Discussing the intersection of telecom business and data analytics
with Eir Ireland’s Managing Director of Sales
(A Six Minute Read)
This is the first interview opening a mini-series of our talks with veteran business execs about their views on Analytics as clients who sponsor analytical and Data Science teams and then consume their outputs.
The interview was conducted a few weeks ago when Catherine was a Managing Director of Sales for eir – the largest telecom in Ireland. Since that time she moved to an independent consultant role and we wish her best of luck on the new path!
Meet Catherine Lonergan. She is a veteran telecom professional whose main focus has always been to look after various customer bases. We are discussing how analytics affects her overall sales strategy and what hurdles she wants to see addressed in the world of data science.
“Managing all the customers from the day we sell, to get them to join, and then making sure that they don’t leave at the other end. “
Could you begin by explaining to us what you are focusing on here at eir as the MD of Sales?
I am Director of Sales for Consumer and Small Businesses, but it’s, in fact, a much wider remit than it sounds. I manage all the sales channels from the retail stores, the website, field sales and telesales and everything behind the scenes in sales operations that make sales happen, including the digital team & strategy which is responsible for the content that goes up, plus SEO and AdWords, which drives traffic to the website..
I’m also charged with the retention of our current 2 million customer base and the end of end process for customer vetting and management of collections to ensure cash is collected from customers.
On top of that, the analytics team is also within my area of responsibility and they work with both – the sales and retention functions to provide data insights but also other areas of eir, such as the finance, regulatory. The common goals for my teams are growth within the commercial unit, revenue for the company, and managing all the customers that we have from the day we sell, to get them to join, and then making sure that they don’t leave at the other end.
“Analytics helps the sales team sell better, sell more, sell in a particular area and drive the type of customer we actually want to bring in. “
That really is a lot to cover! How does analytics help you and your teams?
We use analytics everywhere. I could break it up into two fields: operational and strategic analytics.
For operational analytics we forecast sales, look at opportunities where we are acquiring customers, we monitor products availability and the competition. There’s a lot that goes into the sales analytics to help the sales team sell better, sell more, sell in a particular area and drive the type of customer we actually want to bring in.
More on the strategic side there’s a whole host of analytics and modeling that we use to analyze and understand customer retention and additional opportunities for revenue. We use models predicting customers propensity to churn, propensity to take more products, propensity to pay or if they are in a household – so we can assess the extent of their telecommunications and entertainment wallet we have.
With our Customer Lifetime Value Analytics we use it to ensure that we acquire a customer and keep them for life, so these models help us to look at different ways of achieving this.
Do you see any barriers and blockers for Data Science to be fully adopted and integrated with business decision making?
Many people are suspicious of this new area of business – “data science”. They are wary of what it actually is and how it can add value. I see the value in analytics, but I think other [business] people sometimes struggle with understanding the value of data and maybe say, “Well, you know, it’s great to have but we used to operate and make sales before without analytics, so we can carry on without it.”
I’ve seen examples where the analytical team tried to convince marketing teams of the value in analyzing data and using it in a targeted way, rather than constantly doing mass marketing. The targeted approach will get you a better outcome and the main thing is showing people how delivering good analytics makes a difference versus not having it. So key to this is test and learn and prove value.
But I think that’s a big change and a challenge.
“Data Science needs to become more grounded in everyday business.”
Do you believe Data Science is delivering on its hype?
I definitely believe it’s valuable. Whether it’s performing at its highest level? I wouldn’t be an advocate for analytics just for the sake of it. You have to have substance and you have to really show the commercial value of what you’re doing, just like any other role or area of the business has to do.
But I do think it sits in that hype-realm, especially because of its name “data science” which sounds quite futuristic. It just needs to become more grounded in everyday business, it’s not a new thing anymore and it needs to become the way we work.
Could you give some rock-solid example of Data Science solving business problem for you?
I was managing customer churn and we had a rather small budget. The challenge was to be able to identify high risk customers out of the entire customer base and be clearly focused to only spend the small amount of budget allocated. We developed a new set of Churn Models that assessed the propensity to churn on a customer level.
There had been no models in place before and it really helped in proving the “before and after” picture grounded in a clear structured approach through data and delivering a set of results that backed it up. Clearly churn declined and the budget remained as allocated. Success!
Do you worry that automation is going to replace human jobs?
I think a lot of people are afraid that robotics will take over their job. I feel it will actually free them up to do more productive work . For example, our back-office sales function used to do an awful lot of manual processing of orders. We introduced robots that mirror a lot of what our teams would do manually. This, in turn, freed the team up to add value to orders where they spoke to customers (something the robot is not programmed or effective at doing) and this led to more insightful work and new revenue streams as they sold additional services that the customer may not have originally decided to take.
There are things that robots cannot do: they can’t think, they can’t talk directly to a customer, they can’t problem solve unless you have problem-solved for them already. So no, I’m not worried that automation will replace humans any time soon.
“You need to put the data in people’s hands and get them to understand it and allow them to pull insights out of it themselves”
How can business users be smart when it comes to starting to use analytics in their businesses?
It’s starting with having a discussion with the analytical team and looking at what the business problem is rather than worrying about having too much data and not knowing where to start.
Have a very clear business problem, and then use the analytics to solve that problem, rather than the other way around. Everybody has to have that discipline of not answering the question before you’ve actually looked at the underlying data behind it and you may come up with a completely different solution. .
What do you think the future of analytics will look like? What would you like it to look like?
I think it has to become more of a core discipline within the business. It can’t be an ivory tower of analysts who are the only ones with access to the data — as it’s often the case.
You need to put the data in people’s hands and get people to understand it, allow them to pull insights out of it themselves. I’d love to see an understanding across all businesses that data science has real commercial value and also that it isn’t a purely scientific or mathematical exercise.
If you could give one piece of advice to Data Scientists working with business users — what would it be?
They need to align themselves to the company’s strategy and make sure their work has relevance and applicability. If an analyst is working on something that is not aligned to the company’s strategy, they are wasting their time and people won’t buy into it.
It’s a very fine balance between what the company wants now and what it will want in the future. So keeping an eye on strategic alignment and wagering maybe 10% of the analysts or data science teams time on riskier projects that may pay off in the future. Your stakeholders will see the value that you’re adding to the business now and may be more open to taking on some of the risks of deeper thoughts or analytical approaches.