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Featured in Times of India: How AI + Human Expertise is Revolutionizing Telecalling
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Stay on top of your leads with system-driven management & omnichannel workflows
Once leads enter our system through customer CRMs or manual imports, they go through a checker system. This ensures we adhere to calling regulations. We check for duplicates and DND numbers to ensure no one gets spammed.
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We have a versatile prioritization system that divides leads based on data like city, product interest, age, profession, etc. We also employ Last in First out (LIFO) and First in First out (FIFO) algorithms to effectively maintain the desired turnaround time.
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Once prioritised, leads flow into our dynamic workflow system, which can configure and customise different touch points, like calling, emails, SMS, WhatsApp, IVR, etc. Based on the response from an outreach channel, the lead progresses to subsequent touch points within the workflow.
Our system removes spam numbers to keep the database clean and A/B tests to figure out the best day, time, and channel for reach outs. This ensures our connectivity levels are always within a threshold. If it does falter, our engine automatically performs checks to detect and fix discrepancies immediately.
Our matching engine is a bridge between the telecaller skills a campaign requires and our available pool of agents. This ensures that only the agents within the defined skill range are assigned to the specific task, optimizing the chances of success.
SquadStack ensures that none of your leads slip through the cracks. They are nudged through the funnel with our customisable omnichannel outreach system — Calling, emails, SMS, WhatsApp, etc. We also closely track stage-by-stage conversion to identify improvements. Hence, our lead management system gets a real-time update if there is any drop during the consumer journey.
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Our system actively checks and weeds out agents that engage in fraudulent activities such as misselling, prolonging calls, or providing false qualifications. We then take appropriate action based on severity and issue warnings, partial bans, or permanent removals.
