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Sales is all about the numbers. At the most basic level, the job of a sales (and marketing) department is to generate as many sales as possible for a given investment by the company. The greater the ratio of sales to investment in sales and marketing, the more successful the business is.
It’s no surprise that sales teams were among the first to spot the potential of big data analytics and machine learning. Using technology that ranges from Excel to Apache Spark, eCommerce retailers, manufacturers, and services companies of all sizes are leveraging big data and cloud infrastructure technology to maximize potential sales
In this article, I’d like to take a look at some of the major areas in which sales teams have been able to successfully apply insights provided by big data.
If you take a group of a hundred people, three of them may have a strong interest in buying a specific product, and five of them may make a purchase if they can be persuaded of its benefits. The challenge to a sales team is to identify the right eight people. Cold-calling a thousand people is a waste of time, effort, and money.
The trick here is to determine what qualities constitute a viable lead and which of our thousand potential leads have those qualities. Salespeople have been doing this for centuries, but because we now have access to such a huge volume of data — along with advanced data analytics tools — it’s possible to winnow the wheat from the chaff with great efficiency than ever before. Of course, it’s not perfect, but if a sales team is able to identify twenty possible leads from our group of a thousand for personal contact, they’re a lot better of than they were before.
Using the same basic information we discussed in the previous section, big data allows us to target leads with information tailored specifically to their needs and interests.
Let’s say you manufacture a widget that is of interest to two distinct groups of people: seniors and millennials living in Portland and San Francisco. There’s very little benefit in sending the same marketing messages to both groups. Big data analytics can help us to identify targets for individualized marketing efforts, often letting us drill down to quite specific qualities of groups and even individuals.
Lastly, let’s take a look at price optimization — something that’s of particular interest to eCommerce retailers, where prices may change several times a day in response to changing market conditions. It’s almost impossible to optimize prices for the greatest profit margin and sales if it’s done manually, but some sales teams are using cutting-edge machine learning technology to ingest enormous amounts of information and “learn” optimal pricing strategies that can be applied in real time.
Applying big data analytics to your sales process can bring a substantial improvement to its efficiency and effectiveness — and ultimately to the bottom line of your business.
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