Written by William Flaiz and originally published on Forbes.com on December 5, 2018.
At Bottom Line Strategy Group we work with a range of clients from small businesses using CRM and marketing automation software for the first time to established organizations ready to leverage the massive amount of data they have accumulated to sell more efficiently and effectively.
It is the latter group that we often consult with on implementing an AI solution to further optimize their marketing and selling operations. As part of this process is the education around the difference between machine learning and AI, simply put machine learning can identify patterns in data to make decisions, where AI can take that information to carry out tasks typically performed by humans. Some of the key tasks AI can perform for sales teams are optimizing deals, forecasting pipelines and sales, and automating routine rote tasks.
At any given time, sales reps are juggling three very different priorities: ensuring that the customer is satisfied with the value that they're receiving, closing a deal efficiently, and closing a deal at the best price for the company. Because each of these objectives has very different variables, it's a struggle to balance them effectively. This isn't just a problem for sales reps; it's a struggle for sales managers, too. Sales managers who have limited data-driven insight into the pricing stages of deals may end up focusing on the largest deals or the sales reps that ask for help, rather than the reps that need extra coaching.
Fortunately, AI can address these challenges. AI software can use historical data to identify all important attributes that influence pricing on deals, as well as develop the optimal mix of services and product. Some platforms take attributes like customer footprint, market segment and competitive status into account, giving sales reps meaningful guidance that is customized for each individual deal.
In order for forecasting to be an accurate measurement of growth for your business, it should be treated as a data-driven science. Unfortunately, businesses tend to be too optimistic or pessimistic with pipeline and sales forecasting, either of which can negatively impact a company's growth. Using AI to measure data and intelligently forecast pipelines and sales can transform how a company interacts with its own sales data.
Sales-forecasting software takes the bias out of the process to produce better predictions and higher deal-closing rates. This type of AI software works by gathering data regarding past deals, both those that were successful and unsuccessful. It looks at interactions like emails, meetings, and phone calls and then analyzes the role each had in the sales outcomes. Some AI-poweredsoftware even adds contextual data, such as the weather, traffic, or local events into the sales forecast.
When AI software first came onto the scene, a big concern among critics was that it would put millions of salespeople out of jobs. However, this hasn't been the case; salespeople continue to play a vital role for businesses, and thanks to AI, they're more effective than ever. Building relationships with prospective clients will always require a human touch, but until now, time and energy spent on prospects and closing sales have been divided with countless other tasks. Updating spreadsheets, creating reports, researching the data for meaningful insights are a few manual tasks that AI can now automate.
As a result, sales reps can spend more time actually selling and communicating with potential clients, negotiating, and strategizing how to close important deals, rather than doing menial work.
AI software requires a significant upfront investment in time and resources. However, when done correctly, sales teams gain these resources back exponentially. From optimizing deals and accurately predicting business activity to cutting down on the time that sales reps spend on tedious and time-consuming tasks, AI is a powerful tool that can give your business the edge it needs for success in a sales landscape that has an increasing demand for personalization.
AI relies on historical data to create accurate predictive solutions. Even if you're not ready to introduce an AI-based solution, begin collecting customer data and building a database now so that you'll have this valuable info on hand when you're ready to tap into AI.
While unsupervised learning in AI can conduct exploratory analysis on business data, if that data is not properly labeled, then AI cannot provide insightful information. This stage of data preprocessing is essential, and ensuring that it is carried out properly will ensure that your business is ready to make the transition to AI. Develop a system that automatically labels incoming and outgoing customer interactions and sales data to ensure that the insights produced by AI will be beneficial.
If you're just starting to dip your toes into the waters of AI, it may be tempting to automate every task that you can. However, the transition will go much more smoothly if you have a specific goal and strategy. Starting small and choosing a simple process to automate, like call logging or email drafting, will yield almost immediate benefits.
AI can increase the effectiveness of a sales team, letting them save time and money while maximizing their efficiency. By understanding how AI can be used for administrative tasks, optimizing for the right deal mix, and forecasting, you can create a unique competitive advantage over the competition and unlock the value of the data your business accumulates.
Published by William Flaiz on 01-17-2019