The Importance Of Data Analytics In B2B Negotiations
Big data and analytics is influencing today’s businesses more than ever. Whether it’s helping a business improve its market share or better communicate its goals, data analytics is becoming a valuable asset in business-to-business negotiations.
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 is 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.
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.
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.
