Sales Analytics encompasses analysis technologies that help you generate insights from the data, metrics, and Sales trends. Thus, analysing Sales can give you a better understanding of the past Sales results, allowing you to make effective decisions regarding your prospects and customers, market opportunities, Sales team performance, and product lines. Generally, the best practice is to bring different activities together to set realistic objectives for your Sales team.
In this article, you will get a brief overview of Sales Analytics as a whole followed by a deeper dive into the different types of analytics processes, the importance it holds in a business setup, the working, and its limitations.
Table of Contents
- What is Sales Analytics?
- Understanding the Importance of Sales Analytics
- Why monitor Sales Analytics?
- Top Applications of Sales Analytics
- Understanding Different Types of Sales Analytics
- How do Sales Analytics Systems Work?
- Best Practices for Sales Analytics
- How to Run a Sales Data Analysis?
- Sales Analytics Tools
- Limitations of Sales Analytics
What is Sales Analytics?
Sales Analytics involves leveraging the power of Data Analytics to improve your Sales team’s performance both in the long and short-term. This is helpful when it comes to suggesting recommendations to Sales reps and managers. These recommendations could be regarding the best decisions to make when it comes to capitalizing on the Sales trends or generating models to boost revenue.
Leveraging Sales tools as a part of your workflow can help provide clear insights into Sales pipelines and staff performance among many other things.
Understanding the Importance of Sales Analytics
Successful companies have their customers at their core. To keep flourishing in your market segment you will need to interact with your customers regularly and build relationships on a one-on-one level. As the world gets more data-driven every minute, you need your products to evolve in tandem with your customer’s needs. To stay afloat you need to make Sales Analytics the cornerstone of your business.
Sales Analytics gives you a fair idea about where your prospects are in their buying journey. It will also give you a fair idea about the campaigns you’re running. You’ll be able to keep track of products and campaigns that aren’t faring as well as the others. It will make you aware of market trends in your segment to cater to your customer’s changing needs.
Why monitor Sales Analytics?
Monitoring Sales Analytics can help grow your business, optimize sales activities, and improve accountability. Here’s why incorporating analytics systems into your Sales funnel allows you to stay abreast in a data-savvy environment:
- Clarification of Future Decisions: By utilizing the data obtained from a Sales Analytics system, you can be clear when making decisions for your enterprise. For instance, this can help you understand the products and campaigns that aren’t faring as well as the others. You might want to discontinue these and focus on allocating the Sales resources to products and campaigns showing clear-cut results or change your approach to improve Sales.
- Identification of Missed Opportunities: Analyzing the Sales data gives the companies a sneak peek into the opportunities that might have been overlooked so far to capture a better market share and reel in more customers. You can perform a parallel analysis of your product’s performance with your competitor’s product to understand what’s not working for you, what’s working for them, and what can be done to bridge the gap.
- Recognition of Market Trends: Analyzing marketing trends can provide valuable inputs to bring forth new decisions to increase the revenue generated from Sales. By analyzing historical data you will know when your Sales showed spikes and dips, further delving into which can give you valuable actionable insights.
- Sales Funnel Optimization: By having access to Sales Analytics, you gain the advantage of understanding the reasons behind people’s choices when buying a product. It also allows you to improve the Sales functions, thereby making the transition from a prospect to a customer seamless.
- Increase Efficiency and Productivity: You can use Sales Analytics systems to reveal productivity & efficiency gaps in your Sales process. By becoming aware of your Sales team’s skills you can now decide which employees to assign the most valuable clients and the type of talent that your Sales team might need to increase productivity & efficiency.
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Top Applications of Sales Analytics
Sales Analytics is not just restricted to the Sales activities of an enterprise. It includes almost all forms of business transactions, including those that don’t relate to the closing of a sale. Most of the Sales and Marketing applications rely on Sales Analytics.
