AI is already disrupting sales, today

It’s time for a change. Now. Essentially, CRM hasn’t changed since it was introduced to the market in the late nineties, in such that it requires the end user to log into whatever application they’re working with, create and manage tasks in the software (log calls, emails, create reminders, move leads down the pipeline, etc.) manually. This is cumbersome, time-consuming and redundant. It’s time the software (machine) does its job.

Millions in R&D – are we there yet?

Companies spend millions of dollars trying to make CRM work, and it just doesn’t. Well not completely anyway. We do know that there have been improvements to CRM in general with the use of AI (artificial intelligence) or machine learning. Using statistical methods, mathematics and probability in these algorithms, the technology in these CRMs helps with many tasks. Some examples of AI-infused software in CRM:

  • Improved sales team productivity: Leveraging machines to gain new insights from past sales data, reducing manual analysis and saving valuable time
  • Exceptional support for marketing: AI can assess previous price information, like discounts, promotions and sales history, to calculate price elasticity of the products so price can be optimized.
  • Customer retention: AI can analyze previous sales data that shown when and why a customer left. Now you can see the early warning signs of potential customer churn.
  • Pattern recognition: AI software can now analyze your most valuable customers based on certain data points and then compare this to potential new customers and predict the value of the new customer. This also allows sales managers to reallocate resources more appropriately based on the customer value.

AI and machine learning are readily available now, and we know it can be used to automate much of the repetitive tasks and processes that humans do now. However, AI can’t handle complex decision-making tasks. To complete the full sales process, it requires the full machine-human team working together. For example, the human element is a critical component of the sales process and is something that can never be programmed. Solving business challenges for a customer is intrinsically far more fulfilling than inputting data into a CRM system.

Additionally, AI alone will not be enough to make or close sales. It will certainly be a machine-human collaborative effort to complete the entire process successfully. However, with the assistance of AI, sales managers now can work efficiently and focus on the bottom line to produce better results. Continual use of this AI-infused software and the help of analytics will optimize both the platform and the process.

AI is no longer science fiction – put it work today

Many companies are trying to fix CRM by adding AI-driven “sales assistants,” which are tools that sit on top of CRM and are designed to help sales reps be more effective. However, this approach is flawed because the assistants are using AI to make decisions on poor and incomplete data in the CRM.

Nevertheless, giving credit where credit is due, there are companies rethinking the approach entirely, building an AI-driven platform from the ground up. One example of this type of platform is Spiro, which has pioneered a new approach called Proactive Relationship Management. Proactive relationship management is built on an AI engine, and consolidates CRM, sales enablement, reporting and calls/texts into a single platform.

The value of this single, AI-driven sales platform, is that it:

  • Automatically collects data from emails, texts and calls which, gives sales teams more time to sell
  • Helps salespeople reach more prospects with automatic reminders on follow up activities that can be completed within the platform.
  • Gives leadership better visibility into the pipeline, which drives better management and revenue performance.

Simply put, proactive relationship management technology works by connecting to your email, comes with a built-in phone system and connects to any corporate data source or directory that you have. Using AI, the platform can collect and learn from all this information. As mentioned previously, it uses natural language processing to read and analyze the text of emails to understand what’s going on in the sales process. Calls made through this this platform are automatically logged, and then transcribe and pull the intent from those calls and ensures these important tasks are captured. This allows for the platform to pull information from any ERP, marketing software, or other data source, to make sure you have a well-rounded picture of what’s going on with your prospects.

Based on that information, this technology uses machine learning to predict what your sales team should be doing, and when they should be doing it, relative to these records. Consequently, it proactively reminds them, without any manual data entry. The result of this is sales leaders get an incredible amount of data about what’s going on for their sales team – what their activity is, the calls they’re making and the success of those calls, where the pipeline is strong or where the pipeline is weak. This information is all pulled together with the built-in analytical tool in the proactive relationship management platform, enabling sales managers to make real-time adjustments.

What’s next?

