Artificial intelligence solutions are inundating the market right now: They're penetrating and transforming industries from transportation and agriculture to finance (paywall) and business development.
AI is everywhere. And sales and marketing are no exception to this trend. Many tout AI’s unique ability to recognize and track nuances that a human eye can’t as the “golden ticket” that promises to skyrocket any company’s attempts to understand their customers on a whole new level. I believe AI essentially presents companies with an opportunity to establish longer-lasting, deeper and more meaningful connections with their customers.
How does this happen, exactly?
At its very core, artificial intelligence operates as a brain. Its ability to learn and develop happens through deep learning and neural networks that identify and analyze subtle yet repetitive patterns, thus learning to recognize events, objects and occurrences in the future and produce forecasts.
This is exactly where I believe the true value of AI and deep learning lies for marketers and salespeople -- it has the potential to understand customers' true needs and preferences, start meaningful dialogues and interactions with the clientele and even anticipate their needs, sometimes before they even realize them themselves.
Companies Utilizing AI For Sales
Among the companies that have begun initiating AI integrations in consumer behavior analysis is Deloitte. With its acquisition of Blab in 2017, it hopes to offer its client companies the opportunity for reputation management based on the analysis of 50,000 sources, including news and social channels, and the identification of topics that consumers’ conversations revolve around. As more companies develop predictive capabilities like these, they could change how organizations actively anticipate and respond to consumer needs to increase sales on one side and manage brand development and reputational risks on the other.
It’s not just the larger players that have joined this AI revolution in sales. There are smaller (but fast-growing) companies offering AI tools geared at sales and marketing efficiency improvement. One such solution provider is People.ai, whose website cites large clients like Lyft and Zoom. People.ai automates the capture of contact and customer data, and updates records with their most recent information. A number of other companies are automating sales and marketing tasks, including InsideSales.com, which identifies the most likely conversions in a sales pipeline, and Chorus.ai, a conversational intelligence platform for sales teams.
Companies have untapped potential in the shape of unstructured data about their customers that they can unlock with the help of AI and machine learning. For one, marketing automation through deep learning can help companies develop more personalized experiences for customers and more individualized approaches tailored to each customer’s needs. Deep learning technology already allows this by analyzing and learning based on the history of consumer interactions with brands, like purchasing habits, behavioral traits and digital preferences. Amazon's recommendation engine is one example of how companies can utilize this data.
Another example of such technology applications is Google Optimize 360, a tool in the Google Analytics kit designed to test website versions, tailor them to specific audiences and deliver personalized experiences. According to the website, it can also pinpoint the exact effects website changes have.
Developing And Choosing Solutions
What I believe any deep learning solution should do is help companies identify what the customers need when they need it -- or even better, before they realize they do -- and respond to their customers at the right time. Deep learning solutions should seek to give companies the chance to interact with customers in the right place, at the right time, and retain satisfied customers through meaningful relationships.
What’s more, companies can also find ways to use AI to identify the profiles of the ideal potential customers for the company based on the analysis of its current clients and identify prospects that could become loyal customers. By analyzing potential buyers’ profiles, preferences, hobbies, interests and so on, companies can pinpoint prospects and leads with the highest probability of a positive outcome and offer those potential customers the products and services they will be most interested in.
Of course, automation has already begun completely transforming sales and marketing by accelerating the sales cycles, building stronger customer-brand relationships and making the customer experiences more personalized. However, the emerging solutions shouldn't replace human involvement in the sales processes as we know it; companies should use them to allow their sales teams to focus on more complex tasks requiring human reasoning and an individualized approach.
While AI has the capacity to help salespeople generate more leads, gain insight into customer needs and preferences through behavior analysis, coordinate selling channels and more, companies should still maintain the “human touch” for managing exceptions, taking initiative in situations of ambiguity, using judgment and ultimately outlining the questions that the new technologies are supposed to answer.
By all means, AI opens doors to numerous new possibilities. But one question executives need to ask themselves is how exactly the technology will fit in the current organizational structure and complement existing practices. AI is not a one-size-fits-all solution that can remedy every single issue. Rather, it is a tool that salespeople can leverage to maximize their own potential and focus on more complex issues that require human judgment and expertise. The machine can then take over the lower-involvement tasks, speed up the sales process and help the sales teams achieve their goals faster.
I believe the key to remaining on the cusp of the AI wave in sales and marketing is ensuring that any new solution you create or adopt is naturally integrated into the sales workflow and seamlessly provides insights that improve a process's efficiency instead of disrupting it.
