Category: artificial Intelligence

Google Contact Center AI

Improving Customer Service with Contact Center AI. Reimagine the Customer eXperience through full end-to-end platform expansion

Posted on by Deepak Nair

Announcing Google Cloud Contact Center AI Platform, which offers an out-of-box, end-to-end solution for the contact center. It brings together the advantages of AI, cloud scalability, multi-experience capabilities, and tight integration with customer relationship management (CRM) platforms to unify sales, marketing, and support teams around data across the customer journey.

Contact Center AI Platform is purpose-built for customer relationship management, extending ability to offer personalized customer experiences that are consistent across the brand, whether delivered through a virtual agent, a human agent, or a combination of both. It eliminates many long-running pain points, from managing data fragmentation to replacing rigid customer experience flows with more engaging, personalized, and flexible support. With this addition, Contact Center AI now lets you:

  • Orchestrate the customer journey by creating modern experiences that can be embedded in their chosen channels with mobile/web software developer kits (SDKs), compatible with iOS and Android.
  • Leverage CRM as a single source of insight into the customer experience, to unify content, increase personalization, and automate processing with CRM data unification.
  • Manage multiple channels without pivoting across voice, SMS, and chat support.
  • Predict customer needs and route calls appropriately with AI-driven routing, based on both historical CRM data and real-time interactions.
  • Automate scheduling, schedule adherence monitoring, and manage employee scheduling preferences with Workforce Optimization (WFO) integration.

Provide customers with self-service via web or mobile interfaces using Visual Interactive Voice Response (IVR).

Artificial Intelligence and Machine Learning

Posted on by Deepak Nair

“Artificial Intelligence and Machine Learning” by Vijay Gadepally




video courtesy : MIT OCW hosted on YouTube

This lecture provides an overview of 5 to 6 Decades of Development in the Artificial Intelligence space, Key Ingredients in building AIML Workflows, and examples/details related to Supervised, Unsupervised, and Reinforcement Learning.

Define Marketing Success with ML/AI

Posted on by Deepak Nair

Predictive analytics is the use of data, statistical algorithms and AI techniques to identify possible future outcomes. This can help you stay ahead of the curve and assess the future of your marketing. Here are a few ways that you can use AI and predictive analytics in your marketing.

Optimizing marketing campaigns

With predictive analytics, it works just like the Scientific Method by having a hypothesis and then proving it either right or wrong. You can use the data to determine what customer segments and audiences will be the most effective to reach and create actionable insights.

With accurate reporting, you can accurately tell whether a campaign was successful and optimize where it may fall short. This lays the groundwork for best practices of strategies to follow, not just in marketing, but sales and business decisions as well.

Predicting customer behavior

Data is the most accurate way to predict a customer’s “next move” in your business model- especially online. Using behavioral data with customer journeys, you can predict engagement points on when you think a customer may convert. You can also track “drop-off points” and see where you may be losing people whether it is due to confusing content or a dead end in the journey.

By mapping these patterns, at both one-to-many and one-to-one marketing, you can give insight into the outcomes of campaigns and help drive to the outcomes that you want.You can also use this information to do profile scoring and build customer models.

According to a study by the Aberdeen Group, predictive analytics users are twice as likely to identify high-value customers and market the right offer. By doing all of this you can identify potential leads and prioritize the ones that are most likely to convert.

Personalizing content

By being able to predict customer behavior and build models off that data, you can then personalize your content to target those certain leads. By targeting the right audience at the right time, you can show more accurate paths to ROI.

By using historical data to see the behavior of past customers, you can use that to determine and create personalized messages.

Insights Engine: Go beyond Transactions data: Gauge the Customer Intent

Posted on by Deepak Nair

With Advanced Analytics platform, you can increase your return on investment across marketing, sales, product and category management, by:

Growth for Knowledge (GFK) Consumer Insights Engine
  • Understanding Customers/Prospects
  • Stimulating Demand with Messaging
  • Optimizing Visibility with Marketing Channel Mix
  • Becoming the chosen brand in the target Market
  • Providing the optimal customer product experience
  • Win at every stage of the purchase journey

Empowering Marketing, Sales, Product, and Category Management Business functions to answer key questions with Actionable Insights

  • What triggers the realization of a need to purchase?
  • What channels do consumers use when researching products?
  • What are the important attributes for consumers when deciding to purchase?
  • What do the purchasers think, & talk about the products?
  • How understanding your customer’s purchasing behaviors and decision-making can drive sales.

The Consumer Journey Module is the first, and only solution for Manufacturers, and Retailers in the technology and Consumer Durables Industries to combine the most comprehensive collection of point of sales data with

  • AI Enabled Consumer Review Data
  • Online Consumer Behaviour Data

Source: https://digital.gfk.com/consumer-journey-insights