How Artificial Intelligence can help in Finding Marketing and Sales Leads


Artificial intelligence (AI) has now become an integral part of our lives. It’s how Amazon suggests products, Google answers our searches, and Pandora plays another song.
AI allows scalable growth, and personalizes customer experiences for marketers. AI is a powerful marketing strategy as it allows marketers to outdo themselves in their roles by interacting with their customers through targeted messaging — all at scale.

Here are the five ways AI can assist sales and marketing in finding leads.

1. Support Sales with Appropriate Customer Experiences

Customers now expect personalized experiences and interactions through their favorite channel. AI-driven predictive content tools are allowing marketers to be more tactical, while reducing the workload.  These AI-driven marketing programs can examine your website for case studies, white papers, blogs, articles, videos, eBooks, etc. Once the content gathered, AI foresees what will appeal and ultimately convert each audience section. These Insights can be used to engross visitors across web, email, social, and mobile channels for a complete Omni-channel approach. The outcome is one-to-one value marketing that organizations weren’t earlier able to attain without substantial scaling.
AI-powered marketing engagement platform provide marketers with data-driven intelligence that supports company-wide strategies for a uniform brand conversation.

2. Settle on Qualified Leads from the Start

Today more than ever before, closing deals needs a coordinated arrangement between sales and marketing. Generally, one of the most challenging areas of alliance is approving on what qualifies a lead marketing scenario, what makes for a “perfect customer profile.”
Of course, marketers are well-equipped with a broad range of lead scoring tools and plans, resulting in a computerized qualification process, but even automation has its limits. It can be tough to set up scoring in a meaningful manner that supports wide audiences, product offerings, and sales strategies. Though, predictive lead counting and predictive account scoring offer value where other solutions may not, resulting in a solid foundation between sales and marketing.

3. Personalize Messaging With AI Insights

Google has been using machine learning and AI longer than most sales and marketing teams, and they’re more deeply endowed in it than most of us combined.

RankBrain, Google’s machine learning program, observes user engagement 24/7/365 to offer the best search results for each query that someone types into a Google search bar. The result of that continuous examination is exhibited in each search engine results page (SERP).
If your organization develops EHR software, for instance, data-driven insights into your spectators are one Google search away. Type “EHR” and see the answers Google’s AI is displaying. Is it a description? Are they product pages? What allied searches are enumerated at the bottom?
Those are all bases to understanding the language your viewer is using, the questions they’re requesting, and the key criteria that explain your ideal customer profile—which is vital information for both sales and marketing. You can get this important information by reverse-engineering organic search results.

A Machine Learning and AI program from a reputed institution can give you insight into customer behavior and help you devise appropriate marketing strategy. Premier institutions have experienced faculty and resources to ensure right education for students. 

Comments

Post a Comment

Popular posts from this blog

Predictions: 2019 Data Science Jobs Market

The data science and analytics job market is growing rapidly. What's more, after a year of wages staying comparatively flat, data science and analytics experts can look forward to rises in 2019. That's according to a series of 2019 predictions from quantitative recruitment specialist firm BurtchWorks' managing director, Linda Burtch. If your organization plans to recruit these professionals in 2019, or if you are a data scientist looking to make some career moves this year, these predictions offer some useful insight about what you can assume in the year ahead. What's driving some of the changes? How can you leverage these opportunities? There are several forces in play right now, Burtch told InformationWeek in an interview. Organizations across almost all industries have recognized that analytics can make them more intelligent and more profitable. Career-focused young people are taking degrees to prepare themselves for such jobs. That has extended the talent...
Read more

Know Which Programming Language You Should Use For Big Data Project

Image
You got a big data project. You understand all the ins and outs of the domain, and probably you’ve decided what framework you will use or what infrastructure to use to process all that data, but if you are confused about which programming language to employ for your big data project, this post is for you. Though there are many options to choose from -- Java and Python to R and Scala, picking the right programming language for your big data project can make a big difference between a highly successful project and one that only scratches its potential.  Here's a quick rundown of each programming language to help guide your decision #1 Python The field of big data and data science being relatively new, many erudite folks are to be seen constantly debating about the pre-eminent programming language for big data.  You might know that Python has been present for over a decade, and is more popular in academic circles. Its forte lies in Natural Language Processing (NLP). ...
Read more

Communications in the Era of Big Data

In this digital era, a single click of the mouse on the “Agree” button can provide enterprises complete access to almost all your information. For many business organizations, collecting user information is vital to be able to send the most personalized messages to its customers.   Today, big data has become a fundamental part of communication and marketing efforts to augment value for businesses – as shown by Netflix and Spotify, which have utilized their huge user data to create engaging messages for their targeted audience.   However, this has caused fury amongst those who think that if you are using personal data without adding any value, it’s annoying, not amusing. Owing to the frequent misuse of information and data leakage, there is a growing fear for privacy and security of the data. The advancements in technology are bringing myriad opportunities and possibilities for businesses, but how well are they prepared for the potential risks associated with data use...
Read more