SleekFlow Product Requests

We currently have a VoIP function, but users can only view individual call history logs and basic post-call details manually per interaction. There is no centralised performance dashboard or analytics view to track overall team efficiency, call volumes, missed call rates, or agent-level activity. This leaves teams without visibility into operational bottlenecks or service quality metrics. We suggest developing a dedicated VoIP Performance Dashboard that allows teams to view real-time and historical call metrics, track agent-level efficiency, and review complete session logs from start to finish. This will replace manual tracking, helping businesses reliably measure service quality, ensure SLA compliance, and optimise their overall customer communication workflows.

Users can analyze conversion data over a longer date range and benefit from a more user-friendly analytics experience.

Improve lead analytics so that new conversations from existing contacts are not automatically counted as new leads. The goal is to distinguish between a genuinely new lead and an existing contact who is simply re-engaging or continuing their customer journey. For example, if a contact is classified as an MQL on August 5 and starts another conversation on August 13, the August 13 conversation should not automatically be counted as another new MQL. Instead, it should be recognized as activity from an existing lead/contact. The analytics should ideally provide: Unique lead tracking: Count the number of unique contacts who became leads, rather than treating every new conversation as a separate lead. New vs. returning contacts: Clearly differentiate between a first-time inquiry and an existing contact who starts a new conversation. Configurable lead timeframe: Allow businesses to define a period during which a returning contact should still be considered the same lead. For example: 7 days: A contact re-engaging within one week is not counted as a new lead. 30 days: A contact re-engaging within 30 days is not counted as a new lead. Ideally, this timeframe should be configurable based on the business's lead cycle. Accurate lifecycle tracking: Maintain the lead's existing stage, such as Unqualified → MQL → SQL → Sold, instead of creating a new lead every time they start a conversation. Separate conversations from leads: Analytics should clearly show the difference between conversation volume and the actual number of new leads generated. Re-engagement tracking: Returning contacts should still be visible in analytics as re-engaged or active leads, without inflating the new-lead count. Accurate conversion reporting: Conversion rates should be calculated based on unique leads, making it easier to understand how many leads actually progress from MQL → SQL → Sold. Flexible reporting: Allow users to view both unique leads and total conversations so they can understand overall activity without confusing the two metrics. This would provide a much more accurate view of lead acquisition, lead quality, and conversion performance, while still allowing teams to understand how frequently existing contacts are re-engaging. The key requirement is that the timeframe should be configurable (e.g., 7 days, 30 days, or another period) so businesses can define what qualifies as a genuinely new lead based on their own sales cycle.

Enhance Premium Reports by making analytics dashboards more customizable and easier to share with different team members. The analytics should ideally: Allow users to customize dashboards using drag-and-drop, including adding, removing, and rearranging metrics, charts, and widgets. Allow users to apply and save different filters and views for each report. Provide an option to share reports directly via email, allowing users to send the report or analytics summary to specific recipients. Allow users to choose which report or dashboard they want to share and, where applicable, the specific filtered view. Allow different team members to have access to specific reports or filtered dashboards based on their role or needs. Support appropriate access levels, such as view-only or editing access. Make it easy for teams to create separate dashboards for areas such as sales, marketing, support, and lead performance. This would make Premium Reports more flexible for building personalized dashboards, while also making it easier to distribute and share insights with stakeholders directly via email.

We'd like the dashboard to provide visibility into our entire sales funnel, specifically: Enquiry → Appointment Conversion Rate Appointment → Sales Conversion Rate The data should also be filterable and segmented by: Product category Time period (Month, Quarter, Year-to-Date, Custom Date Range) Rather than limiting the dashboard to the last 30 days or reporting by individual months only, we'd like to be able to view Year-to-Date (YTD) performance. This is because many customer journeys span multiple months. For example, if a customer enquires in January, books an appointment in February, and makes a purchase in March, the dashboard should still attribute and update the conversion across the entire journey, rather than excluding it because the stages occurred in different calendar months. The dashboard should therefore continuously update as customers progress through each stage of the sales funnel, instead of treating each month as a closed reporting period. This allows conversion metrics to remain accurate even when deals are completed across different months.

This will help us target the right persons for our campaigns.

Currently, management and admins have to manually log in and download CSVs to get daily reporting. It would save a significant amount of time if we could configure automated, daily exports of conversation and sales analytics to be sent directly to designated email addresses on a scheduled basis.

Feature Request Description: This request comes from travel and agency-based businesses that handle a high volume of daily inquiries, often from the same customer multiple times within a single day. To maintain service quality and optimize operations, they require more detailed visibility into agent performance and conversation handling. Currently, the exported Conversation Analytics data does not provide sufficient granularity for accurate performance tracking. Critical information such as session-based data (from open to closed conversations), agent/participant names, precise timestamps, and customer identifiers is either missing or not structured for effective analysis. As a result, businesses are unable to reliably measure key metrics, including: First response time Resolution time SLA compliance per interaction Agent-level activity and performance We propose enhancing the Conversation Analytics export to include: Session-based records (each conversation lifecycle from open to closed) Agent/participant attribution for every interaction Detailed timestamps for key events (e.g., first response, last reply, closure) Customer identifiers such as phone number or room/chat ID to distinguish repeated inquiries from the same user Structured, analysis-ready data format for reporting With these improvements, customers will be able to accurately track individual interactions—even multiple inquiries from the same customer within a day—and efficiently evaluate agent performance. This will enable more reliable daily reporting, better SLA monitoring, and overall improvement in service quality using a single exported file.

Users can track messages that were read but did not receive a response, helping identify missed follow-ups and response gaps.

Users can monitor whether response time targets are being met, helping managers track service level performance more effectively.