What is Sales Data Analysis?
Before we directly dig in on the benefits, let us first define sales analysis.
Sales data analysis is a vital way for organizations to maximize their sales capacity, and meet customer needs, in an increasingly competitive world.
In fact, a lack of data should no longer be a reason why your sales data analysis strategy fails, but if it is – here’s how to fix it.
As more and more companies gain the ability to make precise, data-driven sales management decisions, the margin of error is becoming smaller and smaller. Relying on guesswork leaves you open to being disrupted by more data-savvy competitors and startup companies, regardless of what industry you’re in.
Fortunately, a well-designed sales data analysis program can deliver drastic increases in revenue and profit margins by enabling your organization to make better decisions.
Importance and Benefits of Sales Data Analysis
1 Improve Value Propositions and Price Points
By knowing how to analyze sales data, you can ensure that you are always saying the right thing, to the right customer, at the right time.
Most organizations find it extremely challenging to develop value propositions that are effective at convincing each segment of customers they target in their marketing and sales activities. While most companies opt for a one-size-fits-all approach, data-driven companies are able to test many different value propositions on different segments of customers to identify which are the most effective.
By collecting and cross-referencing in sales data analysis, it’s possible to build highly-personalized value propositions tailored to the specific needs of each customer segment.
Another challenge is setting the price of new products and services to ensure maximum sales and revenue. By using market data and dynamic-pricing engines and knowing how to analyze sales data, companies can test many different price points to determine what the optimal price is for each solution, and even for each segment of customers.
Some companies have discovered that, in order to maximize revenue, they actually needed to raise prices. While price increases may cut the number of potential sales, by growing the average size of each sale, you may be able to achieve an increase in overall revenue.
2 Narrow and Refine Product Offerings
Sales data analysis provides many valuable insights that your organization can use to cut costs and improve your product offerings.
By analyzing transactions, you can spot products whose sales are under-performing overall, or under-performing in certain customer segments. Then, you can investigate why they are under-performing, and use the feedback from customers to refine products to better meet their needs.
In addition, you might determine that some products are no longer worth producing or supporting. By cutting these under-performing products, you can decrease costs, and focus more time and resources on products that drive the most revenue and profits.
3 Disruption and Innovation
In order to remain competitive, you must be able to quickly adapt to changing marketing conditions, trends, and customer demands. In such a dynamic, fast-moving business environment, a well-designed data analytics program could easily become your competitive advantage.
Sales data analysis allows you to quickly identify customer needs and deliver personalized solutions faster, more efficiently, and at a lower cost than your competitors.
In every industry, the winners of the future will be the organizations that can leverage data to identify market changes quickly and be the first to respond with solutions that best meet customer needs.
4 Accurate Sales Forecasting
One of the most obvious benefits of sales data analysis is the ability to predict future sales based on historical data. Unlike ambitious goal-setting, historical data gives you an accurate, realistic picture of how much your team should earn within a certain time period.
When leaders can accurately forecast what revenue will be, they can then use that knowledge to allocate resources and manage the workforce more efficiently. Cutting waste allows them to be more agile, and more quickly respond to changing market conditions.
Use historical sales data analysis to compare your organization’s performance to industry averages to see if you’re on track. Share on XHistorical sales data analysis also allows you to compare your organization’s performance to industry averages to see if you’re on track. If you discover that your sales numbers do not align with industry averages, you can now begin the process of identifying the root cause and making the necessary corrections.
5 Performance Assessments and Incentive Plans
When sales managers have reliable data, they can create a sales forecast for each, individual sales rep, and compare their current performance to their performance in the past.
If a sales rep has unusually low performance, sales managers can focus more time on coaching and training that sales rep.
On the other hand, if a sales rep has unusually high performance, sales managers can now acknowledge and reward that rep’s hard work, and have them help train the rest of the team on their methods and tactics.
In addition, by looking at data from your CRM, you can see how reps spend their time, and identify which activities make the most impact when it comes to closing deals and generating revenue.
By making use of sales data analysis , managers can be more effective at correcting performance issues, setting realistic sales goals, incentivizing high performers, and motivating their team.
6 Increase Retention Rates
Research done by Frederick Reichheld of Bain & Company shows that increasing customer retention rates by 5% increases profits by a whopping 25% to 95%.
Your sales team should know who their big buyers are and be focused on taking care of them to ensure the highest customer satisfaction and retention rates possible.
