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10 ways in which AI is affecting organizational sales

Posted by Anurag

Nov 11, 2019 7:20:00 PM

AI and sales

With changing attributes and demands of the customers artificial intelligence is becoming an integral part of every business module. In big organizations many sectors are being handled and simplified by the means of technological updates and artificial intelligence is being one of them. If applied in the most positive direction it can increase the outcomes of any sales team. Artificial intelligence tools are basically used in many fields including the sales module which might ultimately include marketing strategy building such as providing product recommendations and predictive scoring. It is providing support to administrative team as well as the lead score generation. In comparison to the human source intelligent software can contact hundred percent lead with more accuracy and less time. This not only causes efficiency but the revenue potential. Another thing which is responsible for increasing craze of artificial intelligence in the field of sales is the marketing campaign. These campaigns are more customers centric which gives the buyer a better customized and personalized touch. The feel of personalized messages makes customer more valued and loyal to the product or organization.

However, the competition is intense in the field, differentiation of the organization need to be logical in terms of differentiating the brand for the customer to make a notice. In such cases sales team can be very well backed by the data representation and management as most of the team is doing repetitive work that kills their energy as well as the time. This leads to more valuable endeavors, milestone achievement, appreciation as well as the goal achievement.

 Let us see some of the statistics which can support the fact that artificial intelligence is actually revolutionizing the sales module of any organization.

- AI can increase the appointment and engagement of clients an incremental increase of 40% on one hand and reducing the cost by 70% on the other hand.  So in both the ways it is being beneficial. The statistics of the Harvard business reviews reveal a somewhat similar projection

- As per the sales report of state sales research study 62% increases have been observed in the potential opportunity conversion only by adopting the prediction guided selling module. This helps in the ability to mark the preferences of the customer as well as explore the next step anticipated in advance

- As per another sales company report Gartner, it is expected that by 2020 business to business module will employ artificial intelligence augmenting the whole process in centralized form. This is applicable to at least one of the primary sales projects of the organization

- High performing teams of the sales department are either supported or trained in artificial intelligence in some cases the performance can scale up to as high as 4.1 times

- In an organization dealing with business generation and customer identification, artificial intelligence can play a crucial role. These can be in the form of CRM, CPQ, and customer service. The predictive analytical sufficiency is allowing the sales department to excel and flourish

Let us now see some of the points which will evaluate that use of artificial intelligence can make you sell you more.

1. Search for new prospects

Machine learning and artificial intelligence technologies are excelling the pattern reorganization enabling the sales team to search out for highest prospects having potential to buy. This also causes push to enhance the purchasing capacity of the customer by their individual sales technique.  This can be done by matching the new prospects by matching the data profile. In today’s scenario almost every client based organization is enabled by the CRM application system that provides the ability to define various dimensions like characteristics and another specific standpoint that have the power to strengthen the sales background of any organization. This predictive and statistical matching can save almost double the time, increase efficiency by three-fold. To accomplish the similar work it has been calculated without automated technology can take almost 4 years or more.

2. Turning market-qualified leads to sales leads

This is the point that strengthens the whole process in the pipeline stage itself. The area of interest in the collaboration between the sales and the marketing team is lead. AI and machine learning enrich the collaboration with the insight data taken from the sister concern or collaborative organizations. The activities related to the lead generation can be from any other websites, any past conversation or party event.  This allows conversion of natural language generation and natural language processing to help improve the score toward the sale prospects.

3. More accurate forecasting

This process may involve combining the crucial steps like selling, pricing and buying data. This can be stored and placed in one-way ticket in most accurate way helping again in further sales forecasting. This may include accurate feeding.  Artificial intelligence and machine learning module integrated into the CRM and sales management system and planning which then used this accurately filled data to reduce the risk of failure and rollbacks, this also in a way help in formulating better business strategies.

4. Improve customer lifetime value

Lifetime customer value analysis is the prioritization stamp given by the companies to individual customers measuring many things like relationship, purchasing capacity and feedbacks. This design the future role of the customer in maintaining a long term attribute a customer holds with the organization. In fact, many companies are currently using this customer lifetime value analysis as a chance for scoring the respect of customers which can be revived on monthly basis easily.

5. Better reporting and analysis

Remember the days when thousands of reports were prepared in excel? It was humongous, time taking and a monotonous job. Most of the energy and efficiency of the individual were spent on writing and making these reports. Better management of the data helps in time management and decreases the workload to as much as 20-30% causing more scaling of workforce in a better direction.

6. Guided sales techniques

Artificial intelligence and machine learning-based technologies which support the guided selling techniques usually have prescriptive and descriptive analytical approach. This can be in the field of service, selling, feedback and many other roles play of the salesperson. These all are through predictive analysis. This ultimately leads to 62% high performances.

7. Increased productivity

This increases productivity by using artificial intelligence and machine learning to analyze the effective action and behavior that in turn ultimately leads to more sales-oriented activities and closed sales activities. Technological updates can take into account the action and behavior of the customer that will further correlate with higher closing rates, sales manager this ultimately leads to higher performance

8. Better price optimization

Pricing is the crucial area for any sales team or a particular product. Pricing is the area that is learned by most of the organizations or the sales teams through hit and trial or error making methodologies. Being able to detect the data interpretation, read pricing data and purchasing history of any product artificial intelligence can predict the favored sale if a particular product is sold at a particular discount. In short we can say that machine learning can calculate the elasticity of demand for a product in the simplest manner. This helps in better decision making and outcomes through the optimization of the prices which are buyable and achievable.

9. More efficient automation

This makes the content move from another level up. This includes the prospects initiation and continuation from MQL to SQL. Many marketing automation applications like HubSpot are defining the content into the assets for prospect generation since years combining personalization with the analytical approach along who machine learning and automation application help in generation of pre-analyzed tailor-made content that moves along creating opportunities.

10. Faster problem solving

This may include problem-solving and facing the challenges through adaptive engineering module. Supporting the sales system through gadgets and software are deciding the greater amount of time to scrutinize the high-value account. This has the power to solve issues faced by the customer in a given frame of time. This can be understood through the example of chatbots which are providing every second assistance and satisfaction to the customer. They work in a way of problem-solving direction. All these can be done in the most simplified manner in the initial stage through screening and specific measures. Dealing with priority clients will yield different information and techniques that make feedback management so easy and fast.

 

In the end, it can be said that artificial intelligence, as well as the machine earning, is branching out to back up the organization at the umbrella level as well as the team at the ground level to enhance their strategies and capabilities. This allows better selection, decision and policy development. On the other hand customer also feels valued when small things are initiated through automated software like personalized message. The field is immense and change is dynamic hence opening the gates is the only way through which the teams can appreciate the value-added activities in deeper and multi-dimensional prospect causing transformation.

Topics: Artificial Intelligence, Predictive Analytics, Machine Learning, AI, Big Data Analytics, Sales automation

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