Here’s a list of applications that include Sales Analytics:
- Sales Forecasting
- Customer Health Metrics
- Predictive Lead Scoring
- Sales Team Management
- Customer Contact Analytics
- Monitoring Individual Rep Performance
- Multi-touch Sales Attribution
- Sales Process Improvements
- Performance Management
Different types of Sales Analytics are discussed in detail in the next section.
Understanding Different Types of Sales Analytics
With the advent of technology, more and more companies have come to understand the enormous value in the collection and intelligent usage of data. This has led to organizations going for dedicated groups of Data Miners and Analysts to deliver more accurate Sales forecasts, make them equipped to understand and handle risk. Sales Analytics is usually charted into one of the following categories by the Data Analysts when talking about the output:
- Predictive Sales Analytics
- Prescriptive Sales Analytics
- Diagnostic Sales Analytics
- Channel of Distribution
- Store Keeping Units (SKUs)
- Per Comparable Economic Data
- Per Capita
- Category Development Index (CDI)
- Competitive Trends
- Brand Development Index (BDI)
1. Predictive Sales Analytics
Predictive Sales Analytics is a crucial type of analytics since scalable, predictable revenue is an essential goal for Sales managers. It typically deals with Sales forecasting. This is done using models and historical data, which allows your company to get a better forecast and get an idea about what to expect in the future.
A data-driven Sales forecast that takes into account factors like average deal time, size, and management, etc. is more accurate than a Sales forecast that relies on the intuition of the Sales reps. Generally, a Sales forecast would take into account the historical conversion rates of your Sales pipeline.
2. Prescriptive Sales Analytics
Prescriptive Analytics comes into play when you need to understand the findings of Predictive Sales Analytics to recommend the next course of action and, thereby provide the possible outcomes of each of these decisions.
The main focus is on finding actionable insights from your data to steer your enterprise’s growth. Prescriptive Analytics cannot exist on its own as it builds on the results of Predictive Analytics of your Sales data.
3. Diagnostic Sales Analytics
Diagnostic Sales Analytics comes to your aid when you need to find a reason behind specific behaviour. This is the initial step in the data analysis process for most companies, which is performed using techniques like data discovery, drill-down, correlations, and data mining to name a few.
Ir provides you with a crystal clear understanding of your data & helps you answer critical workforce questions. Modern solutions for Diagnostic Analytics deploy machine learning techniques to help the Analysts.
4. Channel of Distribution
When you are selling your products across numerous different places, then analyzing your Sales data by Channel of Distribution becomes essential. Here, an individual retailer should be looked at as a separate entity, but at the same time, he/she should also be grouped by type. For example, online eCommerce platforms, vending machines, and supermarkets are all different Channels of Distribution that need to be assessed separately to get a thorough understanding of your products across space and time.
One common strategy is to first aggregate complete Sales of your products. Then, you can analyze them depending on the distribution of total product Sales across different channels. This allows you to view the changes in data across various periods be it days, months, or years.
5. Store Keeping Units (SKUs)
One common way of analyzing your Sales data is to keep track of your Store Keeping Units (SKUs). Here you take into account the variation and size of each product demarcated by colour, brand, price, geography, etc.
This allows you to identify the trends in the Sales activities across locations and the best sellers in the said locations. Once in possession of this knowledge, you can set out to understand the behaviour of your customers belonging to a given demographic.
6. Per Comparable Economic Data
Comparing your Sales to different sets of economic data gives you another way of analyzing your Sales data. You can measure your Sales numbers against say the GDP (Gross Domestic Product) of a given state or choose from several other valuable economic variables like consumption of basic goods such as gasoline, oil, etc.
This allows you to understand how your products fare when compared to other measures of economic potential and growth.
7. Per Capita
Per Capita is a Sales analysis technique that relatively allows you to gain a new understanding of data. To put it in simple terms, say you have got the annual Sales data from all the different states of a particular region. Now dividing this number by the total population of that state gives you a new Metric: Annual Sales per Person per State.
Using this technique yields several significant and at times quite surprising trends allowing you to fully exploit this newly gained information to your advantage.