You probably know who your largest accounts are, but transactional data can help you identify other accounts that are growing quickly.
You simply can’t afford to lose those large accounts, so you need to be able to identify them, and then make them a top priority for your team.
According to research by Esteban Kolsky, 67% of customers report bad experiences as a reason for churn, but only 1 out of 26 unhappy customers complain.
Sales data analysis gives you the ability to identify the top factors that cause customers to churn, so you can spot at-risk accounts, and proactively reach out to them to address their concerns and make sure they are thoroughly satisfied.
This also includes identifying customers who have signed up for a trial of your product, but haven’t begun using it. When you are able to identify these accounts in the trial stage, you can reach out to them to offer assistance or tutorials to help on-ramp them and help them see the full value of using your product.
7 Increase Repeat Purchases from Existing Customers
According to the book Marketing Metrics, businesses have a 60% to 70% chance of selling to an existing customer, while the probability of selling to a new prospect is only 5% to 20%.
If you want to increase revenue fast, start by reaching out to customers to cross-sell and up-sell products you think could also meet their needs.
Businesses have a 60% to 70% chance of selling to an existing customer, only 5% to 20% to a new prospect. Share on XIn addition, by looking at purchasing patterns among each of your customer segments, you can generate personalized recommendations for what customers can buy next, based on what similar customers have bought in the past.
Using data to identify underserved customers and making personalized cross-sell or up-sell recommendations to them is a quick and sustainable way to boost revenue.
8 Pipeline Management
By comparing leads to historical data on similar customers, you can now segment leads in your pipeline based on how profitable they are likely to be and how engaged they are (an indicator of how quickly they are likely to close).
Instead of wasting time reaching out to leads that aren’t likely to be interested in your products, you can now use your sales data to generate a list of the most viable and profitable opportunities to contact first.
Pipeline data can also allow you to identify and fix weak points and bottlenecks where leads are getting stuck, or falling out of the pipeline completely.
9 Targeted Marketing to Cut Costs and Increase ROI
By utilizing data to improve the targeting of your advertising efforts, you can effectively “clone” your most profitable customers, and avoid wasting money targeting customers that aren’t likely to be a good fit for your company.
Based on data points you have on your most profitable customers, you can now target and acquire more customers that exhibit similar behaviors and characteristics, maximizing the return on your marketing spend.
Over time, you can also gather data on which types of marketing collateral is most effective at converting different types of customers. You may learn that some customers prefer short, concise sales presentations, and other customers prefer more in-depth, detailed demonstrations of the product.
All of this information can be used to tailor your marketing and sales efforts, reduce waste, cut down the sales cycle, and drastically increase revenue and profits.
9 Types of Sales Data Analysis Techniques
1 Sales trend analysis
Sales trend analysis refers to the examination of historical revenue statistics in order to identify patterns. It is a valuable budgetary and financial analysis technique that can signal the beginning of changes in a company’s near-term income growth rates.
It is rarely sufficient to plot a company’s total sales on a trend line and hope to receive meaningful information.
Most businesses offer a variety of items to a diverse range of clients in a variety of places, which means that sales can be segmented into a number of sub-groups and then analyzed using a trend line.
While trend lines can be projected ahead in time using previous data, the sales levels projected by these lines are frequently wildly inaccurate, as they are reliant on the continuance of historical trends into the future.
The following examination of sales trends at a more comprehensive level results in more accurate predictions, as this analysis may disclose a variety of distinct trends.
- Sales trend by product– This study can identify which products are seeing rapid growth and which are plateauing or dropping in sales.
- Sales trend by client– Typically, this data is charted only for the largest customers in order to focus the sales staff’s concentration. When a customer’s sales suddenly decline or flatten, the sales personnel should check promptly to determine if there is a problem with the company’s connection with the customer.
- Sales trend by Channel- A sales trend evaluation by distribution channel usually reveals an early rise in sales when channel utilization is maximized, followed by a major flattening of the sales growth rate.
- Sales trend by region– It is normal for a mature region’s pace of sales growth to slow and then stabilize into a fairly narrow range over time. A fresh region’s sales trend is highly dependent on the establishment of distribution infrastructure, retail locations, and/or a local sales staff.
- Sales trend by contract- While it is possible to evaluate the trajectory of contract sales, forecasting based on historical performance in this area is extremely questionable. It is extremely likely that sales will cease immediately once the contract’s financed amount has been billed, with no warning visible from a basic examination of the trend line data.
