The term "sales data analysis" can sound a bit overwhelming — all three words, individually, can be imposing in their own right.
"Sales" is a difficult line of work to master. "Data" conjures up images of rows upon rows of numbers that could be exhausting to pore through. And "analysis" sounds like a process that requires skills you might not have and considerable effort you'd rather not put in. So when you combine all three of those words, you land on a phrase that can be hard to stomach.
Admittedly, in some cases, there's some truth to that. Some sales data analyses are difficult, exhausting, and far from effortless — but in this day and age, it doesn't have to be that way. With the proper tools and tactics, a sales data analysis can be straightforward without sacrificing thoroughness and practicality.
Here, we'll get some perspective on sales analytics as a whole, a picture of the tactics to do it right, and some background on the best tools for it. Let's jump in.
Predictive Sales Analytics
Companies leverage predictive sales analytics to draw patterns from previous customers' and leads' behavior to predict the inclinations and business potential of future prospects.
Predictive sales analytics enable you to gauge the future success of and identify the most effective strategies for your sales efforts. Programs that automate the process draw from both your historical data and the data you accrue on an ongoing basis to help you pick up on customers' behavioral trends.
These systems typically use AI and machine learning to automatically and intelligently translate your raw data into actionable predictions — ones that become increasingly accurate as time goes on.
Those predictions can help guide different aspects of your sales and marketing approaches, including identifying upselling opportunities, zeroing in on particularly popular products, and improving your sales and marketing messaging.
Though predictive sales analytics programs are practical, convenient, and often effective, they're not necessarily the only means of running a sales data analysis. Here's a step-by-step guide to help you through that process.
How to Run a Sales Data Analysis
Select who or what you want to analyze.
Identify and layout specific, measurable objectives.
Determine how frequently you want to analyze your sales data.
Compile your sales data manually or use sales analytics software on a rolling basis.
Leverage data visualization tools.
Analyze your data and look for trends.
Apply your results to inform better sales efforts.
1. Select who or what you want to analyze.
This one is pretty straightforward, but it's still worth mentioning. You can't run a sales data analysis if you don't know what kind of sales data you're interested in analyzing.
You're going to want to understand what your endgame is from a high-level perspective. A sales data analysis can be very telling into product, department, team, campaign, or rep performance. Know who or what you're trying to better understand first, and go from there.
2. Identify and layout specific, measurable objectives.
Once you have your general idea, it's time to zero in on something more specific — something definitive and measurable. Let's say you run a retail business and recently ran a month-long promotional pricing campaign where all your products were marked 30% off.
Now, you want to see which of your products sold best with the customers attracted by the sale — relative to their normal sales over a similar period outside of the campaign. Here, you'd be interested in measuring product sales by unit over both the campaign and another similar timeframe.
3. Determine how frequently you want to analyze your sales data.
The frequency at which you track your sales will vary depending on the nature of the analysis. In some instances, you're going to want to monitor data regularly — whether that be weekly, quarterly, monthly, or even daily.
Certain reports will warrant ongoing monitoring for the sake of future reference. In our example, the report would rely on previous data accrued monthly to provide a reference point for the siloed month-long campaign.
4. Compile your sales data manually or use sales analytics software on a rolling basis.
Compiling sales data manually is technically still a way to go at this point in the process, so you might choose to leverage spreadsheet software like Excel or Google Sheets for this step. But in this day and age, CRMs and sales analytics software are probably more attractive options. In most instances, this step winds up being a matter of working smarter — not harder.
A CRM can help expedite the process of accruing sales data. It allows you to easily track individual customer information, and many offer resources for keeping logs on sales team performance. And there's a wide range of sales analytics tools that serve a similar purpose.
5. Leverage data visualization tools.
This point is a natural extension of the one above. Those aforementioned sales analytics tools often include a wide array of data visualization resources — features that allow salespeople and managers to easily make sense of sales data as it comes in.
Analyzing sales is an inherently complicated concept. Anything that makes it more readily accessible and easy to digest should come as a welcome, eventually-integral component of your sales analytics infrastructure. That's why data visualization is so important. It allows anyone on your team to make sense of what would otherwise be an imposing jumble of numbers.
6. Analyze your data and look for trends.
Once you've accrued your data, run it through your sales analytics software, and prepared your visuals, it's time to analyze your findings. This is where you reference your measurable objectives to see how the aspects of your sales efforts you were tracking with your sales analytics process held up.
In our example, you would look for the specific sales by unit for each product during the campaign, compare those figures to their counterparts in a similar, campaign-free timeframe, and draw conclusions about which products are most attractive to budget-conscious customers when placed on sale.
