7 Ways Artificial Intelligence in Jiminny is Helping Sales Teams
Artificial intelligence is helping to drive revenue and cost savings across a wide swath of companies and industries, according to the latest State of AI report by McKinsey & Co. The report is based on a global survey that found sales and marketing are among the most popular AI use cases.
Even while we know this is true, the notion of AI can be hard to observe in the wild. For example, sales leaders can’t go out and buy a pound of AI to feed their team. The technology is often embedded in products and we’re not even aware of it.
Our conversation intelligence platform is a good example. AI is a foundational component of Jiminny, but precisely how it helps sales (and other functions, like marketing and support) may not be obvious. This post aims to clarify the subject and help customers better understand the benefits and derive more value from the software.
Intelligence and intelligence
Before we dive into the ways AI helps sales teams in Jiminny, we need to acknowledge that two different definitions of “intelligence” are being used in this post: artificial intelligence and conversation intelligence.
Artificial intelligence (AI) broadly refers to computers or algorithms that have been engineered with the ability to perform human-like tasks. This includes an understanding of semantics and context, which is an acknowledgment of just how far along AI has come.
Conversation intelligence (CI) describes the technology category. CI software like Jiminny correlates and analyses data to paint a picture of conversations sales, support, marketing and other teams are having with customers and prospects. This pinpoints strategic coaching opportunities and effectively forecasts probable outcomes – like deals closing or identifying flight risks.
And now onto those ways, AI is helping sales teams within Jiminny.
1. AI transcribes calls; deep learning summarises them.
Jiminny uses AI to transcribe voice and video calls. This is a crucial starting point for the product because it takes large sets of unstructured data and transforms it into a text format for analysis. AI analyses the text for patterns, trends and actionable insights.
The insights are useful, but sometimes sales leaders want to get a better sense of what was said on a call. The problem is, they don’t have time to read a whole transcript from a 45-minute sales call. That’s where our call summary feature does an amazing job of providing the cliff notes.
Call summary uses a special category of AI called “deep learning” to review the transcript. It analyses the text and determines the key points. In essence, it takes that 45-minute call transcript and gives you just a handful of bullet points you need to understand the call.
2. AI recognises different voices on calls.
Mere transcripts alone are not enough. The AI in Jiminny is smart enough to recognise voices and organise the text in a conversational format. This is crucial both for the AI analysis and also for the human experience with the product.
3. Identifies keywords, themes and moments in communication.
The AI also understands semantics and context, including keywords and themes. Jiminny can identify and graphically illustrate concepts discussed on a call. Typically, the more often the word is used, the larger the graphical representation appears. Some examples of the concepts and themes the software can identify include the following:
- Deal risks;
- Next steps;
- Emotion; and
With this information, the AI, and by extension sales leaders, can identify critical moments in a call. Further, it can even ascertain the stage of a potential deal. This is an important point because it gives business leaders a chance to understand the conversations occurring across the business.
For example, if a company rolls out discounted pricing, that should show up graphically in the dashboard from the ensuing conversations. If it doesn’t, that may be an indication that something is off. Perhaps a team needs more training to get comfortable with the new discounted offer.
4. Surfaces individual customer and prospect engagement metrics.
The AI behind Jiminny can identify an array of individual sales performance metrics such as:
- Question rate – how many questions a rep asks (such as during discovery);
- Talk time ratio – how much a sales rep listens versus talks on a call;
- Patience – the length of time between when a rep stops and starts talking again;
- Longest monologue – the point at which a rep talks for the longest; and
- Talk speed – a numerical measure of words per minute.
These metrics can be eye-opening both for individual sales team members and for coaching. For example, one sales rep we know was surprised to find she talked for 55% of the time on an average call – so she set out to change that and improved her overall performance.
5. Aggregates team customer and prospect engagement metrics.
Jiminny aggregates individual engagement metrics (like those above) and graphically illustrates them across and the entire team. For example, a sales leader can see the average talk time ratio across their direct reports. This provides useful benchmarks for comparing relative performance – and setting goals in sales coaching.
6. Produces team performance insights.
Team insights are quantitative metrics that Jiminny captures over the course of time. Some of the metrics captured include:
- The number of conference calls conducted with customers;
- The number of times the team has dialled an outbound number;
- The number of inbound calls a team has fielded;
- The number of messages sent or received by a team; and
- The number of coaching sessions held and broken out by the three types of coaching.
Such insights mirror traditional metrics sales leaders measure – but the point here is the software is intelligent enough to track this automatically.
7. Generates sales status and deal insights.
Deal insights provide a visualisation of all the activity – between a sales rep and a prospect – around a current deal. It’s a way for sales leaders to understand the health of that deal and determine the probability of it closing.
Like keywords and themes above, it can illustrate these in aggregate across a team, or around a specific deal that’s in play. More specifically the AI will identify what we call “wow moments” from a call, or the “most talked about concepts” such as pricing, features or other themes.
Sales leaders can also configure what we call “nudges” in deal insights. These nudges are like alerts that are triggered when certain words are used or when a threshold is met. For example, if a mention of a certain competitor is deemed an indication of a risk – then a sales leader can configure an alert anytime that competitor is mentioned to nudge them to check on that opportunity.
AI as a foundation of sales performance
The point of Jiminny is to unlock your sales team's potential by analysing all your customer conversations across the company. That analysis provides visibility into sales performance and deal predictability in a way never before possible. Much of that analysis is made possible through AI, which is often silently helping sales in the background.
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