McKinsey claims that ~30% of sales-related activities can be automated. There is potential for salespeople to focus more on value-driven tasks, while their mundane and time-consuming activities can be outsourced to robotic process automation (RPA) bots and digital workers.
In this article, we will focus on 18 RPA use cases in sales processes, such as sales strategy and planning, lead scoring, order management.
Sales & marketing strategy
Sales and marketing strategy is often created by the company’s CMO and CSO, outlining the company’s target position for its products/services in the marketplace and how it will reach that position.
Strategy formulation involves, but is not limited to, the following:
- Monitoring market trends (purchasing behavior, customers’ income level fluctuations, their sensitivity to social and political issues, etc.)
- Monitoring results of advertising campaigns,
- Identifying segments
- Identifying competitive offerings and pricing
These tasks can be partially delegated to RPA bots.
1. Monitoring market trends
Instead of manually watching a graph oscillate and take notes, marketers can leverage RPA bots to monitor it for them, add filters (by region, for instance) if applicable, and automatically extract the useful information to leverage in their marketing strategies.
2. Data extraction and augmentation
To create a targeted campaign, the marketing team should sort out and validate the company’s target audience demographic or customer data.
This would involve cleaning, filtering, copying, and pasting large sets of data from the source onto the company’s database.
RPA bots can be programmed to complete these steps at determined intervals (i.e. every two weeks or so), and by doing so, provide the marketers with a constant flow of high quality data.
If you are interested in learning more about AI and marketing analysis, we have an in-depth article on the topic.
Dynamic pricing is a pricing strategy by which firms can marginally change their prices in real-time relative to their competitors, to get a competitive advantage.
Especially for businesses in competitive markets, such as the hospitality (i.e. hotel room prices) and aviation industry (i.e. airplane ticket prices) where multiple vendors are selling the same products, dynamic pricing can be a profitable strategy.
3. Comparing competitors’ prices
Businesses can leverage web scrapers that use RPA to automatically collect pricing information from competitors’ websites and compare it to their own.
Then, based on predetermined margins, ~5% for instance, the RPA automatically changes the price while a window of opportunity exists. If price movements are so abnormal that they would require staff oversight and approval for any change, a push notification can be sent accordingly.
Learn more about competitor monitoring automation.
Lead identification & qualification
Lead identification and qualification is the process of identifying a potential customer and assessing whether they fit into the company’s “ideal customer” parameters.
There are three steps to lead qualification:
- Collecting leads through email threads or social media,
- Assessing whether the lead fits your parameters of an ideal customer,
- And customizing a proposal to reach out to them, or pursuing a different lead.
These three steps can be automated through RPA.
4. Collecting leads
Businesses usually offer an e-book or demo to showcase their products to potential customers in exchange for their email address, personal (name, age, gender, etc.), and professional (job title, position, responsibilities, etc.) information.
An RPA bot can automatically capture this information from different ERP systems and store it in the database for lead assessment.
For instance, a digital media company automated the extraction of lead generation data from 50 different online publishers, in various formats. The benefit of automation for them was $150K of savings annually.
5. Scoring leads
Once the information is gathered, marketing and sales teams can analyze the data to assess the suitability of the leads (i.e. lead scoring).
For instance, if a software company is marketing its product to professional accountants, it might not make much sense for them to pursue a college student who’d visited their website for a research paper they were working on.
The RPA bot can assess the information of thousands of potential leads by comparing their data to a predetermined set of parameters. For instance, the bot can be programmed to dismiss any user’s data if they have left their “occupation” empty.
6. Customizing proposals
Information such as the product to be purchased or payment preferences can be pulled from the CRM to create custom proposals for potential customers.
Instead of the staff copying and pasting this data from CRM onto a document, RPA bots can be programmed via screen-scraping to automate proposal customization.
Once a lead has turned into a customer, their eventual orders will go through a series of steps to be realized. This is called order management or the order to cash process, a list of sequential steps from the moment a customer places an order to when it’s shipped and the revenue is received and logged into the system.