Knowing what we know about the available technology leveraging machines, this a major gamechanger in the sales industry. Essentially, companies can leverage a machine assistant to accurately and effortlessly handle mundane and routine tasks, from scheduling a meeting, a follow up task, answering customer inquiries via a chatbot, etc. All this while providing real-time predictive modeling to make decisions and adjustments on the fly.

Over time, these tools and processes will only improve. When the software has access to more data, it will be able to provide further insights for decisions. Eventually, the software would be able to make some of those decisions based on a threshold or score level without human intervention. Using technology at this level allows for faster transactions and the best customer experience. Giving extra time back to staff would allow them to work on more advanced or important tasks, improving productivity drastically.

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AI & ML Are Enhancing Marketing and Sales Strategies

In recent years, the marketing and sales domain have seen major transformations as disruptive technologies like Artificial Intelligence and Machine Learning continue to advance. These advanced analytics tools portray a vital role in assisting the marketing and sales teams that they require.

AI and machine learning have already been integrated into some vast areas advancing marketing efforts, such as improving user experience using chatbots, making marketing more personalized, the capability to provide the correct information at the right time in the right environment, enhancing the volume of dark social sharing, and content creation.

Why There is a Need for AI/ML-Based Marketing?

While traditional marketing is carried out without any kind of insights into the customers’ purchasing behavior, modern marketing comes with germane details of customers’ purchasing patterns and other criteria.

Presently, deploying AI and ML is considered essential for gleaning data and reaping its full potential to enhance the bottom line. And leveraging these technologies are becoming widely accepted as a necessary step forward in gathering data.

Besides a large number of advanced tools powered by ML, cloud-based platforms and applications are further creating a paradigm shift in today’s evolving markets. Furthermore, advanced AI-powered features have opened new opportunities for marketing and storytelling. The technology is the modern era of marketing that specializes in accomplishing new levels of customization and targeting within the confines of context.

Importance of AI and ML in Marketing

Owing to rapid innovation in technologies, the marketing landscape is undergoing fast changes that lead to several new apps, tools, and cloud-based platforms development.

There are several benefits of AI and ML-based marketing and sales strategies, providing companies a significant boost into their businesses, include: Improved marketing qualified leads (MQLs); More sales qualified leads (SQLs); Better insights to position marketing strategies; Boost in competitive advantages; Relevant target audience; Smart Point-of-Sale system; Highly precise marketing campaigns; Improved profits and sales; Enhanced customer satisfaction with improved user experience.

AI-based marketing is also providing benefits to companies by forming marketing content, paving the way to marketing prophecy, cutting costs, and eliminating marketing wastes.

As AI and ML-based marketing across sectors are gaining more traction, there are some mistakes that marketing and sales strategists need to avoid when it comes to creating marketing campaigns. Those are including, targeting generic and broad customer personas, following a generic approach, working with insufficient customer data, not analyzing and testing the performance of past marketing campaigns, not addressing regular and returning customers, focusing more on multiple outcomes at the same time, and producing and publishing inappropriate content.

With these stimuli, we can foresee the future of marketing and sales across industries are closely driven by artificial intelligence and machine learning. Even, a large number of big corporations are already taking benefits of it and several small and mid-sized businesses are making their road towards it.

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Chatbot Market: Key Companies Strive to Enhance Customer Experience to Expand User Base

The global chatbot market is extremely consolidated with leading three companies namely Facebook, Google, and Microsoft that collectively held a stupendous 97.5% of the market in 2015, states Transparency Market Research in a new report. Being well-established and recognized, these three companies enjoy brand name and most consumers prefer their products and services.

Yahoo Inc. is another significant player in the chatbot market that is popular among users. Howbeit, the massive volume of communication handled by messaging applications such as WeChat, WhatsApp, Line, Skype, Facebook, Twitter, and Kik leaves very little scope for the entry of new players. Competition in the market is stiff as players vie to outdo rivals by delivering better customer experience. They are striving to offer outstanding customer service to resolve customer complaints and issues which will help them steal a march over their competitors.

