Sales Acceleration Technology Stack

A sales technology stack (sometimes called a sales enablement technology stack) is a collection of software tools and technology used to optimize sales team performance. These tools vary from team to team and can be used at any stage of the sales cycle from lead generation to automating menial tasks.

Sales Acceleration Technology Stack-1

Deciding what kind of tech and tools to use can be a bit overwhelming– there are countless SaaS options– and while every sales manager is keen to streamline processes, support individual reps, and drive revenue, knowing which tools will best help their team isn’t always an easy task. 

It is essential that teams select tools that are right for their team. If your team is already crushing engagement, it might not be necessary to invest in new engagement software, but instead focus on an area that needs shoring up.

Building Your Sales Tech Stack

The first step to building out a sales tech stack is to inspect your sales cycle and take inventory. Most industries follow a similar process, but as you look at the phases specific to your team take care to identify any steps that could be simplified, shortened, or combined with others. Then, ask these questions: 

  • Can any manual processes (data entry, note taking, etc.) be removed or streamlined?
  • Can we improve engagement with leads and prospects at any point in the sales cycle?
  • What can we do to improve prospecting and generate high-quality leads?
  • What can we do to maximize conversions? Where do most sales fall through?
  • What performance indicators are we currently emphasizing? Should we continue this emphasis? What other KPIs need attention? 

If you can simplify or streamline processes, do it. If you can combine two or more tasks into one, do it. Then, start looking for tech solutions to aspects of your sales cycle that need attention.

Sales Tech Stack Starter Kit

Taking the time to inventory your sales cycle and identify areas that can be supported by sales acceleration tech is pivotal. Knowing where your team needs support helps ensure that your sales tech stack includes tools that will specifically support your team. 

That being said, we’ve narrowed the field and identified five types of sales tech that are most commonly utilized as a part of a cohesive sales acceleration strategy and are relevant in most sales sectors:

Lead Database

A lead database is often used in conjunction with lead generation and prospecting tools. A lead database allows ranking of leads, and gathers and organizes data relevant to these leads. A lead database can help teams strategize and develop proactive approaches such as custom pitch decks or outreach approaches. 

Research Tools

Research tools are essential to effective marketing and outreach, and fall in three categories– broad market intelligence, online listening, and direct customer interaction. 

Many tools provide data designed to help teams formulate marketing policies and control market operation. They can even help identify competitors and how they are attracting traffic. Online listening tools gather information about consumers through numerous channels. This information can be used to make content and attract potential buyers. 

Predictive Analytics

Predictive analytic tools are designed to help your team find opportunities to sell more based on individual and market trends. Features of analytic software often include conversion tracking and automated follow-ups, custom deal creation, and sales forecasting.


CRM (customer relationship management) software options usually include email optimization elements that range in complexity. Email software can be used to let reps know when a sent email has been opened, and generate personalized follow-ups. Including scheduling tools in email signatures.

Data Visualization Software

Data visualization software applications render information into graphic form. While Excel graphs can be helpful, there are many additional software options that can make it easier for teams and individuals to quickly and effectively understand massive amounts of data.


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