A well-thought-out automation strategy can help expand customer outreach and lead to increased revenue. Recent research by McKinsey shows that 30% of sales-related activities can be automated. This can be largely explained by the increasing accessibility of advanced technologies like ML, AI, and RPA.
In this article, we will discuss how exactly automation can be applied in sales and outline a few critical tips that will help you start your automation journey.
Lead management
Far too often, companies miss considerable profits from prospective customers who are stuck somewhere in the middle of the sales funnel. Most commonly, these leads are not yet ready to make a purchase and need more information to make a decision. In this case, automation can help, first and foremost, by identifying these leads to be nurtured, and then by methodically guiding them through to the purchase.
There are numerous ways for how modern technologies like ML can identify customers’ buying propensity and automatically start engaging with them. For example, ML-based software tools can analyse customers’ web history, their social interactions with the brand, time spent on the website, etc. By analysing this data, the right combination of promotions can be formed and sent to the customer via a preferred communication channel, be it an email, a messenger, or other.
Once a customer sends conversion signals, a human sales rep can take over and make a personalised offer to the customer. Most importantly, the sales rep would have a detailed customer portrait, enabling him or her to adjust their sales strategy accordingly.
Churn Prevention
When it comes to ecommerce, each customer action is a valuable insight into their propensity to churn. With advanced analytics at hand, custom-built ML models can estimate the probability of churning for every customer based on demographics, customer support interactions, usage statistics, and more parameters.
Not to dive too deep into the technicalities, with the right mix of predictive behaviour modelling and customer lifetime value estimates, companies can identify which customers will churn and when.
Conventionally, churn prediction software would attempt to predict churners based on historical data, statistics, and game theory methods. Nowadays, marketers have found out that it’s far more important how customers’ behaviour changes over time. Modern ML models automatically break an entire customer base into micro-segments, and score each customer based on their ‘segment change’ history. Based on the software predictions, sales staff can target a customer with relevant promotions and discounts at the right time, effectively preventing churn.
RFP Generation
Developing a well-thought-out request for proposal (RFP) is a very important but resource-intensive task. Teams can spend hours drafting requests, which oftentimes get rejected by the upper management. By combining natural language processing (NLP) and robotic process automation solutions (RPA), companies can significantly decrease the time it takes to come up with RFPs.
For example, AI-induced RPA software can autonomously pull all the relevant information from multiple websites, process this data, propose drafts, and send them to the team for review. Not only such a solution would dramatically improve operational efficiency, but it would also streamline RFP collaboration processes.
Post-Sales Automation
The finesse of your post-sale strategy directly correlates with customer satisfaction and retention. Far too often, especially with SMBs, companies focus too much on closing deals, making post-sales management an afterthought.
With RPA and NLP software, an entire post-sale customer journey can be automated and optimised. After the sale is closed, the software can be configured to automatically deliver or activate the product and deal with billing, cancellation, and return processes. While it may seem that these tasks take the minimum amount of time per day, they can burn thousands of hours of sales teams yearly. At the same time, chatbots can resolve the most common customer inquiries without human intervention.