7. Apply your results to inform better sales efforts.
Once you have your results on hand, you need to do something with them. Use them to shape your future sales efforts. Take what you've learned and apply it to better understand and anticipate customer and prospect behavior.
In the retail example we've been running with, you might use the information about how your individual products fare with reduced pricing to selectively discount and promote the products that will appeal most to deal-hungry prospects.
This point might seem obvious, but it still warrants a spot on this list. It's the step that brings everything together. Apply your analyses, and keep running them consistently. There's always more to learn and room to grow.
Now that we've covered the steps of a sales analysis, let's take a look at some of the best tools to help facilitate the process.
HubSpot Sales Hub contains a robust suite of sales analytics resources to create and maintain virtually any kind of data report. The software has the necessary tools to help you track deal forecasts, sales performance reports, and productivity logs — among several other key metrics and analyses.
One feature that sets HubSpot Sales Hub apart from other sales analytics platforms is its accessibility. It has a straightforward interface that reconciles powerful, effective functionality with ease of use. You can maintain multiple customizable dashboards that provide visibility into all of your sales analytics reports.
The platform also has a collaborative element that makes it a solid fit for larger teams. Any dashboard you create contains customizable access privileges, so you can be selective in deciding how many members of your team can view or rearrange your sales analytics reports.
HubSpot Sales Hub has plans starting as low as $40 per month — with higher-priced options to suit your business needs as they evolve and expand. It also features an enterprise-grade option for large businesses.
With its tiered pricing structure and choices to suit companies of any size, HubSpot Sales Hub and its sales analytics features are worth considering — no matter the scale or nature of your business.
Power BI is a sales analytics software and data visualization resource from Microsoft. It's a comprehensive analytics solution that scales from individual salespeople to your company as a whole — affording your entire organization access to an appropriate degree of sales analytics data to inform smarter, more effective sales efforts from top to bottom.
The platform contains an extensive suite of powerful analytics resources. It provides hundreds of different data visualization methods, a variety of built-in AI capabilities, a well-structured Excel integration, as well as pre-built and custom data connectors — all backed and protected by an industry-leading security and data loss protection framework.
The Power BI "Pro Plan" starts at $9.99 per individual user, so it can suit the needs of smaller businesses as they expand. However, that plan is tailored specifically for self-service business intelligence.
Its most robust suite of features comes with its "Premium Plan" — an enterprise-grade solution that starts at $4,995 per month. So while the platform can be leveraged by smaller organizations, its full capabilities are reserved for large enterprises.
MaxG is a product in a class of its own. It's the world's first B2B sales and recommendation engine. The platform offers intelligent performance insight about and straightforward visibility into your marketing efforts — including content like blogs and CTAs.
What really makes MaxG unique comes from what it manages to do with that information. Via the power of AI, the platform can translate those general insights into actionable suggestions on how to improve your sales and marketing efforts. MaxG also contains an industry benchmarking feature that lets you compare your data to your competition.
MaxG's industry benchmarking tool is available for free, but access to the rest of its features starts at $49 per month. It also has an enterprise-level plan available for $4,990. If your business is looking for an analytics solution that can offer quick-hitting, meaningful recommendations to improve your sales and marketing efforts as you go, check out MaxG.
Zoho Analytics is a self-service BI and data analytics software with extensive integrative capabilities and particularly versatile and attractive data visualization resources. The platform allows you to channel massive volumes of raw data into actionable, straightforward reports — all carefully organized on a sleek interface.
The platform is particularly notable for its ability to blend huge amounts of data from a wide array of sources. If you have data stored in multiple sources — like HubSpot, Excel, and Google Cloud Storage — Zoho Analytics allows you to easily combine them and translate that breadth of information into cross-functional reports and dashboards.
Zoho Analytics' pricing structure features plans that suit businesses of any size. Its lowest-priced option costs $22 per month and comes with a wide, dynamic range of features for up to two users.
The remaining tiers are relatively inexpensive and tailored to fit your business's needs as it expands. If you're in the market for a reasonably priced option that will remain reasonably priced as your company grows, take a look at Zoho Analytics.
There's a host of other sales analytics tools worth your consideration, including Databox, Grow.com, Plecto, Demand Sage, Domo, Dear Lucy, and Supermetrics. Finding the right tools for your business will hinge upon the resources you're currently leveraging, the scale of your sales efforts, and which specific metrics and objectives you're looking to account for.
Running a sales analysis can seem like an imposing, inaccessible undertaking, but it doesn't have to be. With the right tools and a little guidance, the process can be simple, smooth, and incredibly useful when trying to improve your sales efforts.
Originally published Aug 2, 2020 11:00:00 AM, updated October 29 2020