News headlines everywhere scream fears of robots taking over jobs, creating an ever-growing pool of unemployable humans who cannot compete with machines. This concern, while understandable, is unfounded. In truth, Artificial Intelligence (AI) will arguably be the greatest job engine the world has ever seen.
Both automation and AI underscore how central and critical human insight and expertise are to business success. And nowhere is this more evident than when we look at the impact of AI in the sales process. Great salespeople are often seen as magicians: they can sell anything to anyone, regardless of the usefulness of their products or the personality of their buyer. But for the bulk of the profession, the perception is often that of a numbers game: if you try to sell enough products to enough people, some of them will want to buy it. This rise of AI in the sales process will help put an end to this stereotype: long-held myths and traditional practices are exposed through the lens of data, making way for discovery and innovation to improve business outcomes and drive a culture of excellence.
More than just automating workflows, AI and its subfields, such as Machine Learning (ML), are helping sales teams gain visibility and control over their pipeline, productivity, and performance. Additionally, ML complements the human touch, enabling sales professionals to scale their outreach without diluting personalisation when and where it matters.
And once sales teams get to better grips with understanding the science behind using this technology, it can be used to give them a cutting edge over competitors.
There is a reason more and more sales-based businesses are beginning to invest in automation software: it shortens the sales cycle and automates menial processes to save precious time and capital resources.
A good sales process is based on data. But all this data needs to be logged somewhere and maintained. The result of this step will be a representation of the data which can be assembled, analysed and updated for work-related processes later. For instance, AI can help manage this by collating data and deciding which content is the most applicable for an individual based around things like historical data, location, and past behaviour.
Once this has been completed, analysed and tested, data can help sales professionals make predictions about their customers and products, and apply this knowledge to the real world. Automating account-based marketing support with predictive analytics and account-centred research, forecasting, reporting and recommendation, can also help free up time for sales representatives to focus on other tasks.
Customer intent is another area that can be automated, this time using Natural Language Processing (NLP). Knowing the intent allows AI systems to understand the situation the salesperson and prospect are facing at that point in time, allowing machine learning to recommend the best response in that situation. Intent-based metrics, such as number of positive replies or number of objection replies to prospect emails, can also be used in A/B testing to identify the most effective playbooks. Machine learning can recommend the best response for sellers, and A/B testing can identify the most effective playbook for a scenario or customer demographic. What’s more, AI/ML provides employees with fast and accurate access to the data and information sales representatives need to improve their understanding of customers; this improves productivity by freeing up time for employees to focus on other tasks at hand.
The sales industry revolves around the customer journey; more and more, this requires a personalised approach for each target. Trying to keep up with each person’s habits, preferences and needs can be challenging, particularly if the salesperson is just starting to build a rapport with the prospect. Using the data collated by the salesperson alongside predictive analysis, AI/ML can propose the personalised journey to improve the sales representative’s one-on-one connections with customers at scale.
At the start of the relationship, using this information helps sales representatives better understand each new customer at a personal level. This gives them a clear roadmap to build a personal customer journey for each prospect, building trust early in the process and enabling junior staff to come across as advisors for each client.
Being able to use data for every customer also helps businesses create a prioritised roadmap based on predictive data of customers more likely to renew, churn and those that are on the fence. This enables sales leaders to identify which clients need more attention and to ensure that they are supported on their personal journey to renewal. This is crucial for forecasting and to manage the workload of sales teams to avoid the mad rush at the end of the quarter.
Achieve greater outputs from inputs
The era of AI in sales has arrived, there is no question about it. AI already performs much of the heavy lifting for many successful sales teams today and, as it continues to learn over time, it will constantly improve to deliver even better information and more accurate predictions.
Teams who choose to embrace ML dramatically push the limits of their potential. Sales representatives that use AI as part of their day-to-day interactions are engaging with prospects and customers in more meaningful ways. And as the technology continues to improve, it will provide sales teams with quicker insight to help navigate their own sales cycle.
It’s time to move past the unfounded fear that AI/ML will replace sales representatives: the truth is, it enables them to become better at their job. The sales industry can look to ML to not only surface actions and insights that will make their teams more effective, but also to improve the lives and livelihoods of the sales representatives by automating administrative tasks. This technology gives time back to sales teams so they can do what they do best—sell.
Tom Castley, Vice President of Sales, EMEA, Outreach