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Winning Customers with AI, Machine Learning and IoT

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Whether consumers know it or not, three next-generation technologies are playing a major role in shaping their experience with brands — and the future of consumer goods marketing: artificial intelligence (AI), machine learning (ML) and Internet of Things (IoT).

To keep pace and effectively compete in an increasingly connected marketplace, brands are investing in these three technologies to continually fine-tune their customer strategies, using hyper-personalized information across touchpoints.

Convergence of Disruptive Technologies 

Have you ever wondered how Netflix makes movie and TV show recommendations, how Facebook prompts friends to be tagged in photos, and how Alexa, Siri and Google Now assist in our day-to-day activities? These are real-life examples of machine learning — a subset of AI. ML uses a customer’s historic data and behavioral patterns to create high-quality predictions of their future behavior.

IDC predicts that applications with predictive analytics will grow 65% faster than those that don’t have this functionality, and that by 2018, most consumers will interact with services based on cognitive computing.

Related: Stepping into IoT – 14 Case Studies

Similarly, IoT is disrupting many industrial business processes. IoT refers to everyday “things” equipped with sensors that generate enormous amounts of data based on use and environmental conditions. Enterprises across industries are deploying next-generation business models around the convergence of two or more of these disruptive technologies to segment and analyze the volumes of data they generate to determine what is meaningful.

Figure 1

In light of this, tech giants such as Apple, Amazon, Google, IBM and Facebook are on an acquisition mission to beef up their ML and AI capabilities. For instance, Google has upgraded its image search and recognition capabilities to identify individuals or objects in photos on the Web. Meanwhile, Apple is investing heavily in AI in areas such as self-driving autonomous vehicles, mapping, image recognition and processing, and voice control.

Rethinking the Customer Experience with AI, ML and IoT

ML is already delivering consistent, gratifying customer experiences across digital channels in three key areas, namely sales, marketing and customer service.

1. Improving Sales Productivity

Sale personnel rely on their mobile devices to stay connected to their company and their customers. Given the enormous amount of data generated by various systems across channels, sales leaders are challenged to qualify leads and identify the right opportunities. By applying ML algorithms, forward-thinking businesses are improving their sales forecasts (predicting credit risk, customer churn, etc.), automating account management and lead-identification activities, and uncovering new upsell and cross-sell opportunities.

2. Better Targeting Marketing Campaigns

As more customer information becomes available through big data, ML will become an essential element of customer-focused marketing campaigns. Among the top challenges marketers face include lead generation, ROI measurement and generating personalized offers/messages in real time by utilizing customers’ personal data, demographics, historical purchase patterns and social sentiments, for example.

3. Enhancing Customer Service

Customer service organizations are increasingly incorporating human-assisted virtual agents such as chatbots to route customers to the right agent and improve the overall quality of service. AI technologies such as natural language processing and speech recognition assist live call center agents by looking up relevant information and suggesting appropriate responses. (To learn more, read: “How Machine Learning Can Optimize Customer Support.”)  Another AI technology, conversational voice interfaces such as Amazon’s Alexa and Apple’s Siri, provides the ability to conduct a natural conversation with customers (and customer support personnel) and suggest the next best action.

[Download]: Stepping into IoT – 14 Case Studies

Looking Ahead

Today’s enterprise systems generate enormous volumes of data that can be fed into AI, ML and IoT technologies to analyze meaningful trends and generate actionable insights. C-level decision makers must understand the important role of this treasure trove of enterprise and customer data in building and maintaining stronger customer relationships, providing hyper-personalized offers and increasing client engagements.

What’s the best way to evaluate where these technologies can be best applied? We recommend:

  • Identifying repetitive business processes that require a lot of manual intervention, often leading to mistakes in order fulfillment, inventory management, shipping, purchasing and billing. When automated, these tasks are predictable and manageable — freeing human resources to focus on more critical tasks.
  • Assessing IT back-office systems and batch processing functions, which are good candidates for intelligent automation.
  • Automating customer service functions for inquiries or tech support with virtual assistants to encourage self-help.
  • Enhancing business processes with ML algorithms to predict employee/customer churn, track equipment conditions and resolve trouble tickets faster by intelligently routing work to the right agent.

As ML, AI and IoT solutions mature, their impact will be felt in more profound ways across the enterprise. The time is now for companies to weave these disruptive technologies into their strategic agendas to enrich the customer experience, streamline processes, drive profitable business growth and transform the way they operate and serve customers.

This article originally appeared on cognizant.com

[Download]: Stepping into IoT – 14 Case Studies

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The importance of data access for digital initiatives

A new report from MuleSoft found that just 37% of organizations have the skills and technology to keep up with digital projects.

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In a global survey of over 1,700 line of business employees in organizations with at least 250 employees, MuleSoft found that just 37% of organizations have the skills and technology to keep up with digital projects.

The resulting report — The State of Business and IT Innovation — reveals four key ideas that IT leaders need to know in order to drive digital innovation forward.

These four key findings are:

  • Collaboration is key 
    • 68% of respondents believe IT and LoB users should jointly drive digital innovation.
  • Keep up the pace 
    • 51% expressed frustration with the speed at which IT can deliver projects.
  • Integration challenge
    • 37% cite security and compliance as the biggest challenge to delivering new digital services, followed by integration (i.e. connecting systems, data, and apps) at 37%.
  • Data access
    • 80% say that in order to deliver on project goals faster, employees need easy access to data and IT capabilities.  

