The end-to-end process of responding to RFPs can be tedious and painstakingly time-consuming to search for answers. A research by Loopio on RFP Response Management & Benchmarks shows how sales teams invest an average of 40-61 hours into every RFP response.
This calls for RFP automation since time is a commodity in sales and must be used to maximize productivity.
In this handy guide, we look at why and how RFP automation transforms your sales cycle and saves valuable resources.
RFP automation is the use of advanced technology like AI, ML, and NLP to automate the creation, management, and evaluation of an RFP (Request for Proposal).
By using such automation technologies, businesses can reduce the need for human intervention, leave no room for errors, and lower the costs and time associated with the entire process.
RFP automation can help you:
Here are a few of the most recurring challenges and pain points B2B companies face when completing RFPs:
The number of administrative tasks in a manual RFP process can be exhausting and laborious. Sales teams can have hundreds of proposals, increasing the number of emails, follow-ups, spreadsheets and documents.
Impact:
Teams may miss deadlines when overburdened with manual tasks such as searching for answers, creating follow-up responses, and customizing templates.
Hurried work to meet deadlines could majorly impact the quality of the information provided and compromise the integrity of responses.
Respondents may struggle to verify and validate responses, especially when dealing with heaps of documents and outdated information.
Impact:
Inaccurate responses can damage the client's relationship with the organization. Correcting data across your knowledge sources manually could take a significant amount of time and further delay timelines.
Each customer may need tailored responses that suit their specific requirements. This could add more complexity as it demands the responder to have a deeper understanding before providing each response.
Impact:
The need for customized responses increases the already heavy workload and could slow down responses. Response quality may also suffer if your team deals with multiple RFP responses.
Data is often spread across multiple data sources, making it challenging for the team to consolidate and find accurate responses.
Impact:
Disorganized information leads to inaccuracies and inconsistencies in responses and can also multiply the time taken by the team to accumulate answers.
Our research suggests that almost 80% of questions in RFPs are repetitive and often rephrased or provided with a different context.
Impact:
Such repetition can only increase the team’s response time, as they must now ensure their responses stay consistent while navigating large amounts of knowledge sources single handedly.
The traditional RFP process involves a ton of manual and repetitive tasks. So, there are substantial automation capabilities in an RFP process, which we’ll discuss below:
Conventional methods involve manually sending stakeholders emails and multiple messages and working tirelessly to gather all information in one place. However, using an RFP automation solution, you can centralize key initial research data and obtain feedback for future RFP responses.
Your team may spend hours copying and pasting old RFPs in an attempt to respond to stakeholders quickly.
AI automation tools, on the other hand, can store and manage RFP templates and boilerplate content, categorize questions, and more to save time.
Since the main goal of most automation tools is to reduce the amount of manual work, they ensure that content is added and organized into a central library for quick retrieval.
For example, AI tools like Docket use advanced Natural Language Processing (NPL) to understand the context of questions and automatically retrieve the correct information from the central library.
Most automation solutions come with a dashboard where you can access insights related to your RFP, such as who is responding to what, average response times, and team productivity rates.
Some tools also offer AI-based recommendations for improving future responses and boosting win rates.
Some tools have automated formatting features that can auto-format responses and ensure a uniform structure that aligns with the business guidelines.
Weighted scoring in RFP automation involves scoring each question or response based on specific predefined criteria to highlight its priority.
This system eases the RFP evaluation process. You can now compare multiple submissions and highlight which proposal accurately meets the client’s demands.
Unlike SMB sales, B2B enterprise sales are more prone to complex RFPs. So here are a few benefits that highlight the importance of RFP automation in B2B enterprise sales:
B2B sales teams must address various components, such as onboarding, security, support, and other technical requirements, and require input from various SMEs.
However, applying RFP automation to routine RFP activities lets teams and managers feel an immediate impact as they now have more time to focus on high-value tasks rather than mundane administrative activities.
Inefficient manual RFP processes are mostly due to siloed knowledge that increases the risk of sending inconsistent, non-compliant responses.
RFP automation in this scenario works best as it introduces centralized knowledge and enables a collaborative environment where the sales team, SMEs, and other RFP contributors work side by side on client requirements, questions, templates, and more.
Automated processes gather data to allow B2B sales to quantify the actual ROI of using an RFP automation solution. Additionally, capturing data helps teams optimize costs, improve process efficiencies, and identify several bottlenecks.
For example, capturing and analyzing sales activity data can uncover inefficiencies in the RFP process, such as excessive time spent reviewing large volumes of text or navigating disorganized data.
Effective data management is crucial in this context, as it directly impacts the team's ability to efficiently track, organize, and access critical information. Without a robust data management system, the RFP process becomes bogged down, leading to delays and potential missed opportunities. Prioritizing data management streamlines the process, enabling the team to focus on delivering high-quality, competitive proposals.
A productive and motivated sales team plays a significant role in bringing in new deals through RFPs. That’s because automation allows them more time to respond to additional RFPs and increases their win potential.
Here’s how Docket AI - An AI sales engineer teammate makes RFP automation possible:
On-time, accurate information retrieval is a major challenge B2B enterprise sales teams face. Docket tackles this in two ways:
Docket AI integrates with your favorite knowledge sources like Google Drive, Slack, PDFs, internal databases, and sales enablement platforms like Highspot and Showpad. This wide integration allows the tool to centralize all relevant information and documents needed for timely RFP responses.
Docket AI ensures continuous data synchronization so the RFP responses are appended with only the latest information, thus minimizing the risk of communicating outdated or incorrect details. For example, product pricing details are prone to frequent changes and must be in sync with the external tools you use.
Here’s how Docket enables automatic information retrieval for timely RFP completion:
Docket AI uses advanced NLP to understand the context and retrieve the most relevant information from the centralized knowledge base. This ensures the team quickly finds accurate answers to specific questions in an RFP.
Users can simply tag Docket in platforms like Slack to instantly pull verified and detailed information, significantly reducing the time spent looking for answers.
Templates make RFP completion faster and easier. Here’s how Docket helps manage such content:
Docket AI stores and manages templates and boilerplate content that’s frequently used in RFP completion. This allows users to quickly copy-paste standard responses and ensure consistency.
Docket can analyze the previously used content and suggest the templates that best fit the RFP requirements, streamlining and shortening the whole response process.
Two ways Docket generates accurate RFP answers on auto-pilot:
With machine learning (ML), Docket generates draft responses to RFP questions based on historical data, such as previously approved answers and other sales content.
Docket AI learns from historically verified and customer-shared RFPs, continuously improving its responses based on these past submissions.
SMEs can vet important or technical responses to ensure accuracy and reliability. This step helps maintain quality standards of critical answers.
Teams can collaborate within the platform, add comments, and make adjustments before the final RFP submission.
RFP automation is one of the most profitable AI use cases in sales. That’s because you can constantly access up-to-date centralized data, save and re-invest time into creating better content, etc., while responding to more RFPs and cutting down the sales cycle.
Avoid performing lengthy searches and doing extra research using Docket AI.
