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10 Applications of Data Science in Sales

  • Manisha Sahu
  • Sep 03, 2021
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In times of high-value data, industries cannot afford to overlook it. They are only legitimate to explore innovative ways to leverage data to their advantage. Today companies can collect and generate a great deal of data on their customers, operations, and performance very quickly. However, ample information from CRM, ERP, and marketing campaigns does not directly lead to improved sales and profit figures.


Data science is the catalyst for transforming raw, multi-source data into actionable insights which improve the fundamental content. Companies can change their business strategy to capture maximum value in their market by accessing greater data-backed insight.


According to McKinsey, 72 percent of the fastest-growing B2Bs claim their analytics help them plan sales compared to 50% of the slowest growers. They say their analytics are effective. To the extent that repetition can be a hallmark of the sales sector, data science can be employed in different aspects.


The only thing left then is to identify the high-potential area to make the most of it. That is why we chose to identify the data science in sales that is the most used and hence the most efficient.


( Recommended blog: B2B Marketing Strategies )



Applications of Data Science in Sales


Data science gives sales a great chance to use their data and transform them into meaningful insights which will ultimately enhance revenue. Let’s take a closer look into it.


Related blog - Data Science Tools


1. Customer sentiment analysis


Customer emotional analysis is a means of extracting emotions from communication. So we may comprehend emotions and make use of this comprehension in our business. The analysis of sentiment relies on the algorithms used to evaluate the general attitude to texts available through social media platforms, blogs, or review sites in text mining. 


Automated sentiment analysis techniques enable a workable insight in real-time with a click. These tools emphasize the subtext of remarks taking facts, emotion, and general views into consideration. Besides the overall classification into pleasant, negative, or neutral observations, these emotions can be widespread.


The feedback of customers is what you should look for. The use of tools to analyze client sentiment is inevitable if you want to understand what customers desire and why.


( Recommended blog: Natural Language Processing )

The image depicts the role of data science in sales. They're - customer sentiment analysis - future sales prediction - customer lifetime value prediction - cross-sell recommendation - price optimization - churn prevention - Market basket analysis - inventory management - chatbots - merchandising and, - implementation of Augmented reality

Role of Data Science in Sales

2. Maximization of customer lifetime value (CLV)


The value of customer life is a critical element for intelligent enterprise decisions. The CLV reflects a customer's profit throughout the full term of brand connection. Knowing the lifetime value of your consumers lets you gain an overview of your future company’s perspectives. 


Several metrics, such as the gross margin, frequency of purchase, mean order value, etc. are employed here. Intelligent algorithms completely take care to track, compare and calculate changes in the data. With all these measures in place, you will be able to maximize your client's lifetime value. 


Customized recommendations, custom newsletter campaigns, client loyalty programs come here. The measurements need to be increased. The following steps are simple: Compare the measurements you take, determine the next weak metrics, and repeat.



3. Future sales prediction


The prospect of future sales gives the companies that work with sales enormous relief. Those who sell — have stock and must manage it intelligently. If there are too many items in stock, they risk having an insufficient room or having to sell at discounts for other items. Instead, when things are too little, the sales decline. Future sales can enable these issues to be avoided and better decisions to be taken.


The model of prediction requires specific data. This includes the number of customers acquired, the number of clients lost, the average sales volume as well as the saison trends. In addition, the expectations of sales - changing conditions that can affect sales dramatically - should be predetermined.


Sales forecast systems search in these data for patterns. The patterns observed are further used to measure the general tendencies in the pipeline for forecasts to be precise.


(Recommended blog: Data Mining Tools )



4. Churn prevention


Now when sales players have the skills to anticipate when a client will make the next purchase, it is necessary to predict when a consumer will quit buying.


Customer churn refers to the percentage of customers who have stopped buying and using the product for a specific period. Machine learning algorithms are used to identify trends and features in the behavior, communication, and ordering of customers who have ceased shopping through customer relationship management information.

The image shows the reasons for customer churn may be various: price, product fit, unsatisfactory customer experience, etc. To prevent or at least to reduce customer churn try to lean on your best customers, provide feedback and prompt communication, offer bonuses, ask about your customers’ opinions, and take it into account.

Churn prevention

It is pretty straightforward to determine individuals who are likely to leave their association with a company compared to the characteristics and the periodic changes in the behavior of existing customers. There may be different reasons for client churn: prices, product fit, insufficient customer experience, etc.


Try to rely on and take into consideration your best customers to prevent or minimize their customer churn, provide feedback and communicate promptly, offer bonuses, inquire about the thoughts of your consumers.



