Sales Analytics: Unique Insights from 6 Years in the Field

Sales analytics is not just a set of numbers or charts, but the art of leveraging data to dissect past sales performances, forecast future opportunities, and provide insights into how sales teams can better meet their targets.

Over the past six years, I was doing technical setups for sales teams: CRMs, automation, reporting, this sort of things. Only to then recently discovered that the biggest obstacle to a company's growth isn't market competition or lack of innovation - it's the absence of effective sales analytics.

We've all heard the buzzwords like "data-driven" and "business analytics" but there's a disconnect somewhere. Why? It's not because sales leaders are averse to using data. Quite the contrary. The real problem is that many can't easily retrieve any sort of insights in a language that speaks to them.

Picture this: you're a top-tier sales leader, focused on driving growth, shaping sales strategy, and steering the ship in the right direction.

SQL queries? Those should be the least of your worries.

Communicating with engineers in their technical jargon? Not your day job.

And yet, here we are. In an age when thousands of software tools promise to make your life easier, it's ironically harder than ever to figure things out on your own if you're not tech-savvy. And let's be honest: Should you even have to be?

This problem leaves sales managers in a difficult spot. How do you focus on a big-picture sales strategy when you're tangled in the nitty-gritty of data analytics? If you've been asking this question, you're in the right place.

In this article I'll try to explain how you can apply sales analytics without anyone's help.

I'll break down how today's sales executives can utilize their customer and sales data. And more importantly, how to make data-driven decisions - a skill that's not just beneficial but essential in 2024.

Is sales analytics really this important?

Let's circle back to this buzzword we all love to throw around: "data-driven" It's not just a trendy phrase. It's a philosophy that puts data at the helm of steering your organization. According to research by NewVantage Partners, a measly 23% of executives claim to have successfully instilled a "data-driven culture" within their companies.

So, what's happening with the remaining 77%? Are they stuck in the Stone Age, devoid of growth? Not exactly. They're growing, but here's the kicker: they could be growing much faster. McKinsey Global Institute reports that data-driven organizations are 23 times more likely to attract new customers, six times more likely to hold onto existing ones, and 19 times more likely to turn a profit.

Let that sink in for a moment. A crazy 600% increase in customer retention might sound like a fairy tale, but it's reality. And here's another jaw-dropper: each day, we generate an unfathomable 2.5 quintillion bytes of data, yet only around 0.5% of it is analyzed. Just imagine the gold mines of insight lying untapped!

Understanding your sales data and sales analysis isn't just a neat trick; it's directly proportional to how effectively you're leveraging your organization's full potential. Even a modest shift in your decision-making approach can rocket you light-years ahead of the competition.

And let's be clear: embracing data doesn't require a Ph.D. in statistics. Plenty of companies still ignore the undeniable power of data analytics, opting instead to "go with their gut." As the legendary investor Charlie Munger once said "Everyone is trying to be smart. I am just trying not to be stupid."

Why many sales teams are not data-driven

The average desk worker uses 11 applications just to get through the workday. Just think about it: your daily sales tech suite probably includes everything from Google Drive and Slack to CRM and ERP systems, not to mention your product databases, meeting software, phone logs, and email archives. But let's get real - how often do you actually mine this data for insights? If you're like most people, the answer is "not often," and it's not because you're lazy or uninformed.

The roadblock? These tools weren't exactly built to serve up insights on a silver platter. They might excel in their individual functions but often fall short when it comes to interoperability. Google might be putting in the effort to extract insights from google suite, but don't expect it to sync seamlessly with your CRM anytime soon. The same goes for all of them. This makes getting a holistic view of your sales landscape nearly impossible without some serious engineering firepower. There're few ways you can try to go about it without loading your already overloaded development team (which I will go about later), but it requires your own time investment and budget allocation.

So now we're turning our question of: “Why Many Are Not Data-Driven” to “Why aren't more companies diverting engineering resources to develop data pipelines or tasking data analysts to set up dashboards in Business Intelligence (BI) tools?” The brutal truth is, for most companies, this simply isn't a priority. There're a lot of cultural barriers, not technology limitations. Companies continue to struggle with challenges relating to organizational alignment, business processes, change management, communication, people skill sets, and simply lack of understanding to enable change.

Is coding necessary to discover insights?

