Data-driven optimization: The critical role of AI in martech

 

AI technology will play a major role in gleaning the value from data offering the ability to organize, link and analyze increasingly large data sets. This will help to curate more successful campaigns and achieve better ROI by making intelligence more actionable than ever before.

 

30-second summary:

  • The application of algorithms, machine learning, and AI to solve major marketing challenges – for example, attribution, intelligence gathering, predictive workflows, and campaign suggestions – will enable the industry to market better, for less money, more success, and happier customers.
  • AI’s ability to understand data at global, regional, and local levels, as well as the most functional kinds of campaigns for different types of businesses, is fundamentally important to delivering optimized results and creating less waste across channels.
  • Data collection, aggregation, and warehousing is not a problem that marketers need to solve – leave this to the software companies. The bigger issue is analyzing and identifying key trends from these channels and their data.
  • This comes in a two-step process – first, determining which solutions offer a quick, affordable way to bring the necessary data together, and second, forming the market vision to know where trends are emerging and the savvy to know how to communicate them to stakeholders.
  • Once you realize that you can bring the same level of insight, analytics and intelligence to the table using AI’s widely accessible to the everyday marketer, it levels the high-value marketing playing field in ways that promote the agility, effectiveness and marketing savvy of independent consultants and agencies as well as small and midsize brands, franchises and media companies alongside their enterprise counterparts.

 

In an increasingly data-driven world, there is tremendous value in capturing and making sense of marketing data, including information on users, accounts, contacts, purchases, downloads, link clicks, form submissions, video plays, transactions, and so on. While this top-level, event data may seem like everything a savvy marketer would need, it is the metadata – data about the event data – that gives it the most valuable context. Metadata can be more revealing than event data itself when collected and analyzed in aggregate. But there is so much data available these days that it can cause paralysis. This is where AI comes in – marketers need to become increasingly adept at using advanced technologies to make data functional.

The rise of AI in marketing

We have been seeing a steady rise in the use of artificial intelligence across industries – marketing is no different. Some of the world’s largest companies rely on AI for all sorts of reasons, but in martech, it holds promises that will bring even more disruption to the industry.

The application of algorithms, machine learning, and AI to solve major marketing challenges – for example, attribution, intelligence gathering, predictive workflows, and campaign suggestions – will enable the industry to market better, for less money, more success, and happier customers.

Two years ago, when the volume of data generated worldwide was estimated to be a staggering 2.5 quintillion bytes of data a day, the industry projected that by this year, 2020, every individual on earth would be generating 1.7 MB of data every second of every day.

While we don’t know where that number actually stands today, it’s likely been driven even higher as a result of the global pandemic. What we do know is that legacy analytics tools are not capable enough to ingest the amount of data being created in today’s martech stacks to make sense of it.

There are more than 8,000 different companies developing software in the space and all the data to go along with them. While the growth of the ecosystem has been empowering, it is also a curse.

Which is why a premium should be put on data integration and management solutions. For much of the industry, one of the fundamental issues is bringing data together efficiently and effectively.

In a multi or omnichannel marketing environment, how you develop actionable insights from a range of different marketing campaigns is one of the things that separates a good marketer from a great marketer.

A great marketer knows how to optimize campaigns, how to leverage historical data, and how to use marketing intelligence to map where to spend their next dollar for optimal impact.

AI in cross-channel optimization

Data density is an important component of artificial intelligence.

While Big Tech companies have enough data density to build predictive algorithms, smaller companies similarly need to be more resourceful to follow suit. Collecting enough data will lay the groundwork to start building their own marketing optimization algorithms.

However, the real challenge is not in-channel optimization. Rather, it’s everything else, like cross-channel optimization, which is a much more interesting problem to solve. And metadata plays a big role.

The ability to understand data at global, regional, and local levels, as well as the most functional kinds of campaigns for different types of businesses, is fundamentally important to delivering optimized results and creating less waste across channels.

Leveraging AI tools to automate data

Combining data from all marketing channels – social, email, mobile, location-based, app-based, targeted or retargeted, PPC or SEM – and tapping into data management capabilities that can help organize, analyze and create intelligence from these channels is a critical step in developing a functional, unified marketing stack.

Data collection, aggregation, and warehousing is not a problem that marketers need to solve – leave this to the software companies. The bigger issue is analyzing and identifying key trends from these channels and their data.

This comes in a two-step process – first, determining which solutions offer a quick, affordable way to bring the necessary data together, and second, forming the market vision to know where trends are emerging and the savvy to know how to communicate them to stakeholders.