5. Inventory Management


The stock referred to the stocking of products and afterward used in crisis times. For enterprises to optimize resources and increase sales, inventory management is therefore vital. Retailers need to effectively manage inventories so that supply stays unimpacted, although sales suddenly rise. The supply networks and inventory chains are thoroughly analyzed to achieve this.


Powerful machine learning algorithms evaluate and supply data in depth and identify buying patterns and correlations. The analyst then evaluates this data and provides a strategy for revenue increase, timely delivery, and inventory management.



6. Cross-sell recommendations


To boost its revenue, cross-sales and up-sellings are exercised by all companies. Complementary products to clients are recommended for buying over-the-counter. During upselling, buyers have the opportunity of buying a top-of-the-line product better than they consider.


Cross-selling advice helps clients maintain and lengthen their relationship with a business. Smart data technologies offer the chance to customize the recommendations that have proved to be a strong tool for upstream sales. Cross-selling requires that a consumer who has previously purchased or intends to buy the extra product being offered.


( Recommended blog: Marketing Management )


The algorithm passes through transaction sales data and provides rules showing that the products are purchased in combination. The role of data science, therefore, is to provide CRM data and transaction data with factual advice. These algorithms assist in deciding which products can be marketed or even put in the catalog on the same page. 


Also, package deals are included for cross-selling. The findings of the study help to make packs of things available at a discount.



7. Merchandising


The objective is to develop tactics to improve product sales and promotions. Customer decision-making via visual chains will be influenced by merchandising. While appealing packaging and branding capture the attention of customers and increase their aesthetic look, rotary goods help to keep their products fresh and new.


Data sets are included with marketing algorithms that gather insights and create customer priority sets that take account of seasonality, relevance, and trends.



8. Price optimization


One of the most arduous tasks of all time is to set the proper pricing. For both sellers and buyers, the price should be satisfactory. It is rather difficult to achieve this equilibrium. Many pricing schemes can be utilized for this assignment. Data science has taken the lead in price definition and considerably enhanced this procedure. Do algorithms contribute to assessing the possible sales promotions?


Models to optimize price evaluate how demand fluctuates with the cost of manufacture and inventory to make the optimal price at various price levels. These models also serve to adjust prices for specific segments of customers. Price optimization affects the satisfaction ratings of clients directly.


Speaking of price optimization, you can also sneak a peek at our blog on Pricing strategies



9. Chatbots — salespeople


The most fascinating application of sales data science seems to be using bots rather than salespeople. Chatbots help automates consumer interactions and reduces the amount of time spent on solving problems. Modern chatbots are enabled to better interpret customer messages through sentiment analysis algorithms.


In addition, chatbots can send hundreds of messages per minute simultaneously. As a result, the selling bots are incredibly efficient. In some circumstances, chatbots have been shown to offer better experiences as the requests are processed instantaneously. The key benefit of hiring a bot is that it saves money.



10. Implementation of Augmented Reality


Augmented reality offers an excellent outlook on sales implementation. The usage of Augmented Reality can provide clients with a much realistically increased buying experience, particularly in online retailers.


Increased reality can primarily be employed to improve product and shelving navigation in real shops and online platforms. Secondly, virtual fitting rooms are available. Customers receive a chance to connect with a product that raises their chances of buying it.


Gamification, entertainment, and visualization are all facets of augmented reality. So last but not the least, Augmented Reality provides a higher level of emotions and feelings and an unforgettable shopping experience to customers.  A new thrilling experience pushes customers to purchase more.



Big businesses that use Data Science


The firms that are using Data Science:




Amazon has access to all customs information, like names, search history, payment methods, and addresses of its customers, which are known worldwide. Amazon makes customized advice and offers effective customer service by taking advantage of all your details.


( Related blog - How Amazon uses warehouse technologies )





The video streaming service provides customers throughout the world with access to all their preferences and viewing patterns. Netflix analyses the information and proposes that the audience discover attractive content and selects movies or series that could serve the interest of particular people.





What does Starbucks do in all its outlets? With the use of data science tools, they study data with them to determine every new opening location in accordance with population, traffic, and client behavior.


It helps them to see if the opening of a new shop in a certain location will succeed and make a considerable profit for the brand.


( Recommended blog: How Starbucks uses technology )





Data science undoubtedly has a positive effect on all industries. Any industry can benefit from data-based and well-structured decisions that are highly accurate. The sales sector is aggressively using data science solutions to their benefit, taking into consideration all the situations in our article. Its advances to sales are largely enhancing customer experience and boosting sales.

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