Data Analytics and Data Science: How to Use Both for Sales Success
While the terms data science and data analytics are sometimes used interchangeably, in reality, they are different processes that offer different advantages. It’s important to make this distinction because when you fully understand what each of them entails, you can apply them to make your sales and marketing operations much more effective. In this article, we’ll explore how you can do just that.
The Difference Between Data Science and Data Analytics
Before we look at the specific ways each process can help your sales and marketing campaigns, we need to clear up the confusion surrounding the two terms.
Data science
Data science is a rather broad term, which encompasses a range of processes to uncover information. It uses scientific methods and models, along with statistics and maths to gain insights from raw data, including making useful predictions. Data scientists use a wide range of potentially complex tools and technologies to get the job done.
Data Analytics
Data analytics, on the other hand, is more specific than data science. This is because, unlike data science, there will usually be a particular goal in mind, which the analyst will be trying to achieve, rather than just looking for any trends or patterns in the data.
Using Data Analytics in Sales
Now we’ve explored the difference between the two terms, let’s look at some of the ways they can be used effectively in your sales strategy, starting with analytics.
Higher Quality Leads
In order for any business to keep growing, it’s essential that it attracts a steady stream of qualified leads who are likely to have a genuine interest in the products and services offered. Analytics gives you the opportunity to identify which types of consumers are generating the most income for you; e.g. those that buy frequently, or make high-value purchases.
By analyzing your data you can find patterns and trends amongst these customers, which will make it much easier to find similar leads. Having a clear picture of these ideal buyers means you’ll have a better understanding of which channels they’re likely to use, and how to target them effectively with the right types of content, for example.
The same goes for B2B. Once you’ve built up a clear image of who your ideal customers are, you can qualify them based on specific firmographic criteria. For example, if you find you’re expanding well into the Norwegian market amongst SMEs, you could use a Norway business directory and filter your search based on company size and sales revenue.
Find the Right Salespeople
Ensuring that you have the right people on your sales team is essential for long-term success. Analytics can help you make better, more informed decisions when it comes to hiring, as well as training your current staff. You can find the particular traits that are prevalent amongst the very best salespeople and then look for new hires who match these criteria.
Sales analytics can also be used in conjunction with data from human resources in order to give you a clear picture of who your best salespeople are, enabling you to then distribute your accounts accordingly, saving the higher value or trickier ones for the best performers.
Make Sure Your Customers Stick Around
Data analytics can be a very powerful tool when it comes to aiding customer retention and avoiding churn. It can allow you to match customers with the best types of products or services from your portfolio, giving them a much more positive experience and also providing you with the opportunity to upsell and cross-sell more effectively.
Analytics can also be used to help identify patterns in customer behavior which will let you know when you’re in danger of losing a customer. You can then react in a timely manner, offering the customer special deals or offers, asking them more about their current situation or implementing a retargeting campaign.
Find the Optimal Price
Finding the perfect price point for your products and services is crucial. Data analytics helps here by allowing you to use a range of data, from market trends to your own sales histories, to determine the maximum your customers will be willing to pay. It can also be used in the B2B sector to good effect. Instead of relying on previous deals and past experiences, you can use dynamic deal scoring to give you a great advantage during the negotiation phase.
Using Data Science in Sales
While data analytics can help you answer important questions for your sales strategy, data science enables you to look into the future. Predictive analytics, for example, gives you the chance to determine the specific factors and events which result in change, and how these changes affect your performance. You can even use demand forecasting to allow you to predict how much demand there will be for your product or service well in advance.
When you have all of these useful predictions to hand, you can develop your sales strategy way before you start launching products, and it also means you can make much better choices based on the insights you’ve gathered. Ultimately it can save you money by reducing waste, taking advantage of upcoming events, and targeting the right people at the right time.
It All Starts with the Right Data
Whether you’re using data analytics or data science, one of the most important factors in generating accurate and in-depth insights is to use clean and complete data. Having errors in your datasets can lead to false insights, which could end terribly if you make the wrong choices based on them.
Whether it’s your own internal sales data, information from market reports, or from a third-party provider as specific as a Qatar business directory, you need to ensure that it is of high quality and from a reputable source.
The differences between data science and data analytics may be nuanced, but it’s important to understand them in order to use both in the most effective way. If you’re looking to really get the most out of your sales strategy, a combination of both methods will allow you to find insights to help achieve your goals, as well as make useful predictions.