There are ways to automate integrations as well as data warehousing, creation, and hierarchical management tasks in minutes.

Regardless, there are companies still trying to solve this problem on their own, in a slow, clunky, expensive, old-fashioned, and error-prone way – doing their own integrations and sometimes hiring another company to create their data warehouses.

That approach won’t sustain a competitive advantage. You don’t have to be a mega-corporation to understand this or to benefit from the insights that can be driven from campaign metadata.

Once you realize that you can bring the same level of insight, analytics and intelligence to the table using technologies widely accessible to the everyday marketer, it levels the high-value marketing playing field in ways that promote the agility, effectiveness and marketing savvy of independent consultants and agencies as well as small and midsize brands, franchises and media companies alongside their enterprise counterparts.


This article is written by Daryl McNutt and originally published here

How AI powers B2B online customer experience and sales

  At a time when pandemic-stressed B2B companies need to develop better customer experience, marketing, and service, artificial intelligence technology can play a crucial role, Aparajeeta Das of ClouDhiti writes.  
COVID-19 has thrown business as usual out of the window for B2B companies across industries. Given the transition to online-only commerce for millions of organizations, it’s more important than ever for your digital presence and customer experience to be seamless. This is where artificial intelligence (AI) comes in. Implementing AI-powered tools into your online operations and customer service strategy can be the difference between inefficient processes and unsatisfied clients, and a thriving business in the face of the crisis. The value of AI is already being realized across industry sectors; in fact, global management consulting firm PricewaterhouseCoopers predicts that the technology will generate $15.7 trillion globally by 2030. Here’s how B2B businesses can leverage AI to drive online customer experience, fine-tune operations management, and ultimately enhance their digital commerce presence.

AI empowers your customers with information

AI-powered chatbots allow online B2B companies to serve their customers with accurate and relevant information, 24/7. For example, you might be experiencing a surge in requests for information from customers or site visitors on such aspects as product availability, features, or other services that you offer. An AI-powered chatbot that’s enhanced with natural language processing and understands conversationally-worded requests can instantaneously provide this information without the need for a human representative. This is vital in times such as now, as the added uncertainty around the pandemic means business customers are seeking reliable answers and ways to adapt— and fast. Semantic search offers another powerful way to ensure online companies meet customer needs with accurate and relevant information. Semantic search technology is able to comprehend a query based on its context, rather than simply the keywords it contains, and searches various types of data to provide the most relevant answer. Empowering your website search engine with semantic search capabilities ensures that site visitors receive the exact answer to their query, even when using conversational language. By being able to answer queries such as “What are the product specifications?” “What are the tax regulations for selling this product?” or “Can you recommend compatible products?” you can increase customer satisfaction by providing them with access to the information you need, and add in an upsell opportunity according to their requests. Semantic search technologies are being increasingly adopted by organizations that prioritize customer access to accurate and pertinent information.

AI lets you better understand and target your customers

Leveraging AI can allow you to segment your customer base using data on their psychographic profile and behavior, and define patterns so as to provide personalized suggestions and offers for relevant products. This customer segmentation also allows you to streamline lead scoring and identify the most likely customers to make certain purchases, as well as making sure you only target them with relevant content. And adoption is growing: The August 2019 CMO Survey reported a 27% increase in the implementation of AI and machine learning in marketing toolkits compared to just six months prior. For example, sellers on Amazon’s B2B marketplace are able to leverage the platform’s segmentation technology to target customers based on demographics, psychographics, situational, and geographical data. This allows them to personalize customer experience and product recommendations both on-site and via such channels as email marketing to maximize the chance of sales. Especially amid the current crisis, business customers are conservative with their expenditure and will only make purchases that are truly necessary. The crisis has altered spending habits of the majority of companies, so it’s important to use data on this behavior to accurately target products that they are likely to need to help them maintain business continuity.

AI allows you to fine-tune operations management

When it comes to operations management AI-powered predictive analytics dashboards can give you vital insights into important aspects of your digital commerce strategy such as inventory management, demand forecasting, and supply chain efficiency. By embedding your operations management with AI, you can better understand the likelihood of future events and plan accordingly—something that’s essential in today’s climate. For example, AI will take into account external factors such as weather and the data gathered from the Internet of Things (IoT) devices present along the supply chain. This could allow you to avoid manufacturing from scratch, ordering too much or too little inventory, or directing your stock in the wrong area, thus driving cost efficiencies. For example, telecom manufacturer Infinera built AI and ML into its supply chain management to analyze production times and logistics routes to accurately predict delivery dates. This allows sales associates and customers to understand what products are available and when they can expect to receive them. AI combines historical data, customer survey feedback and weather reports to provide precise information about when customers will receive goods, enabling them to make better decisions and boosting satisfaction.