8. Category Development Index (CDI)
The Category Development Index or CDI for short is a metric that measures the Sales performance of a category of goods in a specific group. It can be calculated for any territory from cities to countries.
You can understand this better with the help of a simple example. Drawing from the previous examples, you can take the total annual Sales for one product category by state and you can then calculate the Per Capita Sales number for each state and the overall average Per Capita Sales for a whole country.
You can then divide the former by the latter & multiply by 100 to give you the CDI. Taking 100 as the average, any state with a CDI above 100 would be deemed as ‘Above Average Market’ while others that languish below 100 would be deemed as ‘Below Average Market’.
9. Competitive Trends
Competitive Trends allow you to gauge where you stand vis-a-vis your competitors in your designated market. Given the market share your product has in a particular state, and the annual Sales there; it is easy to calculate the total size of your product’s market.
This gives you an exact idea of how many units of your competitor’s products are being sold compared to your Sales figures.
10. Brand Development Index (BDI)
Brand Development Index (BDI) differs from CDI in the fact that it focuses on individual brands within a product category. Using BDI can prove helpful as it allows you to allocate resources for advertising and promotion across territories.
It also differs from CDI because it doesn’t have to be based on the Per Capita Sales for the whole population.
How do Sales Analytics Systems Work?
There is essentially a prediction problem at the heart of sales analytics.
The Sales and Revenue of your company depend largely on the customer experiences you create for them. The Sales Analytics Systems store all the data with respect to the customers, their journey, their interests, and their behavior. Customer interaction with Sales Reps, interactions on all digital platforms, Lead Scores, Sales Attribution, and Key Metrics are recorded and analyzed in the systems. These metrics play a major role in coming up with a Data-Driven Marketing Strategy/Decision.
Best Practices for Sales Analytics
Sales Analytics has made the shift from the days when Sales management used to be about guessing the things that seem to work to relying on concrete data to establish what really works.
The adoption of Analytics tools is still however in its nascent stage so here are a few best practices to keep in mind when incorporating Sales Analysis into your workflow to simplify its usage while focusing on improving productivity and efficiency:
- Presence of Sales Operations Expertise: The presence of Sales Operations Expertise will be helpful when trying to provide discipline and structure while operating Analytics tools. Sales Operations personnel can work as Data Scientists who will be preparing reports, evaluating Sales enablement tools, and setting up these systems.
- Understanding the Key Outputs: Understanding the outputs you are trying to extract from your Sales Analytics systems will be helpful in attaining objectives. You need to be aware of the purpose of using these systems, and the working and nuances of each stage of the Sales process to improve efficiency.
- Ensure Data is Accurate and Complete: The results obtained from your Sales Analytics tool will be dependent on the data it receives from your Enterprise Resource Planning System. Companies lose out on efficiency due to the undisciplined employment of these systems. You should therefore invest time and effort in ensuring the quality of data. One way of doing it is by carrying out a Data Cleansing project on existing data and ensuring the accuracy and completeness of the data.
- Keep it as Simple as Possible: It is easy to get lost in the hubbub while trying to analyze every small activity. This can prove counterproductive as you may get lost in bad analytics; tracking metrics that may have no real impact on your business. Therefore, it is imperative to focus on a certain number of important Key Performance Indicators (KPIs) and discard the rest.
- Coach With Metrics: You need to provide coaching at the Sales rep level based on their skill-set, and create goals based on their individual performance. You need to ask Metric-based questions after you have thoroughly understood how each Sales rep stacks against the other. This will help you go after answers that are driven by data.
How to Run a Sales Data Analysis?
The process of Sales Data Analysis depends on a variety of factors, including use case, and may vary from an organization to another organization. However, there are some common steps that are part of most of the Sales Data Analysis processes. Below are the common steps mentioned to carry out a Sales Data Analysis.
- Select what kind of Sales Data you want to analyze, who or what you are interested in analyzing.
- Zero in on specific, definitive, and measurable objectives.
- Set the frequency of your Sales Data Analysis.
