6 Artificial Intelligence Use Cases in B2B Sales
Would you rather be sold to from a human or a machine? For now most people would answer the first option, technophiles aside. A study of 6,200 customers of financial services companies found that if a customer is aware of their conversational partner being a machine or artificial intelligence application, purchase rates decrease by almost 80%. Findings show that customers perceive the bots as less empathetic and as possessing less knowledge about the product.
Nevertheless, findings suggest that chatbots are actually four times more effective at selling versus inexperienced workers. This might suggest the idea that as artificial intelligence (AI) continues to make strides in development at an incredible rate, it will slowly catch up and possibly overtake its human counterpart. However, while 31% of marketing look to invest in AI technology within the next year, hearsay and vague ideas about what artificial intelligence will be used for is only on the rise.
These unclear predictions about artificial intelligence applications arise from a combination of fear mongering combined with business optimism. For example, 37% of executives believe that managers don’t understand cognitive technologies and this will be a primary significant to AI initiatives. While conversely 79% of executives think that artificial intelligence will make their job more efficient and more effortless.
This uncertainty about artificial intelligence implementations within a business context is sometimes difficult to navigate through as a salesperson trying to understand how artificial intelligence might affect their department. Our article on 10 statistics showing the rise of artificial intelligence in sales gave a picture of AI trends but understanding how to implement AI is another area altogether. Therefore, this article will explore six concrete use-cases for artificial intelligence in sales that are already relevant today.
Sales assistance for Research and Prospecting
Sales reps spend 8.8 hours a week searching for information and 32% of their overall time looking for data to fill into the CRM. Moreover, The B2B Lead found that 50% of sales time is wasted on unproductive prospecting and Gleanster Research established that only 25% of leads are valid enough to be put through to sales. These inefficiencies show a clear opportunity for improvement that AI could be a solution for. AI sales tools have the capability to find missing contact information and update CRM software with precise lead information more precisely and quicker than a human could do manually. Moreover, due to advancements in predictive intelligence these tools can take advantage of historical data logged in the CRM to find promising leads that fit similar profiles.
CRM Data Input Automation
Research by Implicit found that the average sales rep updates 300 crm records per week. These CRM users spend on average almost 6 hours a week working with activity and contact data, costing companies more than $13.200 each year for each user according to Introhive. These non-revenue generating activities were estimated to be 63.4% of all reps’ activities by InsideSales.com. Furthermore, if sales reps’ disregard these activities it leads to low quality data which further hurts the sales process. Sales AI automation can reliably obtain the necessary data, and input it seamlessly into established CRM systems.
Lead Scoring and Qualification
Artificial intelligence can use algorithms to compile historical information from clients, combining it with data about past interactions with the client and even look at additional social listening points to rank leads in accordance with closing probabilities. This type of artificial intelligence application is particularly useful when lead datasets are particularly large. It is difficult for a human to prioritise different leads when looking at hundreds or thousands of different contacts and their respective information, whereas an AI can reliably identify the key leads from a large dataset, ranking them in a systematic way. In general AI brings a level of mathematical logic that would be impossible to replicate manually.
Sales Call Analytics
Another application of artificial intelligence is in sales call analytics. Advanced analytics on sales calls can be used to obtain insights about sales effectiveness and call performance. These are especially useful for sales development representatives that might be in cold calls where keeping to different conversational metrics have a large impact on success. However, as sales call analytics continue to make advancements these analytics could extend to uncovering insights on the performance of account executives in later stage calls.
Artificial intelligence is able to analyse historical sales data to make forecasts about future sales results using various artificial intelligence predictive algorithms. These type of predictions can include forecasting which current deals have the highest probability of being successful. Moreover, they can be used to identify new prospects that are similar to customers who bought the product previously. This can range from finding ideal customer profile fits to social listening to see buying signals from target leads. Simultaneously these predictions can be used internally to predict sales results from the team to prepare for different performance metrics and targets.
In addition to the aforementioned predictive intelligence made possible by AI, certain AI systems might interpret the data to provide insights that go beyond sales forecasts. For example, recommendations may cover which customers provide the opportunity for up-selling or cross-selling. Additionally, recommendations can dive more deeply into which targets are optimal to be contracted and how, as well as how to price or structure a deal. Although we might be some time away from this level of artificial intelligence in sales, this type of automated advice could be helpful to validate the direction a salesperson goes down when fabricating a deal or deciding what approach to take with a prospect. This type of productivity enhancements can also happen on a personal level. Artificial intelligence can be used to observe your historical usage data to help manage your calendar, tasks and other internal processes easier.
To read about some data backed statistics on artificial intelligence in sales, check out the following article: Artificial intelligence: 10 Statistics About AI in Sales