SDR.ai: YOUR SMARTER SALES ASSISTANT
AUGMENTED INTELLIGENCE FINAL PITCH
TEAM CJEMS
I. Executive Summary
Business to business (B2B) sales continue to grow rapidly, but the services available to improve the process and success rate have been slow to keep up. As an intelligence platform that enhances the sales process, SDR.ai is here to change that. SDR.ai helps companies personalize customer messaging and better leverage data, resulting in improved sales results with less spending on internal resources.
II. The Problem and Opportunity
Sales Development Representatives (SDRs) help companies find and qualify sales leads to generate sales pipelines for Account Executives, who work with the customer to close deals. SDRs are a vital part of the sales process, as they weed out people that will not buy to find the ones that likely will, but their work is often repetitive and cyclical. SDRs work with large data sets and follow a clearly defined process, making them ideal candidates to integrate aspects of their jobs with automation. While it is still on the human SDR to understand the pain points of the prospective customer, an opportunity exists to better personalize messaging and make use of the available data to increase the final close rates for sales teams. Current SDR emails already utilize templates, but they do not consider what works and what doesn’t, and while it is possible to analyzing open / click rates of emails, linking this to revenue, or even spending time tweaking emails to add extra personalization, detracts from the time SDRs could spend on the phone with customers.
III. Our Solution
SDR.ai aims to solve this problem by creating emails that mimic what actual SDRs sound like, without the template, considering the available data on what works vs. what doesn’t. It will integrate with existing popular CRMs, including Salesforce and Microsoft’s CRM, to learn from previous email exchanges and aggregate data in one place. Messages can be personalized to the recipient to create a more authentic message. Additionally, and most importantly, SDR.ai can send many more messages, increasing the volume of potential leads and the chances of bringing in additional revenue.
After initial training and manual emails, SDR.ai will continue to build smart responses, with the goal of handling everything up except phone calls, including scheduling and even finding the right person for SDRs to email from a prospective company (by using integrations like LinkedIn and Rapportive). Unlike real employees, SDR.ai is online 24/7, thus making it easier to connect with clients abroad, who normally must take time differences into account, losing valuable time and creating even longer sales cycles.
IV. Target Customer
SDR.ai is ideal for mid-market, high-growth small to medium businesses seeking increased sales and better conversion rates. More specifically, we are targeting B2B software-as-a-service companies (SaaS) that have defined sales processes that include a focus on inside sales. These types of companies are typically much more reliant on inside sales (37% of high-growth companies use inside sales as primary sales strategy). Moreover, many often lack the financial and human capital resources that make them ideal target customers for SDR.ai.
V. Business Model
As a software business, we will rely on a subscription-based business model that will also offer add-on consulting services to customers. On average, compensation for an SDR is high, with a base of $46k and an OTE variable comp of around $72k. Our pricing, therefore, will be based on our ability to reduce overhead spending on SDR’s and our ability to increase revenue per SDR.
VI. Product Development
We will first build a minimum viable product (MVP), focusing on developing initial automatic email creation, Salesforce integration, ability to send a high-volume of messages, and 24/7 availability capabilities. We plan to pilot SDR.ai after an MVP is created to gauge early feedback. To ensure we collect enough data to make the prototype of the product useful and accurate, we plan to partner with software (SaaS) companies that handle a large volume of leads. Given that this product can be tied directly to revenue generation, companies will likely be willing to try the prototype. From here, we could collect data on the most common language used, tied to deals that have been closed historically. By integrating with popular CRMs like Salesforce that already store historical data and emails used, we can determine how many emails on average it takes before deals are progressed from the SDR to the Account Executive. We also can take things a step further by looking at what is useful across different industry verticals, as CRMs already store this type of information.
After the pilot runs its course for a month or so (or whatever the average sales cycle length is), we can review the validation of the emails that were created with SDR.ai compared to those that were not. In short, we can validate that emails were (a) more readily responded to by either picking the right person in the organization (i.e. less emails that pass SDRs from one employee in a prospect client to another) and / or due to shortened response times or (b) opened and responded to by analyzing the language used in each response. The language can be continually refined and tweaked based on #2 until SDR.ai finds the right optimization of length, follow up, and personalization.
VII. Competition
Currently, no direct competitors exist. Instead, there are three categories of potential “competitors.” Existing legacy CRM and sales management software providers do not provide the AI-enhanced capabilities of SDR.ai and will not move into such offerings, as they are focusing on their bread and butter offerings. Companies that offer predictive marketing software are growing, but they currently offer no clear solutions for sales teams. Finally, sales automation solutions providers provide valuable automation tools, but lack the AI-powered learning that SDR.ai would provide companies.
VIII. Risks and Mitigation Strategies
We believe SDR.ai can be an industry catalyst and fundamentally transform sales at large corporations. We recognize, however, that there are a few key risks we must assess and mitigate.
First, CRM providers such as Salesforce could potentially develop their own AI solution and effectively integrate it into their CRM offerings. To successfully mitigate this threat, we propose a two-part plan. First, SDR.ai will immediately file for patent protection in order to prolong our first mover advantage in order to secure as many customers as possible. Second, we will spend the coming years investing in an intuitive UI and integrating with customer databases outside of traditional CRM (e.g., BI tools). This will make customer switching costs higher and help address competitive threats.
Second, as a new platform SDR.ai may face scalability risks. Specifically, customers may find that the platform works well when their sales organization is smaller but that it fails to effectively scale as the database grows. In order to mitigate this, we propose a carefully designed pilot program that chooses rapidly growing companies. This strategy coupled with investment in a talented engineering staff will help address this risk. SDR.ai will not commit to aggressive growth until the pilot program demonstrates that the technology can effectively scale.
VIII. Investment Ask
We’re confident that we have the right team and resources to bring this product to market and grow the business; however, we are seeking $300,000 in seed funding to hire additional engineers to continue to build and refine the SDR.ai offering. While we have the correct business team in place, in order to bring this product to fruition, we need additional engineering resources.
Sources
http://blog.persistiq.com/the-rise-of-sales-development?
https://www.salesforce.com/blog/2014/08/what-is-sales-development-gp.html
https://www.saleshacker.com/day-life-sales-development-rep/
https://resources.datanyze.com/blog/preparing-for-future-without-sdrs