RPA can be employed in the order management cycle to automate the handling of such tasks so that the staff can spend their time better elsewhere. The following steps can be automated:
7. Order submission
Whether it’s through a website or an email, RPA bots can be programmed to collect all order data from various sales channels and input them on the order management software.
8. Inventory tracking
Instead of the sales representative contacting the warehouse to check for availability of the ordered goods, RPA bots can cross check it with the inventory CRM instantly.
9. Establishing shipping timeline
RPA bots can connect to the supply chain and/or logistics CRM to estimate the earliest shipping schedule/ delivery time or shipment.
10. Assessing creditworthiness
The creditworthiness of the customer is instantly checked and assessed via RPA bots by exchanging information with credit agencies such as FEICO.
Explore KYC automation in more detail.
11. Issuing invoice
The data (customer’s name, address, delivery method, payment method) from various sales channels is recorded and printed on the invoice automatically.
For instance, a Turkish appliances manufacturer leveraged RPA to automate the processing of 650,000 product transactions and 55,000 invoices1. The 2,340 hours the company’s supply chain team saved in the process allowed them to focus more on other value-driven and strategically important tasks.
Explore invoice automation in more details.
12. Processing payment
If payment is to be received sometime after the sale is made, or if it is deferred for any reason, an automatic email with a payment gateway can be sent to the customer, politely reminding them of their obligation and giving them the chance to instantly settle their debts.
13. Recording sales
Once the payment has reached the company’s account, the sale information is automatically recorded on the company’s books and the accounts are reconciled.
Post-sales activities involve keeping in touch with the customers for after-sale services, customer care services, customer survey satisfaction, and more.
14. After-sale services
For instance, if the business offers subscription packages (such as Netflix or the New York Times), it’ll be nearly impossible to keep track of all customers and their packages’ expiration dates.
RPA bots can be programmed to collect this data from the customer’s list and automatically reach out to customers a week before their subscription ends.
Transactional email technology, for example, is a tool designed to create personalized emails to send customers for membership renewal notices.
15. Customer services
For customer care service, RPA chatbots can be employed to assist customers with their troubleshooting: The customer care staff would create a command catalog of the most prominent issues facing their customers. Then, when prompted, the chatbot would send the answers along.
16. Customer feedback
For customer feedback, RPA and NLP-based solutions can streamline the collection and categorization of feedback for quicker resolutions.
17. Win-back campaigns
Winning back churning customers is an important sales process. Based on their recorded prior preferences, RPA and AI collaboration can be designed to present “enticing” new options to churned clients.
Netflix, for instance, has one of the lowest churn rates in the industry. One reason is their AI and RPA-based recommendation algorithm that constantly provides users with movies and shows that are in-line with their viewing history. This algorithm accounts for 80% of their users’ activity.
Salespeople must constantly be informed of the amount of sales, customer satisfaction, revenue, sales/marketing expenses, profitability, and other business metrics to understand how themselves and the company are doing.
Data extraction to create reports is a time-consuming activity. Nevertheless, it can be automated by RPA bots.
18. Extracting data for reporting
RPA bots can gather data from a business’ different platforms, such as CRMs, ERPs, social media accounts, and data warehouses, to automatically create analysis reports.
For instance, these reports can be customer churning. By interacting with the data of churned customers (i.e. the amount of time they visited the vendor’s website in a month), reporting solutions use ML to estimate the likelihood that a new customer will churn.
For more on RPA
If you are interested in learning more about the different use cases of RPA, read:
We have covered RPA extensively in the past. Download our whitepaper to learn more about the topic:
And if you believe your business would benefit from adopting an RPA solution, head over to our RPA software hub where you will find data-driven lists of vendors.
And we will help you choose the best one suited to your business:
This article was originally written by former AIMultiple industry analyst Bardia Eshghi and reviewed by Cem Dilmegani
1- Invoice issuance case study.
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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