To drive actionable insights and optimized operations, you need that data consolidated, timely, and structured across meaningful timeframes. That’s exactly what Oxlo’s ASAP data aggregation solution delivers. By pulling together customer satisfaction metrics and performance indicators across multiple dealerships, brands, and survey systems, Oxlo enables dealer groups to make decisions grounded in real, current feedback.
One of the key strengths of Oxlo’s approach is how it handles timeframes. In this blog, we’ll walk through the types of data Oxlo gathers, then explore the timeframes it supports and why those distinctions matter.
Data We Collect
Oxlo’s data gathering solution focuses on collecting and aggregating a set of key customer experience and performance metrics across dealerships and brands. Some of the metrics include:
- Customer Satisfaction Index (CSI)
- Net Promoter Score (NPS)
- Key Performance Indicator (KPI) metrics
- Overall Satisfaction
- Customer Satisfaction Effort (CSE)
- Dealer Recommendation
- Composite Scores (blends of multiple metrics)
- Sales Effectiveness Index (SEI)
- Dealer Satisfaction
- Blended Top-Box Metrics
- Dealer Retention
- Sales Effectiveness
- Service Retention
These metrics typically come via surveys and feedback systems within each dealership, after-sales surveys, service feedback and sales experience feedback. Oxlo exports these data points from websites and aggregates them into a CSV file so dealer groups can easily review the data.
Data Aggregation Timeframes
Year to Date (YTD) Timeframe
The Year to Date timeframe starts from the beginning of the calendar or fiscal year up until the current date. For example, if today is October 1, the YTD period would span from January 1 through October 1.
What It Shows / Use Cases
- Baseline performance: Gives leaders a picture of how the group or an individual dealership has done over the course of the year.
- Progress toward annual goals: Compare YTD metrics to annual targets or prior-year benchmarks.
- Trend smoothing: YTD helps smooth out short-term volatility. For example, a bad week in February won’t overly distort the picture.
- Comparisons across years: YTD metrics are often compared year-over-year to gauge growth or decline.
Previous 3-Month Timeframe
This covers the most recent rolling 3 months (e.g. August, September, October). It’s a relatively short-term but still smoothed view.
What It Shows / Use Cases
- Near-term momentum: Is performance improving or declining over the last quarter?
- Lagging indicators: This timeframe begins to show early signs of significant changes (ex: customer sentiment is trending downward).
- Operational adjustments: Helps catch shifts quicker than YTD and allows mid-course corrections (staffing, training, process changes).
- Dealership comparisons: Identify which stores are improving, flat, or declining in the near term.
Current Month Timeframe
This is the most recent full month’s worth of data.
What It Shows / Use Cases
- Freshest snapshot: Gives the most up-to-date view of how things are going.
- Short-term shifts: Helps spot immediate issues or breakthroughs (ex: a dip in satisfaction after a new process was rolled out).
- Actionable insight: Because it is narrow, you can drill into what went right or wrong (which teams, which service areas, which periods) and respond quickly.
- Operational feedback loop: Adapt more nimbly month to month in response to customer feedback.
Previous Month Timeframe
This is essentially the same as the 1-month timeframe as described above (the month before the current month). Conceptually, it’s often used differently as a reference point to compare against the current month or 3-month trends.
What It Shows / Use Cases
- Baseline for comparison: Use previous month metrics as the basis for evaluating month-over-month change.
- Trend shift signals: If the current month is outperforming or underperforming compared to the previous month, that may signal a change in trajectory.
- Operational context: Helps in preparing monthly reports. For example, last month, this metric was X; this month, it’s changed by Y%.
Current Month Timeframe
This is the running month in progress (Ex: October, from the 1st of the month up to today). It’s not a complete month yet, but a work in progress snapshot.
What It Shows / Use Cases
- Real-time or near real-time monitoring: Gives leadership a live pulse of how satisfaction, performance, or feedback is trending so far in the current month.
- Early-warning signals: If the early data shows a deterioration in key metrics, leadership can intervene mid-month.
- Mid-month course corrections: For example, if a change in process or staffing is having an early negative impact, adjustments can still be made this month.
- Comparing to the same period prior months: For example, comparing the first week of October this year to the first week of October last year (or prior months) to identify shifts.
The Importance of Collecting Data Across Multiple Timeframes
Oxlo’s ASAP solution empowers dealer groups to transform siloed, manual survey data into cohesive, actionable intelligence. By supporting multiple timeframes, Year to Date, rolling 3-month, monthly (previous and current), Oxlo ensures each dealer group has the data in the timeframe they are looking for. We provide options because each dealership and dealer group review data at different time periods.Talk to our team at Oxlo to learn more about how we can aggregate data in the timeframe you’re looking for.