As per estimates of a report by Transparency Market Research, the global chatbot market will rise at a staggering 27.8% CAGR in terms of revenue over the forecast period between 2016 and 2024. Progressing at this rate, the market will become worth US$994.5 mn in 2024 from US$113.0 mn in 2015. In terms of enterprise size, large enterprises is anticipated to continue to remain key segment and generate a revenue of US$626.3 mn by 2024 end. This is because large enterprises extensively employ chatbot for digital marketing applications and also present a massive demand for chatbot to initiate business process automation activities.

Geography-wise, North America holds a massive share in the overall market; the region is anticipated to hold on to the leading spot over the report’s forecast period.

Use of Chatbots in Digital Marketing Activities Drives Growth

The primary factor boosting the growth of the chatbot market is vast development in artificial intelligence (AI), because of which chatbots have evolved from simple answer machines to a smart platform for engaging consumers. Businesses are also using chatbots for marketing needs that demonstrate the large spectrum capabilities and capacities of chatbots.

Apart from this, technological advancements leading to the implementation of artificial intelligence in consumer electronics is aiding the chatbot market to expand its consumer base. Over the past few years, voice and messaging services have become key and are likely to remain so over the forthcoming years. Businesses are increasingly adopting online messaging services and using chatbots for digital marketing campaigns for customer engagement and for lead generation. This is positively influencing the global chatbots market.

Lack of Application Areas Hurting Growth Prospects

The key factor challenging the growth of this market is the significant rise in the capabilities of chatbots which far exceeds the growth in the areas where they can be applied. Further, the growth of this market is restrained due to several hosting issues that need to be resolved. These include chatbot monitoring, management, security, and integration. The failure of hosting services to provide aforementioned services is restraining several enterprises to enjoy the benefits of chatbot. Nevertheless, chatbot as a service is likely to provide significant opportunities to the market in the forthcoming years.


How Marketers Can Start Integrating AI in Their Work



According to Constellation Research, businesses across all sectors will spend more than $100 billion per year on Artificial Intelligence (AI) technologies by 2025, up from a mere $2 billion in 2015. The marketing industry will be no exception.

AI holds great promise for making marketing more intelligent, efficient, consumer-friendly, and, ultimately, more effective. Perhaps more pointedly, though, AI will soon move from being a “nice-to-have” capability to a “have-to-have.” AI is simply a requirement for making sense of the vast arrays of data — both structured and unstructured — being generated from an explosion of digital touchpoints to extract actionable insights at speeds no human could ever replicate in order to deliver the personalized service consumers now demand.

Interestingly, in many cases, the sophistication of AI technologies has already advanced further and faster than most marketers’ ability to actually make use of them. On the one hand, there are the technical challenges of gathering and normalizing data inputs — the act of making different types of data comparable — connecting them to a unified view of the customer, and then aligning the AI-driven decisions to real-world actions. On the other hand, there are also real philosophical, ethical, or at least policydecisions to be made on the value exchange between marketers and consumers when data is shared and used to optimize marketing experiences.

The good news is that, as an industry, we are starting to see meaningful progress on both fronts. For businesses looking to keep pace with innovation and leverage AI, there are steps they can take today. But first, what are some examples of how AI can help make marketing more effective?

Using AI in Marketing

Smart marketers are developing, partnering to build, or integrating AI into their tech stacks to get better at what they do. AI is already being used in ad targeting and customer segmentation, but there are more possibilities in store such as:

  • AI-powered chatbots use all the customer data at their disposal to answer questions and give advice to customers considering making a purchase. Take Sephora’s Kik bot, which quizzes customers about their makeup preferences and then follows up with specific product information.
  • AI-enhanced image search allows users to upload pictures of products they are interested in, to find relevant shopping ideas. For example, companies such as CamFind let you snap a picture of something in the physical world, and get information related to it. Say you see a poster for a movie you’d like to see.  Snap a photo, and CamFind will show you movie recommendations, times, and locations.
  • Personalized training routines and nutrition information can be created based on data from other consumers with similar lifestyles. For example, UnderArmour leveraged IBM Watson’s AI to create a “personal health consultant” that provides users with timely, evidence-based coaching around sleep, fitness, activity, and nutrition.
  • Optimized advertising uses AI to make decisions based on the full range of data available — including unstructured data such as sentiment and mood. For example, IBM, this time as a corporate marketer, teamed up with MediaMath (where I am the CMO/CSO) to activate true AI-driven programmatic marketing using its Watson Cognitive Bidder, to extract predictive signals from exposure to large amounts of data.

