Top 10 applications of big data in marketing and sales

The purpose of this article is to provide an overview of the many trends that have revolutionized marketing and sales for big data. It combines ten related reports and introduces the application of ten big data in today's marketing and sales strategies. One area where growth is fast is pricing: managing prices and disseminating and optimizing pricing through sales networks. Today, with big data algorithms and advanced analytics, price optimization for a given product or service is becoming more and more important. Even in a less flexible commodity-driven industry, simplifying daily pricing decisions is already very common.

Big data's huge contribution to marketing and sales

Current aspects of big data that can assist sales include: improving the quality of potential customers, improving the quality of sales opportunity data, improving target customer development accuracy, regional planning, and winning interest rates. In marketing, big data is also indispensable. In addition to providing conversion rate strategies, sales forecasting, growth revenue and customer lifecycles, there are also ways to help us determine what is most effective at each stage of the sales cycle and how to improve our customer relationship management system. If the company is providing cloud-based enterprise software services, Big Data can also provide information on how to reduce customer acquisition costs (CAC), customer lifetime value (CLTV), and manage many other customer-driven metrics for operating cloud business. It is vital.

Here are the top 10 applications for big data revolutionary marketing and sales:

1. Big data makes it possible to optimize the pricing by maximizing the pricing strategy based on the relationship between each customer and each product.

McKinsey's analysis found that 75% of a typical company's revenue comes from its standard products, and the company can't set the best price at 30% of the annual decision on hundreds of pricing standards. Assuming that sales volume has not decreased, a 1% price increase can result in an increase of up to 8.7% in operating profit, and pricing has a significant potential for improving profitability.

2. Big data can lead to greater customer response rates and deeper customer information.

According to the survey below, Forrester's research found that 44% of B2C marketers are using big data to improve customer response rates, and 36% of marketers use data analysis and data mining to gain more deep customer information. Plan more relationship-driven marketing strategies.

3. Customer Analysis (48%), Operational Analysis (21%), Fraud and Compliance (12%), New Product and Service Innovation (10%) and Enterprise Data Warehouse Optimization (10%) are the most common big data today Sales and marketing case.

A recent study by DataMeer found that customer analytics dominates the use of big data in sales and marketing. There are four key strategies to support this trend: increasing potential customers, reducing customer churn, increasing investment per customer, and improving existing products.

4. Use big data to embed analytics data into contextual marketing.

Many companies' marketing platform technologies are rapidly improving, and the basis for supporting this trend is changing customers, sales, services, and channel requirements that do not match existing systems. This has caused many marketing departments to be unable to fully integrate data and processing. Big data analytics can create scalable system analysis that can alleviate this problem to some extent. The image below is from Forrester's research and is available for free download on the SAS website, combined with intuitive and participatory situational marketing tools and techniques: Corporate Marketing Technical Manual.

5. Big data analysis can improve customer relationships and make marketing programs more successful.

Through big data analysis, defining and guiding customer development, marketers create greater customer loyalty. The picture below is from SAS-sponsored Forrester's research, which analyzes how value is provided throughout the customer's life week (the distance between the two lines in the graph represents the value of the data analysis).

6. The biopharmaceutical industry has begun to use geographic analysis to optimize sales strategies and market launch plans.

McKinsey found that biopharmaceutical companies typically spend between 20% and 30% of their profits on sales and administration. If these companies can accurately deploy sales and marketing strategies in areas and areas with more sales potential, they will be able to immediately reduce this cost.

7. 58% of Chief Marketing Officers (CMOs) say big data has the biggest impact on search engine optimization and marketing, email marketing and mobile marketing.

54% of CMOs believe that big data and analytics will play a crucial role in their marketing strategy development process for a long time.

8. In a recent survey, Forbes conducted in-depth tracking of market leaders in more than a dozen industries and found that they gained greater customer engagement and customer loyalty through the use of advanced big data analytics.

The study found that in more than a dozen industries, sector-specific analysis and the professionalism of big data are key to determining the success of a strategy. At the same time, when the pilot program achieves positive results, the culture of the entire enterprise will undergo a large-scale and profound transformation.

9. Big data allows companies to have a more accurate understanding of each of their business growth points.

Increasing revenue, reducing costs and reducing operating costs, big data is now playing its role in these three key areas and turning into real business value. When effective use of advanced big data analytics, the value-driven points of an enterprise will be more effectively measured.

10. Big value-based customer value analysis has enabled marketers to provide customers with a consistent and versatile user experience across all channels.

Customer Value Analysis (CVA) has recently become an emerging hot topic as a series of big data-based technologies accelerate the sales cycle while maintaining and measuring customer relationships. Today, CVA is a collection of technologies designed to carefully maintain a high-quality, comprehensive customer experience across the sales network.