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.
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
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