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

 

 

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.

The Solution

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.

Data Collection

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.

Validation

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.

Team Members

Marjorie Chelius

Cristina Costa

Emma Nagel

Sean Neil

Jay Sathe

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

 

Problem

The hiring process for new labor has a well-deserved reputation for being inconsistent, time-consuming, and non-democratic. Although some companies have made headway into solving this problem with new technological platforms (e.g., Aspiring Minds) they tend to focus primarily on high-skilled labor. As a result, hiring for high-skilled labor has become more seamless and streamlined. In stark contrast, however, there are nearly 12.3 million community college students in the United States who struggle to find employment. Community college graduates are more likely to seek what is known as “middle-skilled labor” and tend to fill roles that require specific skills and certifications, but typically not a four-year degree (e.g., HVAC technician). At the same time, paradoxically, there are more than 3 million unfilled middle-skilled jobs that these community college graduates could ostensibly fill. The inefficiency in this labor market is caused by a misalignment between job-seeking community college students and prospective employers. We believe that there is no effective channel or signaling mechanism community college students can use to convey their skills to prospective employers. A solution that can bridge the gap between students and employers would be a critical step toward optimizing middle-skilled employment and improving the economic fortunes of community college graduates.

Proposed Solution

We propose an augmented intelligence solution that would sit as a platform between prospective employers and community college students. Traditionally, applicants submit a non-standardized resume that they believe highlights their abilities. Prospective employers then try to match applicant resumes against a nebulous list of criteria they believe are important for the position they are trying to fill. The resulting inefficiency not only means that jobs go unfilled, but it also means that the wrong person may be hired for the wrong role.

In our augmented intelligence solution, we propose that employers use our platform to submit a standard, text-heavy job description. Our platform will translate these job descriptions into simplified skill lists based on a database of similar roles and demonstrated qualifications. This process will require iteration with employers at the outset in order to create a sufficiently large database that can be truly predictive. As these lists are completed, they will be published via the platform as simplified job descriptions reflected in terms of the core skills they require.

On the supply side, community college students create profiles via our platform and fill out all courses they have taken and all relevant work experience. The platform will use a similar database in order to translate disparate coursework and professional work into a list of skills. Students will then be able to access employer job postings and immediately see how well their skills align with employer requirements. Once they apply, employers will not only have access to our platform’s curated list of applicant skills but also traditional application components (e.g., resumes).

We believe that streamlining both sides of this market will enable more efficient hiring and will help close some of the gaps we observe in the middle-skills job market.

Solution Development and Validation

Data Collection

At the outset, we would collect data from prospective employers on some of their commonly filled roles and consult with them to identify the core skills they believe the roles require. On the supply side, we will work with community colleges to translate typical courses into skills (i.e., accounting classes demand a different skill set than a history course).

Model Development / Validation

As the database grows sufficiently large we will begin by conducting a number of small pilots at individual community colleges. Testing the platform in small environments will help us not only validate the efficacy of the model, but will also enable us to adjust the underlying data set in order to make the platform more predictive.

As the model matures, we will expand the pilots to include regions (e.g., Chicagoland) in order to see how combining data from disparate sources changes our model. We ultimately will need to adjust the underlying data set in order to account for differences in how employers articulate skillsets across different parts of the country. A more comprehensive set of pilots will enable us to understand how the data interplays and make these adjustments as necessary

Value Proposition

After sufficient testing of the model, we will provide a beta version for a target list of businesses and students to test.

We have defined a number of benchmarks for both employers and students that can help demonstrate our platform’s value. For employers, tracking average time to hire, average tenure of new hires, and management satisfaction ratings can help validate our platform’s effect relative to the base case.

For students, tracking average number of interviews completed, average time to hire, and post-hire satisfaction can help convey our platform’s value.