FOLLOW

FOLLOW

SHARE

​The Importance Of Data Analytics In B2B Negotiations

Big Data analytics is changing the work force through how businesses communicate and negotiate

2Aug

Big data and analytics are influencing today's businesses more than ever. Whether it's helping a business improve its market share or to better communicate its goals, data analytics is becoming a valuable asset in business-to-business negotiations.

Generating leads and increasing sales

An important objective of business negotiations is to generate leads and boost sales. Big data - the enormous amount of information coming through the internet - can help businesses attain this goal by accurately identifying their consumer pool.

To get the right insight, however, requires businesses to step beyond conventional data analysis. Successful corporations like Amazon and Redbox are using advanced data analytics to identify what Harvard Business Review calls 'the micromarket.' As the reviewers wrote, this business strategy requires aligning sales with opportunity; for instance, using data to identify and cover regions where there are minimal sales coverage and competition and high customer density. HBR cited one chemical company whose account growth rate went from 15 to 25% in just one year, thanks to the micro market strategy. It applied advanced data analysis to identify fast-growing regions where its market share was just 10% and reassigned its sales force accordingly.

First Tennessee Bank is another company that demonstrates how useful data is. The bank wanted to double its annual sales of online accounts. To achieve that goal, it leveraged IBM's Digital Analytics software to gather real-time customer behavior on its website and mobile app; using its campaigning software Unica, FTB was able to see what consumers need and how to sell and cross-sell more online accounts. This resulted in a 600% increase in return on investment on its marketing campaigns, a number beneficial for business negotiations for obvious reasons. Moving to an online presence instead of a typical rented building store has not only increased revenue but turned First Tennesee Bank to an online format. 

Reinforcing sales team

Having a credible sales team is almost mandatory in B2B negotiations; it's not rare for a company to use big data to build its sales force, alongside its IT team.

Data analytics tools have helped reveal traits of talented salespersons and successful marketing tactics. McKinsey's acquisition QuantumBlack, for example, can function to identify certain behaviors of presale experts who are most likely to sell deals. One business used that information to train their employees accordingly, which resulted in 6% reduction in their cost-of-sales and 2% revenue increase.

Decreasing risky business transactions

Businesses like to negotiate with those with an established business credibility; this leads to the necessity of decreasing risky activities that could hinder a business's performance. One bank used QuantumBlack and predictive analytics to identify and stop fraudulent transactions. The software's machine learning model looks at a large amount of data and 'learns' the traits associated with suspicious transactions. The information is then applied to incoming transactions, which receive a 'high risk' score. Finally, bank employees are alerted of risky transactions, allowing them to stop fraudulent activities before they go through.

In the above case, data analytics helped the bank identify around 35 high-risk activities out of several million transactions per day. It was able to catch $100,000 of fraudulent transactions within the first few weeks of using advanced data analytics. Since then, other banks have become interested in data analytics for fraud detection; popular software used include MapR Streams, which runs on Apache Spark, provides Hadoop clusters, and streams credit card transaction events in real-time. Transactions are checked for authenticity using Apache's own machine learning model.

There are numerous unnamed aspects of data analytics that could benefit a business-to-business negotiation; predicting consumer trends; automating pre sale processes; pricing sales items. Such strategies, if applied effectively, can further boost sales and build company credibility. Equally important, big data and analytics help a business more effectively communicate its performance and needs and go into negotiations with full transparency. Many data analytics software packages have reporting tools that can transform data into easy-to-understand visualization so that corporations can better understand each other.

As businesses are depreciating old-fashioned pen-and-paper surveys, data analytics is becoming an obligatory tool for business-to-business negotiations; for increasing a company's sales; improving business operations; building a successful and credible sales team. Further, big data itself is an asset in rapid evolution as the data analytics industry continues to make data processing and computing more efficient, resulting in improved prediction models that can help companies visualize, and realize their current and future sales prospects. 

Data analytics tools have helped reveal traits of talented salespersons and successful marketing tactics. McKinsey's acquisition QuantumBlack, for example, can function to identify certain behaviors of presale experts who are most likely to sell deals. One business used that information to train their employees accordingly, which resulted in 6 percent reduction in their cost-of-sales and 2% revenue increase.

Comments

comments powered byDisqus
Turkeyss

Read next:

What’s On Data Analysts’ Plates This Thanksgiving

i