Tracking IoT devices for supply chain insights

When combined with AI, the analytics gathered from the real-time tracking of IoT devices can provide actionable insights that allow you to respond proactively to prevent issues along the supply chain, such as delays or damage. AI is proving to become a vital tool for digital commerce businesses seeking to keep their operations running smoothly—and reduce costs in the process. In fact, research from McKinsey found that 61% of executives reported decreased costs and 53% said revenue increased after introducing AI into their supply chains. When combined with IoT, AI can also help manufacturers keep track of how machines are performing and any upcoming maintenance that may need to take place, allowing companies to plan accordingly and not lose out due to unplanned downtime. Manufacturers are also using IoT devices to automatically send alerts to customers when a new part is needed, and in some instances, even automatically re-ordering the part through the ecommerce platform. Manufacturers can also use AI to implement predictive maintenance so their customers  receive the part ahead of time, limiting the downtime theywould otherwise experience while waiting for repair. AI is here and it’s here to stay. Given its numerous benefits for B2B companies to drive customer experience, streamline operations, and boost efficiency, the return on investment of implementing AI is especially evident during crisis times. B2B companies that want to remain competitive in today’s hostile market must explore the possibilities of AI to enhance their digital commerce strategy.

This article is written by Aparajeeta Das and originally published here

 

How AI Is Disrupting The B2B Space

Artificial intelligence (AI) as a service is becoming a reality for more and more consumers, and with the major players launching their 5G services last year, many enterprises across different industries have added AI exploration to their agendas. “The convergence of artificial intelligence with internet-connected machines and superfast 5G wireless networks is opening possibilities across the planet,” according to the Wall Street Journal. This means 2020 could be the year of AI deployment. By 2030, AI could increase the global GDP by 14%, or $15.7 trillion. Healthcare, automotive, technology and communications, manufacturing, energy and transportation and logistics are the industries with the most potential for impact. While consumers are benefiting from the implementation of AI across the board, it looks to me like AI presents new challenges for B2B decision makers. According to Salesforce, in 2018, 30% of B2B marketers were using AI “to power the ‘Amazon-like’ personalized customer experiences now expected by business buyers.” As we continue to shift into the era of deployed AI, I believe B2B players need to have a better understanding of how AI is going to change existing business models and what kind of disruptive scenarios the C-suite will be facing moving forward. PwC predicts that 42% of companies will be investigating AI use this year, while 4% are planning to deploy AI across the enterprise. Compared to PwC’s predictions for 2019, the intention to explore AI almost doubled this year, while the intention to implement AI across the entire enterprise declined by 80%. This decline could be because companies have already adopted AI, or it could be a result of the challenges that executives have faced in deploying AI. Enterprises are typically slower to respond to innovation; however, as they are embracing the disruption, the game of being visible for potential customers and finding new partners will likely have to evolve dramatically. Before the era of AI, decision makers researched the B2B space and prepared the vendors list based on the results, so being visible in search and having a thoughtful content strategy were doing the trick for B2B companies. After widespread AI deployment, a much larger variety of factors will likely have to be taken into consideration. I believe you should be ready to identify the customer segment and the stage of the funnel your customers are in and offer personalized messages in real time. You also should be continually enhancing your relevance for customers through the lens of AI. Unlocking the journey could be the key to success. Try to find the equilibrium between providing enough content for each stage of the journey and overwhelming customers. The journey should adapt to the simultaneous needs of buying groups (problem identification, solution exploration and requirements building) and ensure fluidity between buying stages. This is where AI can come into play.  According to KPMG, major enterprise organizations have identified “customer and market insights that will refine personalization, driving sales and retention” as one of the high-priority areas for AI initiatives over the next two to three years. With adherence to all the privacy restrictions, it is feasible to expect that AI should be able to better recognize the buying stage where the customer is at a specific moment. Additionally, AI-based content delivery is capable of generating personalized content to create better engagement and addressing decision maker pain points based on all the available data. B2B marketers are facing the challenge of adopting AI, yet it looks like — with the increasing complexity of the B2B space and customers who expect a personalized experience, content and offers in real time — AI could become a North Star. With the rising volume of metrics that B2B marketers need to decode, they will need a reliable ally, and AI definitely can be on their side. Accelerating digital ecosystems by redefining the role of each component in response to today’s challenges could be a good place to start with AI adoption. Identifying which parts of the renewed ecosystem keep leveraging the AI and which elements may be low-hanging fruit for AI deployment could be the next step. Creating the road map for AI deployment for the remaining part of the renewed ecosystem can set your company on track for AI deployment.
This article is written by Oksana Matviichuk and originally published here

Boosting Contact Centers with Conversational AI

Conversational AI can provide contact centers will critical call volume support amidst the coronavirus pandemic.

