“We have a saying at Altify, which is ‘how do we make it happen as if by magic?’” says Tammy Billington-Dynes, VP of products at Altify, a customer revenue optimisatio n sales platform.
But there’s not a white rabbit in sight on the platform: the San Jose-headquartered firm leverages AI to help guide revenue teams through the sales process.
Its application sits natively in Salesforce – the leading sales platform – and uses AI in two forms, as Billingdon-Dynes tells Encrypt:
“One is augmented intelligence, which is kind of a top-down approach of coaching the seller and the other is a bit of a bottom-up approach which is about productivity features.”
These two approaches reflect the two primary uses of AI across all industries: furthering human capabilities and replacing them entirely by carrying out low-level, repetitive tasks.
The “bottom-up approach” makes use of Salesforce’s own automated tools to carry out tasks such as trawling your inbox for potential connections.
But it is the former – augmented intelligence – that is the key focus for Altify. This takes the form of an AI coach that provides advice to revenue teams throughout the sales process.
As they go through the various stages of a sale, the seller is prompted by the application to enter data, such as who they know within the account and how well engaged the customer is.
“As they do and as the deal progresses we have essentially a rule-driven, advice-based application that takes a look at what we call the key signals,” says Billington-Dynes.
Altify’s engine filters through these key signals – the most important data – and turns it into useful intelligence, such as a potential points of weakness in the sales journey.
Not only does it identify important areas, the application then provides automated coaching to the seller about how to proceed next.
And the augmented intelligence engine can go deeper, says Billington-Dynes, factoring in the seniority of people at the target company and the relationship with that person, as well as competitors for that specific deal.
Target market for AI sales
For sales with a shorter cycle, such guidance might seem like overkill. That’s why Altify’s “sweet spot” is for “B2B complex selling”, according to Altify’s senior director of product marketing Nigel Cullington.
“So big enterprises that have complex products and services that will typically look at long sales cycles, you know – six months plus.”
That could mean, for example, a healthcare division that might have a two year sales cycle with 70 to 80 people on the account plan. Among Altify’s customers are HP Enterprise, Becton Dickinson and United Healthcare.
“So we're sort of the higher end of the contract selling market.”
Altify showed Verdict AI a demo of its platform. Most notably, it provided a clear visualisation of the key people at the company you are selling to and the relationships that connect them, providing alerts where action is needed.
Importance of good data
None of this is possible without data: good AI is dependent on good data. This, is one of Altify’s strengths, says Billington-Dynes.
While it is the revenue teams that are entering the data – which is held and secured by Salesforce – it is Altify that converts it into useful and actionable information. Billington-Dynes claims that it is the scope and variety of data that makes it tick.
“That data is what allows for a richer AI for the seller,” she says.
Crucial to this data-driven approach, and at the forefront of Altify’s model, is "customer revenue optimisation", a term coined by Altify to describe their platform.
“That data is what allows for a richer AI for the seller.”
The idea is to pool the knowledge from everyone at the company that is selling and so gain better intelligence on the company it wishes to sell to.
This, says Altify, “enhances” the standard customer relationship management model by viewing every employee as being “part of the revenue team” adding to the knowledge pile.
And, the more data the revenue team adds, the better the insights. While the technology is very much rule-based, Altify is looking to build on this wealth of data in the future to incorporate machine learning.
“So the application itself can start going 'OK, well when I see an opportunity of this size I have seen that when you don't have access to the key decision maker by stage 4 of the deal, then the deal goes south – unless you go and start building out an inside map and getting that conversation going’,” explains Billington-Dynes.
“So the application itself can learn as opposed to it being poured in just by us and by people reviewing their data through data analytics applications.”
The human touch
Advances in AI might mean that machines do even more of the heavy lifting.
For transactional selling – a sales strategy that prioritises quick sales over building customer relationships – AI is a “big driver of change,” says Cullington, and in some cases can replace the human seller entirely.
However, there is no substitute for the human touch in complex, B2B selling, say Cullington and Billingdon-Dynes.
The technology underpinning robocalls is – to the disdain of many – becoming ever more convincing.
But for complex, multimillion pound deals, automation is simply not viable.
“You need to have a human touch around relationships, understanding and value.”
“If you're selling to enterprise and you're selling complex solutions that you can't automate, selling process at that level, you need to have a human touch around relationships, understanding and value,” says Cullington.
There is no AI substitute for building those client relationships. An AI – for now at least – won’t be able to share a joke with a client, or recognise the emotional signals present in the tone of a customer’s voice.
At this higher level, when a client needs a complex problem solved, a trusted advisor is needed – a human advisor.
Ultimately, it comes down to trust, says Billington-Dynes.
“Trust is the most important point in any decision in a B2B sale. In the kind of enterprise sales situation we're talking about, the customer has to trust their supplier and that's what will help them make their decision. And I do think that the human touch in that situation is what builds the trust.
“People trust the humans that they have interacted with over the process.”