AI has the power to connect your salespeople, processes and customers in ways that completely transform your sales processes. It’s about building a company where everyone and everything is geared for growth.
AI in sales and elsewhere is often discussed in the context of automation. For example, the automated buying of advertising space, or personalising marketing and offers. But the role of AI is much broader than just automation in the digital channel when one is building a Level 3 system in sales transformation.
AI enables the building of highly-effective systems that connect the efforts of various channels. This enriches the internal and external insights that are used for sales success, making it possible to hyper-personalise offerings to customers and the advice given to your own sales personnel. AI can also help in assimilating daily activities into a strategic agenda.
In short, AI automates and enables new behaviours that lead to systemic sales successes across an organisation.
AI is often discussed in terms of its algorithms – Bayesian inference, deep neural networks, regression, kNN (k-Nearest Neighbours Algorithms), etc. – as well as in terms of capabilities like prediction, recommendation, matching, clustering, natural language processing, and more. It is important to have some level of understanding of these topics, but there are already thousands of ready-made tools available that make sense of this complexity.
The challenge is not in understanding the AI tools for sales, but in first knowing which sales problems are worth solving, and then understanding what the most suitable tools are. Whether using existing off-the-shelf tools or ones that are custom-made, one needs to know what the problem is before the tool can be adapted to solve it.
Typically, B2B and B2C sales are seen as very different. B2B is person-to-person driven, B2C is digital-channel driven. But this division is blurring as digital tools bridge the gap, allowing efficient person-to-person interactions with consumers, and intelligent digital interactions with B2B buyers.
Person-to-person sales behaviour is typically at one of two extremes: it either follows a strict one-fits-all process, or it's laissez-faire autonomous and takes neither the client nor the sales person's preferences into account.
AI-powered technologies, however, enable structured and personal support for sales people in areas such as:
Sales can also be seen in light of helping clients succeed via matching their products and services to client needs. Typically, there are many more people coming across client needs than just the sales people. Therefore, in building the Level 3 system it is important to harness every client interaction – even a maintenance visit or a support call – and turn it into an opportunity to identify areas to serve your clients.
One of the systemic changes enabled by the AI-powered sales tools listed earlier is that we can harness everybody in the organisation towards sales. We can help front-line people to identify the right opportunities and connect these to the company offering. In a similar fashion, the insights of front-line people feed insight to a company's dedicated sales personnel.
Companies, especially large enterprises, need to manage complexity by dividing the body corporate into clear functions and smaller units. The downside of this is that we lose a holistic/systemic approach to running the enterprise, where feedback loops could be used to optimise operations.
Data & AI can re-build connections via different parts of the organisation, and enable us to feed insights in various directions with the key aim of helping different functions and units to succeed.
Naturally, some tightly-coupled supply chains – such as in the automotive industry – have been sharing information in the way for a long time, but data and AI enable more and more industries to reach the same level.
Level 3 companies use AI to connect sales to their downstream processes and external value network – all in an automated manner.
While building AI-powered sales transformation, we need to remember that an increasing number of similar tools are available on the buying side too, where customers can optimise the buying process, their selection, and pricing (via auctions, for example).
One interesting consequence of this data & AI transformation on the buyer side relates to marketing, especially brand marketing, where grandiose claims like 'Bank Y – For All Your Banking Needs', are traditionally hard to validate. The power of these claims lies in the fact that buyers do not have the tools or the ability to analyse each offering in detail, and to connect the benefits with their needs.
This may change when we have sophisticated AI-analysis capabilities that compare the offering side to one's own needs, and make minimally-biased decisions on which offering is best for the buying organisation. The natural consequence of this change is that vendors need to make sure they provide data and facts that enable automated comparisons.
The data & AI transformation space is filled with challenges around data platforms that fail to deliver business value, and tech-driven experiments that fail to materialise in production value. Many implementation projects lack a clear business agenda, which alienates business leadership from the efforts.
In order to avoid these pitfalls, we recommend the following:
Learn how to thrive at the turning point of digital sales by reading The Digital Sales Transformation Handbook. Discover how digital sales transformation is changing companies, and how your business can leverage this change through organisational development, customer experience, ways-of-working and technology. Featuring interviews with industry experts, such as Marta Dalton (eCommerce Director for Unilever and Coca-Cola previously), Risto Siilasmaa (Founder of F-Secure) and Antti Kleemola (CDO of VR, Finnish Railways).
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