2 Sales Performance Analysis
Sales Performance Analysis is a technique for determining the current state of your firm in comparison to the desired future.
It makes comparisons based on industry norms, productivity, and other variables.
It identifies discrepancies between your company’s current status and how it should operate, as well as the factors that contribute to the difference.
Sales Data Analysis: 9 Ways to Help You Easily Make Revenue Share on XAs a company grows, some goals and even relations with clients and peers become lost. This is why companies need performance analysis: to reorient themselves.
3 Predictive Sales Analytics
Predictive sales analytics makes sales growth estimates by analyzing sales data to detect trends in customers’ and leads’ behavior.
As a result, you can gain a better understanding of your business’s financial condition in the coming months.
Lead scoring is a common application of predictive sales analytics. In the majority of sales situations, databases contain a lengthy list of leads.
The day-to-day task of a sales representative is to schedule follow-ups on those prospects, make calls, write emails, and qualify leads based on their own subjective assessments.
All of those procedures will consume considerable time, making it difficult for sales professionals to prioritize assets and concentrate on closing deals.
When you spend an excessive amount of time on a lead with a poor likelihood of becoming a customer, you risk missing out on the potential to convert a “better” lead who is more engaged in your offerings.
However, if you employ predictive sales analytic technology, this is not the case.
A predictive analytics tool for sales makes predictions about future behavior based on historical data.
It combines historical and current data to assist you to determine why a lead is taking so long to convert or where your attention should be directed.
Because predictive analytics tools use data science and artificial intelligence, they can assist eliminate analysis errors and improve the accuracy of identifying high-quality leads.
4 Sales Pipeline Analysis
The sales pipeline is critical for sales executives and managers to know what is happening throughout the sales process. It enables you to monitor your customer’s experience.
Thus, when examining your sales funnel, the critical factor to grasp is why a prospect is moving or not.
To create that picture, you must first determine which variables and factors, favorable or bad, are influencing the prospect’s path and how they might be addressed.
One of the primary benefits of pipeline analysis is determining where difficulties could and do occur.
By analyzing historical data, you may identify points in a sales process that frequently result in deal failures and plan for their recurrence.
Not only that but as your comprehension and analytical abilities improve, you’ll be able to deal with hurdles as they arise, increasing your likelihood of winning and forecasting accuracy.
Properly analyzing your pipeline and determining the faults with your sales process enables you to examine prior performance in order to enhance your procedures and make sales rep training more successful.
5 Product Sales Analysis
Companies with multiple products and variances should undertake a regular product sales analysis to ascertain which products are underperforming.
A product sales report analyzes key performance indicators and revenue breaks to determine an item’s success over a certain time period.
Depending on the KPIs utilized, businesses can analyze product sales from a variety of geographic or consumer demands perspectives.
Management can then determine which goods should be withdrawn or pushed.
6 Sales effectiveness analytics
Sales reports are critical for monitoring your marketing reps’ performance and assisting them in identifying selling openings during customer contacts.
Essentially, these analyses are about identifying relevant trends in your data and generating actionable insights to help your business thrive in its sales performance.
Not only can sales effectiveness analytics enhance the quality of your company decisions, but it also enables the automation of time-consuming business operations.
As a result, your sales representatives may spend more time selling and your sales force can strengthen.
7 Diagnostic Analysis
This sales data analysis entails rationalizing patterns and insights in sales data.
For instance, increased rivalry in your business could result in a decline in your sales revenue.
Internal examinations are conducted by sales executives to discover bottlenecks for their staff, document their findings, and create strategies to improve.
The Center for Sales Strategy has developed a diagnostic checklist that you can use to begin auditing your team’s effectiveness.
Sale analysis enables you to assess your sales group’s condition by providing detailed insights into several facets of your business processes.
8 Prescriptive Analysis
Due to the massive amount of data presently available to businesses, it is now simpler than ever to utilize collected data to create real commercial value.
However, determining the optimal method for analyzing such data can be challenging.
Prescriptive analytics is one ideal solution that can aid your company in recognizing data-driven business decisions and avoiding the drawbacks associated with standard data analytics practices; such as devoting precious resources to housing data that does not notify strategic decisions, spending quality time sifting through underutilized data sets, and lacking unique income streams and insight.
9 Marketing Research
Sometimes, good old-fashioned market analysis assists in making sound business judgments.