If you aren’t currently exploring conversational AI (CAI) for your contact center operations, there is a good chance that you will be soon. With the pressure many agents are facing due to coronavirus-related call volume increases, CAI can provide agents with some much needed relief and boost in call center productivity. 

CAI allows contact center operations to automate messages and speech-enabled applications for interactions between humans and computers. CAI bots can communicate similarly to humans, recognizing speech and/or text. Additionally, these bots can go so far as deciphering customer intent in different languages and respond accordingly. 

To learn more about CAI, I contacted Amy Allen, product manager for CSG’s conversational AI solution. Below is the first part of our conversation. 

What is conversational artificial intelligence?

CAI is a new and innovative way to communicate, leveraging AI, and delivers a next-gen platform. CAI isn’t just about voice; it’s a generational shift beyond chatbots to meet customers where they want to interact, regardless of communication platforms (SMS, voice, text, chat, IVR, smart home devices), and in a conversational, smart, and personalized way. 

The hallmark of an AI system is that it’s always learning. A CAI platform has accelerators that speed up AI deployment and training. This solution uses a single AI “brain,” allowing enterprises to reuse interface logic and integrations across several channels or multiple Interactive Voice Responses (IVRs). 

How does it apply to contact centers and unified communications?

CAI improves customer satisfaction (CSAT), call containment, first call resolution, and cost control and shortens customer service representative (CSR) training cycles. Contact centers can deploy an AI-powered virtual assistant in their IVR, chat, social, or text interfaces to resolve a large chunk of inbound inquiries. By combining multi-intent understanding with contextual awareness, CAI has a better grasp of the customer’s intent than standard voice-activated call trees. It can communicate internally and externally through applications and web sites using natural language (voice, text, or gesture inputs). 

Once the virtual assistant is fully integrated and trained, it becomes a true asset for the agile contact center. It adapts to changes in the business’s offerings, processes APIs, and data with minimal manual adjustments and improves the customer’s ability to self-serve through the platform over time. 

CAI can also be a valuable training tool. The AI can assist a CSR through the desktop by listening in on the call, populating the CSR’s screen in real-time with pertinent knowledge based information. It can also give the CSR on-screen prompts of what to say or what required disclaimers to give the customer. The result is that contact centers can shift time away from training CSRs and toward training the AI brain that delivers consistent information — interaction after interaction. 

Is this an out of the box solution or do you have to build it? If it’s the latter, what is the process?

CSG works with the enterprise customer to develop and build the CAI platform to support the customer’s desired use cases and channels. This is supported by pre-built accelerators that enable rapid deployment into multiple verticals for common use cases and pre-built frameworks for easy integration into back-office and third-party applications. 

The process for building a custom CAI solution includes:

  • Discovery: CSG helps the enterprise take massive amounts of raw, unstructured data from multiple sources and classify customer intents and key issues. This conversational data mining and analysis suite unlocks knowledge held in immense volumes of natural language conversations, delivering previously unprecedented levels of big data insight and true “voice of the customer” understanding. Recent projects have sourced data from call transcripts and ticketing systems as examples of inputs.
  • Studio: This tool allows non-experts to easily construct dialogues and business logic using visual flow chart structure. The graphical interface makes it easy to understand what is and isn’t working with the dialogue flow, with the ability to make rapid adjustments as necessary. Adding new dialogues or updating responses is as simple as drag and drop, while one-click publishing ensures any changes are live instantly. The studio tool enables customers to do as little or as much of the dialogue management as they desire.
  • Deployment: Tailored implementations can be completed in as little as six to eight weeks, depending upon scope and specific business goals. Next, the CAI platform is fine-tuned and optimized after delivery to ensure the best possible customer experience.
  • Management: Enterprises can deploy CAI as a fully managed service running in the cloud, including help with expanding the suite of use cases. Alternately, customers can self-manage all or parts of the system.

This article is written by Gary Audin and originally published here