Winnie Palmer, Head of EMEA Marketing at Seismic, gave this presentation at the Future of Sales Festival in June 2021.

My name is Winnie Palmer, and I head up marketing for Seismic in EMEA. In this article, I’ll be discussing how to deliver compelling buyer experiences and maximize your go-to-market effectiveness with AI-guided selling.

My background

As I said above, I head up marketing for Seismic in EMEA. Seismic is a pretty interesting company in my opinion. We’re currently scaling and internationalizing the business globally and pushing for an IPO. We work across capitalization in verticals to help solve go-to-market (GTM) effectiveness problems for many revenue leaders.

A bit about me personally: I've been within the GTM organization for more than 20 years, predominantly in the technology industry and in the b2b SaaS space.

Exploring the broader context

So, let's have a look at the broader context just to set the scene before we delve into the technicalities.

It is quite important to take a note to contextualize the environment within which we operate. A lot of people talk about customer experience being key to help businesses to compete, and to win, especially in the virtual selling world as a result of the pandemic.

Most, if not all, of marketing and sales communication had to migrate online, so it is quite important to think about how we deliver compelling buyer experiences in that environment.

B2C expectations in the B2B world

There are quite a few factors at play, as we all know the expectations of the buyers are changing and a lot of that comes down to you what we as a consumer experience in the B2C world, as the analogy goes.

Let's take Amazon for example, in relation to this talk, a guide to AI-guided selling. How is Amazon doing it in the B2C world? We can now search for products sold through various sellers, we can consider recommendations based on our own past purchases, or even based on other customers' purchase histories.

Additionally, you can read through consumer reviews, and all this information is categorized, analyzed and presented to enable the buyer to make a very truly informed purchase decision right away.

This isn’t just about a purchase decision, but also the product delivery. On Amazon, purchases will arrive on our doorstep very much the next day if not sooner. So these compelling buyer experiences are influencing the expectation in the B2B world as well.

The market is noisier than ever

Now, beyond that, the second point is about competitive dynamics. With the proliferation of products and services available nowadays, it’s becoming increasingly difficult for the customer to navigate through the clutter. It’s very noisy out there, there’s too much.

What that means is that it’s very, very important for organizations to be super crisp with their differentiation. I’ll add that it's not just about the story, but also how the story is told, the execution.

Sellers need support to cope with the evolving landscape

Let's just have a look at salaries for a moment, for sales enablement professionals and sales reps reading this article.

It’s a fact that sellers today need more guidance than ever before. When you think about it, sellers see the increased pressure to meet the expectations of buyers, which are really high now. Simultaneously, they're trying to differentiate the product in this highly competitive market. There's a lot of pressure.

Now, as we think about onboarding new sellers as they enter the workforce. These new sellers are also actually very used to having everything they need to know right at their fingertips on their smartphones.

So your new sellers, and really your entire salesforce, are expecting to get the information they need to be able to sell effectively, ideally in an instant. So this practical reality is changing and shaping how organizations go to market and how GTM professionals execute.

The reality is, and everybody knows this, that over the next 10 years 50% of the leading corporations are likely to churn. The point here is that the companies that are adapting and addressing these challenges will be the one who will succeed.

Buyer experience is key, but their expectations aren’t being met

What’s really quite interesting is that the buyer experience is becoming increasingly important in influencing the buyer’s decisions. Nearly 85% of buyers say that the experience is as important as the product or service itself. However, only about 25% feel like their last buyer experience was a positive one. That’s a big gap.

I’d suggest, if I may, that the customer experience is not a product or service problem, but rather a storytelling problem. The experience your buyer is seeking during the buying process is almost 100% dependent on your GTM team's ability to execute your story.

Whether it’s branded content, sales pitches, your employee advocacy, or your customer referrals. It’s about the content, the message, and how we ensure it enables the entire organization across time zones, geographies, and team languages in order to deliver a consistent, coherent customer experience at scale.

Now, this is the big gap between what customers are expecting and what they're actually experiencing. This is also a huge opportunity. Delivering positive buyer experiences can be a super effective GTM strategy which will help your organization to maximise its GTM performance.

It's not just me saying that: these statements are backed up by studies. Above is an example from a McKinsey study that shows companies that deliver better buyer experiences translate that directly into their business's performance in terms of higher win rates, a faster sales cycle, and greater shareholder values.

AI-guided selling

Now, within that context, let's think about the role of artificial intelligence in data and information. Here's just a quick definition of what AI-guided selling is:

AI-guided selling combines human intelligence and machine learning to create more intelligent buyer engagements

Really, AI-guided selling in the way we would like to define it, is the methodology, the process, or the technique that combines human intelligence with machine learning in order to create a more intelligent, wider engagement. This in turn drives a better buyer experience and better business outcome as discussed before.

So, in the world of virtual selling, think about this a little bit: every buyer engagement generates data that drives and delivers insight into sales activities. For example, how and when a specific piece of content was shared, how the buyers interacted with these content pieces, the particular sales play which led to a close win opportunity. This information is all captured because we are selling virtually.

When data like this is aggregated across multiple systems within your entire GTM tech stack, the organization can then create a comprehensive view of sales activities. When you combine contextual data and collect the data at scale, it becomes a lot more valuable than any data an individual seller can analyze.

Algorithms can be deployed to help identify patterns, behaviours and insights that can lead to new revenue opportunities. In a way, machine learning supports sellers by transforming the sales data to create more focused recommendations for the revenue teams.

Your sellers are enabled with just-in-time information to deliver a truly engaging and compelling buyer experience dynamically in real time.

