4. 24/7 Customer Support
If you’ve worked in a customer support role, then you’re well aware of how many inquiries could be settled by a simple Google search. Rather than bleed payroll, AI chatbots are now able to respond to simple requests to save both time and money. First touch points are handled by AI and then either settled or pushed to an actual representative. This drastically drops off a large portion of inbound requests that are handled by the AI, freeing up the time of your actual employees.
Google recently showed off their Duplex AI system that can replicate human speech with startling accuracy to the effect of even being able to incorporate vocal subtleties like ‘umms’ and ‘ahhs’. The application this could have for not only customer support but also for sales would be unprecedented.
The machine learning component for customer support resides in testing interactions and building more successful responses and conversation pathways. Each company would have an AI chat system that would improve over time based on successful outcomes. The chat system is deployed with a generalized flow at the beginning for measuring and testing purposes before narrowing down on the best ones which are then again tested and refined.
The final product is a customer support team that can now focus on broader issues rather than be inundated with trivial matters.
5. Dynamic Pricing
Dynamic pricing incorporates machine learning in order to deliver varying pricing for every potential customer. What this would look like is a user being served the price of $300 due to being identified as from a high income bracket, whereas another user being identified from a low income bracket would be served with the price of $200. In contrast to A/B testing that requires someone to run it, dynamic pricing is controlled by an algorithm that works in the background. As with any machine learning model, the prolonged use of it will only serve to bolster its efficacy.
Algorithms can even go so far as to look at what competitors are pricing products at, and factor that into the model as well. The data that the algorithms can process ranges from previous purchasing behaviour, financial status, nearby events, location, seasonality, time of day, and more. Ultimately if relevant data on something can be acquired, then it can be integrated into the AI engine.
Demand forecasting can also be a component of what goes into the algorithm and can be used to anticipate heightened or lowered demand in order to reflect the best price.
6. KPI Analysis and Monitoring
Salespeople are losing out on important work time to record and track their KPIs in part due to the widespread presence of customizable performance dashboards for almost all CRMs. When given countless options for dashboard customization, salespeople will divert from their work to select and arrange dashboards to highlight their strengths and bury their weaknesses.
Machine learning models will be able to not only be able to uncover deep insights about KPIs but also record and log them automatically without the need to take salespeople out of their workflows.
With AI, there won’t be a need to set predefined KPIs and track for them. Because AI engines log vast amounts of data on every individual employee, they will be able to identify customized KPIs that are unique to the individual in order to objectively highlight their best and worst metrics.
In order to match an AI’s capabilities, an organization would need to employ a full-time team of data scientists—and even then they would get obliterated on a time, cost, and efficiency comparison, all while the AI engine is working in the background.
Basix has two AI engines that work in isolation yet also feed off one another. The Smart Engine is the master tactician that tees up the next piece of work that’s been predictively labeled as most urgent and impactful. The Insight Engine is the unrelenting bookworm that tirelessly sifts through data looking to find game-changing insights that can be both tested by the Smart Engine and visualized for every team member.
Signing up for a demo will allow you to test drive the AI engines and see for yourself what it means to truly supercharge your team. Book with us now!