[McKinsey] BigDataFr recommends: Unlocking the power of data in sales

power of data in B2B salesBigDataFr recommends: Unlocking the power of data in sales

[…] Analytics plays an increasingly important role in B2B sales—and high-performing sales organizations take it to a new level to differentiate themselves from the also-rans.
 
You don’t have to search
too hard to find breathless paragraphs on the power of analytics. And there are plenty of examples in the sales world where analytics is delivering significant improvements in growth, efficiency, and effectiveness.
 
In our survey of more than 1,000 sales organizations around the world, we found that 53 percent of those that are “high performing” rate themselves as effective users of analytics (Exhibit 1).
 
Yet for all the tangible benefits, analytics is still a bit of a sideshow when it comes to sales. The same survey shows that most sales organizations today (57 percent) do not view themselves as effective users of advanced analytics. Many companies struggle to benefit from even basic analytics, while some have yet to even dip their toes in the data lake at all.
 
Well-designed analytics programs deliver significant top-line and margin growth by guiding sales teams to better decisions. But that only happens when companies can do two things well: focus on areas where analytics can create the most value, and implement wisely.
 
Focus: Four of the biggest sources of value
 
Forward-thinking companies are using the growth of data analytics and artificial intelligence to expand the frontier of value creation for B2B sales and are generating remarkable results in lead generation, people management, cross-selling, and pricing (Exhibit 2).
 
1. Radically improve lead generation. Analytics is well-suited to improving the accuracy of lead generation and automating presales processes as companies use rich data sets to identify the right customer at the right time.
 
Many companies already use historical market information to develop a detailed view of each area’s sales prospects. Some companies are pushing this further by introducing lead-scoring algorithms based on detailed and granular data sets on each of their prospects. Internal data sources on the customer’s previous history are combined with rich external data such as news reports or social media to generate a “360 degree” view of the customer. These algorithms can then predict which factors truly matter in lead conversion and guide sales strategy accordingly. One IT services company used such big-data analytics to predict which leads were most likely to close—and found that established companies were better prospects than the start-ups it had been focusing on. Focusing its attention on established companies raised its overall lead-conversion rate by 30 percent. […]

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By Charles Atkins, Maria Valdivieso de Uster, Mitra Mahdavian, and Lareina Yee
Source: mckinsey.com

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