Now, this is not some far-fetched, fictitious reality. AI-guided selling is here and we are seeing early adopters are beginning to use it.

The number of companies, for example, implementing AI-related technologies has grown by around 270% over the last four years, as artificial intelligence continues to play an increased role in everyday life. For example, again with Amazon, they have Amazon Alexa.

These implementations are becoming more common rather than the exception. AI-guided selling is the digital assistant model, but being adapted for sales. Artificial intelligence and machine learning can and are being used to maximise GTM effectiveness, and changing the dynamic between the sellers and the buyers.

Getting a buyer’s attention through unique experiences

In this world of virtual selling, it’s really challenging for sellers to get the attention of prospective buyers, especially when it comes to retaining their attention and nurturing that relationship.

It’s very, very noisy at the moment in the digital world, and cutting through the clutter is really critical. How do we do that? Ensuring the curation of your messaging is consistent.

This is important because in B2B, the customer experience arguably is tied up with counting consumptions. It comes down to how they consume information, what style they like, and as a whole the presentation needs to have a clear story which is understandable and resonating with their particular problem.

You need to be able to conduct that level of presentation in a highly tailored customized fashion, but it must be at scale for consistency.

If you look at the buyer a little bit more, they're just more educated than ever. Research has shown us that most buyers will engage with about 10 or more content pieces before they even connect with the seller.

We are always searching and buying online, trying to learn for ourselves before we even engage with a seller to have a conversation. So as buyers become more educated, they want consistent linear content experiences in seller engagement. They want it exactly clear to them what they are buying, what the value proposition is, how and why that matters to them.

What artificial intelligence and machine learning can do is to help B2B sellers to deliver that content experience that is very unique to the individual buyer, and providing the experiences that are proven effective based on data and contextual insights.

Let me just be clear, AI-guided selling will not replace traditional seller engagement, AI and machine learning will complement the work sellers do.

So when sellers prepare for buyer engagement, and start thinking about emails, meetings, and so on, artificial intelligence as your digital assistant can provide you with insight-driven recommendations on what content to share or details to keep in mind based on previous experiences and previous data.

That's the algorithm part, the seller still maintains the authority to approve AI-guided recommendations.

How AI-guided selling will work in practice

How it’ll work is like this: the task will be approved by the seller on the seller's input. The seller inputs, for example, the target persona, segmentation, deal lifecycle, even the size of the deal. Then, the AI system goes away and works on the analysis and comes back and fulfills the recommendation by delivering compelling personalised content for buyer engagements.

Sellers in this case will always be the driver, and they will be ultimately responsible for the social and relationship-building aspects of a sale.

AI-guided selling: revenue increases, and costs decrease

Below is another piece of research that I would like to share with you, conducted around businesses that have adopted artificial intelligence and machine learning over the years, for various purposes.

Overall, what we’re seeing is that not only is AI technology able to enable greater productivity by assisting in managing routine tasks, they also help identify patterns that lead to more intelligent business decision-making.

So both of these business advantages support cost savings as well as new revenue generation. Now this study is from McKinsey, and they found that AI-guided selling provided an uptick in revenue cost savings among businesses that adopt AI.

Per this graph, 44% reported cost savings within the business units that have deployed the technology. McKinsey has also found that high-performing organisations that have adopted AI are three times more likely to report revenue gains greater than 10%. So again, this is early evidence of the business impact that AI-guided selling can deliver to the business in concrete terms.

How AI can change sales and marketing

Let’s go a step deeper into the organization, and how artificial intelligence might change the future for sales and marketing, and customer success is potentially part of the picture as well.

Artificial intelligence and machine learning are designed to support the work that GTM teams already perform as support, functioning in this digital assistant model. This technology is very effective in a supporting role that enables marketers to create more effective campaigns, and sellers to have more productive conversations with buyers.

Marketers will be able to get the insights that enable targeted campaigns instead of performing manual research. AI can help with recommendations on specific tactics and perhaps even SEO to increase site traffic.

For sales it can have a strong impact, providing a system for day-to-day activities that could be almost like your personal assistant. It really literally is just that, there to offer a seller recommendations based on the buyer’s previous interaction data. Maybe the data shows this piece of work performed really well with other buyers similar to this particular prospect, and the AI “assistant” will recommend it.

This removes a huge amount of guesswork and then enables the sellers to engage and build stronger relationships with buyers. The highly-technical analysis is enabled by the algorithm, as the patterns are just too complex for an individual person to analyze.

Integration is key

The most important part which we haven't talked about is where the data comes from. It comes down to integration, and integration of course is critical.

If I can give an example from our own business, because we use our own tools and Seismic’s own platform, the tools we use integrate with over 70 sales and marketing solutions. That does provide the gel and the connective ability with all the GTM systems and data sources.

In my team, we rely on a variety of tools to support and house interactions with buyers, from marketing automations to CRM testing starts. When all these systems are integrated with one tool, the Seismic platform for example, the content analytics capabilities then provide quite a comprehensive view of the buyer-seller engagement.

This data is then used to fill the AI engine. Now it is an AI, right? So the good thing is that the system will self-learn. It has the ability to track sales with a seller and almost becomes what we call a sidekick.

Over time, it becomes more and more familiar with the style, and the tool tracks the messaging this particular seller is targeting. So, if you have a seller who is specialising like say technology versus another seller who specialised in financial services, the AI assistant will recognize the difference. It’s individually tailored, and will make different recommendations to these two different sellers.

This engine learns and becomes more intelligent over time, just like you would train Amazon Alexa. The more you train, and the more you work with AI, the more intelligent it becomes.

This is an AI assistant that you can have today.