Data Science Sells: Why Data Scientists Are Key in Sales Today

Data science and sales … really? You might not think the two have much in common on the face of things, because sales is an art and data science is … erm … a science. But you’ll find they do! There’s actually a heap of crossover.

For example, most sales organisations are operating under a deluge of data. Too much to make sense of. Even so, The Salesforce State of Data and Analytics 2023 report found that 94% of business leaders believe they should be getting more value from their data. And shockingly, according to The State of Sales 2023 report, sales reps only spend 34% of their time actually selling, with the rest spent on admin. Enter the data scientist — they help sales teams find valuable insights in raw data, and then visualise it in useful ways for efficient decision-making. 

So, in this blog, we’ll look at the importance of this unlikely duo in modern sales. Are you curious about the role of a data scientist in sales? Do you want to know more about the techniques used? And how they can help you sell more? If so, then you’re in luck — read on.

The role of a data scientist in sales

As you’re probably aware, a day in the life of a sales rep involves lots of administration. You need to be organised given your schedule. Whether you’re answering emails and calls, attending meetings and follow-ups, making notes and forecasts or busy with quotes and negotiations, the list goes on … and that’s a whole lot of data to be dealing with. And it all needs to be recorded and tracked (if you want a good sales process, that is). 

Managing that level of data is impossible for a human, especially if you want to get anything else done with your day, so this is where data science comes in. This is the field that uses algorithmic scientific methods to analyse the large amounts of data you’re working with and derive valuable insights from it. A sales data scientist is someone who uses these techniques with a specific focus on the sales process. The goal is pretty simple: Find data-driven insights that can help improve sales productivity. The result? More high-quality leads and more deals, faster.

“Why is this important to my day-to-day role?”, you might ask. Well, globally 69% of sales reps say that selling is increasingly difficult due to more competition, a tougher economic climate, and new ways of working all to contend with. So, you need all the help you can get, and sales data science is one of the techniques that makes that possible.

For example, it’s critical to the development of CRMs — the technologies that you use to log all that activity listed above. Data science helps make sure your CRM can integrate all those different data sources, understand them, and turn them into usable insights. Without the training that sales data scientists put into CRM, the raw data would stay that way — raw — and wouldn’t be much of a boost to your sales productivity. The CRM wouldn’t produce sales insights — it’d just be a large database that stores information.

Harnessing the power of big data in sales

As a data scientist working in sales, what would you be doing during a typical day? A big part of the job is the analysis of big data. Trying to make sense of terabytes of data is hard. All the traditional methods and techniques used before can’t handle it. It’s too big. So, modern methods like data science are essential to help to understand and learn from it.

Yet, Forrester’s report on AI-powered CRM found that 27% of global business leaders have ad hoc data initiatives and lack a formal strategy to implement them. So, how can the work of data scientists help put in place efficient data management solutions?

Let’s go back to our CRM solution. A CRM collates all this big data into a single, shared view for a deeper understanding of your customers. You might hear this described as a 360-degree view because you can see the customer from every angle — think of it as a circle of information about the customer, with them in the centre. The State of Sales report found that 81% of global sales teams believe this 360 view is important, and Salesforce Einstein 1 offers just that, capturing customer data from every step of their journey in one place. And when you know what makes your customers tick, it’s easier to keep them happy.

Another example could be using big data to inform your pricing strategy. Say you’ve a gut feeling that your products are too cheap and that you should increase their prices. It’s risky to make those changes when you don’t know that for sure — it’s just an instinct. But with big data, you can analyse all the different variables that a change in pricing makes to your business and its sales and profits, so you can understand the impact it would have on your business before you go out on a whim and make a (potentially) bad move.

Predictive modelling for CRM success

Now that all this data has been harnessed, your job as a data scientist isn’t over. How does this data work benefit the wider team? 

Well, one of the main ways is through predictive modelling. In the context of CRM, this means using techniques like statistical algorithms and Machine Learning to analyse historical customer data and predict future outcomes or behaviours. Here, the goal is to anticipate customer needs so that you can provide better service and get closer to your customers. So, what does that look like? Below are a handful of examples.

• Lead prioritisation: These insights help you understand who your most valuable leads are, so you can focus on them first.
• Forecasting accuracy: This will help you forecast potential sales with greater accuracy.
• Predictive insights: These allow you to see what may happen in the future and show the causes of those predictions. 
• Recommended next best actions: With these, you gain an understanding of what you should do next to give you the best chance of a positive outcome.  

Let’s look at this last example in practice. Imagine you’re due to email a customer who hasn’t engaged with your business in months. In this situation, the predictive model might suggest that you send a discount on their favourite product to help you win the customer back. It’s a totally different way of working — one that keeps you a step ahead of your customers, so you can get the best results.

The future of data science in sales

What does the future of data science in sales look like? You don’t need to be a fortune-teller to see that AI will play a big role in the future of sales data science. The world is already experiencing an AI gold rush, and this trend is set to grow further: The State of IT 2023 shows that 86% of IT leaders worldwide believe that generative AI will soon have a prominent role in their organisations. So as a data scientist or a sales professional, you need to engage with AI and see how it can help you.

According to Forrester’s report, The Journey To AI-Powered CRM, 89% of respondents said AI strategy and capabilities are important when partnering with a CRM vendor. Fortunately, with Einstein AI, we’re already ahead of the curve. As the first comprehensive AI for CRM, our solution unifies your data, AI, CRM, development, and security into a single, comprehensive platform. It empowers IT, admins, and developers by aiding the fast development of generative apps and automation. And of course, it helps sales efficiency to close more, higher value deals. It’s no wonder our Generative AI Snapshot Research Series report found that 61% of salespeople believe generative AI will help them better serve customers.

The bottom line? Data science sells

Whether you’re aware of it or not, the truth is that data scientists play a massive role in the world of sales today, and they’re growing in importance. If you’re a data scientist, you’re needed to make the data available to your organisation more useful, and if you’re a sales rep, your job is to use that to close more deals. So, while sales may be an art, it’s time to trust the science, too.