According to HBR, a proactive, prescriptive approach to B2B sales can increase purchase ease by 86%. However, that’s not the only type of sales analytics. There are four types of and while they overlap in some areas, they’re not synonymous:
Descriptive: Analyze historical data to draw conclusions about your sales performance. About 80% of business analytics is descriptive in nature.
Diagnostic: Understand the reasons behind past sales performance by drilling down sales data using data mining techniques.
Predictive: Use statistics, data mining and machine learning (ML) to understand recent data, find patterns and make predictions. Gartner defines it as an approach to data mining that’s not descriptive and takes only hours or days (as opposed to months) to provide outcomes.
Prescriptive: Recommend the best courses of action after analyzing recent sales data. It’s a type of predictive analytics. Dr. Micheal Wu, one of the world’s top authorities on AI and ML, highlights that prescriptive analytics requires a predictive model with actionable data and a feedback system to track the outcomes of the recommendations.
Why is sales analytics important?
While there are numerous benefits to sales analytics, let’s look at the four most prominent ones.
Using lead scoring algorithms, organizations can find the right leads at the right time and estimate their potential. These algorithms tap into historical sales data with external data sets at a granular level to paint a complete picture of each customer. As a result, sales teams can spot and prioritize the best deals as early as possible in the sales process.
According to McKinsey, some companies are experimenting with AI-enabled agents combining predictive analytics and natural-language processing to automate lead generation by handling basic customer questions and automating initial pre-sales questions.
Sales analytics can also help organizations find upsell/cross-sell opportunities to increase a customer’s lifetime value using next-product-to-buy algorithms. It can also reduce churn by spotting customers contemplating switching over to the competitors and reaching out to them before it’s too late.
For instance, a global chemicals company set up a predictive model using over 30 variables, identified ten key factors that pushed customers away and prepared a list of ‘at-risk’ customers. The regional sales managers quickly came up with a plan to engage with these customers and make sure that they stayed loyal to the brand, thereby reducing churn by 25%.
Sales performance analysis is a smarter way of tracking and improving the effectiveness of your sales team reps. For instance, using a platform like Wingman helps you monitor sales calls, track metrics such as total calls made and talk-to-listen ratio and spot patterns differentiating the deals won from those lost.
Equipped with a 360-degree view of each rep, you can identify the skills that must be improved and find suitable training programs. Moreover, sales managers can tailor their coaching tactics to match the needs of each rep, which bolsters the overall productivity and efficiency of each rep.
Managers can also leverage real-time sales coaching to walk their reps through customer-facing conversations. For example, Wingman equips sales teams with cue cards and provides monologue alerts during calls.
Another benefit of analytics is the ability to track the entire sales pipeline (end-to-end) and perform a drill-down analysis. This helps organizations spot bottlenecks — the stages taking up more time — and remove them quickly.
Without such visibility, sales managers have to rely on guesstimates and their instincts to tweak their sales tactics and gauge the performance of their deals. Sales analytics can power real-time analytics dashboards to monitor all deal-related interactions from a single platform.
Sales managers can drill down and filter each interaction according to factors such as deal stage, rep or deal amount. This provides them with adequate context on each deal and intervene wherever reps need more help to coach them through the next steps. As a result, reps can improve their performance and hit essential KPIs around their monthly targets and conversion rates.
Gartner defines sales enablement as the process of providing all the information, content and tools that sales reps need to sell more effectively. Analytics help sales teams build better sales playbooks by analyzing historical data on all customer-facing calls and mapping the data on dos and don’ts of future such conversations.
Sales teams can document winning sales tactics — game tapes — for discovery calls, pitches, negotiations, building trust and handling objections. These game tapes help reps learn from the best and understand customer feedback on products. They also coach the reps to seamlessly navigate tough conversations, especially those around pricing.
Analytics can help sales teams pick the right metrics to track the KPIs on reps, customers and sales strategy. These include metrics such as total sales revenue, quarterly revenue growth, average deal size, MQL-to-SQL ratio and lead-to-close ratio. After all, knowing what to measure is the first step to optimizing sales performance.
Lastly, analytics leads to better sales forecasting. For example, the multivariate sales forecasting method uses algorithms that consider the average sales cycle length, probability of deals closing and performance factors of each rep to make predictions.
What challenges do organizations face
with sales analytics?
Since sales analytics is all about extracting value from data, organizations can struggle when they lack access to high-quality, relevant, credible and useful data. Let’s look at three such challenges.
Data quality and context
Analytics is only as good as the data you collect. 53% of the organizations surveyed by Gartner cite data quality as a challenge. Moreover, several organizations fail to collect data with adequate business context. Bad data coupled with a lack of context makes it harder to understand and use.
Lack of proper tools
A large number of organizations rely on native CRM/sales force automation (SFA) reporting as their primary solution for delivering analytics, according to Gartner. These tools aren’t fully equipped to capture real-time insights or offer end-to-end visibility on sales pipelines.
Additionally, for organizations looking to leverage sales forecasting, conventional CRM tools won’t cut it. They need AI-powered platforms that can automatically process vast volumes of data from several formats (calls, CRMs or marketing campaigns) within minutes.
The Gartner study on the future of sales analytics also found that 57% don’t tap into the potential of sales analytics. While the right tools and data certainly help, the organization has to embrace and nurture a data culture for the tools to succeed.
How can you overcome the challenges
in sales analytics?
Here’s how you can tackle the challenges related to data quality, context, tooling and culture:
Add context to the data you collect. So, before capturing data, know its purpose and audience. It’s also a good practice to contextualize data by explaining any anomalies (like dips during holiday periods) and identifying patterns (higher conversions in the post-holiday season, for instance).
Rather than investing in the best tools, a more prudent approach is to map specific use cases for analytics in sales. This makes it easier to know and understand the capabilities that you need. As a result, you invest in tools that meet the requirements of these use cases.
The first step to fostering a strong data culture is to train your reps to find and extract value from the right data assets. Also, rather than running one-off training sessions, this could be an ongoing process with two-way communication to boost engagement. Laying such a solid foundation is also crucial to help your sales team extract the full potential of analytics tools.
What should you look for in sales
The best way to get started is by defining the use case and purpose for analytics. It helps to think along the lines of:
What needs to be analyzed and why?
Who is it for?
Which metrics should be tracked and why?
What types of visualizations will be required?
Mapping these details makes it easier for sales teams to assess their current strengths and realize which tools they need for their situation.
While each sales organization has a unique set of use cases and requirements, there are a few capabilities that all sales analytics software should possess. The tool should be:
Be user-friendly (as the audience is usually business users)
Allow users to visualize data extracted from multiple sources on a single dashboard
Provide complete visibility of the sales pipeline
Facilitate real-time sales performance analysis, sales enablement and real-time sales coaching
Integrate with your existing sales and marketing tech stack