Sales Analytics Software 2024: Complete Guide

Sales analytics usually comes under the purview of sales operations. Sales Analytics can be descriptive, diagnostic, predictive, and prescriptive. Common sales metrics tracked include sales growth, sales target, effective channels, opportunities, competitive trends, lead conversion, etc.

Good Sales Analytics strategy helps sales guys with required clarity and keeps them focussed on activities that matter to improve performance.

What is Sales Analytics Software?

Who uses Sales Analytics software?

Sales analytics software is mainly used by the sales operations team. They can also be used by the finance, marketing, and central analytics teams.

Sales Operations / Managers

Sales operations teams are mainly concerned with data completeness, data accuracy, and forecast accuracy. This software saves them a lot of time by automating the task of data completeness and maintaining CRM hygiene. It also reduces the forecast prep time, identifies deal risks, and helps automate reporting from a single source of truth and ultimately improves productivity. Instead of focusing on day to day tactics, equipped with the monthly and quarterly forecasts, they can plan better about future sales. It helps to bring all GTM teams on the same page to collectively convert accounts. They can analyze the pipeline by segment, sales rep, stage, or any custom parameter.

Marketing

Not all the data gets entered into the CRM, but this software brings together all the data across teams. Thus it brings marketing, sales, and customer success teams to collaborate together to win deals. It shows the status and risk associated with each deal so that marketing can plan ABM programs accordingly. It allows marketing teams to check their total influence on sales by channel using multi-touch attribution.

Finance

sales analytics software gives in-depth visibility into the key revenue metrics for the CFO. It identifies the deal risk at every stage to avoid quarter-end surprises, which helps finance teams to predict revenues more accurately.

Increase in Revenue

Sales Analytics software helps you forecast your weekly, monthly, and quarterly revenues. They reveal gaps in sales strategy and tactics. Hence you can use these forecasts to plan better i.e. which deals to prioritize, what activities to do for each deal etc. They help sales teams to evolve the approach to hit quota and ultimately increase revenue.

Improve Sales Performance

These tools automate capturing all the sales activities and auto-update the CRM. They also use this data to generate revenue forecasts. All the manual heavy lifting is done by this software which frees up time for sales reps to focus on selling. They also provide leaderboards which keep the motivation of the team-high. More selling time and high motivation directly translate to improved performance.

Improve Sales Process

These tools give direct visibility into the sales pipeline and rep activity. They help track if each sales rep is following the playbook and thus standardize the sales process.

Improve Sales Planning

Having a good forecast means managers know what they can expect in the future in terms of revenue. Understanding what it means for accounts, geographies, product lines, etc can help plan things accordingly. If high growth is expected, hiring and marketing plans can be altered to capitalize on the growth. If a slump is forecasted, managing budgets and revenue expectations becomes extremely critical.

Improve Sales Performance

Forecasts give visibility into future revenues which helps alter sales planning accordingly. On the ground, it directly translates to what the sales rep has to do to hit/exceed quota. Tactically, incentives, and promotions can be designed accordingly to enhance sales performance. Leaders can rally and motivate the sales team to meet targets even if the forecasts look weak.

Sales Forecasting

The biggest advantage of this software is to make the sales forecasting process more accurate and scientific. They do so by removing the biggest hurdles to accurate forecasting which is complete and accurate data. These tools auto-capture all this data from various sources and ensure CRM completeness. They improve the accuracy of forecasts with deal health scores, up-to-minute opportunity data, flexible forecasting (This software can be configured to use different / custom models to forecast), and parallel forecasting. They also standardize reports with win-rate reports, cycle time reports, activity by rep reports, etc. Finally, some of this software uses predictive forecasting that leverages AI and machine learning to analyze historical data to improve pipeline predictability.

Deal Management

Sales Analytics software helps identify all stakeholders including sales reps, buyers, influencers, supporters, detractors, decision-makers, etc, and their sentiment (using AI) around the deal. Using historical and interaction data, they score deals based on desired parameters and define deal risk scores that indicate chances of the deal falling through. They help create a relationship map around a deal and help to make a strategy to leverage supporters and assuage detractors to increase win rates. They also have features to visualize pipelines by stages critical to your sales process.

