Hilda Tingle, Global Head of Digital Marketing & Growth Programs at BNP Paribas Asset Management, explains the firm’s AI strategy and where marketers can get started with theirs.
FSF: Where can AI add the most value?
Hilda Tingle: The low hanging fruit is really around productivity and efficiency. We are always thinking about doing more with less or with what we already have.
We’re looking at automated translations, transcribing meeting notes, summaries of emails and content and creating content. We need to do them because in the end everyone will be using them for productivity and efficiency
Where we want to get to is the cooler stuff: how we use AI to define our return on marketing investment, look at improving lead conversions and directly contribute to revenue.
Why have the internal productivity applications been quicker to take off?
The internal productivity tools are the ones that are already readily available. Translations, transcribing, subtitling and video have all been around for years but not under the category of AI.
People will eventually get to the other ones, but it’s more complicated clearly, because there’s underlying data that needs to be in place such as the sources, structure and taxonomy.
That’s where the difficulty is going to be – do you have all the data in place to be able to do machine learning and use AI to look at lead conversion rates and campaign optimisation?
What are some examples of these more valuable applications?
An example would be campaign optimisation: if you are doing a campaign and generating leads and you want a better conversion rate from leads to marketing qualified leads (MQLs) for sales to try and develop and investigate further.
You want to get better at providing quality MQLs and so you might need to look at lead grading and historical interaction data. External data such as market and company sentiment also come into it. There are a lot more things that need to be thought through and I think the key word is data.
There’s also measuring marketing ROI – where are we investing, what are the channels that are performing well, what audience performs well against which products or is interacting with what kind of content.
If you want to figure this out internally and get better at allocating your budgets you need to take the data from different campaigns. You need that history to be able to apply calculations and formulas to it, and to be able to come up with a recommendation on the best-performing channel or which audience segment is doing well with which products or type of content.
We don’t have an internal GPT in asset management, but at the group level there are various innovation labs and they’re developing and investigating those tools. We will probably have one in the near future, but we don’t have one right now.
What applications do you see in client experience and service?
One is around productivity and efficiency – getting things out quicker and at a lower cost.
When we talk about client experience and service, it comes down to the content itself, not the technology. The technology is going to get us there – so you might be using the best tech, but if your content or the storytelling is rubbish, it’s not going to be a great experience or great client service.
The focus for the marketer is still on the storytelling and content itself: what’s the narrative you’re trying to convey that will deliver that great experience? The tech is the enabler, that is going to get you there faster, and at a lower cost.
How do you manage the relationship between compliance and AI?
The way we’ve been working for the last few years has really changed with compliance and legal; we bring them in right at the beginning, so they understand what we’re trying to achieve with the initiative itself, which helps them. We’re also educating them on new technologies that are being used and where we’re heading in terms of strategy so they can provide the relevant advice.
The other question is around whether AI is going to help the compliance process – I think it can simplify a lot of compliance checks. Say we’ve been tasked with manually checking documents or websites for outdated content, I think AI can simplify that process by identifying certain phrases or words and perhaps providing a score. This would make it easier for both compliance and marketing to spot areas of concern in our content.
I think Generative AI could summarise for compliance as well, whether the content contains products and whether the right disclaimers are being used.
How do you determine AI strategy and where to invest?
It really depends on what you’re using it for. When we talk about productivity, it’s clearly around cost savings, time savings and speed to market.
The other one would be around the quality of the output. If you’re using something like translations, video creation or creative creation, you’re checking how good it reads and how visually pleasing the video is.
It also depends on the specific tool and what that tool does. With video avatars, the KPIs were around cost and time savings and that’s what I’ve been reporting on.
With campaign optimisation: do I see an improvement in campaign performance? Am I getting better conversion rates and better lead to MQL rates?
How can marketers get their data strategies in place to support AI?
You need to clean up your client data and digital interaction data. Your client historical portfolio data, other things like your corporate taxonomy, your content taxonomy, and your data privacy policy. The client interaction data should go back at least a year, a couple of years would be great.
It’s not just about cleaning it and getting it in order, you need to figure out where it needs to be stored and how you connect your data and content together so you can use it for activation.
The other thing is overlaying that with a CDP, a Customer Data Platform. That’s a trending software solution today, and I think it’s quite a pivotal and important tool to have. That brings all your data into one place, and then you can act on the data itself, whether it’s for activation or using AI to kind of figure out the more complex sort of ROI and conversion rates.
I think most companies have a data office, or a data team that should be looking at these things. Get your structure in place, get your marketing taxonomy in place, then I think you can act on them.
Where can people get started on their AI strategies?
I think sometimes you have to start at the end – what is the outcome you desire? Do you want to create great videos, have correct translations in French or Spanish? It’s trying to figure out the end bit that you want to get to and then come back to the beginning.
If I wish to have great videos at a shorter time, at a lower cost, what do I need? AI could be the answer, and then you come back to the start to get your content in place. Do I have the content taxonomy and narrative, the idea for the story, do I need to feature people in the videos?
We started here with all our pilots and proofs of concept – where do we want to get to: better translations in our videos, ads, whitepapers or blog articles? Do we want to get material out within a day, or four days, and then work backwards from there.
Where would you like to be with AI a year from now?
I think productivity and efficiency is where we all need to be, because it’s really a game changer for the way we work, not just in terms of dealing with volume or speed.
But I think it can also increase the quality of what we produce, because it allows us to spend more time on the quality and creativity side of things. Imagine speeding up some of the other aspects, such as the business-as-usual peer reviews, writing meeting minutes, summarising white papers, , some of the boring stuff.
Within a year, I’d love to see not just marketing but our firm using AI as part of our daily business as usual.