- Compile your Sales Data manually or use Sales Analytics tools on a rolling basis.
- Leverage Data Visualization Tools to make sense of vague numbers and metrics.
- Analyze your data and try to identify trends or patterns in it.
- Implement your findings to optimize the Sales Performance of your company.
Sales Analytics Tools
Some of the most popular and useful Sales Analytics Tools are discussed below.
Microsoft’s Power BI is the most widely used Data Analysis Tool for Sales Analytics. Power BI has been in the market since the very beginning of the data revolution. Started as a straightforward Analytics tool, Power BI is now equipped with Machine Learning capabilities for sentimental analysis and integrates effortlessly with other platforms for streamlining the analytics workflows. Power BI’s Machine Learning capabilities can help in customer segmentation by studying customer behavior, interests, and buying patterns.
Power BI, also being a Data Visualization Tool, goes a step further by converting insight into action with Microsoft Power Platforms to build business applications. With Power BI, organizations can add sensitivity labels to data, comply with data privacy rules, and enhance collaboration among departments.
Power BI offers a free desktop version to help individuals learn, but for small organizations, it has Power BI Pro for $9.99/user/month. And large companies can opt for Power BI Premium, starting from $20/user/month, for getting access to advanced features like data management and capacity. With Power BI, companies can also choose a pricing plan based on capacity instead of per user for $4,995/capacity/month.
HubSpot can help you to measure the impressions, clicks, and ROI (Return On Investment) of the campaigns you run for your customers. Using HubSpot, you can also create personalized Email Marketing campaigns and monitor their performance.
Sales activities performed on HubSpot constantly generate a lot of data, which is a rich source of knowledge that can let you make sound decisions on your future ad campaigns. That’s where HubSpot Analytics comes into the picture. You can get a better understanding of your customers, find important clues to attract and retain customers, etc.
Founded in 2003, Tableau Software has acquired many customers, including fortune 500 organizations, as it was a pioneer in supporting online and on-premise workflows. Due to its ease of use and better user interface, Tableau has been a direct competitor for Power BI. Tableau is better in terms of supporting massive datasets and custom visualizations without writing code, making it one of the best options to handle massive amounts of Sales Data.
What makes Tableau stand out for Sales Professionals is that compelling dashboards can be shared among people who do not have Tableau installed. This enables better collaboration among Sales Teams.
Although Tableau Analytics is free to use, you can open new capabilities with its premium plans. Tableau has three models called creator, explorer, and viewer.
Zoho’s Analytics Platform has become quite popular for being affordable and easy to use. Zoho is best suited for businesses that generate most of their Sales from Social Media. Zoho offers advanced Social Media Lead Generation and monitoring features that can help you keep track of posts related to your business.
Zoho Analytics Software provides you with detailed segmentation of customers using your Sales Data. It also comes with extensive integrative capabilities and attractive Data Visualization resources. The platform allows you to blend massive amounts of data from a wide range of sources into actionable, straightforward reports.
Limitations of Sales Analytics
Sales Analytics has certainly revolutionized the usage of data to pull in more customers by keenly studying customer behavior. This has helped so many organizations break out in their market segment, prompting more and more businesses to adopt a data-centric Sales funnel to propel growth. But Sales Analytics is not without its limitations either. Here are a few limitations it poses:
- Carrying out Sales Analysis makes sense for an organization having more than 10 reps, a stable product, and a target market so that the customer behaviour is not too variable for comprehensive analysis.
- For an organization having a Sales operation that can fit comfortably in a spreadsheet, using analytics tools may become unnecessary.
- Some analytics systems suffer from drawbacks associated with modern AI (Artificial Intelligence) systems. For instance, some systems might not be able to provide a coherent explanation for certain results, making them counter-productive.
This article provided you with in-depth knowledge about Sales Analytics, its types, importance, working, and limitations to give you a comprehensive idea about wanting to leverage analytics systems as a part of the Sales workflow of your business.Visit our Website to Explore Hevo
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