“I Got Hooked!” Boomi’s Stephanie Dechamps On The Endless Possibilities Of Data

“I Got Hooked!” Boomi’s Stephanie Dechamps On The Endless Possibilities Of Data
B&T Magazine
Edited by B&T Magazine

Stephanie Dechamps is Boomi’s head of AJP marketing, and a bona fide data-aficionado…

Dechamps sat down with B&T to discuss her career journey, Boomi’s acquisition by TGP and Francisco Partners, and her passion for martech.

B&T: What prompted your career change from journalism to tech? 

SD: I moved from Belgium to Dubai, where I was a journalist, and then I moved to Singapore. I tried to stay in journalism for a while, but for diverse reasons it didn’t work out the way I wanted. I had also done a bit of everything I wanted to. I was writing for financial newspapers, local radio, and I also did a bit of video [and] a little bit of TV. When I moved from Dubai to Singapore, I was working for the Wharton School of the University of Pennsylvania, so it was more like business analyst journalism.

The only opportunities I had in journalism in Singapore was working for a newswire, and I wasn’t really ready to do that, so I was looking for something else.

I got the opportunity from someone who was looking for a candidate who had that experience of news to create a news feed for a social media program. This was the very beginning of social media programs for executives, where you curate the news and write a little blurb for them to post on on LinkedIn.

From there,  I had other roles that were a little bit more around social media buying and social media for corporate. Then I got hooked on digital and data points, and that is how it all started!

How will Boomi’s sale change your role? 

We’re already going for hyper growth – this is also one of the reasons why I was hired – is that [to] go for growth, we need to have a marketing machine in place. But having been acquired by two private equity firms means that we’re going to have a very strong backup to help us go along that growth journey. It’s going to go very fast.

[For] our stakeholders, we look at the growth and how we can deliver results. So that’s really it: accelerate the growth and accelerate that attention to what you deliver as marketing team to help to drive revenue.

How can marketers better utilise data in their campaigns?

Coming from that adtech background [I then moved] into a martech with the opportunity at Oracle. Oracle owns a few martech applications. I really had the chance to to look at the data points and how data can make your life easier, if it is connected.

It’s tracking the click on a banner, all the way to “how does it turn into revenue?” and “how do you make it turn into revenue?”.  The data needs to be connected, and then it’s up to the marketer to interpret the data.

Based on those insights, you can then take a decision to go into what type of marketing channel, or type of marketing mix, you want to put together and what will give the best results.

The great thing about data is that you pilot, you test,  you do A, B, testing and you see if it works, and if it doesn’t, then you can just always change the direction, change one parameter, and see if it makes a change.

 Your work covers Asia Pacific and Japan: do you have any insights about what the Australia and New Zealand market can learn from APAC?

In the ANZ market the consumer, from a B2B marketing perspective, is expecting the marketers to be able to use the data that they have at hand, and they expect a better, a more personalized engagement.

Building the user journey and looking at the data, personalizing with everything we have at hand, we can target specific accounts that are currently showing an intent, and are searching for products. Then we can reveal those accounts, trying to look at which is the best profile and serve them content that’s appropriate for the specific persona. So, you’re not going to treat someone in the IT audience the same way you would treat someone in the finance audience.

A bit like you see in B2C, people are expecting to have a personalized engagement, because everyone knows that we have the data. The personalized touch of engagement in Australia and New Zealand, I would say, is really expected for the company when they do marketing, to deliver that experience. You could still maybe try to do spray and pray in the other markets, but in Australia and New Zealand you can’t.

What are your ultimate business goals for Boomi, and for your work? 

My ultimate goal in business as a marketer and as a marketing lead is to be able to forecast the revenue that that marketing will be able to deliver.

In the past, if you look at adtech, we would just say how many impressions or clicks or how many potential marketing leads we would be able to generate. Then after that, you move to focusing on the pipeline, and how those leads turn into a marketing qualified lead and sales opportunities. A lot of B2B companies are still in that mindset: “what’s the pipeline that I can predict?” The KPI is still around a pipeline.

My ultimate goals is, with the help of data, that I will be able to go to predict not the pipeline, but the revenue. Once we go into the pipeline, [that] is usually the first sales stage. But how can I help and how can I improve the conversion of a marketing lead and a marketing led opportunity for the sales to turn into revenue? At this stage, we often pass the ball to the BDR team, which is a team that’s going to follow up on the leads, and a sales team, and we have little visibility from that first sale stage of that opportunity all the way to that close opportunity.

Do you have any other practises you want to implement?

What I want to bring to Boomi is already in its early stages. But, it’s not yet a systematic approach and this is where I want to bring it.

There is really one thing, aside from that personalisation at scale, that I would like to be able to deliver. [It’s] how we could use predictive intelligence data to help us to not just target better, but also be a trusted advisor for sales, and add even more value to the sales.

Predictive intelligence is using the intent data that’s available – intent data could be search data, but when you work with different media channels, they have information about the database, so they have the visibility on intent data.

Combining that information from a third party with your own information, for us as well we have first party information on the engagement side, together with a third type of information which is the firmographic of the account, and the type of audience we want to engage. [We can then] predict, as much as we can, which account will be more likely to be responsive to Boomi.

Putting those three types of information together: the engagement internally, the intent externally and the firmographic, and the level of the contact or the prospect, really helps to steer towards prioritization of the most likely accounts to be positively responsive to Boomi.

Using that information for a marketing perspective, but also being able to share that information with the sales team and the prospecting team to help them to say, “well, if you target those accounts you have more chances  to have a positive response”.

I used that at Oracle, and I was really able to prove that I can get four times more pipeline if I target accounts with this intelligence. With this data, I can get two to three times more leads, and I can shorten the sales cycle when I target those accounts.

Putting that in place at Boomi will really help me to help to target better. When you do personalization in terms of engagement, you give a better experience. You’re really targeting more likely someone who will be interested in what you’re selling, then you attack them with a messag  that makes sense to them, and therefore the response will be expected to be much more positive.

That’s where I want to steer: using the data at Boomi even more. They’re already in that direction, but using it in a systematic way to be able to give better results from a marketing [perspective].

Then in the future, when all these data sets will be implemented and connected using AI to make decision for me, using machine learning to help me to not just focus on the positive outcome, but focusing on those that are on regular fashion, are converting.

I would use machine learning to be able to tell me which accounts I should go after.

This interview has been edited for length and clarity.

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