Dec 29, 2021
How is Artificial Intelligence Applied in the Sales Industry? — Part1
1. Personalized content/product recommendation
According to the latest survey report released by Salesforce, 62% of B2B buyers expect to receive personalized advice at different transaction stages. In the B2C market segment, shoppers are more popular. And they expect an Amazon-like experience from each brand manufacturer. Segment(The leading Customer Data Platform )pointed out in a 2017 study that only 22% of customers are delighted with the personalized services and products they receive.
Machine learning has become the power behind most content recommendation systems (including Spotify, Netflix, and Amazon), however, it seems to be out of reach for marketers due to price and technical constraints. However, the self-developing customized algorithm is not the only way. There are many plug-and-play solutions on the market, and it is a fast and easy way to use artificial intelligence in marketing. E.g.:
Content AI by Marketo — use predictive analysis and machine learning to display the most relevant content related to user “recommendations” on the company’s website.
CaliberMind — analyzes all company customer data, creates the ideal buyer persona, and advises how to communicate with the company’s audience in a profitable way.
Visely — AI-driven product recommendation engine for Shopify stores.
Read more: What are the Types of Content Relevant Service?
How much influence does artificial intelligence have on personalization?
After analyzing 3.5 billion marketing interactions, BlueShift came to conclusions that:
Creating 3.1–7.2 times improvement in customer participation.
Compared with e-mail, the participation rate of mobile communication has increased by two times.
Over time, the artificial intelligence engine can provide an additional 50% improvement in initial results.
2. Conversational AI product-chatbot
Chatbots are the driving force of automated customer support, but unfortunately, they are underutilized in marketing. After all, marketing is about cultivating good relationships and leading meaningful conversations. Chatbots can now perfectly handle this task, helping companies interact with potential customers through multiple channels at different stages.
For example, Nordstrom allows shoppers to interact with robots while looking for suitable products. After asking a series of essential questions, the on-site assistant will give certain suggestions.
Hipmunk Messenger uses the location of travelers to determine where they are departing from and then conduct the appropriate transaction. Intelligent assistants can also plan travel recommendations and manage hotel reservations for upcoming trips. Generally speaking, the travel industry has taken the lead in chatbots.
Other industries are also catching up. In fact, between 2018 and 2024, the global chatbot market is expected to grow by 31% to reach USD 1.34 billion. According to a survey by CMS Wire, the average cost of an SME messenger chatbot developed for marketing purposes is between US$3,000 and US$5,000.
Read more: Chatbot — One of the Most Popular Applications in the NLP Field
Customized dataset
With the acceleration of the commercialization of AI and the application of AI technologies such as assisted driving and customer service chatbot in all walks of life, the expectation of data quality in the special scenarios is getting higher and higher. High-quality labeled data would be one of the core competitiveness of AI companies.
If the general datasets used by the previous algorithm model are coarse grains, what the algorithm model needs at present is a customized nutritious meal. If companies want to further improve certain models’ commercialization, they must gradually move forward from the general dataset to create the unique one.
ByteBridge, a Human-powered and ML-powered Data Labeling Tooling SaaS Platform
ByteBridge, a human-powered and ML-powered data labeling tooling platform with real-time workflow management, providing high-quality data with efficiency.
Accuracy and Efficiency
- ML-assisted capacity can help reduce human errors by automatically pre-labeling
- The real-time QA and QC are integrated into the labeling workflow as the consensus mechanism is introduced to ensure accuracy
- Consensus — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output
- All work results are completely screened and inspected by the machine and human workforce
In this way, ByteBridge can affirm our data acceptance and accuracy rate is over 98%
NLP Service
We provide different types of NLP in E-commerce, Retail, Search engines, Social Media, etc. Our service includes Voice Classification, Sentiment Analysis, Text Recognition and Text Classification(Chatbot Relevance).
Partnered with over 30 different language-speaking communities across the globe, ByteBridge now provides data collection and text annotation services covering languages such as English, Chinese, Spanish, Korean, Bengali, Vietnamese, Indonesian, Turkish, Arabic, Russian and more.
Cost-effective
A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.
End
If you need data labeling and collection services, please have a look at bytebridge.io, the clear pricing is available.
Please feel free to contact us: support@bytebridge.io
source: http://www.elecfans.com/rengongzhineng/893554.html