This strategy may involve conducting a survey of your clients via phone, email, or in-person. Additionally, you can conduct research on your rivals and basic sales information.
Once you have a firm grasp of economic conditions, you can assess your business success and pinpoint your sales team’s flaws.
Additionally, it finds new business prospects and provides a more complete insight into your consumers’ needs, enhancing your sales performance.
While sales data analysis is based on historical sales data, market research can help fill in the gaps. It acts as a portal into the future for sales directors.
3 Steps to Perform Sales Data Analysis
Step 1: Identify KPIs to track
Prior to delving into specialty KPIs (Key Performance Indicators) to accomplish certain marketing objectives, it is advisable to establish a framework that clearly illustrates all of your marketing efforts.
KPIs, or Key Performance Indicators, are performance measurements used to track specific company objectives across all industries.
Occasionally abbreviated as KSI (Key Success Indicators), when developed and implemented appropriately, they can help establish a business’s direction, provide constructive feedback, and assist in organizing individuals, groups, programs, or entire organizations to maximize performance.
While KPIs vary by sector, and even rivals with many of the same needs may utilize them differently based on their concept and plan, a great place to begin would be with frequent use within a certain industry.
Before delving into the details of a KPI, it is critical to outline your objectives, such as where you may need to improve efficiency.
While this may take time, the more thorough the investigation, the more probable the KPI will yield informative results.
Additionally, it is critical to establish goals that are attainable. KPIs are about focusing on data, not creating lofty goals that can deviate from cohesive initiatives.
Step 2: Choose a Data Analysis Tool
While it’s all too easy to get wrapped up in the potential of big data analytics tools, identifying the system’s primary goals and monitoring a well-designed plan are far more critical than the toolkits themselves.
Begin by focusing on a few critical business problems or opportunities–whether it’s real-time asset monitoring or a better grasp of what your consumers want–and then building your toolset around those basic objectives.
Conduct research to determine which analytics platforms, tools, and skills are being used by others in your business to solve problems or generate opportunities.
For instance, shops may be interested in how other businesses employ artificial intelligence recommendation engines or sentiment analysis to enhance the consumer experience, whereas a banking and finance organization may be more focused on detecting fraud.
To maximize the value of big data, you must establish a comprehensive data strategy that includes integrating big data with knowledge management practices across all levels of the organization.
Step 3: Brief the results with your sales team
As a sales leader, you are well aware that knowledge is power. Hence, a sales analysis report is useful for your team.
Your sales representatives should have a thorough understanding of how prospects can utilize the product and can build personalized strategies outlining the most effective ways to do so.
When it comes right down to it, it is precisely what prospects require from sales representatives.
That is why you need to brief them with the sale data analysis report during your meetings.
Top 8 Sales Data Analysis Metrics & KPIs
1 Click-through rate
This section is entirely dedicated to the performance of your CTA, or Call To Action. It can inform you of the number of times your CTA link has been clicked, independent of its placement.
Typically, a lead generation campaign would include multiple call-to-actions. This means that you should be able to identify each component of your campaign.
You should ascertain the CTA that was clicked and check the click-through rate.
For instance, suppose you create a campaign in which your advertising directs users to a landing page that provides a free e-book.
Thus, the client will enter their data on your landing page. Following that, you’ll email them to request that they verify his email address. That particular visitor will be deemed a lead only if he confirms his email address.
You must track three distinct CTRs in this example campaign:
- CTR for the email verification
- CTR (click-through-rate) for the landing page
- CTR for the PPC advertisement
This is a sales analysis template to check your click-through rate:
Conversion rate is a critical measure to track for any form of campaign.
However, what exactly is a conversion rate? It’s the proportion of leads who took specific actions on your landing page, adverts, or mail.
Several activities can be classified as conversions:
- Purchasing a product
- Activating a button or clicking a link within an email
- Downloading an e-book or other piece of material offered by your brand
- Signing up to your newsletter by email
In a nutshell, a conversion is the culmination of your campaign. However, keep in mind that it does not have to be a sale – it can be anything you believe your firm needs to accomplish.
To determine conversion rates, two steps must be taken:
- Calculate the conversion rate of prospects who completed the conversion target. For example, it could be individuals who validated their email addresses via the link you supplied.
- Define your campaign’s objective. The more precise your objective, the better. For instance, you could establish a goal of generating 50 new leads through your PPC campaign.
