Sales data is at the core of any organization, and it is vital to analyze the data properly to predict future trends.
There are major benefits to this practice, and it’s personalized to the metrics of the organizations.
So, let’s understand what sales analytics is and get a comprehensive understanding of this process.
In this piece, we’ll cover;
- The basics of the Sales Analytics platform
- Why is it essential to an organization?
- Read some sales data analysis examples
- And suggest some important metrics the team should track to get the best out of the analytics tool.
If you want to stay ahead of the game, then explore the market predictions that you can track with sales analytics reports.
What is Sales Analytics?
Before anything, let’s understand what Sales Analytics is and why it is significant.
By definition, Sales Analytics is used to identify and model data in order to understand and predict sales trends and outcomes while helping the team/organizations identify where improvements can be contributed to creating strategies to boost sales.
Image: Show an image that’s tracking sales analytics.
It answers the questions of the trends present in the data, the demographic of our sales products, and how are our sales reps performing- it simply means that Sales Analytics supports the customization of parameters, figures, dimensions, and measurements to get the correct data for all the questions.
And Sales Analytics can also relate to different goals set by the sales teams. It can be used to create strategies for pipeline sales for the team to meet its year’s end goals or any other set target; it can also be used to set a target.
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For instance, if a set target was to be achieved in a month, then sporadically, the sales rep can know their progress using the sales analysis data.
It’ll help establish where the individual, team, or organization stands in terms of their goals and what measures can be taken in the sales strategy to reach that target.
Additionally, Sales Analytics will provide observations to help understand your customers, their buying habits, and the logic behind their behavior. And through this, the task of keeping track of profitable customers and their engagement with the products is made easy. This will be the key to increasing the overall performance and profitability of the organization.
Significance of Sales Analytics
Now that we have defined and determined what is Sales Analytics and have an overall understanding of its importance let’s look at some detailed examples of why it is significant.
Listed below are some of the reasons why Sales Analytics is considered essential to organizations-
1. Market Trends
Sales analytics helps in determining the current market trends. For instance, if the organization is preparing to launch a new product for sale, sales analytics will show an increase in sales of an earlier product due to perhaps a pre-launch buzz strategy which may translate to increasing the sales of the new product.
This can also help understand which product is sold more during the festival season or can help in determining the off-season when the data shows a decreasing trend.
2. Detailed Analysis
The entire data can be funneled to analyze sales product-wise, customer-wise, month and/or year-wise, and location-wise. This is vital information for the organization as it will serve as a foundation for targeted sales practices.
3. Highlighting Opportunities
Analyzing the available sales data can help highlight the untapped opportunities that the organization can use to its advantage. Here, market research, done in collaboration with the field sales rep, will be essential in collecting and presenting the data to compare and set goals to work on these opportunities and understand their practicality and profitability.
4. Customer Behavior
Sales analytics will help determine the behavior and psychology of an individual customer or a collective. This data can provide critical insights into customers, which will help in creating strategies.
Would you like to know 18 psychological customer behavior tricks to sell better?
5. Set Strategies
Sales data and analytics can help an organization set strategies due to their predictive characteristics. It can help determine inventory management, marketing, and sales strategies or may even dictate alterations in the manufacturing process. This analytics can translate to boosting sales or discontinuing sales of a product.
These strategies and decisions will help with the profitability and investment direction of the internal management and the external investors, helping the overall organization.
Let’s Look at some examples of Sales Analytics.
Now that we have established what sales analytics is and why it is essential to a sales team and the organization's overall performance, we’ll illustrate the significance of sales analytics through certain analytics practices.
To operate seamlessly and effectively, a set of baseline analytics needs to be performed by the sales teams. However, sales teams boost their sales analysis in advanced practices that highlight the minute details.
These are some of the analytics practices:
- Sales Channel Analytics
Organizations will sell their products using various sales channels such as retail, e-commerce, direct sales, or resellers. This analysis will identify the most impactful channel, performance-wise, and how it impacts the revenue.
- Pricing analytics
It’s often a good practice for a sales team to collaborate with the product marketing team to understand pricing analytics. This analysis will provide insight into the customer behavior and reactions to the product price and help understand which service or aspect of the product is valuable.
Thus, helping in proper targeted marketing as well sales.
- Pipeline Velocity
The speed at which qualified leads move through a sales pipeline is known as Pipeline Velocity.
Every organization has a sales pipeline, and it’s critical to improve the pipeline. This helps understand what makes sales prospects boost the speed of success and what may cause a decreasing trend etc. This pipeline velocity analysis can spur conversions at a higher rate.
- Predictive Analysis
Timing is a vital element in the sales rep’s arsenal. It is essential to be aware of the timing of when to make a sales pitch via mail or when is it a good time to follow up with the potential client via call. Timing can make or break the deal.
Therefore, analyzing data predictively can signal the reps the best time to communicate with the prospects.
Essential Metrics for the Sales Team to Track
After going through the examples mentioned above, let us now look at Sales Metrics and which metrics should be tracked during Sales Analytics and why.
Sales metrics are data points used to measure and evaluate the organization’s sales performance, the performance of the team, or an individual spanning over a period of time.
Sales metrics indicate the status of the goals and guide the sales reps to boost performance. It does so by providing a detailed analysis of the success of sales initiatives taken and helping identify the areas that would need to be improved.
And sales metrics can be used on various levels such as the Rep-level where the metrics are used to track Rep-level activities, Deal-level where they are used to understand the minute details of deals, and Business-level where metrics are used to measure on a larger scale and how it contributes to the overall business data and operations.
Tracking the right set of metrics brings out the best of the analytical tools used.
Below are some of the tracking metrics used by sales teams-
On a Rep-level, these metrics are employed:
- Call time
- Longest rep monologue
- Talk ratio
- Response rate
- Quota attainment
- Predicted revenue
- Annual recurring revenue
- Sales coverage
On a Deal- level, the following metrics are tracked:
- Revenue per sale
- Sales by lead source
- Average deal size
- Average sales cycle length
- Win rate
- Deals lost
On a Business-level, these metrics are used:
- Customer acquisition cost
- Market Share
- Total Revenue
- Average Profit Margin
- Year-over-Year (YOY) Growth
- Average Customer Lifetime Value (LTV)
Challenges posed by Sales Analytics software
Sales analytics is a great process that is uncontestable- inaccurate metrics may fly under the radar in the short-term; however, the adverse effects are long-term, causing organizations to miscalculate finances or invest in the wrong markets.
We have elucidated all of its advantages and merits. Still, it is also crucial to understand specific properties or characteristics of sales analytics that can be an obstacle.
The process of sales analytics has already been determined as an imperative practice, but it comes with its set of challenges which the organizations may come across, and some of them are listed below:
1. Accurate and Timely Data
Recording data, especially massive amounts of data, is a mammoth task and has a high chance of inaccurately noting down, or there might be a delay. This becomes a very critical challenge.
2. Procuring Data from Customers
Gathering data from customers is often not easy and efficient. It can be detrimental as they are essential in target/goal setting, opinions on the products, the product's value, etc.
3. Comparing Data
Comparing the performance and previous data with the current one may be difficult as there may be a drastic change in some metrics, rendering them statistically irrelevant. Thus, comparisons may make it challenging to extract results and clear conclusions.
4. Data from Different Sources
Data gathered from various sources and applications is essential for a holistic approach to data analysis. However, procuring, compiling, preparing, and editing the data is incredibly difficult and not efficient.
Now that you have a deeper understanding of the Sales Analytics process and how it can help your organization achieve its goals, why not try out the Sales Analytics software on Convin’s platform for free.
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