How to increase sales with Data science

Data is becoming the building blocks across all industries. 

For sales leaders to understand and use the sales, information is the key to a higher return on fx. Advertising spent, higher conversation rate, better and faster lead generating, a bigger return on investment, etc. 

Our data scientist has previously increased a production companies revenue by +65% pr. customer just by optimizing their sales based on a data science analysis. Read about the case here. 

The following chapter will cover 7 areas where salespeople, sales leaders, and executers can increase their sales using data science analyses. 

1. Cross-sell

A cross-sell analysis gives insight about what product an individual customer is most likely to buy, affected by products added to the chart or product click ad.

The cross-sell recommendation is based on previous customers’ behavior similar to the real-time customer. This insight gives the sales team the ability to show specific products the customer most likely will buy – when they are looking at or buying product A. 

Using data science to optimize your cross-sales is done by an algorithm based on previous datasets: sales transactions, customer demographics, customer behavior, etc. The algorithm looks for patterns and relations through the different datasets. It illuminates the factors that affect a sale – with a high level of accuracy. That means the data science provides fact-based individual recommendations to your customer based on previous CRM- and transaction data. 

Cross-sell analysis is one of the most normal Data Science analysis, and I am sure you have seen it if you shop online.  

When you shop online and find a product you like, you will on most webshop be presented for similar or other products based on the product you are looking at. That is an algorithm predicting which products to show you by analyzing previous customers’ behavior. 

2. Predecting sales

Whit a sales predicting analysis, you can predict which sales you will make in the future, whom there will buy which product, and when they will buy it. This gives the sales team the knowledge to increase their sales extremely. It also gives the production guidelines for how many items of each product there has to be stuck. Furthermore, the marketing team will have the insight to create individual campaigns that will get the highest possible conversation rate, better return on ROI, etc.  

Sales forecast

The ability to predict sales helps all departments in the companies. It is creating whole new possibilities for optimization and customer understanding.

The data science algorithm is finding the patterns and clusters from various datasets.

After the patterns are identified, the algorithm sorts the customers into boxes based on which product the specific customer most likely will buy in the future, and when they will buy it.

3. Improve lead generation 

Data science makes it possible to improve a company’s lead generating and optimize the sale process. It is possible to identify the right customer at the right time. Based on previous customers’ behavior – data science finds the patterns there makes it possible to divide the customers after who would most likely buy a specific product. 

Let us take a call company as an example: 

By the use of data science, a call company can divide its prospects up into different groups, based on the results from previous datasets: 

  • Group 1: Customer who are 40% likely to buy product A
  • Group 2: Customers who are 25% likely to buy product A
  • Group 3: Customers who are 14% likely to buy product A
  • Group 4: Customers who are 2% likely to buy product A

If the sales team only has a specific amount of time to sell product A, it makes sense to start focusing on people in group one and then work your way through the groups. 

But the opportunities don’t stop here. You can make the groups more specific by dividing them further and adding more layers to the analysis. 

As an example:

We know that 40% of the customer in Group 1 most likely would buy product A, but when are they most likely to buy the product? 
When adding another layer, we can divide the people in Group 1 once more:

  • Group 1.A: People who are most likely buy product A between 15.00 and 17.00
  • Group 1.B: People who are most likely will buy product A between 11.00 and 13.00
  • Group 1.C: People who are most likely will buy product A between 07.00 and 10.00 

Lead generating based on data science can be more specific than this example. But the bullet point is that it gives you the opportunity to identify which sales strategy there will give you the best turnover possible. 

4. Understanding customer sentiment

Customer sentiment analysis is useful in understanding the customer’s real-time emotions. If you want to improve your service, product, or customer experience – you have to search in your customer’s feedback. Customer sentiment analysis is unavoidable if you want to understand what your customers want when they want it and why they want it.  

The analysis relies on a text-mining algorithm, thereby studying the attitude towards different texts from the company’s platforms as comments on Social Media posts, chat messages, customer service e-mails, etc., can generate real-time actionable insight.

The algorithm identifies and translates the subtexts from the comments and mails into account information, mood, and general opinion.

Besides the general classification as positive, negative, or natural comments, the algorithm identifies the extent of these emotions. 

customer sentimental buttons

5. Improving customer lifetime value (CLV)

Having the CLV insights gives a company many opportunities to maximize the CLV, personalized product recommendations, personalized newsletters, customer loyalty programs, etc. 

A CLV analysis reflects the profit a specific customer brings in the entire interaction period with your brand. Identifying the loyal and appropriate customers is an easy task, but predicting the time of customer attrition, understanding the customers’ behavioral changes there affect the CLV is a more demanding task. 

Whit the use of data science and various dataset as sales transactions, average order value, customer demographic, the geographical placement of the customer, etc. is it possible to identify the areas where a company can cut costs, build retention strategies, formulate sales pitch or improve the inventory plan with the right products. 

6. Churn prevention

Customer churn refers to the percentage of customers who will stop buying your product or services in a particular period. Even if you know your CLV, is it important to predict which of your customers who will stop buying. A churn prevention analysis gives the company the insight to change the factors there make their customer churn, creating a closer customer relation, and change the customer’s experience for the better. 

The reason for Customers’ churn can be many different things as price, product fit, bad customer experience, inefficient webshop, etc.

Data science had made it possible to create an algorithm; there can identify patterns and common trails in customer behavior, communication, and ordering for those customers who are most likely to churn. When you know the characteristics of the customers who are most likely to stop buying your product or service, you have the ability to change the factors that make your customers stop buying. 

7. Price optimization

overpriced logo

Setting the right price is one of the most challenging tasks for companies. The price must be acceptable for sellers and buyers and competitive towards the competitor’s price. Setting the right price for your product or services directly influences your customers’ satisfaction, and it will help you increase your customer lifetime, CLV, and break even.

Data science models calculate how the product demand at different price levels compared to the cost of production, competitors’ prices, and time.

Based on the mentioned datasets, the data science model will generate the price; there will give the biggest revenue. 


There is no doubt about the value and positive effects data science can bring to all types of industries. 

The knowledge and information data science brings to the sales team does not only improve the amount of sales but also increase the customer experience. 

The sales team will reach their KPI’s with fewer efforts, and the ROI will increase. 

All companies have enough data to make one or all of the mentioned analysis. 

We at Borbaki will help you find the analysis for your company. You tell us what insight and results you want – then we will create the algorithm for it. 

Do you want to know more? Just send us a quote, and we will get back to you.