Sales executives are increasingly caught in a frustrating loop: "I don't know SQL, so I can't analyze data. My CTO knows SQL, but they're swamped. The analysts on my team can only field a couple of my questions per week, yet I have dozens daily. How on Earth am I supposed to be data-driven?"

Some might argue that learning to code could be the game-changer. The idea is appealing: by mastering SQL or Python, you could dive directly into the data pool and analyze key customer and sales metrics at will. But let's pause and consider the reality.

Managing a sales team and spearheading corporate strategy are already full-time jobs. Do you really have the bandwidth to also become a data engineer? And that's not even touching on the hours it would take to learn coding skills proficient enough to glean meaningful insights.

Sure, knowing Python or even basic SQL would give you an edge, but let's get real: the likelihood of you being able to single-handedly manage data pipeline engineering and maintain a data pool is slim to none. Learning programming languages from scratch is a marathon, not a sprint. We're talking months of evening sessions after long workdays.

So, does coding proficiency break down barriers to sales and marketing analytics? Without a doubt. But is it practical for a sales leader to learn coding just to be data-driven? Personally, I don't think so. Every case is different, but generally speaking, the ROI you'll get from focusing on sales and strategy far outweighs the benefits you'd reap from learning to code.

4 practical approaches to data-driven sales

Now, let's pivot to some actionable steps you can take to utilize the power of data without needing to become a tech wizard. I'll keep it simple and avoid diving too deep into the technical weeds, so you get a clear picture of how to stay ahead of the curve.

Approach 1: Hiring or collaborating with a data analyst

This is often the first port of call when you're swimming in data. Having a dedicated analyst on your team to continually analyze key business metrics can be a game-changer. Before you decide to add a new seat to the table, though, weigh these pros and cons:

Pros

• Accuracy is King: Human-level accuracy in translating text to SQL (or the business requirement to the actual code) hovers around 92.96%. This high level of accuracy is perhaps the most compelling reason to have an in-house data analyst.

• Data Quality: With a dedicated analyst, you're ensuring that someone is always keeping an eye on data collection and its overall quality. It minimizes the 'garbage in, garbage out' scenario.

• Enhanced Security: Given that data breaches increased from 310 million to 422 million instances in 2022, having someone adept at data security is not just a plus—it's a necessity.

Cons

• Operational Tug-of-War: This individual will likely report to the CTO and be aligned with engineering priorities. While you may get some of their attention and could even convince them to implement Business Intelligence software for your team, be prepared to jockey for priority against other projects.

• High Costs: Expect a starting salary of around $80,000 in the U.S., and the price tag only escalates with the complexity of your data needs.

• Time-Intensive: Finding the right fit isn't a walk in the park. You'll need to navigate the hiring process, align with stakeholders, conduct interviews, and clear other procedural hurdles.

In summary, if you're data-light or budget-conscious, this option might not be the best fit, at least for now. However, having a dedicated analyst is an invaluable asset for any team in the long run. At some point, every company will need a specialized sales analytics unit. But if that's a financial stretch at the moment, here the other things you can do.

Approach 2: Learning SQL (language to analyze data)

I'd be remiss if I didn't mention this. Learning SQL can be a game-changer for your sales analysis. I mean it. But before you dive in, there are a few things to consider:

1. Make sure your engineers are keeping the data clean and reliable.

2. You need to feel okay asking your tech team about what's what in the database.

3. Ideally, you'd have a central place where all your data lives. This could be a unified database that pulls from your CRM, your ERP, and any other customer facing tools you use.

4. And let's be real—you've got to have the time to actually learn SQL.

If everything above checks out then you're in a pretty good spot to actually get ahead. Try to give SQL at least an hour a day. Use that time to practice pulling data and see how it lines up with your day-to-day questions. But before you start making big decisions based on your queries, double-check them with someone who knows what they're doing. Your CTO or a dev team member can help.

If you're looking for some straightforward places to learn SQL, I found these sites super useful:

• SQLBolt: Learn SQL the Interactive Way

• Mode’s SQL Tutorial

Trust me, learning SQL isn't just for tech nerds. It's a skill that can help you make better decisions.

Approach 3: Engaging with CTO and engineers

Here’s another great option if you have good relationships with your engineers. If your company is relatively small, or if your CTO is not too busy (or highly supportive of what your department is doing), you can gain a lot of insights from them. As straightforward as it may seem, there are some considerations to keep in mind:

• Group your questions in one ask. Try not to bother your technical team too often. While urgent questions are exceptions, generally, asking all your questions at once will minimize disruptions and garner more assistance.