How to Put AI Into Practice

There are three key areas of consideration to help you get started leveraging AI for marketing today: affirm your consumer data policies, make your data actionable, and select the right AI partner.  Let’s look at each in more detail:

Affirm your consumer data policies: Before getting started, it is important to confirm your policies regarding the handling of consumer data, transparency, and control.  Your company needs to make sure that you’re complying with the EU’s new General Data Protection Regulation (GDPR). Consumers should be able to interact with connected devices — from web browsers to mobile phones to voice assistants — knowing that their data is being used in transparent ways, in a manner consistent with their preferences and expectations, to which they have explicitly consented.

Make your data actionable: There are three pillars here to consider:

  1. Common identifier: With the explosion of devices, digital identity today is deeply fragmented, leading to head-scratching experiences where, for example, a gym franchise might roll out a great new membership offer…to someone who is already a member. The first step is to establish a common identifier, usually an alpha-numeric string, across various touch points and data sets to help create a unified view of the customer. Emerging solutions such as DigiTrust are helping marketers tie together their various touchpoints in a safe, respectful, and scalable way.
  2. Data gathering: AI can make sense of all your data and extract insights from it, but only if you can collect and normalize it before activating it. Choose a data management platform (DMP) that incorporates the identity solution selected above and can handle data from a wide range of sources, so you can collect, organize, and centrally manage all your data, segment it into granular audiences, and activate it for marketing in real-time.
  3. Data end points: The best customer experiences cut across touchpoints, whether they are “paid” (online, Connected TV, or digital audio ads), “owned” (your stores, websites, call centers) or “earned” (PR, blog posts, social media). Although there are as yet few platforms that can deliver marketing across all of these touchpoints, technology such as IBM’s cloud-based Universal Business Exchange can build upon the common identifier and data gathered to drive consistent execution across a range of platforms and tools.

 Select the right AI partner: With the policies, data assets, and pipes in place, the stage is set for AI to make optimal decisions and drive real business performance. For best results and fastest time to real return on your investment, be sure to choose a partner that has true AI, not simply rules-based decisioning, which is impossible to scale for the volume of data and combinations of interactions that marketers are managing today. In addition, be sure to choose partners with real experience addressing your particular use cases and working with your existing technology partners. Finally, as with any vendor relationship, be sure to check on your partner’s ethical and philosophical approach to AI, as this is still an emerging technology that demands thoughtful guardrails to produce effective results in a responsible manner.

AI is no longer just hype — it’s a “have to have” that you can start integrating into your marketing today with the steps outlined above.

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About The Author

Dan Rosenberg is CMO/CSO at MediaMath.

Preparing your business for an AI future

Artificial Intelligence is a powerful tool that has the potential to fuel new ways of working, improve decision-making, and increase efficiency in businesses across the board. In fact, innovations in AI are happening so fast that the future is nearer than it ever has been before, so the adoption of this new technology is key to future-proofing your business.

However, because everybody’s talking about AI, it can be hard to make out who is speaking the truth…and who’s just hyping it up. This can make it difficult to know where to start when deciding the ways in which AI can help your organisation’s operations, and cloud any judgement on where to start.

Know which problems AI is best placed to tackle

A common misconception surrounding AI is that it describes some kind of superintelligence that can be applied to any situation and, like a human can, have an intuitive idea of what needs to be done, with limited information. This is simply not the case. AI is only good at solving really specific problems, and you have to map out a clear target towards which it can be trained.

This is why the problems you choose to tackle with AI must be well-defined and goal-orientated. The task itself can be complex and difficult to solve, but it must have a clearly defined objective, with clearly defined parameters. It’s important to be able to measure the success of the AI algorithm according to specific KPIs.

