligence are available for customer centric brands and builders that truly care about delivering great experiences to their customers.

The key to unlock these insights already sits in your CRM or customer data warehouse today.  What is this magic we speak of?  A simple plain text email is all it takes.  The days of email being nothing more than a virtual inbox to deliver more and more low cost SPAM may not be going away, but there is clearly more value to be derived from a simple little email.  Value that can help make those emails key inputs into building a dynamic AI empowered customer learning engine.

Before we get too far down the path let’s take a quick step back.  Over the last two decades companies have invested millions of dollars in a variety of CRM initiatives to build out the elusive Single View of the Customer (SVoC).  This worked perfectly in a world where everyone had a postal address and a home phone. Join credit card data that is linked to a physical address and brands could access valuable demographic data while consolidating shopping and spend data to do everything from building out segments to tailoring messages to a more precise audience.  The good ole days.  No need to worry about the various forms of digital identity like social network IDs.  No need to consider the multiple devices carried by each of your customers.

Life was good. Then along comes the Internet, followed by social networks, the iPhone and broadband mobile networks.  That further empowered even more devices.  Now that curated SVoC seems a bit outdated.  What is a customer centric builder to do with the proliferation of identity that has occurred over the last five years alone?  That takes us back to the modernised capabilities of advanced graph algorithms and AI to play in the modern world of sales and marketing and avoid the impersonal carpet bombing that consumers so frequently tune out completely.

How do we bring the old and the new together?  Let’s view this thru the lens of our old friend Wilma and a fictional customer journey with one of her favourite shoe brands.  One day she is an anonymous shopper on FaveShoes.com and she really likes what she sees in their new releases.  So much so that she decides to sign up for their email marketing program.  Wilma doesn’t realise that her email was verified in less than a second and that the email has been deemed legit.  As she clicks around the website she notices that there are three main sections on the web page…one for hiking, one for sandals, and another for nice casual wear.  Wilma may be new to the site but she feels right at home.

This is no accident or blind luck.  FaveShoes.com is powered by connections to a web of APIs that marry a graph algorithm identity resolution engine that is linked to a social affinity engine and an email verification ping.  This is the way customer centricity needs to work in the 21st century.  Personalisation starts on day one…not after a few visits over several months.

To be sure, we must continue to deliver great value to our best customers.  Loyalty and rewards programs are not going anywhere anytime soon. But hotel points or airline miles are table stakes now. What are the little things you can do to stand out from the herd? What if you could make your customers feel like you know them by delivering personalised customer experiences like the one Wilma received.  An experience that gets better as the relationship builds over time.  This example is as salient for B2B brands where the target is professional buyers.  They are unique humans just like Wilma.

In a world that is seemingly over-run with data – messages, communications, advertisements – how does this work get done?  Breaking through the noise requires knowledge and insight. Identity resolution is the precursor to all of it.  Data is deconstructed, analysed, and placed in a probabilistic graph where the strength of those relationships and the quality of those characteristics are determined through real-world observations. As new data enters the system they are assessed in real-time using a layer of intelligence that gets smarter and more accurate as the graph increases in size.

The beauty of the technology lies in how far reaching the impact on the customer experience can go without ever crossing the line into creepy (always a real concern).  How about a well placed email asking Wilma to tell her friends about the new sandals she purchased.  FaveShoes understands that she has a large social following and is a micro-influencer with over 2500 followers across Pinterest, Twitter, Instagram, and Facebook.  This kind of insight goes well beyond the retailer in this example.  Hotel chains might surprise and delight customers that are not top-tier loyalty members based on factors like influence.  Ditto for airlines.  Entertainment and media companies can get a jump start for new shows or movie releases.  And maybe, just maybe, we all benefit from a few less crappy ads that have nothing to do with products or causes that might work for Betty (whoever she is) but not Wilma.  And this same principle applies if you are a B2B account-based marketing pro in need of better insights to cut through the clutter of your prospects’ inbox.

This is the beauty and power of comprehensive digital identity when linked to CRM or your existing Customer Data Warehouse.  The maths is simple…1+1=3 (or more).  Innovative brands and builders will lead the charge at creating next generation customer experiences.

We would be remiss if we did not acknowledge that some people might not appreciate, let alone embrace, that level of personalized data-driven marketing. That’s an important point. We should do everything we can to respect people’s privacy, while also increasing transparency about what will happen to the data that feeds into their digital identities. That means giving customers more control of their data and then offering them more value in return for sharing it.