• Be specific but not overly so. Communicate the end result you'd like to see, not the activities. If you need a specific metric, explain how you'd like it calculated and presented. Also, offer some details about the tools you use and where the data resides. Whether it should be taken from your CRM or the product’s database, don't leave them guessing.

• Leverage CEO influence. I've emphasized the C-suite because I assume most readers are part of small to medium-sized organizations without a dedicated analytics team. Your CEO can push engineers to prioritize your requests as he might better understand the sales process . The key is to clearly explain the 'why.' Make the benefit to the company apparent, and you're more likely to get what you need.

Approach 4: Mastering business intelligence software

You might have heard this term before: Business Intelligence (BI). Maybe you used it at your last company, read an article, or even have one right now. For those who don’t know what that is, here’s a great article from IBM. But to put it shortly, it’s a sales analytics tool that allows you and your sales reps, without any coding knowledge, to create reports from your database and dashboards with elements you’d like to track.

Here's some of the sales reports BI tools allow you to visualize:

• Deals pipeline

• Real time sales pipeline

• Market and industry data

• Sales funnel conversion rates

It’s really powerful when maintained properly and when you're completely aware of how to use it. And this is where the devil is hidden.

There are 2 important things to keep in mind if you decide to adopt a solution like this:

1. Complex setup. To make it work, your engineers or some consultant/freelancer have to put a lot of technical work upfront. Any BI tool is as good as you set it up. In simple terms, you need to first get all the data flowing from different locations (like CRM, ERP, etc.) into one spot.
   
   Then it should be merged correctly to create a holistic picture of prospects' and users' journeys. And in the end, this spot (database) has to be connected to the actual BI tool which will allow you to create the sales reports.
   
   

2. Upfront learning. The more flexibility you need, the more time you will have to take to understand how this software works. For example, the average time to learn PowerBI (probably the most popular tool from Microsoft) is 4-6 weeks, which you could double given your already limited capacity. It also involves some tool-specific learning; for example, for PBI, you will need to understand DAX to utilize it to its fullest potential.

That being said, if you’re mostly interested in static data like sales revenue, pipeline, and sales cycles you can get by with whatever your developers configure for you. Obviously, you can request them to update the dashboard or add some charts if needed.

Just don’t expect it to happen on the same day. So, it might be a good idea to invest some of your time in understanding the basics of the software, so you can be less dependent on your engineers/analysts and create some reports independently.

The most popular BI tools are Tableau and PowerBI. Just look into those first if you’d like to adopt a solution like this. I’d say each company should have a BI tool to have an overall glance at the sales trends. However, it’s not a good choice for one-off reports that you will never generate again or for reports that change all the time.

What is a data pipeline and why do you need one

When your data is scattered across various tools, it’s really hard to analyze the overall sales performance. You run different campaigns through different platforms, and most likely right now, you use Excel or Google Sheets to aggregate these numbers. Well, you then know better than me that it’s a pain in the rear. So, here’s where a data pipeline comes into play.

Without delving into too many details (which you can do by following this IBM article, the whole idea of creating a data pipeline is to take data from different sources and aggregate it in one place. In other words, it's a substitute for manually jumping between multiple platforms, trying to collect the analytics yourself, and then crafting some pivot tables in Excel.

This brings a lot of value when you use multiple channels for your sales. For example, you may keep client data in your CRM, such as the time it took the deal to go through the pipeline, but then you might not know how much revenue they generated if you don’t connect your product database to your CRM. But even if you do, there are tons of things you won’t know unless you have a well-structured data pipeline, such as the churn rate by channel, customer LTV, and ROI.

Picture this: You do emails and LinkedIn outreach using Sales Navigator, send emails via HubSpot, and make calls using Google Voice. How do you calculate the ROI of each channel? What difference does it actually make for your bottom line or your Northstar? To answer this, you need to pull data from all of these channels into your main database, where you have all the current customer information (like transactions, purchase frequency, volume, etc.).

And to do this, you will practically need to ask your engineers to work on it (try leveraging the CEO influence we talked about earlie) or hire a freelancer to handle it (Be prepared to pay at least 2-3k for a quality job).

And keep in mind, this will only allow you to aggregate the data in one place, which is essential to make data-driven decisions. You will still have to implement one of the steps above to actually retrieve the insights and analyze your sales team performance.

