AI-DRIVEN B2B SELLING USING MACHINE & ARTIFICIAL INTELLIGENCE

AI-DRIVEN B2B SELLING: USING MACHINE & ARTIFICIAL INTELLIGENCE

The most productive use of COGnitive AUTOmation (COGNAUTA) is using it as tool for helping human performance rather that a standalone solution. Indeed, AI-DRIVEN B2B SELLING is the optimal combination of intelligent autonomous automated tools and human effort. In Cognitive Automation language (Cognauta), we call it a humabot.

The pillars of an AI-DRIVEN B2B SELLING are the analytical capability and making it without a “human involvement-feelings”.

Identifying Potential Markets:

One of the best tools to discover new target niches is anomality detection. It permits the identification of “rare” items, events or observations which deviate significantly from most of the data.

For instance, if a group of potential clients have a significant deviation from most of the clients, it′s possible that the “rare” client segment is either a good group of prospects that should be approached or, on the contrary, a segment that is not a target.

Sales Forecasting

Sales forecasting is crucial for determining the market opportunity and the minimum results expected from a selling team.

The most valuable attribute of using Machine Learning rather than statistical models for estimating the future demand, is the fact that in Machine Learning, the trained Neural Networks can be used in much diverse scenarios and it′s also possible to improve the model′s prediction capabilities by retraining it periodically.

The most common applications of Machine Learning in selling are the following[1]:

Lead Scoring and Prioritization:

AI looks to data from very diverse sources without involving sentiments in the analysis, letting out the “gut instinct and incomplete information”.

Customer Relationship Management[2]: AI-Driven CRM is fundamental because it permits in the continuous scoring a prioritization of leads. The most common Machine Learning models used in AI-Driven CRM are:

Customer Relationship Management[1]: AI-Driven CRM is fundamental because it permits in the continuous scoring a prioritization of leads. The most common Machine Learning models used in AI-Driven CRM are:

• Association Rules Mining
• Classification
• Clustering
• Forecasting
• Regression
• Sequence Discovery
• Visualization

Also, the most common machine learning algorithms used include:

• Association Rule
• Decision Tree
• Genetic Algorithm
• Neural Networks
• K-Nearest Neighbor
• Linear As Well and Logistic Regressions

Artificial intelligence can look dispassionately at large datasets from a number of sources and tell you which leads you should prioritize, based on the scores the AI has given them.

AI will help you in your help efforts regardless of your knowledge of skill on the matter. Indeed, there are several platforms that are easy to use, for example:

• Drift
• Conversica
• Exceed.ai
• Crayon
• Salesforce

Why we are we giving you those names? Because at Cognauta:

WE TALK BUSINESSES: Technology is a tool; we have developed the best technologies for what does not exist, and we use/adapt the best that exists. Indeed, WE DON'T REINVENT THE WHEEL, BUT GENERATE MORE CASH FLOW.

Therefore, we have at Cognauta a unique method to combine the mentioned tools and others in our platforms at a very convenient price for you.

Don′t wait to build an insurmountable competitive advantage. Contact us now for a FREE trail: [email protected]

[1] Trends in Machine Learning Applied to Demand & Sales Forecasting: A Review. Usuga, Lamouri & Grabot. LAMIH CNRS, Arts et Métiers Paris. Tech, Paris, France. LGP, ENIT, Tarbes, France

[2] Application of data mining techniques in customer relationship management: A literature review and classification. Ngai, Chau. The Hong Kong Polytechnic University, Hong Kong, PR China. Tsinghua University, Beijing, PR China