3 Conversion Time
Wondering how long a web user takes to convert into a qualified lead? To be sure, you can only quantify this with the time-to-conversion statistic.
It is critical to monitor the total time required to convert at each phase of your sales funnel. This way, you may obtain a precise estimate of the duration of the sales process.
Time to conversion tracking is rather basic. All you have to do is determine the total amount of time your visitors spend on your website before completing your business’s conversion goal.
Then, divide that figure by the overall number of leads acquired.
4 Return on Investment (ROI)
Return on investment (ROI) is the most crucial indicator in any campaign. While we have already emphasized the relevance of the indicators described above, ROI is something quite different.
To gain a better knowledge of Return on Investment (ROI), the proportion or percentage number is what every client or member of the C-suite wants to know.
Is the business profitable enough to justify your investment?
For instance, suppose you make $15 per lead and spend $12 to acquire each lead. This equates to a 25% return on investment.
That is, however, a very simplistic example. In truth, calculating your ROI accurately requires a lengthy process. It’s considerably more complicated for firms that offer sophisticated services and products.
To obtain a holistic view of your ROI, you should calculate for the following:
- Total revenue that you may earn from each of your clients
- Costs associated with each stage of your sales funnel
You should keep in mind that your campaign will incur certain recurrent expenditures. For instance, the fees associated with Facebook advertising.
There are also one-time expenditures to consider, such as the money spent on developing valuable content (e.g. e-book).
Consider every dollar spent on your campaign when calculating your ROI.
5 Cost per click (CPC)
Large companies rely on advertising to increase traffic. Therefore, if you use paid advertising, you must implement a cost-per-click statistic.
The cost per click (CPC) is the amount you must pay for each click from anyone who views your advertisement.
By tracking the cost per click (CPC) of your purchased advertisements, you can establish the entire cost of increasing revenue.
While seeing a large number of new visitors as a result of a new advertisement you prepared may seem nice, you must still decide whether those leads convert into paying clients.
You don’t want your CPC to skyrocket when all you’re getting are ineffective visitors who can’t even be turned into genuine leads.
6 Sales Closing Ratio
The Sales Closing Ratio compares the number of prospects engaged by your sales staff to the number of deals closed.
This is used to determine your sales funnel’s efficiency. For instance, it should track or account for the number of formal quotes issued by your sales staff in comparison to the number of completed deals.
To determine your sales ratio, you must first define a discrete stage in your sales process, such as giving out a quote.
This sales data analysis metric will narrow the scope of your study to a single event and allow you to focus just on qualified leads within your funnel.
7 Average Purchase Value
Average Purchase Value is a metric that indicates the average sales value of each transaction that you perform.
This is a critical sales KPI for your organization to know because it will aid in the development of revenue forecasts and estimates.
By gaining an understanding of customer purchasing behaviors, you may build tactics to reinforce particular behaviors, such as encouraging people to purchase higher-end products.
8 Cannibalization rate
Cannibalization Rate quantifies the effect of new items on existing service income.
As your business introduces new products, the focus and desire for existing products may wane.
In business, cannibalization can present difficulties for sales teams centered on an established product line.
While the majority of businesses strive to innovate and bring greater items to market, new releases are not always risk-free.
This is especially true if the value propositions of the new and existing products appear to be dissimilar, if not competitive.
If you introduce a new product that renders an existing one obsolete, you run the risk of losing current customers.
Numerous software companies mitigate the risk of new product cannibalization by offering new goods at a discounted pricing to existing clients.
This sales data analysis metric can assist you in recruiting satisfied customers to participate in your official product launch.
Conclusion
You can only derive valuable insights from your sales data analysis if you’re collecting it in the first place.
The benefits of sales data analysis are clear and abundant. Unfortunately, sales reps rarely see the value in collecting all of this data, and few take the time to manually enter it into your company’s CRM.
To make things worse, the process of manually entering sales activity and customer data into a CRM like Salesforce™ is often extremely tedious and frustrating, further decreasing the chances that your sales reps will actually take the time to do it.
Inaccurate and incomplete data is often just as unreliable as having no data at all.
While getting sales reps to enter accurate and thorough data is an uphill battle, you simply can’t afford to not have this data.
The only viable solution is to find a way to eliminate manual data entry entirely and make the whole sales data analysis process automatic. However, until now, there has been no reliable technology capable of doing this.