“This research shows data is one of the most critical assets that businesses need to move fast and thrive into the future,” said MuleSoft CEO Brent Hayward

“Organizations need to empower every employee to unlock and integrate data — no matter where it resides — to deliver critical, time-sensitive projects and innovation at scale, while making products and services more connected than ever.”

Want to read through the whole report? Download it from MuleSoft

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Where is the financial value in AI? Employing multiple human-machine learning approaches, say experts

According to a new study, only 10% of organizations are achieving significant financial benefits with AI.

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AI is everywhere these days — especially as we work to fight the spread of COVID-19

Even in the “before times,” AI was a hot topic that always found itself in the center of most digital transformation conversations. A new study from MIT Sloan Management Review, BCG GAMMA, and BCG Henderson Institute, however, prompts a crucial question:

Are You Making the Most of Your Relationship with AI?

Finding value

Despite the proliferation of the technology and increased investment, according to the report, just 10% of organizations are achieving significant financial benefits with AI. The secret ingredient in these success stories? “Multiple types of interaction and feedback between humans and AI,” which translated into a six-times better chance of amplifying the organization’s success with AI.

“The single most critical driver of value from AI is not algorithms, nor technology — it is the human in the equation,” affirms report co-author Shervin Khodabandeh.

 

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From a survey of over 3,000 managers from 29 industries based in 112 countries — plus in-depth interviews with experts — the report outlined three investments organizations can make to maximize value:

  • The likelihood of achieving benefits increases by 19% with investment in AI infrastructure, talent, and strategy.
  • Scalability. When organizations think beyond automation as a use case, the likelihood of financial benefit increases by 18%.
  • “Achieving organizational learning with AI (drawing on multiple interaction modes between humans and machines) and building feedback loops between human and AI increases that likelihood by another 34%.”

According to report co-author Sam Ransbotham, at the core of successfully creating value from AI is continuous learning between human and machine:

“Isolated AI applications can be powerful. But we find that organizations leading with AI haven’t changed processes to use AI. Instead, they’ve learned with AI how to change processes. The key isn’t teaching the machines. Or even learning from the machines. The key is learning with the machines — systematically and continuously.” 

Continued growth

While just 1 in 10 organizations finds financial benefits with AI, 70% of respondents understand how it can generate value — up from 57% in 2017.

Additionally, 59% of respondents have an AI strategy, compared to 39% in 2017, the survey found. Finally, 57% of respondents say their organizations are “piloting or deploying” AI — not a huge increase from 2017 (46%). 

One of the biggest takeaways? According to co-author David Kiron, “companies need to calibrate their investments in technology, people, and learning processes.”

“Financial investments in technology and people are important, but investing social capital in learning is critical to creating significant value with AI.”

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Bringing DX to the food supply chain in a pandemic

In a new paper, supply chain stakeholders share how COVID-19 has affected the transformation of the sector.

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There’s little doubt that COVID-19 had a profound effect on the food supply chain.

As one example, just think back to roughly March of this year, when virus transmission was rapidly picking up speed. Remember the reports of food and beverage companies only producing their most popular or essential products? Or how it would take slightly longer than usual to restock certain products? What about the rush to integrate — or quickly improve the efficiency of — digital and e-commerce. 

Panning out a bit, think about food safety and quality professionals. The need to stay safe — and in many cases, stay at home — meant performing the very hands-on job of monitoring, auditing, inspecting at a distance, i.e. digitally. 

When the food supply chain was hit by storages, delays, breakdowns, and lockdowns, the end result was — like in so many sectors — a rapid digital transformation.

As The Food Safety Market — an SME-powered industrial data platform dedicated to boosting the competitiveness of European food certification — elaborates in a new discussion paper, “technology has played an important role in enabling business continuity in the new reality.”

The paper — Digital Transformation of Food Quality & Safety: How COVID-19 accelerates the adoption of digital technologies across the food supply chain — features industry experts from companies like Nestlé, Ferrero, PepsiCo, McCormick & Company, and more discussing the effects of the pandemic on the supply chain.

A few highlights from the paper:

  • John Carter, Area Europe Quality Director for Ferrero put the issue of food access into perspective at the start of his interview:

“The production of food defines our world. The effects of agriculture on our daily lives are so omnipresent that they can be easy to overlook; landscapes and societies are profoundly influenced by the need to feed our growing population. But much has been taken for granted. Only occasionally are we forced to consider: ‘where does our food come from?'”

  • Ellen de Brabander, Senior Vice President of R&D for PepsiCo provided insight on the cost benefits of digital transformation:

“The need for customization is a big driver for accelerating digital transformation and moving away from a ‘one size fits all’ approach. This means that the cost to develop and produce a product must be lower and digital technologies provide a clear opportunity here.” 

  • Clare Menezes, Director of Global Food Integrity for McCormick & Company brought up one area where digital tools need to go:

“There aren’t any areas where digital tools “fail”, but there is a need for tools that ‘prove out’ predictions around where the next integrity event will play out and how it could lead to quality or food safety failure. These tools are an obvious candidate for AI given the number of PESTLE factors that might come into play.” 

Want to read all of the interviews? Check out the paper here.

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