What’s more, AI needs vast amounts of data to learn from. The amount of data you have must be one of your first considerations when defining the problem you want to tackle. On one hand, if there isn’t enough data produced by the operation towards which you are deploying your AI, then there won’t be enough data for it to be taught the minute differences and combinations that it needs to be able to accurately identify patterns and make predictions. On the other hand, if there aren’t enough data-points in the process in the first place, then it might not be the right place for you to implement AI, as it won’t be able to offer you any meaningful insights.

Taking the time to consider the problem and data involved first will save you money and time on experimentation and development further down the line. This doesn’t mean that you should just limit the scope to the easiest problems, but it means defining your goals to the point that you don’t waste time training an AI algorithm that misses the point of what you’re trying to achieve.

Potential AI projects should be orientated around minimising the costs and making strong predictions for core business problems, opportunities or challenges. Just like any other technology in business, AI should be viewed as a tool that can help make your organisation more effective, profitable or streamlined.

For example, when it comes to sales, an AI model can accurately predict a business’s sales using patterns found in historical sales data. In manufacturing, AI can predict any machine malfunctions before they even happen, potentially saving a company thousands of man hours and millions of pounds. In farming, AI can use satellite imagery and weather data to deliver accurate predictions that tell them what to plant, how often to water it, and how to fertilise it – saving a lot of guesswork and money.

Choose the right AI solution for you

A new generation of tools is emerging that provide end-to-end AI, allowing organisations to operationalise the technology at speed and for a reasonable cost. Whereas before, AI had to be developed in labs by highly-skilled technicians, this new generation of tools means that much of the nebulous technical work is done for you. Using an accessible interface, you can set the parameters, input your data, and define the desired goal that you want your model to be applied to.

The rapid development happening in AI software and hardware means that you want to choose a solution that’s scalable and future-proof. At the same time, there’s no point in implementing an AI that’s not bespoke to your enterprise. It’s therefore important to invest in an AI platform that’s flexible and capable of adapting to your specific needs. That’s why it’s important to review the technical capabilities of the platform and have a good idea of the different types of AI itself. For example, traditional Machine Learning can solve far fewer problems than Deep Learning, so opt for the latter if you want a viable solution that lasts for posterity.

In-house competencies must also be a top consideration – while it’s important to pick a solution that you can tailor to your own needs, you’ll also want to make sure that your tech team are able to manage and maintain your AI tools without the need for too much extra training or external consultancies.

Budget of course underpins all of the above considerations. Not only must you consider the future costs of running your AI solution, but you also want to make sure that the solution is robust and won’t start requiring expensive hardware – in this case, cloud-solutions are your best bet to keep the total cost of ownership to the minimum.

Spread the word

If you think that AI has the potential to transform your company, educate your organisation and senior management.

Because of AI’s complexity, demonstrating its possibilities to non-technical audiences and those working in other departments within your organisation may seem like a daunting task. But taking the time to make sure your colleagues are informed will not only lead to the faster adoption of AI by decision-makers, but it will also improve the efficiency with which you implement AI into your company once the decision has been made.

Company-wide basic knowledge of AI will be hugely valuable and will encourage certain changes in data practice management. This doesn’t mean everyone has to be or become an expert. The point is to begin to shape the mindset and strategy of the organisation around the core principles that allow AI to work, and what it’s particularly good at doing.

To harness the real power of AI, you can’t just go to an AI consulting firm and ask them to optimise your profits. The process of building an AI model must start within the organisation itself. To be more specific: it must start at the top.


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Luka Crnkovic-Friis, CEO and co-founder, Peltarion
Image Credit: Shutterstock/Mopic

Constellation Research : 46% of Companies are Investing in AI for Sales and Marketing Purposes


Artificial intelligence (AI) adoption is relatively modest across large enterprises, but marketing and sales organizations are embracing the technology most quickly.

Forty-six percent of companies are investing in AI for sales and marketing purposes, and 50% are deploying AI projects for commerce and customer service, according to a report released Thursday by Constellation Research. By contrast, 48% of firms said they do not plan to invest in AI for finance, legal and administration.

