As the Pre-Sales Consultant, you will be responsible for providing troubleshooting assistance for customer orders, account statuses, and relevant problems. The other responsibilities will be as follows:
Compile daily lists of leads and delegate individual leads to sales team associates.
Maintain an organized and accessible file system for administrative and sales professionals.
Generate leads and cold calling potential clients
Partnering with direct sales and executive sponsors to educate existing and prospective customers about the superiority of the company’s services – this includes conference calls,online meetings, site visits, presentations, technical evaluations, technical objection handling, proposals, and follow-up on all customer related questions.
Collaborating with Sales, Marketing and other internal teams to ensure that customers get appropriate updates, upgrades and event-oriented solutions.
Respond to Request For Proposals (RFPs) and Request For Information (RFI) by providing fully-documented solutions with the required technical details.
Review proposals created by Account Executives to provide necessary input and ensure technical accuracy.
Conduct research on business ideas and proposals.
Key skills
Sales Executive
Direct Sales
Sales Support
Customer Experience
Sales Support Executive
Pre-Sales
TCP
DNS
Enterprise Security
Desired Candidate Profile
Relevant industry experience in General Enterprise verticals
Experience in technical pre-sales/sales engineering or similar role delivering technical products or services
Experience with Internet technologies (TCP/IP, DNS, HTTP/HTTPS, etc.), including an understanding of CDN and Mobile technologies
Experience with Internet or Enterprise Security and an understanding of basic security architecture and functional elements including firewalls, DDoS protection, etc
Proven ability to collaborate across all roles (presales, sales, post-sales, engineering) in a global, geographically distributed organization.
Extensive experience solving business problems using quantitative approaches. Comfort. with extracting, manipulating and analyzing complex, high volume, high dimensionality data from varying sources”.
Proficient in analytic tools like SAS, SPSS and R.
Technical competencies:
Proficient in Advanced MS Excel (Pivots, calculated fields, Conditional formatting, charts, dropdown lists etc.), MS PowerPoint and MS Access.
Familiarity with relational databases RDBMS and intermediate level knowledge of SQL, and VBA coding, Excel Macros etc.
Experience working with large data sets.
Behavioral Competencies:
Ability to manage conformity to established procedures & processes.
Comfortable in handling large volume of data.
Ability to work under pressure and have flexible working hours.
Excellent communication skills.
Excellent in driving strong relationships with stakeholders.
Highly results focused.
Strong passion for data driven research for answering hard questions with data. Flexible analytic approach that allows for results at varying levels of precision.
Ability to communicate complex quantitative analysis in a clear, precise, and actionable manner.
Timeline Definition for Ad-hoc Requests.
Responsible for Time Estimation (for completion of request) and Timeline Definition for every approved Request.
Responsible for Execution of Ad-hoc Requests (including data extraction and preparation).
Resolve by self or escalate to the Revenue Management Analytics Lead, any issues which may arise to ensure minimum TAT (turnaround time).
Present data and reports in an easy to ‘read and comprehend’ manner.
Proactively get reports/analysis validated by the Revenue Management Analytics Lead or Zone. Director Analytics (if required) and incorporate feedback provided.
Responsible for sharing of reports/analysis/updated models with the request generator.
Responsibilities:
Suggested new products in to market and finding out the best combination of products lineto maximizing the reach of consumers and market-share by analysis and to fix the price of the products.
Conducted consumer research, in-market studies and retail landscape evaluation to develop predictive models as part of a concentrated effort to improve customer reach and product placement.
Create a model incorporating social media data into customer analytics. Developed a market test to determine pricing elasticity of various product offerings and collaborated with large cross-functional team to roll-out price changes.
Developed Statistical Techniques like Factor, Cluster, Regression analysis and come up with insights.