Privacy statements are notoriously complex. Almost no one reads them. So it’s important to clearly explain how a company plans to use customer data, and to provide an obvious, simple mechanism to opt in or out.

We also need to respect customers who share their data by showing them how it benefits them directly. Most people don’t object to a grocery store using data to send coupons on the food they buy most frequently. When Amazon suggests buying socks with the shoes someone bought, they can see the value.

Taking the time to understand your customers, and offering them personalised content and services with obvious value, will help you build a happy, loyal customer base who will often advocate on behalf of your brand. Using data to reward your best customers or prospects shows you care enough about them to listen to them. Unlike that guy at the party who keeps forgetting who Wilma is.

About the authors: Scott Brave (left) is currently CTO at FullContact. He was also the founder of m.sound creations and a founder and CTO at Baynote, Inc. Prior to Baynote, he was a postdoctoral scholar in the Department of Communication at Stanford University where he served as lab manager for the CHIMe (Communication between Humans and Interactive Media) Lab. Scott received his Ph.D. in Human-Computer Interaction, and B.S. in Computer Systems Engineering from Stanford University, and his M.S. from the MIT Media Lab. Scott is an inventor of numerous patents and co-author of over 30 refereed publications in the areas of human-computer interaction and artificial intelligence. Rick Porter (right) is Head of Services & Solutions at FullContact Inc.  Previously, FullContact acquired nGame, of which Rick was a co-founder.  At nGame, Rick was responsible for product management and strategy. He has a rich and diverse background focused on building and delivering solutions to enterprise customers. Prior to nGame, Rick held senior Product Management roles at SAP where he owned a portfolio of Analytic Applications. In this role he was responsible for the full product lifecycle from ideation to global launch and field enablement. 

The post Back to the future: How AI can empower personalisation at scale appeared first on AI News.

Serving as a plug and play solution for vendors, the Canadian IoT-based firm extends the life of Point of Sale terminals (POS), without having to upgrade them to new cloud-based devices through its DataTap technology.

The DataTap technology captures all the sent data from the POS to the receipt printer, eventually sending it to the cloud so that it can be presented in a dashboard for management to monitor key pertinent information and make vital business decisions. The value of this data across many retail locations provides imperative business intelligence. Thus, Datametrex is seeking the application of blockchain platforms to give the data further authentication, verification, and integrity across every specific network of retailers.

This will be a great help for retailers as the integration of blockchain into the DataTap technology will offer tremendous value to brands, wanting an insight on POS data for products sold through their retail distribution channel. Once Datametrex completes the acquisition of Nexalogy (a social media analysis firm), which has the most reliable data from the Datametrex blockchain – it can add key artificial intelligence (AI) techniques to further enhance the data.

Nexalogy helps brands, corporations, and governments to analyse information with the help of its AI platform, which provides valuable insights and analysis from a variety of data sources. This allows organisations to study the information and make better decisions in areas such as security, marketing, and overall operations.

The post This company aims to combine AI and blockchain and enhance point of sale appeared first on AI News.

The firm, alongside strategic moves and acquisitions, is also planning to use the money to add several software and system developers to support the completion of its new customer facing portal and expand its capabilities around enterprise managed services.

Robert Tallack, CEO of ConversionPoint Technologies, said: “ConversionPoint Technologies delivers that elusive ‘how’ that keeps e-Commerce marketers awake at night. That is, how do I get shoppers to see my product? How do I increase conversions? How do I keep buyers coming back? This financing will support the development of some amazing new key elements for our platform that will make it an even more powerful and comprehensive e-Commerce B2B solution that delivers the ‘how’ for brands, agencies and companies worldwide.”

AI was also the topic of discussion at the recently held IP EXPO Europe in London. Commenting on the opening of IP EXPO Europe 2017, Bradley Maule-ffinch, EMEA Portfolio Director at IP EXPO event series said: “AI and machine learning are becoming the norm for organisations striving to extract value from the growing quantities of data available in the modern world. Our speakers and exhibitors at IP EXPO Europe today have addressed key questions around AI, from its impact on society, to practical, legal and ethical questions about how to apply the technology now. We’re looking forward to seeing this conversation develop further tomorrow.”

The post ConversionPoint Technologies raises $2 million in funding for AI-enabled eCommerce appeared first on AI News.