The Problem / Opportunity
Sales Development Representatives (SDRs) help companies find and qualify sales leads to generate a sales pipeline for the Account Executives, who then spend their time working with the customer to close a deal. The SDRs are a vital part of the sales process, as they need to 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 take into account 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.
SDR.ai aims to solve this problem by creating emails that mimic what actual SDRs sound like, without the template, taking into account the available data on what works vs. what doesn’t. It will integrate with existing popular CRMs, like Salesforce, to learn from previous email exchanges and aggregate data in one place. Messages can be personalized to the recipient in order to create a more authentic message. Additionally, and most importantly, SDR.ai can send so 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 have to take time differences into account, losing valuable time and creating even longer sales cycles.
Pilot & Prototype
To ensure that we are creating a product customers actually want to use, we plan to pilot SDR.ai after an MVP is created in order to gauge early feedback. On average, compensation for an SDR is high, with a base of $46k and an OTE variable comp of around $72k. We can convince companies to be part of our pilot program by showing how we ultimately can either reduce the need for so many SDRs or bring in additional revenue per SDR.
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.
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