It’s much easier for marketers and sales teams to adopt AI because of the breadth of plug-and-play options in the market, said Courtney Sato, director of research development at Constellation and co-author of the report.

“They’re already using a CRM platform or some enterprise software that has an automated tool set built in,” she said. “All they have to do is turn it on and pay an extra fee.”

Despite marketer uptake, the report, which surveyed 50 C-level executives at large companies across 12 verticals, found enterprise investment in AI is modest across the board. Most companies (51%) are investing less than $1 million in AI, although 60% plan to grow their budgets by at least half this year.

“Even early adopters are not spending a lot on AI yet,” Sato said. “The budgets are pretty modest compared to everything we’ve been hearing about AI.”

Most companies are in the early stages of adopting AI. Forty percent of respondents said they’re investing in building a data lake and big data analysis capabilities, which can take up to two years. Just 30% are investing in predictive analytics and machine learning, while 22% are working on natural language processing and image recognition. And only 8% are building deep learning neural networks.

“Companies are investing in the foundational technologies,” Sato said. “They need data lakes and statistical analytics, so they’re going to invest in those first. They don’t have the appetite for the more cutting-edge technologies.”

How they’re doing it

Depending on what the AI is being used for, companies have three options for deploying it: build their own infrastructure, leverage computing power from providers like Google or Amazon or license off-the-shelf tools from platforms such as Salesforce or Oracle.

Each option has its pros and cons, Sato said. Building AI infrastructure is a huge and long-term investment, whereas working with a vendor or cloud provider can allow companies to start using AI more quickly. But organizations choosing to leverage or license should be wary of getting locked in with a cloud provider and aware that out-of-the-box AI tactics aren’t very flexible.

“Contracting with a vendor is helpful if you don’t want to build the back-end infrastructure,” Sato said. “The drawback is it’s not as customizable, and then you face lock-in.”

Companies that want to own their AI technology need to source from a limited and competitive talent pool. Eighty percent of respondents told Constellation they need to bring on more employees to implement AI solutions, and 40% said they’ll need to hire a “significant” amount of new talent.

Attracting talent will be tough, especially when competing with large tech and consulting firms with deep pockets, Sato said.

“If you have someone good, big tech companies are going to come after them,” she said. “It’s going to be a difficult decision for the employee not to go there.”

Because of these challenges, most companies are taking a hybrid approach to adopting AI. Twenty percent of respondents said they are both building and working with a cloud provider or vendor, while 26% said they’re doing all three.

“This is the fastest way to dip your toe in the water and then decide which strategy works for which project,” said R “Ray” Wang, principal analyst at Constellation.

Friction and oversight

Companies are cautious about deploying AI because it will fundamentally change the way their organizations function. It will also demand new roles and skills and affect roles beyond manual jobs.

Fifty-four percent of executives told Constellation they’ll need to understand how to restructure their business to accommodate AI, while 50% say they’ll need to acquire data expertise. Forty-eight percent said they need to learn how to manage an AI-augmented team.

Just 6% of executives said they don’t expect their roles to change as their organization adopts AI.

“Most executives are going to have to understand how an algorithm works and how to identify false outcomes,” Sato said. “After a while, I’m not sure how much value an executive that doesn’t have those competencies will be.”

Many executives are anxious that AI will replace their jobs and automated technology won’t be able to do tasks as well as humans, Sato said.

“There is fundamental distrust of the technology and what it’s going to do to people’s livelihoods,” she said.

There are also looming questions about how AI will be regulated. The General Data Protection Regulation in Europe and the Facebook-Cambridge Analytica debacle mean US data privacy regulation could be around the corner – and companies should put it high among their priorities.

“AI can create personal data,” Sato said. “You might give consent to share information A and B, but when those two things come together, they make information C that you didn’t consent to share.”

AI, however, gets smarter by consuming as much data as possible. Tech companies may now be gathering as much data as possible before potential regulation, Sato said.

“It’s against their interest to curb data collection,” she said. “They can’t help themselves.”


This article was originally posted at AdExchanger –