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CRM has recently become the world’s biggest software market, and like many other areas of business, is becoming increasingly influenced by machine-learning and AI. This has big implications for both businesses and their customers.

Customer relationship management, commonly known as CRM, refers to ‘practices, strategies and technologies that companies use to manage and analyze customer interactions and data throughout the customer lifecycle, with the goal of improving customer service relationships and assisting in customer retention and driving sales growth.’

It has also now become the world’s largest software market according to Gartner. Worldwide CRM software revenue amounted to $39.5 billion in 2017 overtaking database management systems revenue, which reached $36.8 billion in the same year.

“In 2018, CRM software revenue will continue to take the lead of all software markets and be the fastest growing software market with a growth rate of 16 percent,” said Julian Poulter, research director at Gartner.

CRM has now become the world’s largest software market with revenue of $39.5 billion in 2017.

This growth has been driven by amongst other things, lead management, voice of the customer and field service management. Gartner also claims to have witnessed a rise in the use of marketing and sales technology within the CRM sphere of business operations, leading to the major vendors offering CRM suites covering sales, commerce and service showing stronger than average growth.

With the introduction of marketing and sales technology to the CRM sector, the demand for software presenting siloed customer information has dropped off, as users increasingly seek a wholistic, 360-degree view of their customers and the ability to integrate with AI and machine learning to dramatically increase their CRM effectiveness.

This is a fascinating example of new buzz-worthy technologies such as big data, analytics and machine learning impacting traditional areas of business and transforming the very basics of how these functions work.

Big data and analytics software can now collect massive amounts of customer data like never before, aggregating till point transactions from all retail stores countrywide and using analytics to discover things such as seasonal trends, big sellers and identify cross-and-up-sell opportunities. More detailed knowledge of customers and buying patterns is affecting sales forecasting and pipeline management.

More detailed knowledge of customers and buying patterns is affecting sales forecasting and pipeline management.

Machine learning technology is increasingly being used to automate best-practices decisions based on strategic input from top-level management. And CRM efficiencies are being optimized via AI technologies such as chatbots, which are used to facilitate and guide customer interactions.

It’s all very exciting, or perhaps scary depending on which side of the fence you sit, and the market seems to agree. Aviso, a predictive sales startup which provides sales forecasting enhanced with machine learning for businesses, announced a funding round of $8 million at the beginning of last year, on the back of having raised $15 million in its previous funding round two years prior.

Bigger firms are getting in on the act as well. In 2015 LinkedIn acquired the predictive sales and marketing firm Fliptop in an effort to boost development of its own Sales Solutions. By integrating Fliptop’s predictive analytics into its Sales Solutions and Sales Navigator business functions, it aimed to better target individuals via these functions and enhance the attractiveness and usability of these products for their users.

And in 2016 eBay acquired SalesPredict, which leverages advanced analytics to predict customer buying behavior and sales conversion, in a move designed to improve its “artificial intelligence, machine learning, and data science efforts.”

Thus using big data and predictive analytics to tailor products and services to customer needs more closely, businesses can effect a win-win situation, potentially driving further sales for themselves as well.

New machine-learning and AI-powered marketing tools promise win-win scenarios for both resellers, and their end-user customers.

So, AI & analytics promises to be a boon to the customer. But what about for the ICT channel business?

As we’ve mentioned on this blog before, here too analytics will play an integral role. By identifying customer buying histories, analytics will be able to provide distributors and resellers with opportunities to cross- and up-sell appropriate products to their downstream customers. In fact, the technology will not only be useful in a sales context, but in an operational one too. This same software can also help channel businesses identify which products or brands to order at the right times, show when products need discounting or replenishing, and help them optimize and efficiently manage their discounts and product markdowns.

By correlating both structured and unstructured sets of data, and driving conversions by providing the right customers with the right messages at the right times, these new machine learning and AI-powered marketing tools promise win-win scenarios for both resellers and their end-user customers.