Sales Activity Management

The biggest pain point of any sales manager is CRM data completeness. These software attack that precise problem by automatically capturing the activities from email, meetings, notes, calls, SMS, and other channels and auto-updating the CRM. Sales activity data gives insights into where the salesperson is spending his time, which deals are being prioritized, rep-deal engagement, etc. Managers now have complete visibility of the pipeline activity which can be leveraged to relook at sales strategy and tactics at a deal level.

Some software uses AI to recommend the next steps for the sales rep to follow the sales process.

Top KPI that Sales Analytics Software impacts

• Sales Growth

• Sales Target

• Sales Funnel

• Pipeline Velocity

• Average contract value

Auto-capture and Auto-update CRM

This software can integrate with your mail, SMS, phone, etc, and AI can auto-capture sales activities from all your touchpoints with your customers and auto-update the CRM. This keeps the CRM completion rate high.

Predictive Forecasting

Predictive reporting uses AI and machine learning to analyze historical data to accurately predict future sales. The most advanced systems use time-series databases that can record data at scale any time it’s updated, tracking change through time, then applying AI and machine learning processes to derive insights.

Deal Scoring

The AI uses lots of data points about sales activities (like time spent on each deal, customer historical conversion rate, engagement touchpoints, etc) and historical trends to score deals in terms of likelihood to convert. Some software goes further to recommend the next steps to improve the deal health scores.

Deal Level Recommendations

AI can be used to determine deal level recommendations for the next steps to be taken by sales guys to improve deal conversation rates.

Other Features of Sales Analytics Software

Data visualization

This is one of the most powerful features of analytics software. Managers can visualize sales KPIs and metrics in any way they want. They can create multiple dashboards for multiple teams and have custom metrics tailored for that team's requirements. Top capabilities include a wide range of charts, graphs, scorecards, dashboards, real-time updates, segmentation, data exporting, etc . Reporting can be customized and collaborative. Some tools even allow predictive analytics by analyzing current and historical trends.

The software differentiates themselves mainly on technology (how well they use AI).

Different software uses AI differently to deeply integrate with your existing systems (email, SMS, calls, etc) to capture data sales activity accurately and make a bottom-up accurate forecast, thus eliminating manual process in forecasting. AI is leveraged across the sales process to identify time-wasters and automate them improving sales productivity

Some software standout in their data visualization capabilities. They have a lot more options in terms of dashboards, charts, graphs and flexibility in which they can combine data and visualize it. This can be really powerful to do detailed analytics and structure a narrative around sales activities.

Size of the Organization

Business Requirements

Sales teams facing some of the following problems should consider buying sales analytics tools

• Too much time spent in capturing sales activities data

• Incomplete CRM data

• A lot of manual work in getting sales forecasts done

• No transparency/consistency in sales forecasting across sales teams

• No single source of truth for data used to make the forecast

• Poor macro of the pipeline activity

• A lot of quarterly business review QBR questions are answered on gut feel and not data

• Lack of transparency

Horizontal Solutions vs Point Products

Pointed solutions include software that is meant only for sales analytics purposes like Clari, Topopps, Looker, etc. While horizontal solutions include software that offers sales analytics capabilities as an additional feature while it’s mainly designed for other use cases like CRM are mainly designed for pipeline management but also offers sales analytics capabilities.

As a company, if you already have a CRM with a large sales fleet and are looking for more specialized needs around analytics (like better forecast accuracy, custom forecasting models, auto-capture/update CRM data, etc), it is recommended to buy analytics software.

Integration

To make full use of sales analytics software, the entire organization data should easily flow into the tool in the desired format. This could mean changes in the way data is currently handled, readying it for the tool or generating new data for the tool. This also implies that existing systems need to be integrated with the tool and it can be time-consuming. Making the sales analytics tool successful is an enterprise-wide commitment. Make sure you are ready for that.

Challenges in Adopting Sales Analytics Software

The biggest challenge with sales analytics solutions is that it is hard to establish an ROI on the purchase. It is hard to estimate how much time and (manual) effort it saved for sales guys, and how much of that got translated to selling activities and ultimately revenue.

Other main concerns users pointed out were around data security and privacy as permissions and access control were not complete.

Some of these analytics solutions integrate with your engagement, enablement and commission tools. Depending on all the integrations and customizations, time to value can range from a month to three months.