{"id":10190,"date":"2024-04-12T09:56:59","date_gmt":"2024-04-12T09:56:59","guid":{"rendered":"https:\/\/www.salesforce.com\/?p=10190"},"modified":"2024-04-12T10:01:01","modified_gmt":"2024-04-12T10:01:01","slug":"why-data-scientists-key-in-sales","status":"publish","type":"post","link":"https:\/\/www.salesforce.com\/eu\/blog\/why-data-scientists-key-in-sales\/","title":{"rendered":"Data Science Sells: Why Data Scientists Are Key in Sales Today"},"content":{"rendered":"\n

Data science and<\/em> sales \u2026 really<\/em>? You might not think the two have much in common on the face of things, because sales is an art and data science<\/a> is \u2026 erm \u2026 a science. But you\u2019ll find they do! There\u2019s actually a heap of crossover.<\/p>\n\n\n\n

For example, most sales organisations are operating under a deluge of data<\/a>. Too much to make sense of. Even so, The Salesforce State of Data and Analytics 2023<\/a> 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<\/a> report, sales reps only spend 34% of their time actually selling, with the rest spent on admin. Enter the data scientist \u2014 they help sales teams find valuable insights in raw data, and then visualise it in useful ways for efficient decision-making.\u00a0<\/p>\n\n\n\n

So, in this blog, we\u2019ll 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\u2019re in luck \u2014 read on.<\/p>\n\n\n\n

The role of a data scientist in sales<\/strong><\/h2>\n\n\n\n

As you\u2019re 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\u2019re answering emails and calls, attending meetings and follow-ups, making notes and forecasts or busy with quotes and negotiations, the list goes on \u2026 and that\u2019s 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). <\/p>\n\n\n\n

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\u2019re 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<\/a> that can help improve sales productivity. The result? More high-quality leads and more deals, faster.<\/p>\n\n\n\n

\u201cWhy is this important to my day-to-day role?\u201d, you might ask. Well, globally 69% of sales reps say<\/a> 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.<\/p>\n\n\n\n

For example, it\u2019s critical to the development of CRMs<\/a> \u2014 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 \u2014 raw \u2014 and wouldn\u2019t be much of a boost to your sales productivity. The CRM wouldn\u2019t produce sales insights \u2014 it\u2019d just be a large database that stores information.<\/p>\n\n\n\n

Harnessing the power of big data in sales<\/strong><\/h2>\n\n\n\n

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\u2019t handle it. It\u2019s too big. So, modern methods like data science are essential to help to understand and learn from it.<\/p>\n\n\n\n

Yet, Forrester\u2019s report<\/a> 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?<\/p>\n\n\n\n

Let\u2019s 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 \u2014 think of it as a circle of information about the customer, with them in the centre. The State of Sales<\/a> report found that 81% of global sales teams believe this 360 view is important, and Salesforce Einstein 1<\/a> 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\u2019s easier to keep them happy.<\/p>\n\n\n\n

Another example could be using big data<\/a> to inform your pricing strategy<\/a>. Say you\u2019ve a gut feeling that your products are too cheap and that you should increase their prices. It\u2019s risky to make those changes when you don\u2019t know that for sure \u2014 it\u2019s 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.<\/p>\n\n\n\n

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Integrate All Customer Data into One Streamlined View with Salesforce Einstein 1<\/h2>\n\t\t\t\t\t\t\t

Want to personalise every experience along the customer journey on the world’s #1 CRM?<\/p>\n\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t

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Predictive modelling for CRM success<\/strong><\/h2>\n\n\n\n

Now that all this data has been harnessed, your job as a data scientist isn\u2019t over. How does this data work benefit the wider team?\u00a0<\/p>\n\n\n\n

Well, one of the main ways is through predictive modelling. In the context of CRM<\/a>, this means using techniques like statistical algorithms and Machine Learning<\/a> 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.<\/p>\n\n\n\n