Lavender is an email intelligence company designed to provide an AI-powered sales email coach to help teams humanize the sales process and write better emails faster. Lavender aims to empower reps with email scoring, data-driven personalization, writing assistance, template ranking and aggregated coaching insights and analytics, helping sellers around the world get more replies faster.
The Lowdown
Lavender’s AI-powered sales email coaching platform is designed to blend deep learning and communication psychology to help sales reps 2X-3X their cold email reply rates. It does this by providing real-time suggestions to optimize the email — such as making sentences shorter, balancing the ratio of I’s and you’s and mobile friendliness — so that reps can focus on building relationships and selling products.
Lavender users can write fully personalized emails in less than five minutes, instead of the 15 minutes it would otherwise take — and they average a 20.5% reply rate, according to the company.
Back-Of-The-Box Details
Lavender aims to provide an in-inbox email writing coach, research and personalization tools and a coaching dashboard, offering the following capabilities:
Email Scoring
Lavender assesses and scores emails in real time based on their likelihood of eliciting a response. This guides users in enhancing their email communication and raises the probability of a successful sales conversation.
Data-Driven Personalization
Lavender’s AI extracts data on the recipient and their company to generate personalized email content. This allows users to connect with recipients on a personal level and increases the relevance and impact of their outreach.
Writing Assistance
Lavender’s AI identifies specific moments in an email where a user can be hurting their chance of a reply. In those moments, Lavender offers intelligent writing suggestions, helping users craft clearer, more persuasive messages based on data. Each suggestion includes an explanation, so the user can make them their own if they want or they can use the suggestion as given.
Aggregated Coaching Insights and Analytics
Unlike traditional sales enablement platforms, Lavender offers a unique, highly granular view of sales email performance. This capability not only provides a comprehensive understanding of email performance but also helps pinpoint precise areas for improvement.
Template Ranking
Lavender ranks templates according to their performance, assisting users in identifying the most effective strategies. This provides a critical advantage in selecting the most successful email formats and content, and in understanding scenarios where particular types of email messaging have success.
Lavender integrates natively with Gmail, HubSpot, Outlook, Outreach and Salesloft, and it offers integrations for Google Docs, LinkedIn and more through Lavender Anywhere.
Who It’s For
The primary Lavender users are sales professionals, including sales representatives, sales managers and sales enablement specialists.
Lavender technology isn’t just helping salespeople, however — Lavender was offered for free to job seekers during the pandemic, and it still is today. Lavender’s email coaching has helped countless job seekers improve their outreach and more quickly land a new job.
What It Solves
The average email reply rate is 1%-2%, which burns 99% of leads. But sales management often has no visibility into why email isn’t working. Lavender is built to increase reply rates by coaching salespeople and helping them write more effective emails faster. It blends content intelligence with performance data, so managers finally know how to help.
What Makes It Special
Lavender is trained on nearly 200 million sales emails (and growing) and can measure and understand, down to the individual level, what is driving email performance.
Lavender provides specific recommendations that reps can review and resolve in real time as they draft their email and coaches them to be better writers. Lavender can draft emails in various tones and styles using just a few bullet points, and the platform continuously improves its suggestions as reps and teams use it more often.
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