The key metrics every sales team should track

There's already a heaps of information online to guide you on this topic. What I aim to share are the key insights that have influenced decisions made by the Sales leaders I have worked with. I hope this will help you achieve your own goals as well.

One of the most important lessons I've learned is to always align your KPIs with what is important to the business. It's easy to get bogged down in metrics like the number of cold calls your team should make each week, especially when the real opportunity might lie in warm outreach to existing customers to achieve negative revenue churn. This is just one of thousands of examples.

Your first step should be to determine the priority, or North Star metric. This helps define the direction in which you're heading, and it's often not merely revenue. For Amazon, for example, it is the Number of purchases per month. Once you identify this North Star metric, align your entire team and KPIs around it.

Taking Amazon as an example:

• Number of calls per purchases per month

• Customer-facing meetings attended per purchases per month

• Number of purchases acquired per one hour spent on a specific sales activity

If one Sales Development Representative (SDR) makes 10 calls and brings in 20 purchases a month, versus another who makes 400 calls and secures just 5 purchases, you should absolutely incentivize the first person.

So, the main advice here is to focus on quality metrics, not vanity metrics. Whatever you measure, always trace it back to what is essential for the business. Consistently align your low-level KPIs with high-level KPIs. If you have a monthly sales targets, consider it a lag indicator. Understanding and tracking the activities that lead to reaching these sales goals are critical.

How Netflix used sales analytics to boost customer retention

According to Netflix, over 75% of viewer activity is driven by personalized recommendations. This strategic move increased user retention and outperformed the retention rates of Hulu, YouTube TV, and ESPN Plus by 30% from 2014 to 2019. Although Hulu has caught up in 2023, Netflix still maintains a high retention rate of 72%.

The main challenge for Netflix was to compete with numerous other players offering essentially the same thing: movies. So they had to be really creative to retain customers on their platform, especially back in 2016 when people had compared to today. far fewer paid subscriptions

Netflix's main advantage lay not just in the volume of data collected but also in its analysis. Every pause, rewind, or fast-forward, as well as every trailer watched but not followed by a movie, is recorded. This data was then utilized for:

1. Consumer Behavior Analytics: Analyzing viewing habits, times, and patterns to understand what, when, and how users watch.

2. Content Recommendation: Factoring in multiple variables such as viewing history, user ratings, and the time spent deciding what to watch.

3. Content Production: Investing $100 million in "House of Cards" after analytics revealed a significant fan base for movies directed by David Fincher and starring Kevin Spacey, as well as interest in the British version of the show.

And none of this would have been possible without data-driven executives who truly believed that data was the cornerstone for guiding the company. Some will always try to argue that Netflix had the advantage of a large budget and big analytics teams, but let's not forget that they have 238.39 million paid subscribers. While I struggled to find well-documented examples from smaller companies, I've seen similar successes firsthand.

For instance, Facebook once paused all development for an entire month during a period of rapid growth to focus on setting up analytics and better understanding the customer interaction data. One of it’s zero employees shared it with me on a conference.

Utilizing the power of data correctly within your organization can be a game-changer. Data in isolation offers little. Its true power emerges when properly analyzed and interpreted. I hope this article saves you the countless hours I spent researching and implementing the data solutions and enables you to just become a more data-driven, better sales leader.

And for those who find this all too complicated or time-consuming, we created a brand-new solution that we believe is a better alternative for many teams that want to be more data-driven. Datalynx bridges the gap between AI and your data, allowing you to:

• Pull your data sources, like CRM, database or Cloud Drive in one place with a few clicks

• Connect a smart strategic AI advisor to this hub

• Ask analytical questions as you would to a data analyst

• Keep the data on your servers for security reasons

• Create multiple AI advisors for different data sets

• Achieve all of the above without the need for developers or extensive training

Give Datalynx a try

Datalynx is a sales analytics assistant built with simplicity and the needs of sales leaders in mind. We've spent years analyzing available solutions, trying to make them work for our own use and to understand their limitations. So it's not just another analytics tool.

The idea behind Datalynx is to provide AI with the context of your data, allowing you to retrieve any insights you'd like using plain English, without having to follow all the exhaustive steps described in the article, all while maintaining a high level of security.

Join our waitlist, and we'll be excited to share the first version with you for free. And if this message resonates with you, do spread the word to your executives friends. Friends don't let friends guide companies with their gut.