At Dashbot, our mission has been to decipher language, in order to help businesses deliver magical, authentic customer experiences by unlocking the insights hidden in their conversational dark data. In our day-to-day business interactions, language is not just a tool for communication; it's a reservoir of untapped potential. Every customer conversation holds a wealth of insights, but the sheer volume and complexity of this data can make it seem like an insurmountable challenge to extract them. That's why at Dashbot, we're excited to introduce the Data Slicer, a revolutionary no-code data workspace designed to transform your conversational data into actionable intelligence.
Dimensions: Accelerating business impact with A.I.-extracted insights
Leveraging our near-decade of experience ingesting unstructured conversational data and with the recent technological advancements in the industry such as with Large Language Models (LLMs), we’ve developed the capability to extract many different insights – called dimensions – from your conversations at scale. Imagine tapping into your different customer conversations across chat, social, agent calls, chatbot messages, survey data, etc. and instantly extracting the following insights from the data:
- Predicted CSAT scores
- Product Names
- Tonal Emotions
- Categories + Reasons behind why customers are reaching out
- Predicted Solutions
- Summaries
- And much, much more
By pulling in both our A.I.-generated dimension as well as your own metadata fields (e.g. customer LTV, agent names, location names, etc.) into Data Slicer, you now have infinite ways to “slice-and-dice” (get it?) your data to extract the insights that matter most to you.
We also provide many new ways of visualizing the same data, with a user-friendly UX to segment the data, expand & contract areas in the workspace, drill in & out, and apply filters.
And of course, all of these insights always tie back to the actual conversations associated with them (what we call the “ground truth”) for you to review conveniently and easily in an expandable + collapsable side panel:
Leveraging Dimensions across the rest of the Dashbot Platform
Insights on their own oftentimes aren’t enough to lead to true actionability. So we’ve extended these dimensions in Data Slicer to be accessible throughout the entire platform so that you can not only have infinite customizability of how to visualize these insights, but you can configure hyper-targeted dashboards to drive true actionability across your organization.
By leveraging A.I.-generated insights with our highly configurable reporting capabilities, we wanted to fully empower our users to extract actionable insights that can not only be easily understood by different stakeholders through the organization, but that can lead to quantifiable R.O.I. to improve sales, lower churn, and reduce costs.
Use Cases
1. Expanding VoC Coverage
Most businesses measure customer satisfaction using surveys (such as with tools like Qualtrics or Medallia). The problem with this is that survey response rates are dropping, your customers are prone to survey fatigue, and these surveys are just a snapshot in time (can’t measure CSAT continuously unless the same customer keeps filling out a survey which leads to survey fatigue).
But what if you could leverage your existing “goldmine” of unsolicited data across ALL of your channels (phone calls, chats, social, surveys, etc) in order to give you an overall understanding of customer satisfaction and increase your Voice of Customer coverage to 100% of your conversations? – all without forcing you to hire a data scientist to interpret it all.
We worked with one business that had a 3.8% response rate through their surveys and wanted to get a better understanding of true customer satisfaction utilizing their other data sources. By ingesting their agent call and live chat data into Data Slicer and deriving a Predicting Rating across all of these conversations, they were able to uncover an additional 12.7% or over 100k+ poor experiences happening in these other channels, with actionable insights in how to remediate these experiences.
2. Chatbot + IVR Optimization
Businesses that currently deploy chatbots and/or IVRs understand that it’s a never-ending process to continuously improve the performance based on new + unanticipated use cases that come in. This is the typical process we see from our customers on how to identify + release new use cases for their chatbots & IVRs:
- Identify a trend that wasn’t on the radar (e.g. increase in a certain topic, or similar phrases that are not accurately accounted for)
- Understand context (i.e. examples of what customers are communicating)
- Prioritize which new use case would drive the biggest impact for the business
- Deploy a “fix” (i.e. a new intent or adapting an existing intent)
- Monitor results
The problem is that this process can take up to several days and can involve different teams/team members. With our new Data Slicer capabilities, we’ve seen one of our customers take this process from 2 weeks down to just 5 minutes. In one view, you can quickly get a snapshot into:
- A.I.-extracted reasons that triggered the unhandled intent or led to a dropoff/escalation
- Trending categories of dissatisfaction
- Summaries of longer-form interactions
- Top negative drivers based on our Predicted Ratings
- User journey analysis around all the Reasons that led to dropoffs + escalation
By quickly getting a pulse into what new use case to prioritize, you can export the necessary training phrases to either create a new intent or update an existing one, all within 5 minutes. This speed to insight + action results in the ability to recover customers within a significant pain window, resolve systemic issues before they become widespread, and enable a more proactive service recovery.
3. Financial Impact Scoring
Insights on their own oftentimes aren’t enough to lead to true actionability – they require some type of prioritization metric to prioritize what to take action on first. Quantifying financial impact (churn risk or revenue growth) is the most important metric we’ve seen businesses use to prioritize action planning.
Because Dashbot can ingest any additional sources of data – including financial data – we can tie our A.I.-generated insights to financial metrics such as customer LTV, MRR, AOV, etc. By tying our insights to your financial data, you can quickly quantify impact such as:
- Churn risk based on customers/cohorts with low Predicted Ratings
- Negative Drivers of dissatisfaction and how much churn risk is associated with each
- Upsell opportunities based on new products or services being mentioned
- Financial performance by branch/location based on positive & negative drivers
- Financial risk by cohort tracked over time
For one business, Dashbot helped ingest their customer LTV & cohort data to easily quantify financial risk associated with their CX data. Immediately, Dashbot was able to surface $9.3M in potential churn risk, as well as all of the supporting data & recommendations to take action to prevent it.
Looking Ahead
As we stand on the brink of a new era in customer experience, we believe our Data Slicer (and more to come) will emerge as a pivotal tool in the evolution of conversational analytics. Our tool not only shines a light on hidden insights but also quantifies their impact, empowering businesses to make informed decisions swiftly. It's a leap toward understanding customer needs at a granular level, and with Data Slicer's A.I. capabilities, the voice of the customer is now a clear guide to strategic action. Join us in this transformative journey where your data's untapped potential becomes your strongest asset in driving success and innovation.
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